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All Journal Journal of Information Systems and Technology Research
Aviolla Terza Damaliana
UPN Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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OPTICS-Based Clustering of East Java Regencies and Cities by Divorce Factors Cesaria Deby Nurhalizah; Aviolla Terza Damaliana; Dwi Arman Prasetya
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1227

Abstract

Divorce is a social phenomenon that occurs when a married couple decides to legally end their marriage. This decision is influenced by various factors such as conflict, economic pressure, domestic violence, and deviant behavior. The aim of this study is to group regencies and cities in East Java Province that share similarities in the main causes of divorce, in order to understand the patterns that emerge across regions. The OPTICS (Ordering Points to Identify the Clustering Structure) clustering method was chosen for its ability to identify cluster structures with varying densities. The modeling process was conducted using a proportion-based approach for each causal factor, with optimal parameters obtained through manual grid search using min_samples = 2, xi = 0.05, and min_cluster_size = 0.1. The analysis identified three main clusters, each dominated by conflict, economic hardship, and deviant behavior, respectively. The quality of the clustering was evaluated using a Silhouette Score of 0.588, indicating reasonably good results. These findings are expected to serve as an initial understanding of divorce causes in East Java and can be used as input for the formulation of more targeted social policies.
Forecasting USD to Rupiah Exchange Rate with the Fuzzy Time Series Singh Approach Reghina Ajeng Santika; Aviolla Terza Damaliana; Mohammad Idhom
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1238

Abstract

The exchange rate plays a crucial role in determining a country's economic stability, especially for countries like Indonesia that rely heavily on international trade. In recent years, the fluctuations in global currency values have intensified, particularly after the trade war between the United States and China began in 2018. These fluctuations have significantly impacted the exchange rate between the Indonesian Rupiah and the US Dollar, which in turn affects the competitiveness of Indonesian exports, increases the cost of imports, and influences key economic decisions made by investors, importers, and exporters. The problem of this research lies in the challenge of predicting exchange rate movements amidst economic uncertainty and currency volatility.  This study aims to address this problem by forecasting the exchange rate of the Indonesian Rupiah against the US Dollar using the Fuzzy Time Series Singh method. This method is chosen due to its ability to capture complex data patterns with high accuracy and simpler computational requirements. The primary objective of the research is to evaluate the effectiveness and accuracy of the Fuzzy Time Series Singh method in predicting the exchange rate of the Rupiah against the US Dollar. The results of this study show that the forecasting model achieved an accuracy rate with a MAPE value of less than 10%, indicating that the method can provide highly reliable predictions, which can assist economic actors in making better-informed decisions in the face of currency volatility.
Comparative Analysis of Deep Learning Models for Wind Speed Prediction Using LSTM, TCN and RBFNN Firly Setya Wardani; Mohammad Idhom; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1298

Abstract

Wind speed forecasting plays a vital role in various sectors, including renewable energy management and disaster preparedness for extreme weather events. Accurate prediction models are essential to support decision-making processes, especially in regions with dynamic seasonal patterns. This study compares the performance of three time series prediction models Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Radial Basis Function Neural Network (RBFNN) for forecasting daily wind speed. The dataset consists of historical wind speed data that underwent multiple preprocessing steps, including seasonal-based missing value imputation, stationarity testing, supervised transformation, normalization, and hyperparameter tuning to optimize model performance. The models were evaluated using four standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared (R²), and Mean Absolute Percentage Error (MAPE). The results show that the TCN model outperformed the others, achieving an MAE of 1.117, RMSE of 1.524, R² of 0.120, and MAPE of 20.95%. The LSTM model ranked second with competitive performance, while the RBFNN model produced consistent but slightly lower accuracy. The findings highlight the superiority of TCN in capturing complex sequential and seasonal patterns in wind speed data. The unique contribution of this research lies in integrating seasonal-based preprocessing with a comparative evaluation of three advanced models under varying conditions, including extreme weather scenarios. This study serves as a foundation for developing more accurate and reliable wind speed forecasting systems to support renewable energy planning and enhance disaster risk mitigation strategies.
Determination of Tuberculosis Risk Clusters Based on Health Factors in East Java Using Fuzzy Gustafson Kessel Naila Qalbi; Mohammad Idhom; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1579

Abstract

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis and remains a major public health problem in Indonesia, particularly in East Java Province. This study aims to group tuberculosis risk levels across 38 districts/cities in East Java Province based on health determinants using the Fuzzy Gustafson-Kessel (FGK) clustering method. The data were obtained from the Central Statistics Agency of East Java Province and the East Java Provincial Health Office in 2024, using four main variables: the number of Diabetes Mellitus (DM) patients, malnourished toddlers, Human Immunodeficiency Virus (HIV) patients, and productive-age active smokers. The FGK method was applied because it can form elliptical clusters through the Mahalanobis distance approach, making it suitable for data with non-homogeneous distribution characteristics. The optimal number of clusters was determined using the Modified Partition Coefficient (MPC). The results show that the four-cluster solution achieved the highest MPC value of 0,987 indicating good cluster partition quality. These four clusters represent tuberculosis risk groups categorized as low priority, medium priority, and high priority across districts/cities in East Java Province. The clustering results can serve as a basis for determining intervention priorities and supporting more targeted public health policy planning
Co-Authors Cesaria Deby Nurhalizah Dwi Arman Prasetya Firly Setya Wardani Mohammad Idhom Naila Qalbi Reghina Ajeng Santika