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All Journal ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics Neo Teknika: Jurnal Ilmiah Teknologi International Journal of Advances in Data and Information Systems MEANS (Media Informasi Analisa dan Sistem) Jurnal UNITEK Journal La Multiapp Jurnal Pengabdian Masyarakat (ABDIRA) Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Mandiri IT Jurnal Locus Penelitian dan Pengabdian Jurnal Algoritma Jurnal Masyarakat Madani Indonesia Indonesian Journal of Community Empowerment (IJCE) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Informatika dan Kesehatan INOVTEK Polbeng - Seri Informatika
Sri Mujiyono
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Journal : Jurnal Algoritma

 1 
Prediksi Fluktuasi Berat Badan Berdasarkan Pola Hidup Menggunakan Model XGBoost dan Deep Learning Mujiyono, Sri; Sanjaya, Ucta Pradema; Wibisono, Iwan Setiawan; Setyowati, Heni
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2253

Abstract

The global obesity rate has tripled since 1975, driving the development of technology-based solutions for predicting body weight to mitigate disease risks. This study implements three models—Decision Tree Regressor, XGBoost Regressor, and Deep Learning—to project final body weight based on physiological variables (age, gender, BMR), nutritional factors (caloric intake, surplus/deficit), and lifestyle factors (physical activity, sleep, stress). The multidimensional dataset from community health posts includes TDEE calculations and BMR estimates using the Harris-Benedict Equation. Evaluation using RMSE and R² indicates XGBoost as the best-performing model (RMSE: 5.65; R²: 0.974), outperforming the Decision Tree (RMSE: 10.68; R²: 0.908) and Deep Learning (RMSE: 10.4; R²: 0.913) models. Key challenges include overfitting in the Decision Tree and Deep Learning's inability to capture outliers due to vanishing gradients. The analysis identifies energy balance, representation of extreme data, and regularization as critical factors for model stability. Hyperparameter optimization (learning rate, max\_depth) and data augmentation are recommended to enhance generalization. These findings offer an innovative framework for data-driven health technologies, reinforcing the role of artificial intelligence in precision public health interventions. Practically, the study advocates for the adoption of optimized predictive models integrating multidimensional variables for high accuracy, while highlighting the need for outlier handling and further clinical validation to ensure relevance in real-world scenarios.
Implementasi Algoritma XGBoost Untuk Prediksi Status Gizi Balita Berbasis Website Pangestu, Andi; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2390

Abstract

Malnutrition among toddlers remains a serious public health issue in Indonesia, with a stunting prevalence of 21.6% in 2022—still above the WHO standard, which sets the maximum threshold at 20%. Traditional methods for assessing nutritional status are time-consuming and prone to human error, highlighting the need for a more efficient and accurate approach. This study aims to develop a system for predicting toddler nutritional status using the XGBoost algorithm, implemented in a web-based application utilizing anthropometric data. A quantitative approach with applied research methods was used, analyzing 5,489 anthropometric records of children from RSUD DR. Gondo Suwarno during the 2017–2023 period, selected through purposive sampling. The dataset included parameters such as age, sex, height, weight, arm circumference, and head circumference of children aged 0–59 months. After data cleaning, 5,169 high-quality samples were retained and divided into 80% training and 20% testing sets with balanced class distribution. The XGBoost model was optimized using Grid Search with 3-fold cross-validation to achieve the best hyperparameter configuration. Results showed that the XGBoost model achieved an accuracy of 97.17%, precision of 97.16%, recall of 97.17%, and F1-score of 97.16% in classifying three nutritional status categories: Normal, Overnutrition, and Undernutrition. Feature importance analysis revealed that weight was the strongest predictor, contributing 42.52%, followed by age (16.79%) and height (15.49%). The system was successfully implemented in a user-friendly web application that allows the input of anthropometric data and provides real-time prediction results. This research produced an effective screening tool for early detection of toddler malnutrition, improving healthcare service efficiency and supporting government programs aimed at reducing stunting rates.
Aplikasi Rekomendasi Menu Makanan Harian Menggunakan Algoritma Metode KNN Nafi, Tri Maula; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2491

Abstract

This food menu recommendation app utilizes the K-Nearest Neighbors (KNN) algorithm to provide menu suggestions that suit the nutritional needs and preferences of users. The system analyzes various factors, including calories, protein, fat, and carbohydrates, in order to generate accurate and relevant recommendations. Users can enter information related to their nutritional needs and food preferences, such as their favorite types of food and allergies, to get the right menu suggestions. Through this application, users are expected to receive healthy menu recommendations that suit their individual needs, which in turn can increase awareness of the importance of a nutritious diet and overall health. With a data-driven approach, this app is an effective solution for those who want to make healthier food choices and support the achievement of their desired health and nutritional goals. In addition, this app can also contribute to building better eating habits among the community.
Sistem Pendukung Keputusan untuk Menentukan Guru Terbaik dengan Metode Analytical Hierarchy Process (AHP) : Studi kasus: SDN Bergas Lor 01 Khafid, Ahmad Noor; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2543

Abstract

Teacher performance evaluation is very important in ensuring the quality of education. However, this process is often difficult because it must consider various aspects, such as personality, competence, achievement, and innovation. In this study, we developed a decision support system (DSS) using the Analytical Hierarchy Process (AHP) method to help determine the best teachers objectively. This method helps identify relevant criteria and weights in determining teacher performance evaluations. This method is capable of producing more consistent and transparent decisions. The designed system is expected to simplify the decision-making process in determining the best teachers more efficiently, thereby supporting the improvement of teacher performance.
Penerapan Algoritma Naive Bayes dengan optimasi genetic algorithm untuk memprediksi kedisiplinan siswa Dewanto, Bernadus; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3084

Abstract

The grouping of student misconduct data for the second semester is used to assess student discipline levels. This data classification uses data mining methods to determine student discipline objectively. The data mining method used in this study is Naïve Bayes. This data classification uses manual calculations with the Gaussian Naïve Bayes method, which uses an integer approach. It is not only tested manually but also with RapidMiner tools. The technique used in Rapid Miner to divide the data into several parts or folds, where the training and testing data parts are divided by cross-validation. This technique aims to make the evaluation results more accurate. The evaluation is made with a confusion matrix with curation, precision, and recall calculations and F1 score. Data grouping is divided into two categories, namely disciplined and undisciplined. The results of the study using Naïve Bayes with GA optimization obtained an accuracy value of 89.47% using the cross-validation technique with stratified sampling type, which helped produce a more stable evaluation.
Co-Authors Abdul Rohman Abdul Rohman Ade Pratama Amalia, Alifah Anton Anton Aripin, Ahmad Nurul Dewanto, Bernadus Ela Suryani Fahrizal Alfian Dante fifi maqfiroh Fiktina Vifri Ismiriyam Galih Agil Febri Hidayatullah Hani Latifah Heni Setyowati Hesti Yunitiara Rizqi Ilham, Dimas Muhammad Iwan Setiawan Wibisono Kartika Yuni Purwanti Khafid, Ahmad Noor Lisa Virdinarti Putra Nafi, Tri Maula Novan Syaiful Nugroho, Aventura Suryo Nugroho, Ivan Pangestu, Andi Purnamasari, Yessy Sabilla Sanjaya, Ucta Pradema Syarifudin, Nanang Irfan Wahyu Kristiningrum Widayati Widayati Yeni Fitri Afidah Zulmi Roestika Rini