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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini MEANS (Media Informasi Analisa dan Sistem) Didaktika: Jurnal Kependidikan Jurnal Algoritma Jurnal Cakrawala Informasi (JCI) Jurnal Masyarakat Madani Indonesia "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Journal of Future Artificial Intelligence and Technologies Jurnal Informatika dan Kesehatan Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat
Iwan Setiawan Wibisono
Universitas Ngudi Waluyo
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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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.
Sistem Pakar E-Rapor untuk Prediksi Minat Bakat dan Roadmap Pendidikan Siswa dalam Pemilihan Sekolah Nurohmah, Siti; Wibisono, Iwan Setiawan
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.22-1.2280

Abstract

Choosing a school and university that does not match students' interests and talents often leads to regret later on. According to Irene Guntur, an Educational Psychologist from Integrity Development Flexibility (IDF), 87% of students in Indonesia feel they are in the wrong major. In addition, the Minister of Education, Culture, Research, and Technology (Mendikbudristek) Nadiem Makarim stated that 80% of students in Indonesia do not work in accordance with the major they took. This is due to students' lack of understanding of their interests and talents, as well as the influence of friends, family, or people closest to them in the decision-making process. This study aims to develop an integrated e-report system that is able to identify students' interests and talents based on academic data from elementary, junior high, to high school levels. This system provides recommendations for relevant schools and universities, and functions as a promotional platform for educational institutions through profile information, vision, mission, and blogs. The development of the system follows the Waterfall method which consists of the stages of needs analysis, system design, implementation, testing, and maintenance. . Student academic data in grades 6, 9, and 12 is the basis for system analysis. The results of the study show that the system is able to increase the accuracy of recommendations by up to 95%, while providing an effective promotional medium for schools and universities. This system is expected to help students make better educational decisions, minimize external influences, and encourage educational institutions to improve their competitiveness and service quality. These findings contribute to the development of innovative, effective and predictive technology-based educational information systems.
Co-Authors Abdul Rohman Achmad Nuruddin Safriandono Akhmad Dahlan Alfiano Aldo Pamungkas amalia fatima zahra Aqmal Putra R.R Arga Aditia Purnama Aya Sofia Dinafa C, Annisa De Rosal Ignatius Moses Setiadi Efaforito Gulo Farah Zakiyah Rahmanti Heni Candra Kirana Heni Setyowati Hesti Yunitiara Rizqi Hesti Yunitiara Rizqi Himmah Taulany inneke fitri ade muliya Kartika Yuni Purwanti Kurniati Pratami Lisa Virdinarti Putra Mamluatul Husnia Marsiska Ariesta Putri Nurohmah, Siti Ojugo, Arnold Adimabua Sanjaya, Ucta Pradema Sekar Febri Medianza Sri Mujiyono Suamanda Ika Novichasari Ucta Pradema Sanjaya Zulmi Roestika Rini