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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics MEANS (Media Informasi Analisa dan Sistem) Didaktika: Jurnal Kependidikan JURNAL ILMIAH GLOBAL EDUCATION 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 Harmony Philosophy: International Journal of Islamic Religious Studies and Sharia INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer
Iwan Setiawan Wibisono
Universitas Ngudi Waluyo
Religion 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.
Pemantauan Daya Listrik Real-Time Menggunakan IoT untuk Efisiensi Energi Rumah Tangga Munir, Misbahul; Setiawan Wibisono, Iwan
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.22-2.2391

Abstract

Teknologi Internet of Things (IoT) hadir sebagai penggerak utama dalam era transformasi digital yang memungkinkan perangkat saling terhubung dan beroperasi secara otomatis. Penggunaan daya listrik yang tidak terkontrol dapat menyebabkan pemborosan energi dan peningkatan biaya operasional. Penelitian ini merancang sebuah sistem monitoring pengelolaan konsumsi daya listrik berbasis IoT (Internet of Things), sehingga memungkinkan pengguna untuk memantau tingkat konsumsi energi secara real-time. Sistem ini memanfaatkan sensor daya PZEM004T yang terhubung ke platform smartphone berbasis aplikasi Blynk melalui mikrokontroler NodeMCU ESP8266. Metode yang diterapkan dalam penelitian ini adalah Research and Development, yang mencakup tahapan perencanaan, pengembangan, serta evaluasi sistem. Hasil pengujian menunjukkan bahwa sistem mampu menurunkan konsumsi daya listrik hingga 20%, meningkatkan akurasi sensor sebesar 3%, serta menurunkan latensi transmisi data hingga 75%. Temuan ini menunjukkan bahwa sistem mampu meningkatkan kesadaran pengguna terhadap pola konsumsi energi dan mendorong perubahan perilaku ke arah yang lebih hemat energi. Selain memberikan solusi praktis untuk pengendalian energi rumah tangga, sistem ini juga menawarkan potensi pengembangan lebih lanjut, seperti integrasi kecerdasan buatan (AI) dan energi terbarukan. Penelitian ini memberikan kontribusi penting terhadap pengembangan sistem IoT di bidang efisiensi energi dengan menghadirkan pendekatan yang aplikatif, hemat biaya, serta ramah lingkungan, sekaligus memperkaya khazanah penelitian sebelumnya di bidang monitoring konsumsi energi berbasis IoT yang belum banyak mengeksplorasi integrasi sistem dengan aplikasi mobile secara langsung dan real-time.
Sistem Informasi Penjualan Tembakau Berbasis Web dengan Laravel: Implementasi Metode Waterfall dan Pengujian Black-Box Anugrah, Harun; Setiawan Wibisono, Iwan
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.22-2.2511

Abstract

Tobacco sales by local businesses often face challenges in recording transactions and managing customer data. This study focuses on the design and development of a Laravel-based sales information system using the Waterfall methodology approach. The development of this system involves a series of processes that include needs identification, architecture design, design, testing, and system maintenance. This system contributes to the digitization of tobacco MSMEs with a time efficiency in recording of up to 45%. System testing indicates an increase in effectiveness in transaction data management and sales report presentation. With this system, it is hoped that business actors will achieve improvements in digitizing their business processes effectively and measurably.
SmartTraffic-CNN: Deteksi dan Estimasi Jumlah Kendaraan Secara Otomatis Menggunakan Deep Learning dan Ekstraksi Fitur Putri, Marsiska Ariesta; Riyono; Setiawan Wibisono, Iwan
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.22-2.2943

Abstract

With the rapid pace of urbanization, the number of vehicles traveling between cities has increased significantly. As a result, various traffic-related problems have emerged, such as congestion and excessive vehicle volume and types. To address these issues, comprehensive road data collection is essential. Therefore, in this study, we developed an intelligent traffic monitoring system based on You Only Look Once (YOLO) and a Fuzzy Convolutional Neural Network (CFNN), which records traffic volume and vehicle-type information from the roadway. In this system, YOLO is first used for vehicle detection and combined with a vehicle-counting method to calculate traffic flow. Then, two effective models (CFNN and Vector CFNN) along with a network mapping fusion method are proposed for vehicle classification. In our experiments, the proposed methods achieved an accuracy of 90.45% on a public dataset. On this dataset, the average precision and F-measure (F1) of the proposed YOLO-CFNN and YOLO-VCFNN vehicle classification methods reached 99%, outperforming other approaches. On real highways, the proposed YOLO-CFNN and YOLO-VCFNN methods not only attained high F1-scores for vehicle classification but also demonstrated remarkable accuracy in vehicle counting. Furthermore, the system maintained a detection speed of over 30 frames per second. Thus, the proposed intelligent traffic monitoring system is well-suited for real-time vehicle classification and counting in real-world environments.
Co-Authors Abdul Rohman Achmad Nuruddin Safriandono Akhmad Dahlan Alfiano Aldo Pamungkas amalia fatima zahra anggita maharani donal Anugrah, Harun Aqmal Putra R.R Arga Aditia Purnama Aya Sofia Dinafa C, Annisa De Rosal Ignatius Moses Setiadi Deta verrensyah Efaforito Gulo Farah Zakiyah Rahmanti Heni Candra Kirana Heni Setyowati Hesti Yunitiara Rizqi Himmah Taulany inneke fitri ade muliya Kartika Yuni Purwanti Kurniati Pratami Lisa Virdinarti Putra Mamluatul Husnia Marsiska Ariesta Putri Marsiska Ariesta Putri Marsiska Ariesta Putri Misbahul Munir Moch Nurcholis Majid Muhammad Ari Wibowo Muhammad Diva Livarizi Nurohmah, Siti Ojugo, Arnold Adimabua Rahman, Fitri Anisa Riyono Sanjaya, Ucta Pradema Sekar Febri Medianza Siska Nadeak Sri Mujiyono Suamanda Ika Novichasari Tatik Wulandari Wahyuningtyas Hesti Widya Pertiwi, Nur Annisa Zilni, An Zulmi Roestika Rini