Jurnal Varian (e-ISSN. 2581-2017) is one of the scientific journals at Universitas Bumigora (former STMIK Bumigora Mataram) . This journal aims to provide a forum or means of publication for lecturers, researchers and practitioners both in the internal and external environment of Universitas Bumigora. This journal is published 2 (two) times a year in the Even (April) and Odd (October) periods. The Jurnal Varian focuses on publicizing Statistics, Mathematics and their aplication . For more information, contact the admin via email: varian@universitasbumigora.ac.id e-ISSN. 2581-2017
Panel Data Regression Modeling with Weighted Least Squares Method Using Fair Weights
Panel data regression is a robust method for analyzing relationships between dependent and independent variables by combining time-series and cross-sectional data. Its reliability hinges on key assumptions, particularly homoscedasticity. Violations, known as heteroscedasticity, lead to inefficient estimates and biased inference, as estimators fail to meet the Best Linear Unbiased Estimator criteria. The Weighted Least Squares (WLS) method addresses heteroscedasticity by weighting observations based on the inverse of their variance. WLS assumes prior knowledge of the heteroscedasticity structure, which is often impractical, creating gap in evaluating its effectiveness compared to alternative methods. The purpose of this study is to examines life expectancy in South Sulawesi as the dependent variable, with expected years of schooling, per capita expenditure, and average years of schooling as independent variables. The research methode used WLS with reasonable weighting, successfully addressing heteroscedasticity. The fixed-effects model was identified as the most appropriate, with an R-squared of 99.45%. Life expectancy was explained by the model. Results shows all variables positively and significantly influence life expectancy. In conclusion, the WLS method effectively overcomes heteroscedasticity in panel data regression, providing reliable estimators. This study highlights the importance of method selection in panel data analysis and offers insights for policymakers aiming to improve life expectancy in South Sulawesi.
Copyright (c) 2025 Muhammad Ferdiansyah, Raupong Raupong, Siswanto Siswanto http://creativecommons.org/licenses/by/4.0
Evaluating Different K Values in K-Fold Cross Validation for Binary Logistic Regression to Classify Poverty
Data mining is essential for decision-makers to analyze and extract insights from data efficiently. Classification is one of the data mining techniques used to organize data based on its features, helping to identify patterns and make predictions. This study evaluates Binary Logistic Regression (BLR), a type of generalized linear model that suitable for binary outcomes, for classifying poverty depth across Indonesian regencies/cities in 2022, with a focus on the impact of different K values in K-Fold Cross Validation. The dataset includes 514 regencies/cities, with the Poverty Depth Index as the target variable, categorized into high (1) and low (0) levels, using 11 predictor variables. K-Fold Cross Validation was performed with K values of 3, 5, and 10, using accuracy and Area Under Curve (AUC) as evaluation metrics. The mean accuracy values for BLR are 75.7% for K=3, 74.3% for K=5, and 75.1% for K=10. Results show that K=3 offers the highest accuracy in classifying poverty depth in Indonesia, with the lowest standard deviation of 0.03. However, K=10 demonstrates superior discriminative ability in BLR, reflected by a higher AUC value. This study highlights the significant influence of K values in K-Fold Cross Validation on BLR performance.
Copyright (c) 2025 Julia Oriana Sinaga, M. Fathurrahman, Sri Wahyuningsih, Memi Nor Hayati http://creativecommons.org/licenses/by/4.0
Analysis of Gold Price Forecasts Using Automatic Clustering Method and Fuzzy Logic Relationship
Gold is often chosen as an investment due to its lucrative potential. To maximize profits and avoid losses, investors need to understand the volatile price movements of gold. This research aims to forecast the price of gold in the next period. In this research, the forecasting method used is Automatic Clustering and Fuzzy Logical Relationship (ACFLR). ACFLR is a method that uses the concept of fuzzy logic for modeling time series data. The forecasting process includes data sorting, cluster formation, interval determination, fuzzification, FLR and FLRG formation, and calculation of forecasting values. Based on this method, the result of the gold price forecast in Padang City for the next period, namely January 2024 using the ACFLR method is IDR 978,796.9. with a MAPE value of 0.9%, which means this method is very good. For further researchers, it is hoped that the Fuzzy Time Series method can use other forecasting models in order to obtain the most optimal method for forecasting gold prices.
Copyright (c) 2025 Ro'i Khatul Jannah, Dina Agustina http://creativecommons.org/licenses/by/4.0
Rainfall Forecasting Using the Singular Spectrum Analysis (SSA) Method
This study aims to evaluate the accuracy and performance of rainfall data forecasting in the city of Parepare using the Singular Spectrum Analysis (SSA) method. Situated in South Sulawesi Province, Parepare City is characterized by high rainfall intensity, which increases the likelihood of natural hazards such as flooding and landslides. These disasters have the potential to negatively impact key sectors, including economic activity, tourism, and transportation. Therefore, reliable rainfall prediction plays a crucial role in establishing a robust disaster early warning system. Monthly rainfall measurements from two stations, Bukit Harapan and Bulu Dua, are analyzed. The results reveal a Root Mean Square Error (RMSE) of 191.0566 for Bukit Harapan station and 346.023 for Bulu Dua station, underscoring the method's forecasting accuracy. A 12-month forecast predicts consistently high monthly rainfall in Parepare City, with the highest rainfall expected in December 2024 at Bukit Harapan station and in January 2024 at Bulu Dua station. Conversely, the lowest rainfall at both stations is anticipated in July 2024. Forecasts predicting increased rainfall during certain periods, especially in December and January, provide critical insights for strengthening disaster preparedness and informing mitigation strategies. This information also plays a key role in minimizing adverse effects on the economic, transportation, and tourism sectors, while promoting more efficient and sustainable management of water resources.
Copyright (c) 2025 Nurhikmawati Nurhikmawati, Aswi Aswi, Ansari Saleh Ahmar http://creativecommons.org/licenses/by/4.0
Simulating the Dynamics of Early Marriage and Marital Stability Using SERH Mathematical Models
This study aims to develop a mathematical model of SERH (Susceptible, Engaged, Risk, and Stabilization) to analyze and predict the incidence of early marriage in South Sulawesi Province. The research employs method a combination of theoretical and applied approaches, utilizing differential equations to model the dynamics of early marriage spread. Data were collected through questionnaires distributed to 400 couples selected using the Slovin technique, representing a population of 57,789 couples. The SERH model parameters, including the rate of couple interaction , transition from engagement to risk , and recovery from risk to stability , were derived from the collected data. Simulations were conducted using Maple software to visualize the spread of early marriage under different scenarios. The results of the analysis revealed two equilibrium points: a marriage-free equilibrium and a stable endemic equilibrium. The basic reproduction number was calculated to be 3.97, indicating that one couple can influence 3-4 others in their social environment. However, with effective interventions such as education and counseling, the R₀ value can be reduced to 0.45, significantly lowering the spread of early marriage. This study provides valuable insights for policymakers to design targeted prevention programs and highlights the importance of early intervention in reducing the prevalence of early marriage.
Andi Muhammad Ridho Yusuf Sainon Andi Pandjajangi
Copyright (c) 2025 Nadiyah Khairana, Suwardi Annas, Syafruddin Side, Andi Muhammad Ridho Yusuf Sainon Andi Pandjajangi http://creativecommons.org/licenses/by/4.0
Multiple Regression Model on Selling Price, Sales Volume, Raw Material Costs, and Direct Labor Costs on Profit
During its operations, a company always aims to achieve increased profits. The case of PT Ciomas Adisatwa RPA Unit Banjarmasin from 2019 to 2022 demonstrates a trend of rising profits. Several factors can influence a business's profitability, including selling price, sales volume, and incurred costs. The objective of this study is to analyze how the profit of PT Ciomas Adisatwa RPA Unit Banjarmasin is influenced by selling price, sales volume, raw material costs, and direct labor costs. This research applies a quantitative descriptive approach using secondary data. The monthly data utilized spans from 2019 to 2022, comprising a total of 48 data points. The results of the study show that selling price, sales volume, and raw material costs have a significant effect on the profit of PT Ciomas Adisatwa RPA Unit Banjarmasin, while direct labor costs do not have a significant influence. These findings imply that pricing strategies and increased sales volume are the main factors that can enhance the company's profitability. Additionally, controlling raw material costs is a crucial aspect that must be managed efficiently to avoid burdening the cost structure. On the other hand, direct labor costs can be minimized through production efficiency and the use of technology without compromising production output. Therefore, the company should focus on pricing strategies, marketing, and raw material cost efficiency as key measures to sustainably increase profits.
Copyright (c) 2025 Ines Saraswati Machfiroh, Afna Maulida http://creativecommons.org/licenses/by/4.0
Lasso Regression and Elastic Net in Analysing Factors Affecting the Open Unemployment Rate
This study aims to identify and analyze the variables that affect the open unemployment rate in Banten Province, Indonesia. The analyzed variables include population density, average years of schooling, labor force participation rate, minimum wage, Provincial GRDP, total labor force, and the number of poor people. The method used in this study is multiple linear regression analysis with secondary data from the Central Bureau of Statistics (BPS) for the period 2017–2022. The analysis revealed multicollinearity in the average years of schooling variable, with a Variance Inflation Factor (VIF) >10. To address this issue, Lasso regression and Elastic Net regression were applied. The results of this study show that Lasso regression produces a model with a Mean Squared Error (MSE) of 1.3234857, while Elastic Net regression yields a model with a lower MSE of 0.180683, indicating better predictive performance. The best model for predicting the open unemployment rate in Banten Province is the Elastic Net regression. The variables that significantly affect the open unemployment rate are average years of schooling, labor force participation rate, minimum wage, Provincial GRDP, total labor force, and the number of poor people. The conclusion of this study is that Elastic Net regression is more effective in predicting the open unemployment rate than other methods. The implication of these findings is that the generated model can serve as a basis for formulating more effective labor policies to reduce the unemployment rate in Banten Province.
Copyright (c) 2025 Anita Mustikasari, Andi Daniah Pahrany http://creativecommons.org/licenses/by/4.0
Exploring Crime Problems from A Statistical Point of View with Negative Binomial Regression
Criminality is a complex issue in Indonesia that is very important to the government, law enforcement agencies, and society. The underlying causes of Indonesia's crime problem are complex and impacted by various circumstances. The aim of this research is to model the crime problem in Indonesia and determine the influencing factors. The method used in this research is Negative Binomial Regression. The results of the study show that the negative binomial regression model can be used to model criminal problems because the variance value is more significant than the average. Based on the parameter significance test results, both simultaneously and partially, the open unemployment rate, Gini ratio, average years of schooling, and prevalence of inadequate food consumption significantly affect the crime rate, with an Akaike’s Information Criterion Corrected (AICc) value of 698,098. These findings suggest that addressing economic inequality, unemployment, education, and food security could help reduce crime in Indonesia. Policies aimed at improving job opportunities, reducing income disparity, and enhancing education and food security are crucial in mitigating crime. This study provides valuable insights for policymakers and law enforcement agencies, offering a foundation for more targeted and effective crime prevention strategies. Future research could employ the robust Poisson Inverse Gaussian Regression method to avoid the overdispersion problem.
Copyright (c) 2025 Andrea Tri Rian Dani, M. Fathurahman, Ludia Ni'matuzzahroh, Regita Putri Permata; Fachrian Bimantoro Putra http://creativecommons.org/licenses/by/4.0
Forecasting the Amount of Water Discharge Based on the VARIMA Model
Water is an absolutely necessary substance for every living thing. Clean water is the main requirement for ensuring human health and the environment PT. Air Minum Giri Menang (Perseroda). The purpose of this study is to determine the model and then predict the water discharge of PT. Air Minum Giri Menang using the obtained model which will be useful for the community and agencies so that the management, distribution, and use of clean water are more optimal. The method used in this study is VARIMA (Vector Autoregressive Integrate Moving Average) which can process data for more than one variable. The data used in this study is water discharge data produced and distributed in the period January 2018 to December 2021. The results show that the best model obtained is VARIMA(0,1,1) with model accuracy for water discharge data that produced and distributed based on the MAPE value of 4% and 5% which states that the forecasting results can be categorized as very good. This means that the VARIMA (0,1,1) model has provided very accurate results in predicting water discharge with very small forecasting errors, thus indicating that the model is very effective. Suggestions for further research are look for the alternative forecasting method that are overcome non-stationarity data other than data transformation.
Copyright (c) 2025 Hesti Meliyana, Mustika Hadijati, Lisa Harsyiah http://creativecommons.org/licenses/by/4.0
Stock Price Index Prediction Using Random Forest Algorithm for Optimal Portfolio
With a majority Muslim population in Indonesia, Islamic capital markets such as the Jakarta Islamic Index (JII) are a relevant choice because the JII is an investment index that complies with Sharia principles. This research aims to predict stock prices in the JII using the Random Forest (RF) algorithm and form an optimal portfolio with the Mean-Variance Efficient Portfolio (MVEP) model. The data used is the daily closing price of JII stocks from April 2023 to March 2024, obtained from the Indonesia Stock Exchange and Yahoo Finance. The RF method is used to predict stock prices, with model performance evaluation using Mean Absolute Percentage Error (MAPE). The results showed that the application of ML with the RF algorithm in predicting stock prices produced very good predictions because the evaluation results using MAPE were in the 0%-10% range, namely a value of 2.522% for ACES shares; 1.222% for ICBP shares, and 0.760% for INDF shares. The optimal portfolio formed using MVEP produces a stock composition with a weight of 7.64% for ACES, 22.46% for ICBP, and 69.90% for INDF. The optimal portfolio’s estimated expected return and risk are 0.0546% and 0.0103%.
Copyright (c) 2024 Putri Humairah, Dina Agustina
The Weibull Regression Model Analysis of Mahakam River Water Pollution Potential
Mahakam River has a vital role in the lives of the people of the East Kalimantan province, including providing a raw source of clean water. The multi-activity of the Mahakam River watershed, as a water traffic lane, mining, fisheries, hotels, restaurants, and resident houses, has the potential to produce waste into the water. Increasing waste in the water flow can increase the pollution potential of river water, threatening people’s health. Therefore, precaution is necessary. In this research, statistical prevention was proposed, providing information to the East Kalimantan people regarding the factors affecting the pollution potential of the Mahakam River through Weibull regression (WR) modeling on dissolved oxygen (DO) data 2022. Research data was secondary data provided by the Life Environmental Department of East Kalimantan province. The WR model is a Weibull distribution that is directly influenced by covariates. WR model consists of Weibull survival regression, cumulative distribution regression, hazard regression, and Weibull mean regression. This research aims to obtain the factors affecting the pollution potential and to provide the pollution potential information of Mahakam River 2022. The research concluded that factors influencing the pollution potential of the Mahakam River were watercolor degree and nitrate concentration. Applying the WR model to DO data 2022 was able to provide the pollution potential information of Mahakam River, namely the probability of river water isn’t polluted is 0.6555, or the probability of the polluted river water is 0.3445, the pollution rate is 6 locations are polluted for every 10 mg/L DO, and the DO average of river water is 5.7450 mg/L. Increasing water color degree and nitrate concentration will decrease the probability of the Mahakam River being polluted, increase the probability of the Mahakam River being polluted, increase the pollution rate, and reduce the DO of Mahakam River water.
Copyright (c) 2024 Zalva Pradipa, Suyitno Suyitno, Meiliyani Siringoringo
Application of VAR-GARCH for Modeling the Causal Relationship of Stock Prices in the Mining Sub-sector
Accurate modeling is expected to minimize risk and maximize profit in investment portfolios, one of which is in stock price modeling. This research aims to model the causal relationship between stock prices using the Vector Autoregressive - Generalized Autoregressive Conditional Heteroskedasticity (VAR-GARCH) model. The VAR-GARCH model is used to overcome heteroscedasticity and model dynamic volatility. The data used for the modeling consists of daily stock prices from July 2023 to May 2024 for mining sub-sector companies listed on the Jakarta Islamic Index (JII), including ADMR, ADRO, and ANTM. The results showed that the VAR(1) model is stable, but this model indicates the presence of heteroskedasticity or ARCH effects. Therefore, the VAR(1) model was combined with the GARCH model, and the results showed that the best model is VAR(1)-GARCH(1,1). The VAR(1)- GARCH(1,1) model is appropriate and meets the homoskedasticity assumptions for modeling the stock prices of the mining sub-sector in the Jakarta Islamic Index (JII). This indicates that the VAR-GARCH model could successfully handle the volatility of stock price data. In general, this research is in line with previous research, i.e., the VAR-GARCH model showed a better model for capturing the volatility patterns in the data.
Copyright (c) 2024 Muhammad Nasrudin, Endah Setyowati, Shindi Shella May Wara
Modeling the Number of High School Dropouts Using GWGPR
In 2022, the high school dropout rate is the highest compared to other levels of education in Indonesia. Seeing the urgency of the 12-year Compulsory Education program, completing education up to the high school level is an important thing that needs to be considered. Thus, it is necessary to know the factors that influence the dropout rate in the hope that this problem can be reduced. This study aims to model the high school dropout rate using geographically weighted generalized poisson regression (GWGPR) based on the factors that influence it. GWGPR is used if the response variable is overdispersed and depends on the location observed. The results of this study indicate that each province has a different regression model. The GWGPR model with the adaptive tricube kernel weighting function is the best model because it has the smallest AIC value compared to other weighting functions. In Central Sulawesi Province, the GWGPR model with the adaptive tricube kernel weighting function formed is µË†26 = exp (8, 1267 − 0, 1267X4 + 0, 0344X5 + 0, 0957X6 + 0, 1173X7). With the significant variables are the average length of schooling, the percentage of the population aged 7-17 years who receive PIP, the open unemployment rate, and the percentage of children who do not live with parents.
Copyright (c) 2024 Nur Azizah, Nurul Fiskia Gamayanti, Junaidi Junaidi, Hartayuni Sain, Fadjryani Fadjriyani
A Kernel Logistic Regression Approach to Understanding the “Banyak Anak Banyak Rezekiâ€ Stigma
Indonesia, the world’s 4th largest country with a population of 270 million in 2020, faces many challenges due to rapid population growth, including biodiversity loss and increased consumption of natural resources. One of the cultural factors underlying the high rate of population growth is the perception of “Banyak Anak Banyak Rezeki“ that develops in the community. This study aims to identify and model the factors that influence the “Banyak Anak Banyak Rezekiâ€ stigma and find solutions to overcome this problem. The research method used was quantitative, with a sample of 384 people in South Sulawesi, consisting of Bugis, Makassar, Toraja, and Mandar tribes. The variables studied include religiosity, tradition, number of children, and cognitive dissonance. The analysis techniques used were logistic regression (LR) and kernel logistic regression (KLR). The results showed that religiosity, number of children, and cognitive dissonance had a significant effect on the “Banyak Anak Banyak Rezekiâ€ stigma. The accuracy of the LR model reached 87.01% and increased to 93.51% after using KLR.
The “Banyak Anak Banyak Rezekiâ€ Stigma
Assyifa’ Nur Qalby. A. Tjabbe. Suwardi
Copyright (c) 2024 Assyifa’ Nur Qalby. A. Tjabbe.S, Muh. Alwi Sanur, Nurul Mutmainnah Chaerunnisa, Azzahra Dwi Nur Maulina, Bobby Mutmainnah Poerwanto
Mathematical Modelling and Simulation Strategies for Controlling Damage to Forest Resources Due to Illegal Logging
Forests are one of the natural resources that provide many benefits for the welfare of living things. The dense population causes people to depend more on forest resources. One of them is illegal logging. Various strategies to control forest damage due to illegal logging have been carried out, namely by direct handling to improve damaged conditions while preventing the recurrence of forest damage. The purpose of this research is to build a mathematical model of forest resource damage control strategies due to illegal logging, determine assumptions, formulate the model, and conduct analysis and problem solving including: determining the equilibrium point, determining the stability analysis of the equilibrium point, and conducting numerical simulations of the equilibrium point. The last step is to interpret the results of the analysis obtained and make conclusions. Based on the research and simulation results of the model, it can be concluded that taking into account the variable of forest resource damage control strategy due to illegal logging, the result shows that if the density of forest resources has been affected by the disturbance of population density around the forest, it is necessary to have a forest resource damage control strategy in order to compensate for the people around the forest who do a lot of illegal logging. In order to maintain the forest so that the forest does not quickly become extinct and can overcome drought, prevent flooding, maintain groundwater quality, protect animals, reduce air pollution, climate control, reduce dust particles, prevent the greenhouse effect, supply natural fertilizers, prevent erosion, and maintain springs.
Copyright (c) 2024 Irene R Naben, Elinora N Bano, Fried M. A. Blegur
Clustering of Study Program Using of Block-Based K-Medoids
The purpose of this research is to classify Study Programs based on eleven mixed data from Internal Quality Management System (QMS) indicators. This grouping can provide a clearer picture of how QMS affects the performance and quality of study programs. By understanding these clusters, universities can identify and design more effective strategies to improve the quality of education. The data used comes from the National Accreditation Board for Higher Education (BAN-PT) and the website database, which consists of seven numerical variables: number of lecturers, percentage of doctors, percentage of professors and associate professors, student enumeration, percentage of graduates, program experience, and availability of laboratories. Meanwhile, the categorical variable consists of four variables: National Accreditation Board of Higher Education (BAN-PT) research ranking, accreditation, international recognition, and level of community service. The clustering method used is the blockbased k-medoids (block-based KM), and multivariate analysis of variance (MANOVA). We applied the Deviation Ratio Index based on K-Medoids (DRIM) to determine the number of clusters. This research results that the optimal number of groups that must be formed is three. Based on MANOVA the results showed that the group consisting of 12 study programs had better QMS outcomes than the other two groups.
Copyright (c) 2024 Asa Nugrahaini Itsnal Muna, Kariyam
Analyzing Lightning Strike Susceptibility Using the Elliptical Fitting Method with a Principal Component Analysis Approach
Lightning is a high-current discharge that occurs in Cumulonimbus clouds, with CG (Cloud to Ground) lightning strikes posing significant dangers, especially to human life. Pasuruan, located in the highlands between mountains and the ocean in Indonesia, is particularly vulnerable to such strikes. This study aims to mitigate the impact of lightning strikes, particularly in industrial areas like Pasuruan, by delineating lightning-prone areas using a sophisticated methodological approach. Our research employs a robust Ellipse Fitting Method, parameterized with Principal Component Analysis (PCA), to accurately define the boundaries of these high-risk zones. The Ellipse Fitting Method, which involves forming an ellipse from the intersection of a plane and a cone, uses five key parameters: a center point, two vertex points, and two focus points. PCA is then applied to these parameters to determine the ellipse’s configuration, with the center point derived from the mean of all data points. The major and minor axes are defined by the first and second eigenvalues of the principal components, respectively. The size of the ellipse correlates with the confidence level, with higher confidence resulting in a larger ellipse. The result of integrating these advanced techniques is the generation of two PCA models from data collected across 28 sub-districts in Pasuruan, with findings indicating a high level of vulnerability in Lumbang District and a moderate level of risk in Gempol District. This methodological framework not only enhances the precision in identifying lightning-prone areas but also provides a scalable approach for similar studies in other regions. Suggestion for the further research are to overcome extreme points or extreme points in the PCA confidence ellipse such as MVEE.
Copyright (c) 2024 Helmalia A. Lovytaji, Rozikan Rozikan, Djati C. Kuncoro, Ria Dhea Layla Nur Karisma
Analysis of Underdeveloped Regency Using Logistic Threshold Regression Model
Regional development inequality causes some regions to lag behind other regions. An underdeveloped regency is a regency where territories and people are less developed than other regions nationally. The government has set a Human Development Index (HDI) target of 62.2 to 62.7 to accelerate the development of underdeveloped regency and prevent the regions from lagging. This study aims to evaluate the HDI target and obtain the HDI value that reduces the risk of underdeveloped regency and acquires variables that affect underdeveloped regency’s status. The logistic threshold regression model is used in this study with HDI as the threshold variable, 22 indicators for determining underdeveloped regency as explanatory variables, and the underdeveloped regency’s status as the response variable. Threshold regression can handle non-linear relationships between response and explanatory variables, including various types of threshold models such as step, segmented, hinge, stegmented, and upper hinge. By applying a hinge threshold regression model using the R package ’chngpt,’ this study addresses non-linear relationships and categorical responses. The results showed a threshold effect with a threshold value of 62.9, indicating that the HDI target can reduce the region’s risk of being left behind.
Copyright (c) 2024 Annisa Nur Salsabila, Siskarossa Ika Oktora
Finding the Factors Influencing the Severity of Traffic Accident Victims in Sleman Regency Using Ordinal Logistic Regression Analysis
Special Region of Yogyakarta (Daerah Istimewa Yogyakarta, DIY) is well-known for its tourist, cultural, and educational attractions, but it also has a high accident rate. Sleman Regency is among the DIY regions with the greatest number of traffic accidents. According to Yogyakarta Police records, Sleman Regency had 1,825 traffic incidents in 2022, while 637 accidents occurred there in a short period of time in 2023, specifically from January to April. To stop the rising number of people injured in road accidents, this issue needs to be taken into account. The objective of this study was to examine the profile of traffic accidents that happened in Sleman Regency between January and April of 2023 and use the ordinal logistic regression method to find characteristics that influence the severity of traffic accidents. Sleman Regency traffic accident data was used in this study. The opponent's vehicle factor, with the category of four or more wheeled vehicles and non-motorized vehicles, is one of the elements that influences the severity of traffic accident victims in Sleman Regency, according to the study's findings.
Copyright (c) 2024 Amalia Rizqi Cahyani, Mujiati Dwi Kartikasari
Deterministic Economic Resilience Through Gross Regional Domestic Product Using Nonparametric Geographically Weighted Regression Spline Truncated
Megatrends are large-scale global movements with huge impacts, influenced by socio-economic, political, ecological and technological factors. As a developing country, Indonesia faces challenges such as political instability and limited infrastructure, so strengthening economic resilience through increasing Gross Regional Domestic Product (GRDP) is important. The aim of this research is to analyze Indonesia's GRDP data in 2022, which shows significant spatial variability between provinces to see the resilience of the Indonesian economy. The method used is Nonparametric Geographically Weighted Regression - Spline Truncated (NGWR-ST). The NGWR-ST approach is well suited because it allows location-specific parameter variations, captures complex nonlinear relationships through spline functions, and minimizes the influence of extreme values ​​using truncation. The results indicate that an optimal model is achieved with two knot points (GCV = 0.293) and a fixed kernel bi-square weighting function with a 19.174 bandwidth (CV = 974.621), providing optimal spatial weighting. Among the factors analyzed, the Human Development Index (HDI) and the Rate of Return (ROR) are identified as having a significant influence on GRDP, contributing insights for strengthening Indonesia’s economic resilience. Thus, this study will contribute to formulating appropriate regional policy strategies to strengthen the economy in facing the World Megatrend in 2045
Copyright (c) 2025 Nurul Mutiara Annisa, Dhita Hartanti Octavia, Muhammad Ridzky Davala http://creativecommons.org/licenses/by/4.0
Comparison of Naive Bayes Classification Methods Without and With Kernel Density Estimation
Halal certification is important to give confidence to Muslim consumers around the world regarding the halalness of products. The Halal Product Assurance Organizing Body (BPJPH) is the official auditor in Indonesia that is responsible for the halal certification process. This study aims to address the need for verification and validation of data for halal certification applications in Indonesia by using the data science approach and machine learning technology. In this study, the Naïve Bayes classification method was used to optimize the data verification and validation process. However, this method needs to be improved by applying optimization methods such as Kernel Density Estimation (KDE) to improve classification results. The results showed that the Naïve Bayes classification method with KDE optimization produced better performance than the Naïve Bayes method without optimization. The performance of the Naïve Bayes classification model without optimization achieves 87.6% Accuracy, 85.4% Recall, 88.8% Precision, and 87.1% Fmeasure. Meanwhile, the Naïve Bayes classification model with KDE optimization achieves 97.5% Accuracy, 95.9% Recall, 98.9% Precision, and 97.8% Fmeasure. Thus, it can be concluded that the Naïve Bayes classification algorithm with KDE optimization results in a performance increase of 9.9% compared to the Naïve Bayes method without optimization. This research has important implications in handling complex and non-normally distributed data and providing solutions for BPJPH in the process of verifying halal certification.
Copyright (c) 2024 Agus Hermawan, Siswanto, Andi Kresna Jaya
Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications
Schistosomiasis is a tropical disease caused by Schistosoma mansoni (intestinal schistosomiasis) and Schistosoma haematobium (urogenital schistosomiasis). Schistosomiasis in Indonesia is endemic to Central Sulawesi and is commonly found in the Napu Valley and Bada Valley areas, which are administratively included in Poso District and Sigi District. One approach to obtain information on schistosomiasis endemic areas is by mapping the population profile of villages with schistosomiasis cases. This mapping is intended to provide an overview of the social and demographic conditions of villages with schistosomiasis cases. One of the many analysis methods that can be used is cluster analysis. Cluster analysis is a method for grouping data based on the extent of their similarities. Data with similar characteristics will be grouped together, while data with different characteristics will be placed in different groups. Among several types of methods in cluster analysis is Clustering Large Application (CLARA). CLARA is a clustering method which is more robust to unusual data and can be applied to handle large volumes of data. The results of this study are obtained two optimum clusters, each possessing distinct characteristics as determined by Schistosomiasis cases indicators. Cluster 1 with low schistosomiasis cases and cluster 2 with high schistosomiasis cases.
Copyright (c) 2024 Mohammad Fajri, Rais, Nurul Fiskia Gamayanti, Siti Natazha Dg Mabaji, Shalsa Yunita Rahman Jati, Rizwan Arisandi
An Implementation of K-Medoids Method in Provincial ClusteringBased on Education Indicator Data
Data mining is searching for interesting patterns or information by selecting data using specific techniques or methods. One method that can be used in data mining is K-Medoids. K-Medoids is a method used to group objects into a cluster. This research aimed to obtain the optimal number of clusters using the K-Medoids method based on Davies-Bouldin Index (DBI) validity on education indicators data by province in Indonesia in 2021. The results showed that the optimal number of clusters using the K-Medoids method based on DBI validity is 5 clusters. Cluster 1 consists of 1 province with a higher average dropout rate, average length of schooling, and well-owned classrooms compared to other clusters. Cluster 2 consists of 15 provinces with an average proportion of school libraries lower than Clusters 3 and 4 and higher than Clusters 1 and 5. Cluster 3 consists of 9 provinces with an average proportion of school libraries, proportions of school laboratories, net enrollment rates, and higher school enrollment rates than other clusters. Cluster 4 consists of 8 provinces with a higher average enrollment rate than the other clusters. Cluster 5 consists of 1 province with a higher average repetition rate and student-per-teacher ratio than other clusters.
Copyright (c) 2024 Annisa Zuhri Apridayanti, M.Fathurahman, Surya Prangga
Classification Classification of Criminal Events Based on Biplot Analysis
Kriminalitas merupakan suatu perilaku yang melanggar hukum dan aturan dalam masyarakat. Penelitian ini dilakukan untuk menganalisis data biplot jumlah kejahatan di berbagai provinsi di Indonesia. Biplot merupakan analisis yang berguna untuk menafsirkan hubungan antara variabel dan objek dalam bentuk grafik tunggal. Sumber data dalam penelitian ini adalah data sekunder yang berasal dari website Badan Pusat Statistik yang berjudul “Statistik Kriminal 2022â€. 34 kepolisian daerah yang mewakili setiap provinsi di Indonesia menjadi objek pengamatan dan 9 klasifikasi kejahatan menjadi variabel. Metode penelitian ini menggunakan analisis biplot dengan bantuan fiton. Dari nilai Dekomposisi Nilai Singular, keragaman data yang dapat dijelaskan sebesar 73,714%. Pada grafik analisis biplot hubungan antar observasi diperoleh bahwa observasi atau objek polda dari setiap provinsi tersebar terpusat pada satu kuadran. Hubungan antar variabel yang paling tinggi adalah korelasi antara variabel kejahatan narkotika dengan kejahatan yang berkaitan dengan penggelapan, penipuan, dan korupsi, sedangkan hubungan yang paling rendah adalah korelasi antara kejahatan narkotika dengan kejahatan terhadap ketertiban umum. Dalam hubungan observasi dengan variabel diperoleh 4 kelompok. Keberagaman variabel yang paling tinggi terletak pada kejahatan terhadap kebebasan masyarakat, sedangkan keberagaman variabel yang paling rendah terletak pada kejahatan terhadap kesusilaan.
Copyright (c) 2024 Doni Muhammad Fauzi, Sanda Insania Dewanty, Farah Fauziah Putri, Alya Rahma Inneztiana, M. Fariz Fadillah Mardianto, Dita Amelia, Elly Ana
Daily Rainfall Forecasting with ARIMA Exogenous Variables and Support Vector Regression
There is a seasonal element every year, with the dry season often lasting from May to October and the rainy season lasting from November to April. However, climate change causes the changing of the rainy and dry seasons to be erratic, so it is necessary to anticipate weather conditions. Prediction of rainfall is used to see natural conditions in the future with time series modeling. The rainfall modeling method at the six Surabaya observation posts used is the Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) and Support Vector Regression. The exogenous variable used is the captured seasonal pattern of rainfall. The SVR model uses input lags from the ARIMAX model and parameter tuning uses the Kernel Radial Based Function. Selection of the best model uses the minimum RMSE value. The results showed that the average occurrence of rain at the six rainfall observation posts occurred in January, February, March, April, November and December. The ARIMAX method in this study is well used to predict rainfall in Gubeng and rainfall in Wonorejo. The SVR input lag ARIMAX method is good for predicting rainfall for Keputih, Kedung Cowek, Wonokromo and Gunung Sari. Nonparametric methods are better used to forecast rainfall data because they are able to capture data patterns with greater volatility than parametric methods, one of which is the SVR method.
Copyright (c) 2024 Regita Putri Permata, Rifdatun Ni'mah, Andrea Tri Rian Dani
A Regression Model of Public Interest in COVID-19 Vaccination Ahead of MotoGP
The lack of a threshold value for herd immunity against COVID-19 ahead of the MotoGP 2022 Event in the Mandalika Circuit Area, Central Lombok Regency is the reason for the author to conduct this research. The objectives of this research are 1) to make a mathematical model of the interest of the people of Central Lombok Regency in the Covid-19 vaccination in welcoming the 2022 MotoGP Event and 2) to determine the influence of MotoGP Event 2022 and other factors on the interest of the people of Central Lombok Regency in carrying out the COVID-19 vaccination. This research is a type of quantitative descriptive research. The independent variables in this study are the variables of ease of getting the Covid-19 vaccination (X1), the efficacy of the COVID-19 vaccination (X2), trust in the government (X3), and the 2022 MotoGP event (X4), while the dependent variable is the variable of interest in covid-19 vaccination (Y). To achieve the objectives of this study, the authors collected data through a questionnaire that was distributed to 332 respondents, they’re people who received the full vaccine aged 12-70 years. The questionnaire was tested for validity and reliability. Data must first be transformed into interval data using the Method of Successive Interval (MSI) then analyzed using multiple linear regression with classical assumptions including normality, multicolllinearity and heteroskedasticity. The results showed that the 2022 MotoGP event did not have a significant effect on the interest of the people of central Lombok Regency to take part in the COVID-19 vaccination. The biggest factor that influences is the factor of people's trust in the government.
Copyright (c) 2024 Elok Faiqotul Himmah, Riana
Stunting Prevalence Modeling Using Nonparametric Regression of Quadratic Splines
The nonparametric regression approach is used when the shape of the regression curve between the response variable and the predictor variable is assumed to be of unknown shape. The advantages of nonparametric regression have high flexibility. A nonparametric regression approach that is often used is truncated spline which has an excellent ability to handle data whose behavior changes at certain sub-sub intervals. The purpose of this study is to obtain the best model of multivariable nonparametric regression with linear and quadratic truncated spline approaches using the Generalized Cross Validation (GCV) and Unbiased Risk (UBR) methods and to find out the factors that influence the prevalence of stunting in Indonesia in 2021. The data used were the prevalence of stunting as a response variable and the predictor variable used was the percentage of infants receiving exclusive breastfeeding for 6 months, the percentage of households that have proper sanitation, the percentage of toddlers who get Early Initiation of Breastfeeding (IMD), the percentage of poor people, and the percentage of pregnant women at risk of SEZ. The results showed that the best quadratic truncated spline nonparametric regression model in modeling stunting prevalence was quadraic truncated spline using the GCV method with three knot points. This model has a minimum GCV value of 7.29 with an MSE value of 1.82 and a R2 value of 94.07%.
Copyright (c) 2024 Tutik Handayani, Sifriyani, Andrea Tri Rian Dani
Natural Disaster Mapping on Java Island Using Biplot Analysis
Indonesia is located in the ring of fire region. This condition causes Indonesia to have the potential to experience various disasters, such as volcano eruptions. In addition, rapid population growth has led to rampant land conversions that cause floods, landslides, tornadoes, droughts, and forest fires. The research aims to map natural disasters in Indonesia, especially Java Island to find out the provinces and their natural disasters tendency using Biplot analysis. Based on the results, Central Java, East Java, and West Java have a tendency to have floods and landslides. East Java tends to undergo earthquakes and Central Java has the potential to experience volcano eruptions. Through the natural disasters mapping, the government, especially the BMKG, will be able to find various solutions to overcome the natural disasters that have great potential to occur in provinces in Indonesia, especially Java Island as the manifestation toward SDGs Target 2030.
Copyright (c) 2024 Pressylia Aluisina Putri Widyangga, M. Fariz Fadillah Mardianto, Firda Aulia Pratiwi, Andi Vania Ghalliyah Putrie, Putu Eka Andriani, Dita Amelia, Deshinta Arrova Dewi
Naive Bayes Algorithm with Feature Selection Using Particle Swarm Optimization
The COVID-19 vaccine in Indonesia has led to the emergence of public opinion which is conveyed on social media such as Twitter. One of the analyses that can be done to produce various information from public opinion is sentiment analysis. Sentiment analysis is used to determine whether an opinion tends to be positive or negative. This study aims to classify the public opinion of the COVID-19 vaccine in Indonesia with sentiment analysis and to visualize the location of the sentiment of the COVID-19 vaccine tweet data in Indonesia. To achieve this aim, the Naïve Bayes algorithm with Particle Swarm Optimization (PSO) feature selection was used. This study uses opinions into positive and negative class sentiments towards 2,547 tweets related to the COVID-19 vaccine in Indonesia from January to June 2021. The results show that the distribution of positive and negative class sentiments is 2,328 and 219, respectively. In addition, the positive sentiment for the COVID-19 vaccine was dominated by people on the island of Java based on a random number matrix initialized by the PSO method. The classification of public opinion on Twitter media provides accurate and optimal performance results using a combination of the Naïve Bayes algorithm with PSO feature selection. The results of the combination of these methods have accuracy and F1 score values of 91.28% and 95.38%, respectively. The visualization of geo-spatial mapping showed that positive sentiments related to the COVID-19 vaccine exist in almost all regions in Indonesia but are dominated by the Jabodetabek area.
COVID-19; Classification; Naïve bayes; particle swarm optimization; Twitter.
Copyright (c) 2024 Siswanto, Iwan Kurniawan, Sri Astuti Thamrin
Estimating and Forecasting Composite Index in Pandemic Era Using ARIMA-GARCH Model
Many industries have suffered financial losses as a result of the COVID-19 epidemic. The stock market's movement has been impacted by this circumstance. Due to the influence of some people, a large number of individuals with limited trading knowledge are attempting to participate in the stock market. Market volatility should be understandable in order to gain profit instead of having losses. Therefore, it's essential to comprehend the market of the future through analysis of the data. The purpose of this study is to use ARIMA-GARCH to predict the Indonesian stock market price during. The period covered by the dataset is January 2020–December 2022. The training data indicates that ARIMA (2,1,2) is the best model for ARIMA. The results showed that data residual fitted by ARIMA (2,1,2)-GARCH (1,2) exhibits heteroscedasticity, according to the residual analysis. The MAPE score is 2%, which is relatively small. It means that ARIMA (2,1,2)-GARCH (1,2) is good enough for forecasting the Jakarta Composite Index.
Copyright (c) 2024 Agus Sofian Eka Hidayat, Gilang Primajati