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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JSiI (Jurnal Sistem Informasi) Jurnal Telematika Jurnal Informatika Global EDUMATIC: Jurnal Pendidikan Informatika Jurnal UNITEK Jurnal Algoritma Jurnal Masyarakat Madani Indonesia AL-UMRON: Jurnal Pengabdian kepada Masyarakat The Indonesian Journal of Computer Science
Sanjaya, Ucta Pradema
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Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Journal : Jurnal Algoritma

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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.
Sistem Pendukung Keputusan Pemantauan Stok dan Restok Otomatis Berbasis Web Wibisono, Bagas; Sanjaya, Ucta Pradema
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.22-1.2301

Abstract

Problems in stock management are often caused by delays in monitoring and the absence of a system that provides accurate restock recommendations. This study designs and implements a web-based decision support system to monitor stock and recommend restocks automatically, using the Laravel Framework. This system applies rule-based logic by considering remaining stock, average daily sales, and delivery time. The calculation process compares available stock with minimum requirements predicted from historical sales patterns. The development methodology follows the Waterfall model. The implementation results show that the system is able to accelerate the monitoring process and provide accurate restock notifications, as well as support managerial decision making in product distribution. This system also succeeded in reducing the number of expired goods to only 2%, which shows an increase in distribution efficiency. An informative dashboard interface also makes it easier to monitor stock conditions in real time. This system makes a real contribution to improving the efficiency and accuracy of inventory management.
Co-Authors Ade Pratama Ais, Salma Rihadatul Damayanti, Hevi Alvina Diah Anggraini, Diah Dirgantoro, Guruh Putro Fauziyah, Dewi Nur Fetrika Anggraini, Fetrika Heni Setyowati Hesti Yunitiara Rizqi Hevi Alvina Damayanti Ilhamy, Rifqi Syahrul Irsyada, Rahmat Ita Aristia Sa'ida Iwan Setiawan Wibisono Nita Cahyani Oktana, Weka Brilliant Jaya Prastya, Ifnu Wisma Dwi Pribadi , Teguh Saputra, Agus Bima Sri Mujiyono Wibisono, Bagas Zakki Alawi Zayn, Afta Ramadhan