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All Journal International Journal of Electrical and Computer Engineering Engineering, Mathematics and Computer Science Journal (EMACS) An-Nas
Hidayat, Muhammad Fadlan
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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Forecasting Poverty Ratios in Indonesia: A Time Series Modeling Approach Hidayat, Muhammad Fadlan; Henryka, Diva Nabila; Citra, Lovina Anabelle; Permai, Syarifah Diana
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 6 No. 3 (2024): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v6i3.11968

Abstract

Poverty is one of the main problems still faced by Indonesia today. To help find the right solution, an annual prediction of the poverty rate in Indonesia is needed. This study uses data on the 'Ratio of the Number of Poor People in Indonesia per year from 1998 to 2023' obtained from data.worldbank.org. The prediction methods used in this study include the Naïve Model, Double Moving Average, Double Exponential Smoothing, ARIMA, Time Series Regression, and Neural Network, with a total of 26 models. Of the 26 models, only 19 models passed the model comparison stage. Based on the evaluation results using the RMSE, MAE, MAPE, and MDAE metrics, it was concluded that the NNETAR Neural Network model showed the best performance among the six methods used to predict the poverty ratio in Indonesia.
Machine Learning Approach: A Comparative Analysis of Classifiers in Predicting Obesity Type Tedjasulaksana, Jeffrey; Dinata, Ferry Jaya; Krisnadi, Rafael; Reksosamudro, Matthew S.W.; Wen, Wilbert; Hidayat, Muhammad Fadlan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15268

Abstract

Obesity is a growing global public health concern that increases the risk of chronic diseases and significantly affects quality of life. Traditional diagnostic methods such as Body Mass Index (BMI) have limitations in accurately representing body fat distribution and individual health conditions. This study aims to comparatively evaluate the performance of various machine learning and neural network models in predicting obesity levels using a multiclass classification approach. The dataset consists of 2,111 observations with 12 predictor variables and seven obesity categories, obtained from a publicly available source. Data preprocessing included duplicate removal, outlier handling using the interquartile range method, feature scaling, and categorical encoding, followed by a 60:20:20 train–validation–test split. Several classifiers were implemented, including Logistic Regression, Support Vector Classifier, Random Forest, Extra Trees, Gradient Boosting-based models (XGBoost and LightGBM), Multilayer Perceptron, K-Nearest Neighbors, and TabNet. Model performance was evaluated using macro-average F1-score and confusion matrix analysis. The results indicate that LightGBM achieved the highest predictive performance with an F1-score of 0.96, demonstrating strong generalization across obesity categories. XGBoost and Random Forest also showed strong performance, while Support Vector Classifier exhibited consistent results across training, validation, and cross-validation. These findings suggest that ensemble-based models are highly effective for obesity classification, while model selection should consider accuracy, interpretability, and computational constraints.
Co-Authors Alam, Islam Nur Citra, Lovina Anabelle Dinata, Ferry Jaya Henryka, Diva Nabila Krisnadi, Rafael Nabiilah, Ghinaa Zain Purwanto, Eko Setyo Reksosamudro, Matthew S.W. Syarifah Diana Permai Tedjasulaksana, Jeffrey Wen, Wilbert