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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Indonesian Journal of Environment and Disaster Journal of Mathematics, Computation and Statistics (JMATHCOS)
I Gusti Ngurah Sentana Putra
IPB University
Computer Science & IT Earth & Planetary Sciences Education Environmental Science Mathematics

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Evaluation of Tree-Based Models for Predicting Social Assistance Recipient Status Based on National Socio-Economic Survey (SUSENAS) 2024 Yani Prihantini Hiola; Zulhijrah; I Gusti Ngurah Sentana Putra; Syella Zignora Limba; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xyyv0f37

Abstract

Abstract. Poverty is a major socioeconomic challenge in Indonesia that affects the effectiveness of social protection programs. In response to this challenge, the government has created social assistance programs to improve the welfare of the people. However, the distribution of social assistance is often considered to be inaccurate, resulting in households that are deemed eligible for social assistance not being identified as recipients. One solution to improve the accuracy of distribution is the application of machine learning in the context of classification. Several tree-based models, such as LightGBM, Random Forest, and XGBoost, were selected because of their superior capabilities compared to classical models such as logistic regression, especially in handling complex data and fulfilling model assumptions. This study compares the performance of these three models in predicting social assistance recipient status using data from the 2024 West Java Provincial National Socioeconomic Survey (SUSENAS). Model evaluation was conducted on several data pre-processing scenarios involving outlier handling, class balancing, and feature engineering. The results show that LightGBM consistently outperforms the other models on six metrics, namely Accuracy, Balanced Accuracy, F1-Score, ROC-AUC, PR-AUC, and Brier Score, out of a total of eight evaluation metrics used. SHAP analysis identifies Social Assistance History and Asset Score as the most influential features for model prediction. Friedman and Nemenyi nonparametric tests confirmed significant performance differences between LightGBM and other models based on the F1-Score, PR-AUC, and Brier Score metrics. These findings indicate that tree-based models, particularly LightGBM, can support the development of a more targeted and data-driven social assistance targeting system. Keywords: Social Assistance; Tree-Based; SHAP; SUSENAS; Hybrid Bayesian Optimization
CLASSIFICATION OF CARDIOVASCULAR AND CHRONIC RESPIRATORY DISEASES UTILIZING ENSEMBLE MODELS WITH DATA EXPLORATION TECHNIQUES I Gusti Ngurah Sentana Putra; Amri Luthfi Najih; Unique DA Resiloy; Rachmat Bintang Yudhianto; Erfiani Erfiani; Anwar Fitrianto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9311

Abstract

Non-communicable diseases, especially cardiovascular and chronic respiratory conditions, contribute significantly to Indonesia’s healthcare burden and BPJS expenditure. Health claim data often suffer from class imbalance, multicollinearity, and outliers that impair model accuracy. This study evaluates the impact of essential data exploration techniques such as winsorizing, correlation and VIF analysis, variable selection, and SMOTE on the performance of ensemble classifiers. The dataset comprises 497,439 BPJS health insurance claims from 2022, including 27 predictors (14 numerical and 13 categorical). Two data pipelines were compared: one without preprocessing and another incorporating systematic data exploration. Five ensemble models were tested, namely Decision Tree, Extra Trees, Random Forest, XGBoost, and LightGBM. Model performance was assessed using F1-score, balanced accuracy, and G-mean across 20 stratified cross-validations. The results show that preprocessing substantially improves classification fairness and accuracy. Bagging models, particularly Random Forest, achieved the highest improvement, with balanced accuracy and G-mean increasing from around 0.93 to 0.99. Boosting models showed modest gains. These findings highlight that rigorous data exploration enhances ensemble classifier performance, enabling more reliable disease classification and supporting fairer, data-driven decision-making in BPJS health management.
MODELING CLIMATE CHANGE IN THE ASEAN REGION USING CO₂ TRADE NETWORK-BASED SPATIAL-TEMPORAL GRAPH NEURAL NETWORK (ST-GNN) AND IMPACT ANALYSIS ON INDONESIA I Gusti Ngurah Sentana Putra
INDONESIAN JOURNAL OF ENVIRONMENT AND DISASTER Vol. 5 No. 1 (2026): Indonesian Journal of Environment and Disaster
Publisher : Disaster Research Center, Universitas Sebelas Maret, Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijed.v5i1.2669

Abstract

Climate change in Southeast Asia is intensifying due to rapid economic growth, industrial expansion, and cross-border carbon transfers. This study employs a Spatial-Temporal Graph Neural Network (ST-GNN) to model CO₂ emissions embedded in ASEAN’s trade network, with Indonesia as the focal point due to its dominant role in regional emissions. Using Our World in Data (OWID) datasets (1990–2023), the ST-GNN framework captures interdependencies between trade-linked emissions and temperature change, outperforming traditional models with an RMSE of 0.011 when optimized (90% top features). Key findings reveal that Vietnam and Australia exert the strongest influence on Indonesia’s emission-driven temperature rise, while Singapore acts as a high-centrality hub in the carbon network. Permutation importance analysis identifies land-use change (CO₂_including_luc), energy consumption per capita, and coal-based emissions as the top predictors of warming trends. The temporal attention mechanism highlights critical periods, such as the 1998 financial crisis and post-2008 recovery, where economic shocks amplified emission impacts. Policy recommendations emphasize regional carbon accounting frameworks, deforestation control, coal phase-out strategies, and ASEAN-wide climate collaboration to mitigate transboundary emissions. This study demonstrates that ST-GNNs enhance climate modeling by quantifying spatial-temporal emission dynamics, offering actionable insights for decarbonizing trade-dependent economies.
DETECTION OF ADULTERATION IN COCONUT MILK USING CUCKOO SEARCH-OPTIMIZED XGBOOST ON HIGH-DIMENSIONAL FTIR SPECTRAL DATA I Gusti Ngurah Sentana Putra; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.8376

Abstract

Coconut milk adulteration is an important issue because it can reduce food quality and endanger consumers. This study aims to develop a rapid and accurate detection method for coconut milk adulteration using a combination of FTIR spectroscopy technology and the XGBoost machine learning algorithm optimized with the Cuckoo Search Algorithm (CSA). FTIR spectral data from traditional and instant coconut milk samples were analyzed using Standard Normal Variate (SNV) and Savitzky-Golay (SG) preprocessing to reduce noise and clarify spectral features. The XGBoost model was then optimized through CSA with hyperparameter tuning. The results showed that the combination of SNV+SG preprocessing increased the model accuracy by 84.44%, with a precision of 92.73% and an F1-score of 79.94%. In addition, CSA optimization provided a 19.7% increase in accuracy compared to the model without tuning. These findings prove the effectiveness of the CSA-XGBoost approach in analyzing high-dimensional spectral data and is a potential solution in efficiently detecting the authenticity of coconut milk. In conclusion, this approach has the potential to be widely applied to test the authenticity of other food products quickly, non-destructively and accurately.
Co-Authors Agus Mohamad Soleh Amri Luthfi Najih Aulia Rizki Firdawanti Bagus Sartono Budi Susetyo Cici Suhaeni Erfiani Erfiani Fitrianto, Anwar Gerry Alfa Dito Kusman Sadik Rachmat Bintang Yudhianto Syella Zignora Limba Unique DA Resiloy Yani Prihantini Hiola Zulhijrah