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All Journal Jurnal Pendidikan Sains dan Komputer Journal of Computer Science and Informatics Engineering
Latief, Ibrahim
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Energy Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Other

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Classification of Nutritional Status in Toddlers Based on Anthropometric Data Using Random Forest Imung, Mundirin; Idawati, Idawati; Latief, Ibrahim
Journal of Computer Science and Informatics Engineering Vol 4 No 4 (2025): October
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i4.1175

Abstract

Stunting is a chronic nutritional problem that remains a major challenge in improving child health in Indonesia. This condition has long-term impacts on physical growth, cognitive development, and future productivity of children. Early detection of toddlers' nutritional status is crucial for effectively preventing and addressing stunting cases. This study aims to develop a machine learning-based classification model for toddlers' nutritional status using simple anthropometric data, namely age (in months), sex, and height (in cm). The dataset used in this study was sourced from the 2022 historical records of the Health Department and the Community-Based Nutrition Recording and Reporting System (E-PPGBM), comprising 120,999 entries categorized into four nutritional status classes: normal, tall, stunted, and severely stunted. Data preprocessing included label encoding and feature standardization. The model employed is the Random Forest Classifier, evaluated using accuracy, precision, recall, and F1-score metrics. The training results show that the model achieved a classification accuracy of 99.93% on the test data, with F1-scores for each class as follows: Normal = 0.9998, Severely Stunted = 0.9985, Stunted = 0.9975, Tall = 0.9997. Feature importance analysis indicates that height is the most influential feature in the classification task. These findings demonstrate that machine learning algorithms, particularly Random Forest, are effective for predicting toddlers’ nutritional status and have strong potential to be integrated into digital applications that support Indonesia’s stunting reduction programs. However, the model's limitation lies in its use of only basic anthropometric features—age, sex, and height—without considering additional variables such as weight, disease history, dietary patterns, socioeconomic status, or immunization history, which may also influence a child's nutritional status. To improve the model's accuracy and relevance, it is recommended to incorporate other related features, such as body weight, nutritional intake, health history, and social-economic indicators, in future research.
Model Prediktif Kelulusan Mahasiswa Berbasis Machine Learning Menggunakan Pipeline Terintegrasi dan Hyperparameter Tuning: A Machine Learning-Based Student Graduation Prediction Model Using an Integrated Pipeline and Hyperparameter Tuning Mundirin, Mundirin; Hedin, Deden; Idawati, Idawati; Latief, Ibrahim; Lili, Mohamad
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 01 (2026): Artikel Riset, February 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i01.7795

Abstract

Delays in student completion are a critical issue in higher education because they impact academic efficiency, program accreditation, and graduate quality. This study aims to develop a machine-learning-based model for predicting student graduation using an integrated pipeline. This pipeline encompasses data processing, model building, and hyperparameter optimisation. The dataset was obtained from eight semesters of student academic data, totalling 146 credits. This dataset includes both numeric and categorical variables, such as GPA, number of credits passed per semester, study load, and student background characteristics. Preprocessing was performed using ColumnTransformer, which combined StandardScaler for numeric features and OneHotEncoder for categorical features. A classification model was developed using the Random Forest algorithm and optimised with GridSearchCV to identify the optimal hyperparameter settings. Model evaluation was performed using accuracy metrics, confusion matrices, and classification reports. The findings of this study indicate that the model achieves an accuracy of 81%, suggesting a strong ability to classify students as on-time or late graduates. Feature analysis shows that the average Grade Point Average (GPA), the number of Semester Credit Units completed each semester, and consistency in study load are the main factors influencing the timeliness of study completion. The implementation of an integrated channel has proven effective in maintaining preprocessing consistency and reducing the possibility of data leakage. The developed model can be implemented as an early warning system to support data-driven academic decision-making.
Co-Authors Hedin, Deden Idawati Idawati Lili, Mohamad Mundirin Mundirin Mundirin, Mundirin