Jurnal Bumigora Information Technology (BITe)
Jurnal Bumigora Information Technology (BITe) merupakan salah satu jurnal milik Universitas Bumigora yang dikelola oleh Jurusan Ilmu Komputer. Jurnal ini dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi.
Optimizing Language Models and Text-Based EconomicSystems with Proximal Policy Optimization
Background: This study investigates the use of the Proximal Policy Optimization (PPO) algorithm in two text-based case studies: alignment of large language models (LLMs) with human preferences and dynamic pricing based on customer reviews. In the LLM case, PPO combined with preference-based learning significantly improves alignment, BLEU, and human-likeness scores. Objective: This research aims to evaluate PPO’s effectiveness in text-based decision-making through these two cases. Methods: The method employed is reinforcement learning experimentation using the PPO approach. For the LLM case, PPO is integrated with preference learning to enhance alignment, BLEU, and human-like output. Meanwhile, in the economic scenario, PPO produces adaptive pricing strategies with high accuracy or low Mean Absolute Error (MAE) and the best cumulative rewards, outperforming the A3C and DDPG algorithms. Cross-validation and ablation studies assessed PPO’s generalization capability and the contribution of reward components, clipping, and exploration strategies. Result: The findings demonstrate that PPO excels across distinct domains and offers a stable and efficient solution for text-based tasks. Conclusion: The findings confirm its flexibility for various NLP applications and intelligent decision-making systems
Copyright (c) 2025 Irwan Darmawan, Nilam Ramadhani, Mohammad Nazir Arifin, Ubaidi -, Nindian Puspa Dewi, Muhammad Innuddin http://creativecommons.org/licenses/by/4.0
Naive Bayes Algorithm Implementation for Hair LossPrediction
Background: Hair loss is a common problem that can affect a person’s self-confidence. Early prediction of the risk is important to help with more appropriate treatment. Objective: This study aims to apply the Na¨ıve Bayes algorithm to predict hair loss based on personal data and clinical factors such as age, gender, stress levels, hormones, and family history. Methods: The Na¨ıve Bayes method was chosen because it efficiently handles categorical data. The data used in this study were obtained from a public dataset available on the Kaggle platform, which contains individual information about the risk of hair loss. Result: The developed prediction model can classify risks based on various causal factors, but its performance is still low with an accuracy of 55.5%, AUC 0.593, and MCC 0.113. Conclusion: These results indicate that the model is unreliable for practical applications. The implication is that this system can be the basis for further development with more complex algorithms, the addition of clinical features, and stronger validation so that it can be applied effectively in medical contexts and personal consultations.
Copyright (c) 2025 Ani Yoraeni, Syifa Nur Rakhmah http://creativecommons.org/licenses/by/4.0
Enhancing Vehicle Communication on Highways through Modification of the On-Demand Distance Vector Routing Protocol Using Learning Automata Approach
Backgroud: Vehicle-to-vehicle communication has become a crucial element in the development of intelligent transportation systems. However, conventional routing protocols face limitations in coping with dense and dynamic traffic conditions. Objective: The objective of this study is to improve communication efficiency between vehicles by modifying an on-demand routing protocol using a learning automata approach. Method: This study employed a simulation method with traffic modeling using traffic modeling software and network simulation tools, based on data from highways in the Soekarno-Hatta International Airport area. Result: The results of this study show that the developed protocol increases the packet delivery ratio to 87.7% and reduces latency by 6.5%. Conclusion: The conclusion of this study is that the application of learning automata in vehicle routing enhances communication reliability and supports the implementation of a more adaptive and efficient transportation system.
Copyright (c) 2025 Bryan Jonathan Hutapea, Ketut Bayu Yogha Bintoro, Helna Wardhana http://creativecommons.org/licenses/by/4.0
Property Database Integration Using Schema Matching with Linguistic and Constraint
Background: The rapid development of technology has driven progress across various sectors, including the property industry in Indonesia. However, property data integration on Lombok Island still faces challenges due to the diversity of attribute naming, which hinders efficient information retrieval. Objective: This study aims to integrate four property databases (Saduthama, Salva, SJP, and Garden View) using a schema matching method based on linguistic and constraint approaches. Methods: The linguistic approach is used to identify similarities between attributes, even when their names differ, using the Bigram technique, which proved effective in identifying attribute similarities with a threshold of 0.7. Meanwhile, the constraint approach evaluates the compatibility of attributes based on additional criteria such as data type, attribute length, null values, and uniqueness, ensuring that the integrated attributes work compatibly. The integration process includes preprocessing, generalization, and attribute matching. Result: The evaluation results show precision (P), recall (R), and F-measure of 90%, with an average accuracy of 84%. Conclusion: This result outperforms previous studies that achieved 100% precision, 60% recall, and 75% F-measure.
Copyright (c) 2025 Muhammad Hariri, Bambang Krismono Triwijoyo, Gallih Hendro Martono http://creativecommons.org/licenses/by/4.0
Comparison of Decision Tree-Based Methods in Lung Disease Detection
Background: Lung disease is a leading cause of death globally, with more than 4 million cases each year, including 500,000 new cases in Indonesia, most of which are detected at an advanced stage. Objective: This study aims to compare the performance of three decision tree algorithms, XGBoost, C4.5, and Random Forest, in detecting lung disease and to determine the best method based on evaluation metrics. Methods: A total of 30,000 data samples from Kaggle were processed through a cleaning stage using the IQR method, categorical attribute coding, and data division into 80% for training and 20% for testing. The classification models used include XGBoost, C4.5, and Random Forest. Model performance evaluation used a confusion matrix, accuracy, precision, recall, and F1-score. Result: The results showed that the C4.5 algorithm had the best performance with an accuracy of 94.33% and zero false negatives. XGBoost followed with an accuracy of 93.18%, while Random Forest was the lowest (90.07%). Conclusion: These findings indicate that C4.5 has great potential in an accurate early detection system, helping to reduce the risk of misdiagnosis, especially in false negative cases, and supporting clinical decision making in health facilities.
Copyright (c) 2025 Lely Kurniawati, Dadang Priyanto, Neny Sulistia Ningsih, Moch Syahrir, Ria Rismayati http://creativecommons.org/licenses/by/4.0
Security Analysis of University Websites in West Nusa Tenggara against SQL Injection, Cross Site Scripting and Insecure Direct Object Reference Attacks through Penetration Testing
Background: In the digital era, cybersecurity is important for universities in protecting academic information and user data. The focus of this research is to identify and analyze the security vulnerabilities of higher education websites in West Nusa Tenggara against three types of attacks, namely SQL Injection, Cross Site Scripting (XSS), and Insecure Direct Object Reference (IDOR), which can compromise the integrity of higher education data and information systems. Objective: This research aims to evaluate the level of vulnerability and severity of the risk of the three types of attacks on the websites of higher education institutions. Methods: This research uses penetration testing methods, and assesses the severity of vulnerabilities based on the Common Vulnerability Scoring System (CVSS) version 3.1. Result: This research results show that 50% of the ten college websites tested are vulnerable to XSS attacks, 30% to SQL Injection, and 20% to IDOR. The highest severity was found in the SQL Injection vulnerability with a CVSS score of 9.0 critical category. Conclusion: The implications of the results of this study indicate that higher education institutions need to immediately strengthen system security with strict input validation, WAF implementation, and adequate authorization mechanisms to prevent future exploitation of similar vulnerabilities.
Copyright (c) 2025 Dhira Wahyu Febrian, Raphael Bianco Huwae, Ahmad Zafrullah Mardiansyah http://creativecommons.org/licenses/by/4.0
Classification of Learning Styles of Junior High School Students Using Random Forest & XGBoost Algorithm
Background: Accurately identifying students' learning styles so that educators can adjust their teaching methods accordingly is a challenge in the field of education. However, the application of Machine Learning for learning style classification has not yet been implemented in schools in Mataram City. Objective: This study aims to classify the learning styles of students at Junior high school (SMP) Negeri 2 Mataram using Random Forest and XGBoost algorithms. Method: Data were collected through questionnaires completed by students in grades 7, 8, and 9. The results of data exploration (EDA) show data imbalance in the collected classes. Result: These results indicate that both algorithms performed well in classifying learning styles, with XGBoost showing slightly better performance. However, the accuracy obtained is not yet optimal, likely due to the limited dataset size. To address data imbalance, the SMOTE technique was applied. Initial evaluation showed that both XGBoost and Random Forest achieved an accuracy of 80%. After Hyperparameter Tuning, the accuracy of XGBoost increased to 84%, while Random Forest reached 82%. Conclusion: This study contributes to the application of Machine Learning in the education sector and highlights the need for further research to enhance model performance.
Copyright (c) 2025 Christine Eirene, Dian Syafitri, Neny Sulistianingsih, Khasnur Hidjah, Hairani Hairani http://creativecommons.org/licenses/by/4.0
Development of an Information System for Admission of New Students Using Waterfall to Increase the Efficiency of Registration
Background: Problems faced in the previous manual system include long queues, data inaccuracy, and slow administration, which hamper services to prospective students and parents. Objective: This study aims to improve the efficiency of the New Student Admissions (PPDB) process at Junior High School (SMP) Islam Abdul Wahid through the implementation of an online PPDB website. Method: The method used in this study is a software development approach with a waterfall model, which includes the stages of needs analysis, design, implementation, and system testing. Research data were obtained through interviews with administrative staff and questionnaires to prospective students and parents. This website is designed using web technologies to ensure reliability and ease of use. Result: The results of this study are that the online PPDB website is able to speed up the registration process, increase data accuracy, and significantly improve the service system. Conclusion: The conclusion of this study is that the implementation of the online PPDB website provides an effective solution in improving the efficiency of school administration and can be applied in other educational institutions as a form of digital innovation in the modern era.
Copyright (c) 2024 Mochammad Faid, Novaldo Jaka Setiawan, Yanuar Ricky Alfiandri
Web-Based Pistol Storage Security Monitoring System Optimization for Database Effectiveness
Background: Gun storage security is an important aspect in supporting operations, but manual monitoring often creates obstacles to the effectiveness and accuracy of gun storage data management. Objective: The purpose of this study is to optimize the gun storage security monitoring system with web-based technology to improve the effectiveness and accuracy of the database. Methods: The method used in this study is the development of a web server-based system that is integrated with real-time monitoring features, automatic notifications, cloud-based data storage to facilitate data access and tracking. Result: The results of this study are the creation of a system that is able to improve the accuracy and responsiveness of gun storage monitoring, as well as improve the efficiency of database management. Conclusion: In conclusion, the implementation of this web-based system has the potential to reduce security risks and significantly increase the effectiveness of monitoring in gun storage.
Copyright (c) 2024 Ahmad Lukman Nur Rokhim, Mokhammad Syafaat, Kasiyanto Kasiyanto, Dekki Widiatmoko, Aguk Sridaryono
Detection of Lumpy Disease in Livestock Using the MobileNetV2 Architecture Method
Background: Lumpy Skin Disease (LSD) causes skin lesions, decreased milk production, and death in livestock such as cows. Objective: The purpose of this study is to detect LSD disease quickly and accurately using the Convolutional Neural Network (CNN) MobileNetV2 method based on android application. Method: This study uses a quantitative method with a reuse-oriented development approach and the MobileNetV2 algorithm trained with augmentation data and LSD disease image classification. Result: The results of this study are that the MobileNetV2 classification model is able to detect LSD with an accuracy of 95.91%. The developed application makes it easier for farmers to detect diseases early so that they can accelerate preventive measures. Conclusion: The implications of this study indicate that the MobileNetV2 model can improve the effectiveness of disease detection in livestock and can be applied in animal health applications in the field.
Copyright (c) 2024 Dion Pratama Putra, Giri Wahyu Wiriasto, Paniran Paniran
Lung Sound Classification Using Mel Frequency Cepstral Coefficients and Convolutional Neural Networks
Background: Challenges in diagnosing respiratory disorders are often caused by the lack of technological tools capable of accurately recognizing lung sound patterns, thereby reducing the potential for subjective misdiagnosis by medical personnel. Objective: This study aims to develop a lung sound classification model that is able to detect respiratory disorders early and accurately. Methods: The method used includes a combination of data augmentation techniques and Mel Frequency Cepstral Coefficient (MFCC) feature extraction to improve the performance of Convolutional Neural Network (CNN) in classifying lung sounds. A total of 1,350 lung audio recordings were categorized into nine classes, including normal and abnormal sounds. The augmentation techniques applied include the addition of white noise, pitch scaling, time stretching, and random gain to enrich the variety of training data. Result: The results show that the E-CNN2D model is able to achieve an accuracy of up to 95%, surpassing the previous model, which had an accuracy range of 83-93%. Conclusion: With these results, this study has the potential to be a fast and accurate diagnostic tool solution so that it can support medical personnel in reducing the risk of subjective misdiagnosis in respiratory disorders.
Copyright (c) 2024 Sayyidis Syariful Halim, Bulkis Kanata, Syamsul Irfan Akbar
Improving Large Language Model’s Ability to Find the Words Relationship
Background: It is still possible to enhance the capabilities of popular and widely used large language models (LLMs) such as Generative Pre-trained Transformer (GPT). Using the Retrieval-Augmented Generation (RAG) architecture is one method of achieving enhancement. This architectural approach incorporates outside data into the model to improve LLM capabilities. Objective: The aim of this research is to prove that the RAG can help LLMs respond with greater precision and rationale. Method: The method used in this work is utilizing Huggingface Application Programming Interface (API) for word embedding, store and find the relationship of the words. Result: The results show how well RAG performs, as the attractively rendered graph makes clear. The knowledge that has been obtained is logical and understandable, such as the word Logistic Regression that related to accuracy, F1 score, and defined as a simple and the best model compared to Naïve Bayes and Support Vector Machine (SVM) model. Conclusion: The conclusion is RAG helps LLMs to improve its capability well.
Copyright (c) 2024 Sirojul Alam, Jaka Abdul Jabar, Fauzi Abdurrachman, Bambang Suharjo, H.A Danang Rimbawa
Classification of Atopic Dermatitis and Psoriasis Skin Diseases Using Residual Network (ResNet-50)
Background: Atopic dermatitis and psoriasis are common skin diseases with similar symptoms, characterized by abnormally red or inflamed epidermal lesions and varying degrees of skin thickening. However, they are distinct conditions, making it crucial to understand how to differentiate between them. This understanding can help reduce stigma and the risk of comorbidities, thereby improving patients' quality of life and preventing more serious health risks. Objective: The aim of this research is to increase accuracy in classifying the skin diseases atopic dermatitis and psoriasis using the Residual Network (ResNet-50) model without overfitting, and compare it with the MobileNet model to find the best approach. Method: The method used in this study is the ResNet-50 architecture for skin disease classification, namely atopic dermatitis and psoriasis. The selection of the ResNet-50 model is based on the use of shortcut connections that allow the application of deeper networks without experiencing the problem of vanishing gradients. Result: The results showed that the best accuracy reached 92.75% for training data and 88.00% for testing data, with a data ratio of 80%:10%:10%. In addition, the confusion matrix results from the best model showed that the precision, recall, and F1 score values for both diseases were between ≥80% and ≤96%. Conclusion: The ResNet-50 method in scenario 1 outperformed other scenarios, improving classification accuracy and enhancing diagnostic effectiveness and medical practice development.
Copyright (c) 2024 Rakhimatulfitria Mekacahyani, Badie’ah Badie’ah, Imam Much Ibnu Subroto
KosyFinder E-Commerce System Based on Web Geographic Information System and Expert System
Background : The current problem of students and the community in searching for boarding house information only relies on information from friends, relatives or manual searches in searching for boarding house availability. Currently, the availability of information service provider platforms for boarding houses is now abundant, especially those based on websites or android-based. Boarding house information services based on websites or android-based only provide general information related to boarding house information, have not been able to provide comprehensive information. Objective : The purpose of this study is the KosyFinder e-commerce platform integrated with the Web Geographic Information System (WebGis) and Expert System which can help users in choosing a boarding as needed. Method : The research method used is Research and Development using the waterfall development model and the certainty factor (CF) method in diagnosing the health of the residential environment around the boarding house. Result : The results of this study were able to produce an e-commerce application product KosyFinder that was able to be integrated with WebGis and expert systems. The results of expert testing and user testing obtained a final value of 3.8 (very suitable), which means that the resulting system can be used. Conclusion : The platform that develops is functioning properly and as expected in accordance with the results of expert and user testing in the category is very appropriate.
Copyright (c) 2024 Hardiana Hardiana, Siaulhak Siaulhak, Andi Jumardi, Iriansa Iriansa
Performance Improvement of The Random Forest Method Based on Smote-Tomek Link on Lombok Tourism Analysis Sentiment
Background: Tourists visiting Lombok Island can access various sources of tourist information and can share their views and tourist experiences through social media such as positive and negative experiences. Objective: This research aims to analyze the sentiment of Lombok tourism reviews using the Smote-Tomek Link and Random Forest algorithms. Methods: The research was carried out in several stages, namely collecting the Lombok tourism dataset, text preprocessing, text weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method, data sampling using SMOTE-Tomek Link, text classification using Random Forest, and the final stage was performance testing based on accuracy. Result: The research results obtained using the Smote-Tomek Link and Random Forest methods in sentiment analysis analysis of tourist reviews about Lombok were 94%. Conclusion: The use of the Smote-Tomek Link and Random Forest methods in Lombok tourism sentiment analysis produces very good accuracy.
Copyright (c) 2024 Khairan Marzuki, Lalu Ganda Rady Putra, Hairani Hairani, Lalu Zazuli Azhar Mardedi, Juvinal Ximenes Guterres
Implementation of Multi-Attribute Utility Theory (MAUT) Method in Gaming Laptop Selection
Background: This time, laptops are an essential piece of technology because these instruments can be used for various tasks, including work, play, and education. Objective: This research aims to develop a decision support system using the MAUT method for selecting gaming laptops. Methods: The method used in this research is MAUT which is applied to the selection of gaming laptops. Result: Based on calculations that have been carried out using the MAUT method, it is found that the MSI Cyborg Katana laptop (B13VGK) is the first ranked gaming laptop with a utility value of 0.7035. Conclusion: The implementation of MAUT for gaming laptop selection is proven to be applicable.
Copyright (c) 2024 Suci Ramadhani, Wahyu Ardiantito S, Stacyana Jesika
Analysis of Tourist Sentiment towards Tourist Attractions in the Mandalika Special Economic Zone Using the Naïve Bayes Method
The Mandalika Special Economic Zone has become one of the most popular destinations for both domestic and international tourists. This popularity highlights the importance of understanding the views and feelings of the tourists. Therefore, this study aims to analyze tourist sentiment towards the attractions in the Mandalika Special Economic Zone. The data analyzed was obtained from 1,144 reviews on the TripAdvisor platform. The research stages included data collection, data labeling, data preprocessing, data transformation, data classification, as well as data analysis and visualization. The results of this study indicate that the majority of tourists have a positive sentiment towards the attractions in the Mandalika Special Economic Zone. Furthermore, testing with the Naïve Bayes algorithm successfully classified tourist sentiments accurately, with consistent accuracy rates obtained from each fold: fold 1: 89.08%, fold 2: 89.96%, fold 3: 88.21%, fold 4: 87.34%, and fold 5: 90.79%.
Sentiment Analysis; TripAdvisor; Mandalika; Naïve Bayes
Copyright (c) 2024 Teguh Iman Pribadi, Fahry Fahry, Muharis Muharis, Ega Dwi Putri Marswandi
Analysis of Rclone Implementation for File Sharing Security on OneDrive Cloud Storage
Background: OneDrive provides a reliable infrastructure for storing and managing user data. However, along with the benefits, the security challenges associated with storing and sharing files in the cloud are also increasing. One of the main aspects of cloud storage security management is the file-sharing process. While OneDrive offers a few security features, such as access control and two-factor authentication, there remains a need to strengthen additional layers of security, especially when sharing files with external parties. Objective: This research aims to analyze the effectiveness of Rclone in improving file-sharing security on OneDrive, especially File Backup and File Encryption. Methods: This research uses the Network Development Life Cycle (NDLC) method, which includes six stages. This research focuses on three stages: analysis, design, and simulation prototyping. Result: The application of Rclone for file sharing security on OneDrive can increase data security in cloud storage, as evidenced by three backup attempts on .doc files with an average time of 2.033 seconds and successful encryption of 4 files with an average time of 37 seconds. Conclusion: Using Rclone as a tool to manage data security on OneDrive improves data protection and provides flexibility and efficiency in file management, making it an effective solution for file sharing security needs in cloud storage
Rclone, File Sharing, Onedrive, Cloud Storage
Copyright (c) 2024 Lilik Widyawati, Muhammad Azwar, Hairudin Alafi, Iqra Asif
Rancang Bangun Sistem Penyiraman Otomatis Berbasis Internet of Things Pada Tanaman Hias Sirih Gading
Kemajuan teknologi dari waktu ke waktu telah berkembang dengan pesat sehingga dapat memberikan banyak kemudahan bagi manusia untuk melakukan pekerjaan sehari-hari seperti melakukan perawatan pada tanaman hias yang berada di dalam rumah. Salah satu contoh tanaman hias yang ada dirumah yaitu tanaman sirih gading. Untuk merawat tanaman diperlukan air untuk melakukan penyiraman. Penyiraman yang teratur merupakan rutinitas penting di lakukan untuk menjaga tanaman terus tumbuh dan berkembang. Pekerjaan yang dilakukan secara rutin dan terjadwal dapat dilakukan dengan memanfaatkan teknologi sensor dan internet of things (IoT) untuk melakukan kontrol dan pemantauan. Teknologi tersebut dapat digunakan untuk membangun sistem yang berfungsi untuk melakukan perawatan dan penyiraman pada tanaman hias sirih gading. IoT merupakan sebuah sistem yang memungkinkan setiap device dapat berkomunikasi, melakukan kontrol dan pemantauan melalui jaringan internet. Hasil yang dicapai setelah penelitian ini dilakukan yaitu dihasilkan sebuah sistem penyiraman otomatis berbasis internet of things dengan menggunakan NodeMCU yang terintegrasi dengan Telegram untuk melakukan perawatan dan penyiraman pada tanaman hias sirih gading. Hasil dari penelitian ini yaitu suhu lingkungan pada tanaman berada pada rentang 24oC–29oC dan rata-rata error pengukuran suhu menggunakan sensor DHT11 yaitu sebesar 2,07%. Pengukuran kelembaban tanah pada tanaman hias sirih gading berada pada rentang 47%-65%.
Copyright (c) 2020 Putri Ayu Wulandari, Phyta Rahima, Sirojul Hadi
Internet Machine Prototype with a Coin Payment Using a Rock Pi Microcontroller
Background: Coins are widely used in game machines to play game applications. Internet use is usually done by purchasing an internet package. This method is often used, but coins are rarely used. Objective: The aim of this research is how to make a machine that is capable of using coins as internet payment, especially hotspots. Methods: The research method was carried out using an experimental method with stages of analysis, prototype design, and testing using a descriptive approach. Result: This research produced 1 one-unit prototype machine equipped with an application for internet hotspots by inserting IDR 500 and IDR 1000 coins with connection limits according to the value of the coins entered. Conclusion: This research produces prototypes and coin internet application software.
Copyright (c) 2024 Raisul Azhar, Heroe Santoso, I Putu Hariyadi, Husain Husain, Mahsus Afriandi
Feasibility and Performance Evaluation of Microprocessor and Fuzzy Logic-Based Overcurrent Relay in Electrical Protection Systems
This research focuses on the development and testing of a microprocessor-based Overcurrent Relay (OCR) with the implementation of fuzzy logic control for adaptive setting of TMS values. OCR is a crucial component in the electricity protection system which aims to detect and respond to disturbances due to excessive current, preventing damage to electrical equipment. The aim of this research is to test the feasibility and performance of microprocessor-based OCR with fuzzy logic control in setting TMS values, and compare it with the international standard IEC 60255. The method used in this research is the implementation of the Sugeno type fuzzy logic algorithm on the Atmega 328P microcontroller. This algorithm is used to control the TMS value based on the rotating frequency of the detected overcurrent. The results of this research are a characterization of the OCR response to variations in streaming current and different PS settings. The lowest TOp deviation was observed at higher PS settings, with the best performance in the IDMT OCR type. The use of fuzzy logic control is able to increase the OCR response to overcurrent disturbances, although under normal conditions it sometimes results in a longer TOp. The conclusion of this research is that microprocessor-based OCR with fuzzy logic control can provide protection performance that is adaptive and responsive to dynamic operational environmental conditions. This implementation has the potential to increase the efficiency and leakage of the electricity protection system
Copyright (c) 2024 Bima Romadhon Parada D.P, Citra Dewi Megawati, Eko Nurcahyo, Taufik Hidayat, Rachmadi Setiawan, Kurniadin Abd Latif
Determining a Digital Marketing Strategy Using a Combination of Analytical Network Process (ANP) and Profile Matching
Background: Many MSMEs in the city of Matram are experiencing diculties in determining the digital marketing strategy to use. This is due to the many digital marketing strategies that can be used and the many factors that serve as criteria for selection. Objective: Develop a decision support system using a combination of ANP and profile-matching methods to assist MSMEs in determining the digital marketing strategy to be used. Methods: The method used in this research is a combination of ANP and Profile Matching Methods. Result: The combination of methods (ANP) and Profile Matching in determining digital marketing strategies has an accuracy of 83.33%. Conclusion: The combination of ANP and Profile Matching methods in determining digital marketing strategies has successfully recommended the best digital marketing strategy.
Copyright (c) 2024 Muhammad Mujahid Dakwah, Mohammad Najib Roodhi, Djoko Suprayetno, Iwan Kusmayadi, Abdurrahman Abdurrahman, Rifqi Hammad
Utilization of the Internet of Things in Monitoring Hydroponic Lettuce Cultivation
Background: Urban residents who create agricultural systems with hydroponic techniques are very busy and do not have time to monitor plant growth. Objective: This study aims to utilize the Internet of Things (IoT) in monitoring hydroponic lettuce cultivation with the Nutrient Film Technique (NFT) system using ESP32. Methods: The method used in this study is Rule-Based Automatic Control for monitoring hydroponic lettuce cultivation. Result:The results of this research are successfully building a monitoring system with a web interface and mobile interface has been successfully built. The system that was built successfully controlled pH and hydroponic nutrients automatically with a pH sensor accuracy of 97.19% and a nutrient sensor of 97.815%. Conclusion: The implementation of a control and monitoring system for hydroponic lettuce cultivation can be applied because it has high accuracy.
Copyright (c) 2024 Heri Nugroh, Joni Maulindar, Ridwan Dwi Irawan
Shoe Dryer Using Fuzzy Logic Controller with Internet of Things-Based Monitoring
Background: When the rainy season arrives, the main problem faced is the drying process of shoes due to uncertain weather. Moreover, drying the shoes directly under the sun can reduce the glue's durability and damage the shoes' material. Objective: This research aims to create an automatic shoe dryer that can be monitored online and uses fuzzy logic to maintain a stable temperature to maintain shoe durability. Methods: This research uses the fuzzy Mamdani method with ESP32 as a microcontroller to regulate the temperature stability of the shoe drying box. Results: Sneaker shoes were found to dry at 52% humidity and stable temperature at ± 410C within 82 minutes. Meanwhile, leather shoes dried at 62.9% humidity, and the temperature stabilized at ±350C within 20 minutes. Conclusion: The application of Mamdani fuzzy logic has an effect in stabilizing the temperature; if the drying box is tested without using a controller, the temperature will continue to increase, and the humidity will drop quickly, resulting in shoes not being completely dry.
Copyright (c) 2024 Aqsal Renaldi Permata Dewata, Parama Diptya Widayaka, Puput Wanarti Rusimamto, Farid Baskoro
Implementation of the Multi Attribute Utility Theory (MAUT) Method for Selecting the Best Affiliate Marketing
Background: Digital marketing has become crucial for companies to remain relevant and grow. One common marketing strategy used is through collaboration with affiliate marketing. However, many companies still need help selecting the best affiliate marketing, often relying on subjective approaches. Objective: This research aims to develop a Decision Support System (DSS) using the MAUT method to choose the best affiliate marketing in the digital marketing industry. Method: This research method involves applying the MAUT method to identify normalization value variations among affiliate marketing options, resulting in a ranking of affiliate marketers. Result: The results of this research show that Sugiharto, Rifan Jauhari, and Nina Dwi Lestari rank the highest, with respective preference values of 0.8750 and 0.8625. Conclusion: The developed DSS successfully manages affiliate marketing data, providing valuable information for decision-making processes. This study contributes significantly to data management and marketing and has potential applications in various business contexts.
Copyright (c) 2024 Wahib Mubarok, Tino Feri Efendi, Siti Rokhmah
The Autoregresiive Integrated Moving Average and Fuzzy Time Series Cheng Hybrid for Predicting Stock Price
Background: PT Telkom Indonesia Tbk is the largest company in the telecommunications sector in Indonesia. PT Telkom's share price always rises every year, attracting investors to invest. In investing, it is very important to analyze shares in order to know the situation and condition of the shares. Objective: This research aims to predict the share price of PT Telkom Indonesia Tbk. Methods: The method used is the Autoregressive Integrated Moving Average (ARIMA)-Fuzzy Time Series Cheng hybrid method. Cheng's FTS model is able to overcome nonlinearity problems in ARIMA model residuals. In this research, the first modeling uses the ARIMA model, where the data is divided into two, namely January to November 2019 data used as training data, and December 2019 data used as testing data. Next, residual modeling was carried out with FTS Cheng. Hybrid forecasting is obtained by adding up the results of ARIMA and FTS Cheng forecasts. Result: Model evaluation is based on MAPE values and in this study the MAPE value of the ARIMA-FTS Cheng hybrid model was obtained at 1.03\% for training data and 1.09\% for testing data. Conclusion: The hybrid model has a MAPE value of less than 10\%, so it can be concluded that the ARIMA-FTS Cheng hybrid model can predict PT Telkom Indonesia Tbk stock closing price data accurately.
Copyright (c) 2024 Ignasia N.G. Neyun, Winita Sulandari, Isnandar Slamet
Designing a Promotional Media Website for Pottery Products Using the Waterfall Method
Background: Penujak Village is one of the oldest pottery tourism villages that has succeeded in providing pottery production for foreign collectors. After the Bali bombing incident, Penujak pottery experienced a decline, which could trigger several other factors, including the development of promotions. Promotions used by the Penujak community still use traditional word-of-mouth promotions. Objective: This research aims to create a website to help promote pottery products in Penujak Village. Methods: This research uses a qualitative approach with the waterfall system development method. Result: The research produced website promotion interval values of 29, 1 being > 26, 6 31, 2 which can be categorized as 4 = Agree. Conclusion: Pottery promotional media in the Penujak tourist village is suitable for use as promotional media. Tests were carried out on promotional media using usability theory with seven questions fulfilled.
Copyright (c) 2023 Miftahul Madani, Haryono Haryono
Translation Motion-Based Backpack for Indonesian Army Soldiers as a Source of Energy for Military Communication Devices
Background: Energy is an aspect that is very necessary in human life, because every activity we do consumes and produces energy. Among various sources of energy, human physical activity is a potential energy source that can be utilized. Objective: This research aims to design a backpack with translation motion as an energy source for military communication devices. Methods: The design of this tool uses the research and development method. Result: The results of the study indicate that walking produces a voltage of approximately 3.863 volts, climbing stairs produces 4.56 volts, and running generates 5.39 volts. The voltage generated increases with the level of human movement involved. Conclusion: In conclusion, the more human movement is involved, the higher the voltage generated. The calculated spring constant is Ktotal = 22.612 N/m, with a unit spring constant of 11.306 N/m. F
Copyright (c) 2023 Jerry Barto Naga, Dekki Widiatmoko, Kasiyanto Kasiyanto
E-Marketing Electronic Products Using Cross Selling
Background: CV Permata Hijau Delapan is a shop engaged in Electronic sales that has not implemented a strategy on product sales and marketing, this shop has introduced a sales system through social media, but has not used a recommendation system on the website. Objective: To examine the application of cross selling strategies in the e-market of electronic products owned by CV Permata Hijau Delapan. Methods: The system can recommend products that are widely purchased by consumers of CV Permata Hijau Delapan by applying the cross selling method. Result: The system built accommodates the process of sales transactions and product orders online. Survey through google form to customers. It can be seen that the average percentage of each customer satisfaction is towards the product by 65.1%, towards the price by 74%, towards customer service by 75.6%, and towards the shopping experience by 79.8% overall. It can be seen that the dominant customer satisfaction in the survey is towards the shopping experience of 79.8%. Conclusion: This study shows that the implementation of cross-selling strategies in the e-market of electronic products CV Permata Hijau Delapan is efective in increasing sales.
Copyright (c) 2023 Abdullah Abdullah, Megawaty Megawaty
Technology Biomechanic-Based Design of Knee Protector Generator for Portable Electricity Generation to Support Military Operations in the Field
Background: In the 5.0 era which depends on electrical energy, innovation is needed to overcome the limitations of batteries in electronic devices. An innovative device has been created that uses knee joint movement to produce renewable energy. Objective: Knee movements are converted into electrical energy through a special gearbox that rotates a generator, converting the energy to a power bank as a storage medium. Methods: This study used an experimental method. Result: This research produces 5.6 Watts of power, providing an eective solution for charging batteries in mobility conditions. Conclusion: By utilizing knee joint movements and biomechanical analysis, this tool can utilize dierences in force, angle and speed of movement to produce the required electricity.
Copyright (c) 2023 Andik Sugiarta, Kasiyanto Kasiyanto, Dekki Widiatmoko, Mokhammad Syafaat, Afif Achmad, Iqra Asif