International Journal of Engineering and Computer Science Applications (IJECSA)
This journal covers all areas of computer science research, and literature studies including hardware, software, computer systems organization, computational theory, information systems, computational mathematics, data and data science, computational methodology, computer applications, learning science and technology, and knowledge management. (12.12.21) ISSN 2828-5611
Designing A Web-Based Content-Based Filtering Method for A Culinary Marketplace
The development of information technology has made various aspects of life easier, including product marketing through marketplace applications. In Pasar Bengkel Village, known as a culinary center, vendors have experienced a decline in the number of customers due to the operation of the Medan–Tebing Tinggi toll road, which has reduced visitor traffic. The purpose of this study is to design a marketplace application with a recommendation system that can help vendors increase product visibility and attract customers back. The research method is the application of a Content-Based Filtering-based recommendation system with the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to process product description data and generate relevant recommendations for users. The result of this study is a marketplace application that is able to provide personalized product recommendations based on the frequency of word occurrences in product descriptions using the TF-IDF technique. In conclusion, this application is expected to be an innovative solution in increasing the competitiveness of local culinary vendors by expanding their marketing reach and maintaining the sustainability of their businesses..
Copyright (c) 2025 Zulfikar Shiddiq, Suendri Suendri, Fathiya Hasyifah Sibarani http://creativecommons.org/licenses/by-sa/4.0
Clustering Regency in Kalimantan Island Based on People's Welfare Indicators Using Ward's Algorithm with Principal Component Analysis Optimization
Cluster analysis is used to group objects based on similar characteristics, so that objects in one cluster are more homogeneous than objects in other clusters. One method that is widely used in hierarchical clustering is Ward's algorithm. This method works by minimizing the sum of squared distances between objects in one cluster (within-cluster variance) to produce optimal clustering. However, one important assumption in using this method is that there is no high correlation between variables, or in other words, the data must be free from multicollinearity. Multicollinearity can cause distortion in distance calculation, resulting in less accurate clustering results. To overcome this problem, a Principal Component Analysis (PCA) approach is used to reduce the dimension and eliminate the correlation between variables by forming several mutually independent principal components. This research aims to cluster 56 districts/cities in Kalimantan Island based on 19 indicators of people's welfare in 2023, using Ward's algorithm optimized through PCA. Validation of clustering results is done using the Silhouette Coefficient value to assess the quality of clustering. This research method is a combination of Principal Component Analysis (PCA) and hierarchical clustering using Ward’s algorithm. PCA was applied to reduce 19 welfare-related indicators into four principal components that retained most of the essential information in the dataset. The clustering process based on these components resulted in two optimal clusters, as determined by a Silhouette Coefficient value of 0.651, which indicates a moderately strong cluster structure. The results of this research are that the first cluster consists of 47 districts/cities characterized by relatively low welfare levels, while the second cluster comprises 9 districts/cities with comparatively higher welfare conditions. These findings imply the existence of considerable disparities in welfare among regions on Kalimantan Island. The results can be used as a reference for policymakers in formulating more targeted and equitable development strategies
Copyright (c) 2025 Eva Lestari Ningsih, Siti Mahmuda, Memi Nor Hayati http://creativecommons.org/licenses/by-sa/4.0
A Gaussian Mixture Model Approach to Profiling Stunting Risk Across Indonesian Provinces
Stunting is still a major health problem in Indonesia, with notable differences between provinces. Although the national rate has decreased over time, regional gaps continue, emphasizing the role of data in helping to explain what contributes to the issue. This study aims to segment 38 provinces in Indonesia based on maternal and child health indicators associated with stunting prevalence. The variables used include the percentage of low birth weight (LBW) infants, the percentage of infants born short, the percentage of pregnant women with chronic energy deficiency (CED), exclusive breastfeeding (EBF) coverage, prevalence of diarrhea in toddlers, and prevalence of acute respiratory infections (ARI) in toddlers. The clustering analysis was performed using the Gaussian Mixture Model (GMM) with the number of clusters varied from 2 to 7. Model selection was based on the Bayesian Information Criterion (BIC), where the lowest value indicated the optimal model. The results show that the model with two clusters was selected, with a BIC value of 1358.24, which indicates the best balance between model fit and complexity. This clustering reveals that provinces are grouped based on similarities in maternal and child health profiles, not on geographic proximity, meaning that the GMM method does not rely on spatial location to form clusters.
Copyright (c) 2025 Masithoh Yessi Rochayani, Iut Tri Utami http://creativecommons.org/licenses/by-sa/4.0
Animation Design Stage in Merarik as a Medium for Introducing Culture and Customs
The creative industry today, animation is one of the popular media used by many entertainment industries and to introduce the customs and culture of a region. Animation is a process in which an image or object is manipulated to create a predetermined movement flow. Merarik is the escape or kidnapping of a girl from the supervision of her guardian and her social environment has been formed as a cultural heritage that is passed down from generation to generation for the Sasak people in general and merarik has several stages that must be passed through to run it. The purpose of this design is where the designer wants to provide information about the meaning and stages in the design. The methodology used in this design is the pipeline method, which is a method that includes several stages (pre-production, production and post-production). The results of this design are in the form of animations of the stages in merarik which are designed with 2D techniques as a medium that explains the process of elopement (merarik) in Sasak culture. Animation is made with the limited animation genre which is usually done in Japanese animated films. The implications of the results of this study as a medium for learning and preserving the Merarik culture which has recently been rarely found among the Sasak people
Copyright (c) 2025 Christofer Satria, I Gede Mugi Raharja, I Kt Suteja, I Wayan Swandi http://creativecommons.org/licenses/by-sa/4.0
Computational Aesthetics in Improving Regional Election Mascot Branding Using Hue Saturation Value Method
The digital era has a visual appeal that plays a crucial role in strengthening identity and public involvement in political campaigns, particularly in the election of the regional head of Mataram City. The role of visual mascots is important in attracting public attention. Through the intensity of color in digital media, it can attract attention and persuade the public to like it, thus building trust in the branding of the Mataram city election. However, the deviation value from the dominant color intensity in the Mataram City election mascot on the computer screen remains unclear. The core issue lies in identifying the exact value of the color intensity in the mascot design, which serves as a digital aesthetic element in strengthening the branding identity of the Mataram City election. This study aims to analyze the color intensity of the mascot design as a reflective attraction in the regional political campaign in Mataram City. The method used is Hue, Saturation, Value (HSV), with cultural and psychological color analysis image processing techniques to attract public participation and perception. The results of the study indicate that the color intensity based on HSV segmentation increases. The average color Hue has a deviation of 0.0319, Saturation has a deviation of 0.0255, and Value has a deviation of 0.9409, all of which are attributed to the dominant color in the mascot design on the computer screen. The HSV color intensity in the design of the Mataram city election mascot, especially on the screen, has an average value that intersects with each other, resulting in unity and forming an aesthetically pleasing visual. In conclusion, the brightness of the dominant color is a crucial aspect of creating aesthetic elements that capture the audience's attention. The implications of the study's results serve as a basic reference for creating graphic designs using computer technology.
Copyright (c) 2025 Hasbullah Hasbullah, I Wayan Swandi, Anak Agung Gde Agung Udayana, Alit Kumala Dewi http://creativecommons.org/licenses/by-sa/4.0
Handling Imbalanced Data in K-Nearest Neighbor Algorithm using Synthetic Minority Oversampling Technique-Nominal Continuous
Classification is a part of data mining that aims to predict the class of data using a trained machine learning model. K-Nearest Neighbor (K-NN) is one of the classification methods that uses the concept of distance to the nearest neighbor in creating classification models. However, K-NN has limitations in handling imbalanced class distributions. This core problem can be addressed by applying a class balancing technique. One such technique is the Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC), which is suitable for datasets containing both nominal and continuous variables. The aim of this research is to classify Honda motorcycle loan customer data at Company Z using the K-NN method combined with SMOTE-NC to address data imbalance. This research method is experimental, using a 10-fold cross-validation approach to partition training and testing data. The input variables include gender, occupation, length of installment, income, installment amount, motorcycle price, and down payment, while the output variable is payment status (current or non-current). The results of this research are: the optimal K value for classification using K-NN with SMOTE-NC is K = 1, with an average APER (Average Probability of Error Rate) of 0.143. The best result is found in subset 8 with an APER value of 0.033. In this subset, out of 61 data points, 34 current-status customers are correctly classified as current, and 25 non-current-status customers are correctly classified as non-current, with only one misclassification in each class. The conclusion of this study is that the combination of SMOTE-NC and K-NN (K=1) provides high classification accuracy for imbalanced data, and can be effectively used to support credit risk assessment in motorcycle financing.
Copyright (c) 2025 Anjani Anjani, Memi Nor Hayati, Surya Prangga http://creativecommons.org/licenses/by-sa/4.0
Feature Extraction in Eye Images Using Convolutional Neural Network to Determine Cataract Disease
The eye is one of the vital human senses and serves as the main organ for vision. One of the visual impairments that requires special attention is blindness, and cataracts are a major cause of it. A cataract is a condition in which the eye’s lens becomes cloudy due to changes in the lens fibers or materials inside the capsule. This cloudiness blocks light from entering the eye and reaching the retina, significantly interfering with vision. Early detection of cataracts is essential to prevent blindness. An efficient image-based classification model is needed for cataract detection. This study aims to test the Convolutional Neural Network (CNN) model for early cataract detection by exploring the use of several optimization algorithms: Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMSprop), Adaptive Gradient Algorithm (AdaGrad), and Stochastic Gradient Descent (SGD). The research method follows an experimental approach, where eye image datasets are trained using the same CNN architecture but with different parameter configurations. The results show that the Adam optimizer, with a data split of 70% for training, 15% for validation, and 15% for testing over 50 epochs, produced the best results, achieving accuracies of 94%, 93%, and 93%, respectively. Other optimizers performed reasonably well but could not match Adam's stability and accuracy. The implication of this research is that the choice of optimizer and hyperparameter configuration plays a crucial role in improving the performance of image-based cataract detection models.
Copyright (c) 2025 Fitra Rizki Ramdhani, Khasnur Hidjah, Muhammad Zulfikri, Hairani Hairani, Mayadi Mayadi, Ni Gusti ayu Dasriani, Juvinal Ximenes Guterres http://creativecommons.org/licenses/by-sa/4.0
Prediction of Student Major Selection at High School Using a Machine Learning Approach
The primary objective of this research was to develop and evaluate a machine learning prediction system that matches Senior High School (SMA) Nusa Putra Kota Tangerang students with their potential school majors based on their academic interests and performance levels. This research method employs machine learning algorithms, including Random Forest, Support Vector Machine (SVM), logistic regression, K-Nearest Neighbor (K-NN), and Naive Bayes, using academic records, interest tests, and questionnaires for data collection. The data was processed and analyzed to train and test the algorithm. The findings of this study indicate that the Random Forest algorithm achieved the best performance among the models, with an accuracy of 85%, a precision of 82%, a recall of 88%, and an AUC score of 0.92. The factors that affected the prediction of major selection were Grade XII Mathematics scores and Science Interest Test results. The research implications suggest that Random Forest technology within Machine Learning (ML) enhances major selection accuracy while promoting fairness, providing superior educational choices and increased student satisfaction. Future studies should investigate additional factors that influence this phenomenon.
Copyright (c) 2025 Nasril Sany, Dody Dody, Esa Firmansyah Muchlis, Muhaimin Hasanudin
Bidirectional Encoder Representations from Transformers Fine-Tuning for Sentiment Classification of Cek Bansos Reviews
Social assistance programs are essential government initiatives aimed at supporting underprivileged communities. One such program is facilitated through the Cek Bansos application, which enables users to check their eligibility for social aid. However, user experiences with the application vary, leading to various sentiments in their reviews. Understanding these sentiments is crucial for improving the application’s functionality and user satisfaction. This study focuses on sentiment analysis of user reviews of the Cek Bansos application by leveraging a fine-tuned Indonesian-language Bidirectional Encoder Representations from Transformers (BERT) model. This research aims to evaluate the BERT model's effectiveness in classifying sentiments in user reviews and provide insights that could improve the Cek Bansos application. This research method is the BERT model was fine-tuned using hyperparameters such as a learning rate of 3e-6, batch size of 16, and 9 epochs. The dataset consisted of 8,000 reviews, divided into training (70%), validation (20.1%), and test (9.9%) sets. Review scores were manually categorized, where ratings of 1 to 2 were classified as negative sentiment, 3 as neutral, and 4 to 5 as positive. The results of this research are as follows: the fine-tuned model achieved an accuracy of 77%, with additional evaluation metrics such as precision, recall, and F1 score, demonstrating the model's effectiveness in identifying positive, negative, and neutral sentiments separately. This study concludes that the BERT model provides a reliable method for sentiment classification of user reviews, which could support developers and policymakers in refining the Cek Bansos application to enhance user experience. Additionally, a web-based application developed using Streamlit allows government officials to visualize sentiment trends in real time, improving their understanding of user feedback. Future research could further explore alternative machine learning models and additional linguistic features to improve sentiment classification accuracy and the overall user experience.
Copyright (c) 2025 Erna Haerani, Alam Rahmatulloh, Souhayla Elmeftahi http://creativecommons.org/licenses/by-sa/4.0
Analysist of User Satisfaction of the High School Student Admissions Website using the User Experience Questionnaire Method
In the era of digitalization of public services, web-based student registration systems have become an important instrument in the education sector. The Website for New Student Admission (PPDB) of Senior High Schools (SMA) and Vocational High Schools (SMK) in Central Java Province has been implemented as a single platform for new student registration, but the main problem identified is the lack of a comprehensive evaluation of the level of user satisfaction with the quality of interaction experience with this platform, especially after the emergence of several complaints on the official PPDB social media regarding the system flow, services, and website appearance. The purpose of this study is to measure and analyze the level of user satisfaction with the PPDB website of SMA/SMK in Central Java Province using the User Experience Questionnaire (UEQ) approach which covers six aspects of user experience. This research method is descriptive quantitative with a survey approach using a standardized UEQ instrument consisting of 26 question items, involving 30 respondents of class X students of SMA Negeri 1 Karanganyar Demak selected using a 10% sampling technique from the population. The results of this study are indicate that the efficiency criteria obtained the highest score of 1.125, while the novelty criteria received the lowest score of 0.792, with the benchmark comparison diagram indicating a position below average (poor) in the criteria of attractiveness (1.061), clarity (1.092), accuracy (0.983), and stimulation (0.992), while in the criteria of efficiency and novelty, they are in a position above average (quite good). The implication of these findings underlines the need for further development in the aspects of visual appeal, clarity of information, accuracy of functions, and interaction stimulation to improve the overall quality of the user experience of the PPDB website.
Copyright (c) 2025 Ardian Adi Prabowo, Ahmad Fathoni, Anjis Sapto Nugroho, Kristiawan Nugroho, Omar Farooq
Analyze Threats in a Virtual Lab Network Using Live Forensic Methods on MetaRouter
This research identified critical network anomalies in the MetaRouter virtual environment, focusing on IP activity related to routers, networks, and client devices. Suspicious interactions were observed between IP 192.168.1.100 (router) and IP 172.16.205.53 (client), including reused TCP port numbers and incomplete SYN sessions, indicating potential spoofing attempts. Invalid route information involving 192.168.1.100 highlights malicious modifications to the routing table, indicating an attempt to manipulate the routing information. Packet inconsistencies, such as “TCP Previous segment not capturedâ€ and “Spurious Retransmission,â€ revealed interference between the client and router, possibly caused by an external attacker exploiting network protocol vulnerabilities. The aim of this research is to analyze threats in virtual lab networks using live forensic methods on MetaRouter to detect anomalies, with a focus on Border Gateway Protocol (BGP) and TCP deviations in MetaRouter. This research method is a controlled prototype experimental setup in a virtual laboratory consisting of two routers and two client devices. This method simulates real-world network operations to identify malicious activities. Wireshark is used for real-time packet-level monitoring and analysis because it has powerful visualization and filtering capabilities, surpassing tools like tcpdump. This research integrates live forensic techniques to collect and analyze routing logs, packet data, and protocol behavior. The results of this research are the identification of suspicious behaviors, such as reused TCP port numbers, incomplete SYN sessions, and unauthorized route announcements, indicating potential spoofing and BGP hijacking attempts. Packet data irregularities, including “Out-Of-Orderâ€ messages and abrupt session terminations, are also detected, revealing disruptions in traffic flow caused by malicious activities. The results of this research are highlighting the effectiveness of the forensic framework in identifying and documenting network anomalies in virtual environments have significant implications for improving security in cloud-based and hybrid networks. This research provides a scalable and replicable methodology that can improve real-time anomaly detection and response, paving the way for future advances in network security.
Copyright (c) 2025 Firmansyah Firmansyah, Bayu Wibisana, Muhammad Jordan
Optimization of Renewable Energy From Solar Panels For Environmental Monitoring Using Arduino
Renewable energy is an alternative energy in the midst of the issue of fossil-based energy running out. One of these alternative energies is solar panels that generate electricity to achieve environmentally friendly energy. The problem researched in this study is the need to optimize or energy efficiency of solar panels. The type of research is development research with a prototype model. The purpose of this study is to optimize the efficiency of the solar panel tracking system equipped with artificial intelligence and sensors to monitor the environment based on the internet of things. This research method is an integration of fuzzy logic internet of things for tracking solar panels based on light sensor input. The use of solar panels using polycrystalline types, and the addition of temperature and humidity sensors is important to utilize. The result of this study is that the highest efficiency is obtained at 9.96%, meaning that the energy received by the system is 9.96% into useful power that is lost. In conclusion, solar panels are highly dependent on weather conditions and the focal point of solar energy capture. The implication of this study is that the improvement of environmentally friendly energy efficiency, although not too high, can be further developed. Recommendations for further research suggest that data collection be carried out in sunny weather, and improved using solar panels with a larger capacity and energy storage from batteries. The use of machine learning algorithms can be an alternative to study large amounts of solar panel data.
Copyright (c) 2025 I Wayan Sugianta Nirawana, Fahmi Romisa, I Gede Karang Komala Putra, Nouval Aulia Rahman, Ayisa Rahmadani Fitria
Comparison of Lexicon-Based Methods and Bidirectional Encoder Representations for Transformers Models in Sentiment Analysis of Government Debt Market Movements
The State Budget of Indonesia (APBN) is the main tool for implementing fiscal policies and serves as a budgeting guideline for development execution in Indonesia. One of the funding sources in budget financing is Debt Financing, which consists of Government Securities (SBN) issuance and Loans. Overall, SUN contributes IDR 5,824.34 trillion, highlighting its significant proportion in debt financing. Understanding public sentiment toward SUN is essential in developing effective government policies. This research conducts sentiment analysis on tweets from the social media X over the past 7.75 years to assess public perception and propose strategic recommendations. The aim of this research is to compare the BERT model and the Lexicon-Based method to determine which achieves the highest accuracy in sentiment analysis. The findings can help the government develop strategies for issuing SUN, especially in improving public involvement and investor trust. This research method is based on a deep learning pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, specifically IndoBERT, with fine-tuning, and a Lexicon-Based approach utilizing the InSet lexicon. The results of this research are as follows: on the overall tweet dataset, the BERT model with optimal hyperparameters outperformed the Lexicon-Based method, achieving an accuracy of 70.28% compared to 55.77%. Similarly, on an annual basis, BERT exhibited higher accuracy than the Lexicon-Based method, except in 2021. Public sentiment on SUN in social media X is categorized as 49% positive, 30% neutral, and 21% negative. These findings indicate a generally favorable perception of SUN but also highlight areas for improvement in public communication. Overall, the BERT model demonstrates superior performance over the Lexicon-Based method. Considering the opportunities available, the government could leverage social media through Key Opinion Leaders and enhance transparency in explaining policies such as Tapera. This approach could maximize public participation in investing in SUN in Indonesia.
Bidirectional Encoder Representations for Transformers (BERT)
Copyright (c) 2025 Firda Rachmawati, Ulil Azmi, Rahmania Azwarini
Assessing Twitter User Sentiment Regarding Divorce Issues Using the Random Forest Method
The issue of divorce remains a complex and sensitive topic within Indonesian society, influenced by various factors such as repeated disputes, domestic violence, lack of harmony, financial difficulties, and other socio-cultural aspects. With the rise of social media, particularly Twitter, public discussions regarding divorce have become more widespread, allowing individuals to express their opinions and sentiments on the subject. These diverse perspectives create a wealth of sentiment data that can be analyzed to understand public perception and societal trends related to divorce. This study aims to classify public sentiment on divorce-related discussions using the Random Forest algorithm, providing insight into how people perceive and react to divorce issues. The research adopts a quantitative approach with a case study framework. The methodology involves data collection through web scraping techniques to gather approximately 1500 tweets containing discussions on divorce. The collected data is then preprocessed, including text cleaning, tokenization, and feature extraction, before being used to train and evaluate the Random Forest model. Sentiments are classified into three categories: negative, neutral, and positive. The classification model's performance is assessed using accuracy and F1-score metrics derived from the confusion matrix to determine its effectiveness in categorizing sentiments. Experimental results indicate that the Random Forest algorithm achieves an accuracy of 70%. The relatively low accuracy is attributed to the imbalance in sentiment class distribution, where negative sentiments dominate while positive sentiments are underrepresented. This imbalance affects the model's ability to predict positive sentiments effectively. The implications of this research contribute to a better understanding of public sentiment dynamics regarding divorce, which can be beneficial for policymakers, psychologists, and social researchers in analyzing societal attitudes towards marital dissolution.
Copyright (c) 2025 Muhamad Azwar, I Putu Hariyadi, Raisul Azhar http://creativecommons.org/licenses/by-sa/4.0
Design of a Quick Response Code-Based Infrastructure Management Information System
The management of infrastructure and facilities at MTs Mambaul Hasan Sumberrejo Paiton Probolinggo is currently conducted manually, resulting in significant issues such as data inaccuracies, misplacement of items, and difficulties in tracking asset movements. These challenges reduce efficiency and hinder effective inventory management. The aim of this research is to design and develop a Quick Response (QR) Code-based management information system to enhance the efficiency and effectiveness of infrastructure and facilities management at MTs Mambaul Hasan. This research method is based on Research and Development (R&D) with a quantitative approach and a case study framework. The process includes system requirements analysis through direct observation and interviews with school staff, followed by system design using the Object-Oriented Analysis and Design (OOAD) approach. A prototype is then developed and tested to gather user feedback, and system evaluation is conducted to refine the system before full implementation. The results of this research are a QR Code-based infrastructure and facilities management information system that simplifies asset registration, enhances tracking accuracy, and reduces manual workload. Usability testing with school staff revealed an 82,67% satisfaction rate, indicating a significant improvement in efficiency and traceability of assets. The implementation of this system provides a practical and effective solution for managing infrastructure and facilities at MTs Mambaul Hasan. This study concludes that the QR Code-based system improves efficiency, accuracy, and traceability in inventory management. The implications of these findings suggest that other educational institutions can adopt similar technological solutions to modernize their management processes, with potential future integration of mobile and cloud technologies for enhanced usability and scalability.
Copyright (c) 2024 Moh. Sukron, M. Raihan Ramadhan, Ahmad Sihabillah
Clustering Analysis of Umrah Pilgrim Data Based on the K-Medoid Method
The Umrah pilgrimage is becoming increasingly popular among Indonesians, with millions of participants yearly. This trend creates a need for service providers to understand the characteristics of pilgrims to improve service quality, marketing strategies, and competitiveness. Analyzing data on pilgrims helps service providers develop more effective strategies and tailor packages to match their needs, ensuring competitiveness in a growing market. This study aims to clusters Umrah pilgrims based on age, gender, district, and chosen package using the K-Medoid clustering method. This research uses the K-Medoid method for the reason that it is more resistant to noise and outliers compared to other clustering methods. The most centrally located point in the data set is called a â€medoid,â€ which is an object in a cluster that has the lowest difference to all other objects in the cluster. The results of this study are that the K-Medoid method successfully grouped pilgrims into three clusters: Cluster 1 with 63 members, Cluster 2 with 25 members, and Cluster 3 with 25 members. The findings indicate that the Milad Mastour package is preferred by older pilgrims, primarily from Mataram and West Lombok. The Arbain package is favored by younger pilgrims from the same regions, while adult pilgrims mostly choose the Regular package. The implication of this research is that it can provide insights for service providers to design more specific programs that align with the profiles of pilgrims based on age and district.
Copyright (c) 2024 Dias Nabila Huda, Anthony Anggrawan, Hairani Hairani
Website-Based Expert System for Diagnosing Epilepsy in Children Using the Forward Chaining Method
Information technology has been used in various sectors of life, including the health sector. One of them is the use of expert systems in diagnosing disease. Disease diagnosis carried out by an expert has weaknesses along with the expert's biological weaknesses. One technology that can be a solution is an expert system. This research aims to build a web-based expert system for diagnosing epilepsy in children, along with things that parents can do when treating epilepsy patients. The method used in this research is forward chaining, and system testing is carried out using the Black Box method. From the system design that has been created and tested, a web-based expert system application for diagnosing epilepsy in children has been produced. The black-box testing results show that all menus function well and as expected. The results of expert testing and user testing results obtained a final score of 3.8, which means the assessment is in the very suitable category. Apart from that, it will provide information and education to the public, in this case, the parents of epilepsy patients, regarding the type of epilepsy the child suffers from and how to treat it, which can be accessed anywhere and at any time.
Copyright (c) 2024 Rahmawati Nasser, Subhan Subhan, iin karmila putri
Electric Vehicle Sales-Prediction Application Using Backpropagation Algorithm Based on Web
The accuracy of predicting future product sales is needed to minimize losses and gain profits. Inventory of goods carried out manually or improper product inventory planning causes the number of goods to accumulate due to the small number of requests, so the goods are damaged. Therefore, a sales prediction system with high accuracy is needed to assist in stocking electric vehicles. This research aimed to predict electric vehicle sales using the web-based backpropagation method. This study uses the backpropagation method to predict electric vehicle sales data from 2015 to 2022. The data is divided into 84 instances as training data and 12 instances as testing data. The result of this study was that the backpropagation method obtained a MAPE error rate of 6.25%. Thus, the backpropagation method can be used for predicting electric vehicle sales because it has a very accurate performance level.
Copyright (c) 2023 Ramadhanti Ramadhanti, Hairani Hairani, Muhammad Innuddin
Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation
The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Copyright (c) 2024 Riosatria Riosatria, Hairani Hairani, Anthony Anggrawan, Moch. Syahrir
Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach
The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task.
Copyright (c) 2024 Hairani Hairani, Mengas Janhasmadja, Abu Tholib, Juvinal Ximenes Guterres, Yuri Ariyanto
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods
Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Copyright (c) 2023 I Nyoman Switrayana, Diki Ashadi, Hairani Hairani, Afrig Aminuddin
Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms
Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.
Combination of Smote and Random Forest Methods for Lung Cancer Classification
Lung cancer is a network of cells that grow abnormally in the lungs. Lung cancer has four severity levels, namely stages 1 to 4. If lung cancer is not treated quickly, it is at risk of causing death. This research aimed to combine Synthetic Minority Over-sampling (Smote) and Random Forest methods for lung cancer classification. The method used was a combination of Smote and Random Forest. Smote was used to balance the data, while Random Forest was used to classify lung cancer data. The results showed that the combination of Smote and Random Forest methods obtained an accuracy of 94.1%, sensitivity of 94.5, and specificity of 93.7%. Meanwhile, without Smote, the accuracy is 89.1%, sensitivity is 55%, and specificity is 94.5%. The use of Smote can improve the performance of the Random Forest classification method based on accuracy and sensitivity. There was an increase of 5% in accuracy and a 39% increase in sensitivity.
Copyright (c) 2023 Christopher Michael Lauw, Hairani Hairani, Ilham Saifuddin, Juvinal Ximenes Guterres, Muhammad Maariful Huda, Mayadi Mayadi
Analysis of Vulnerability Assessment Technique Implementation on Network Using OpenVas
Vulnerability Assessment is an important method for identifying and analyzing security vulnerabilities within a network system. This research aims to identify security vulnerabilities within the PT. Dutakom Wibawa Putra network using OpenVAS as a research tool. In the vulnerability analysis phase, OpenVAS is utilized to scan the PT. Dutakom Wibawa Putra network and identify existing vulnerabilities. Subsequently, an evaluation is conducted on the identified vulnerabilities, including risk assessment and necessary mitigation recommendations. The outcomes of this study provide a clear overview of the security vulnerabilities present in the PT. Dutakom Wibawa Putra network. This research using method analysis of vulnerability assessment technique implementation on network using OpenVas, several significant vulnerabilities that could impact the security of the network and systems have been identified. The findings of this analysis report can serve as a foundation for developing improved security strategies and implementing effective mitigation measures. In conclusion, this study successfully applies Vulnerability Assessment techniques to the PT. Dutakom Wibawa Putra network using OpenVAS. The identified vulnerability analysis results offer valuable insights into security weaknesses that need to be addressed. It is hoped that this research can serve as a reference for PT. Dutakom Wibawa Putra and similar organizations in enhancing their network security through the implementation of effective Vulnerability Assessment techniques.
Implementation of Vulnerability Assessment Techniques
Copyright (c) 2023 Muhammad Muharrom, Afif Saktiansyah
The Utilization Of The Conjugate Gradient Algorithm For Predicting School Year Expectations By Province
Expected Length of School (HLS) is the length of school (in years) that is expected to be felt by children at a certain age in the future. It is assumed that the probability that the child will remain in school at the following ages is the same as the probability of the population attending school per total population for the current age. Length of School is also a benchmark for evaluating government programs in improving Human Resources that excel in the competition of technological advances. The purpose of this study is to apply the Conjugate Gradient Algorithm with the Best Performance for Predicting School Life Expectancy in Indonesia. Research data on the Expectation of Schooling in Indonesia consists of 10 Provinces obtained from the Central Statistics Agency from 2016 to 2021. This study uses 5 architectural models, namely 2-10-1, 2-15-1, 2-20-1, 2-25-1 and 2-30-1. Of the five architectural models used, the best architectural model is 2-3-1 with an MSE of 0.000000002 in two seconds. Based on this best architectural model, it will be used to predict the Expectation of Old Schools in Indonesia for the next five years, namely from 2022 to 2026.
Artificial Neural Network, Backpropagation, Cojugate Gradient, Prediction
Copyright (c) 2023 Astri Rismauli Simbolon, Solikhun Solikhun
The Performance Machine Learning Powel-Beale for Predicting Rubber Plant Production in Sumatera
This study aims to predict rubber plants in Sumatra; rubber plants have a relatively high economic value; rubber sap must be cultivated because it is a product of the rubber plant, which is the raw material for the rubber industry, so in large quantities. Therefore, rubber sap, the selling value will increase so that it can increase farmers' income. Rubber production in Sumatra experiences ups and downs; therefore, this study aims to predict rubber plants using the Powell-Beale algorithm method, one of the Artificial Neural Network methods often used for data prediction, implemented using Matlab software. That supports it. This study does not discuss the prediction results. Still, it discusses the ability of the Powell-Beale algorithm to make predictions based on datasets of rubber plant production in recent years obtained from the Central Statistics Agency. Based on this data, a network architecture model will be formed and determined, including 6-10-1, 6-15-1, 6-30-1, 6-45-1 and 6-50-1. The best architecture is 6-15-1, with the lowest Performance/MSE test score of 0.00791984.
Copyright (c) 2023 Siska Rama Dani, Solikhun Solikhun, Dadang Priyanto
The Implementation Of The Fletcher-Reeves Algorithm In Predicting The Growth Of Forest Plant Cultures
Forest protection and development are essential because forests are the world's lungs. In addition, the HTI organization (modern manor backwoods) began to hide again. However, due to the great interest in wood to be used as raw material for material and property production lines, large organizations started to develop hamlet wood which was then marketed abroad, such as pressed wood, rattan, sawn timber, and done jobs for individuals in the area around the hamlet. By making a prediction, knowledge about the growth of forest plants can be known so that they can anticipate or minimize the risks that may arise. They can assist in determining policies and making decisions. This study aims to predict the growth of forest plants in the following year using an Artificial Neural Network Algorithm. The information used in this study is from the Central Bureau of Statistics from 2011 to 2022. The method of implementing this research uses the Fletcher-Reeves Algorithm, one of the Artificial Neural Network methods using 5 models, including 7-10-1, 7-15- 1, 7-20-1, 7-25-1, and 7-30-1. Of the five models, the structural model is 7-20-1 with an MSE value of 0.00037397. It can be said that this model can be used because it produces a fast combination and a short period of time.
Neural Network, Fletcher Reeves, Prediction, Forest
Copyright (c) 2023 Dwi Ramahdhani, Solikhun Solikhun
The Application of the Fletcher-Reeves Algorithm to Predict Spinach Vegetable Production in Sumatra
Determination of spinach plant predictions is one of the most critical decision-making processes. In predicting spinach plants in each period, it depends on each period, both the previous and subsequent periods. The production of spinach plants that change every period causes uncertainty in predicting. The method used to indicate the data is the Fletcher-Reeves algorithm, it is an appropriate development technique compared to the backpropagation strategy because this strategy can speed up the preparation time to arrive at the minimum convergence value. This paper does not discuss the prediction results. Still, it discusses the ability of the Fletcher-Reeves algorithm to make predictions based on the spinach production dataset obtained from the Central Statistics Agency. The purpose of this research is to see the accuracy and performance measurement of the algorithm in the search for the best results to solve the prediction of spinach plants in Sumatra. The research data used are spinach vegetable production data in North Sumatra. Based on this data, a network architecture model will be formed and determined, including 2-20-1, 2-30-1, 2-35-1, 2-45-1, and 2-50-1. After training and testing, these five models show that the best architectural model is 2-20-1 with an MSE value of 0.00608399, the lowest among the other four models. So the model can be used to predict spinach plants in Sumatra.A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety.
Artificial Neural Network, Fletcher-Reeves, Vegetable Plant.
COVID-19 Suspects Monitoring System Based on Symptom recognition using Deep Neural Network
The outbreak of the Corona virus or COVID-19 was still a global concern even though it has been declared an endemic in several countries in the world, including Indonesia. However, with the emergence of new variants of this virus, preventive efforts continue to be made to prevent its spread. To prevent the spread of this virus, early detection was important, especially in knowing prospective clients who are positive and reactive to this virus, thus enabling early isolation measures for prospective patients who are taking action. This identification can be carried out in public areas that are the center of community activities. In this study, an intelligent system will be developed that can detect people suspected of COVID-19 through fever and breathing problem symptoms that can provide solutions to prevent the spread of this virus. Identify these symptoms through thermography-based image processing sourced from thermal camera sensors and then look for the possibility of suspected and reactive COVID19. Furthermore, the AI model was used by the early detection system of people suspected of being positive and reactive for COVID-19 using the Deep Neural Network method. This study aims to identify symptoms of fever and respiratory infection through image processing sourced from thermal camera sensors and further diagnose prospective patients who are suspected of being positive and reactive for COVID19 using the CNN method as an intelligent system for early detection of suspected positive and reactive COVID19 patientsIn the process of testing the classification training model, the performance results in the CNN classification process have an accuracy value of more than 88%. Furthermore, a comparison was made between the CNN classification and other classifications, such as SVM, Naive Bayes and Multi-Layer Perceptron (MLP). The results obtained from this comparison have an average percentage of accuracy above 80%. MLP has the lowest accuracy among its classification methods of 83.56%. CNN has the highest accuracy value compared to other methods of 88.68%. Therefore, CNN can be chosen to be the right one for use in the COVID-19 suspect detection system through the recognition of symptoms and respiratory disorders. Based on these performance measurements, the process of detecting COVID19 suspects indicated by health symptoms can be applied to real data.
Designing a Honey Quality Tool Based on Gas Sensor and Color Sensor
Honey has many benefits. Thus, honey counterfeiting often occurs with the ever-increasing demand. However, many do not know how to distinguish between real and fake honey, even honey breeders and hunters find it difficult to tell the difference. The honey used to test the quality of honey is honey produced by Apis dorsata bees or wild bees, the nectar consumed by these bees is the kesambi tree, the kesambi tree that grows a lot on the slopes of Mount Tambora, Bima district. Honey contains a lot of antioxidants such as vitamin C, pinocembrin, chrysin, pinobaksin, catalase, and many other ingredients that are very beneficial for the health of the body. Testing the authenticity of honey using two sensors, namely a gas sensor and a color sensor, the tool has been connected to a database and web application to display test data. The web application can be accessed from any location as long as it is connected to the internet network. The results of the research are the ideal distance for measurements is carried out as far as 2 cm. The success rate in testing pure honey with a mixture of honey has different values, such as the value of pure honey has a gas sensor voltage of 3.3 volts while the value of mixed honey with 50% pure honey and 50% sugar, the value of the gas sensor voltage is 2.54 volts. Mixed honey has a voltage below 3 volts. The results of the color sensor test, namely the sensor output have different values, the RGB value for pure honey is red 206, green 246, and blue 182 while the RGB value for mixed honey is for 20% pure honey and 80% sugar mixture produces an RGB value of Red 156, Green 210, Blue 171. The color sensor can distinguish between real honey and mixed honey for trigona honey.