Journal of Dinda : Data Science, Information Technology, and Data Analytics

Systematic Literature Review : Population Density Mapping Using Data Mining

Sat, 28 Jun 2025 22:52:57 +0800

Mapping population density plays a crucial role in designing and developing urban policies. Traditional methods are often unable to capture complex spatial patterns, making the application of data mining techniques crucial. In this study, we conducted a Systematic Literature Review (SLR) of various data mining techniques, including K-Means, KDE, DBSCAN, Random Forest, linear regression, Cellular Automata, and Fuzzy C-Means. The findings of this study show that although K-Means proved to be effective, it is quite sensitive to the presence of outliers. On the other hand, DBSCAN successfully detects irregular distributions, while KDE is able to track trends despite being computationally intensive. Random Forest and linear regression can predict growth, but both require large datasets to provide accurate results. Meanwhile, Cellular Automata and Fuzzy C-Means offer flexibility, but also require comprehensive data. For future optimization, we recommend using AI-GIS hybrid models.

Implementation of Random Forest Algorithm with RFE and SMOTE on Cardiotocography Dataset

Muhammad Ahsani Nur Taqwimi, Buang Budi Wahono, Harminto Mulyo — Sat, 28 Jun 2025 22:56:59 +0800

Having a healthy baby is a dream for mothers. However, the high rate of maternal and fetal mortality is still a serious problem, so more accurate fetal health monitoring is needed to prevent pregnancy complications. One of the devices used is Cardiotocography (CTG), which produces data on fetal conditions. The CTG dataset used in this study faces challenges in the form of class imbalance and a high number of features, which can reduce classification performance. This study aims to overcome these challenges by implementing the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) technique for class balancing and Recursive Feature Elimination (RFE) for feature selection. The dataset used is "Fetal Health Classification" from the Kaggle platform, which consists of 2,126 data with three classes: Normal, Suspect, and Pathological. The test results show that the RFE method is able to reduce the number of features from 22 to 18, while SMOTE increases the proportion of minority data. The model built produces good classification performance with an accuracy value of 95%, precision 93%, recall 89%, and F1-score 91%. The ROC-AUC value for the Normal class is 0.9881, Suspect 0.9789, and Pathological 0.9985. Although the model is able to predict the Normal and Pathological classes with high accuracy, the performance on the Suspect class still needs to be improved. Overall, the integration of Random Forest with SMOTE and RFE has proven effective in improving the accuracy of fetal health classification.

Evaluation of the Information System (Smart Deer System) at BKPSDMD of Bangka Belitung Islands Province

Aditya Ahmad Fauzi — Sun, 13 Jul 2025 07:56:58 +0800

Improving the quality of human resources (HR) is one of the important factors in the development of a region. To realize superior, competent, intelligent, and educated human resources, a fast, easy, and useful information system is needed in the management of further education in the BKPSDMD Prov. BaBel, therefore, introduced an information system called "SI Pelanduk Cerdik" which aims to make it easier for State Civil Apparatus (ASN) in the process of submitting competency development. Therefore, the purpose of this research is as feedback to correct the shortcomings of the "SI Pelanduk Cerdik" application. The qualitative description method is the method used in this study. The results of the study show that the use of "Si Pelanduk Cerdik" in BKPSDMD Prov. BaBel is very useful. This application makes it easier for ASN in the process of submitting competency development, with quick and easy access anytime and anywhere. The level of satisfaction of ASN with this application is also very high. Before this application, the process of applying for further education by ASN was manual and time-consuming. However, with the existence of the "SI Pelanduk Cerdik", the time needed for ASN to apply for competency development can be significantly reduced, in just about 30 minutes. The app lives up to the desired expectations

Unveiling Risk Patterns of Disability Progression A Clustering Based Transition Matrix Analysis Using Indonesian National Data

Ariyono Setiawan, Abdul Razak Bin Abdul Hadi, Erwin Faller, Aji Prasetya Wibawa — Sun, 13 Jul 2025 22:09:52 +0800

This study investigates the progression of disability severity from "some difficulty" to "a lot of difficulty" using a transition matrix framework. It aims to identify risk patterns and classify severity clusters based on national survey data from Indonesia between 2010 and 2023. The study draws on the theory of functional limitation progression, which assumes that individuals with mild disabilities face varying probabilities of developing severe limitations depending on contextual and demographic factors. It also incorporates clustering theory to group similar progression behaviors. We utilize 20,604 data points from multiple disability types (cognitive, hearing, mobility, etc.). The transition rate is computed as the ratio of individuals with "a lot" difficulty to the total with "some" and "a lot" difficulty. Statistical analyses include descriptive summaries, Pearson correlation, and K-Means clustering via the FASTCLUS procedure. Heatmaps are generated to observe annual and typological patterns. The average transition rate is 66.77%, with a maximum of 99.6% in some subgroups. Three distinct severity clusters emerged, centered at 31.27%, 58.62%, and 82.20%. Transition rate negatively correlates with "some difficulty" prevalence (r = –0.45, p < .0001), indicating progressive concentration of severity in smaller populations. Heatmaps reveal consistent risk escalation over time, especially in cognitive and self-care disabilities. This study enables policy actors to stratify intervention priorities and monitor disability risk more accurately using dynamic, data-driven indicators. This is the first study in Indonesia to apply a large-scale transition matrix combined with clustering to map functional disability progression. It offers a novel quantitative method to uncover hidden severity patterns and informs future decision-support systems for inclusive health planning.

Enhancing Prediction Accuracy of the Happiness Index Using Multi-Estimator Stacking Regressor and Web Application Integration

Rofi Nafiis Zain, Nisa Hanum Harani, Syafrial Fachri Pane — Tue, 15 Jul 2025 01:36:25 +0800

This study proposes a novel approach to enhance the prediction accuracy of the Happiness Index using a multi-estimator stacking regressor model and web application integration. By combining diverse regression models, such as decision tree, random forest, gradient boosting, LGBM, and support vector regressor (SVR), the proposed ensemble architecture achieved superior predictive performance with an score of 0.9814. A custom Happiness Score was formulated using weighted indicators derived from Pearson’s correlation analysis. Furthermore, SHapley Additive exPlanations (SHAP) were used to interpret model predictions, revealing the Human Development Index, Female Labour Force Rate, and Life Expectancy as key contributing features. The final model was deployed via a Python Flask-based web dashboard, enabling stakeholders to visualize happiness metrics interactively. The results suggest that stacking-based regression, when combined with interpretability techniques and real-time deployment, can offer a powerful solution for socioeconomic modeling and supporting urban policy.