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All Journal Teknik dan Ilmu Komputer Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METIK JURNAL Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Green Intelligent Systems and Applications International Journal of Multidisciplinary Sciences and Arts MDP Student Conference International Journal of Mechanical, Industrial and Control Systems Engineering jurnal informasi teknologi dan sistem Jurnal Informasi Teknologi dan Sistem (INTEKSIS) Global Science: Journal of Information Technology and Computer Science
Genrawan Hoendarto
Universitas Widya Dharma Pontianak, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Energy Aware Reinforcement Learning Approach for Dynamic Production Scheduling Optimization in Sustainable Smart Manufacturing Environments Yogiek Indra Kurniawan; Krisna Widi Nugraha; Rosyid Ridlo Al-Hakim; Erick Fernando; Rian Ardianto; Genrawan Hoendarto; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 4 (2025): December :IJMICSE: International Journal of Mechanical, Industrial and Control
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i4.408

Abstract

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.
Design of Diabetes Prediction Interface Using E-ss and Classification Tree Algorithm Venecia Venecia; Genrawan Hoendarto; Tony Darmanto
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3370

Abstract

Diabetes was a chronic disease that continued to increase globally, making early detection essential to reduce long-term complications. This study aimed to develop a desktop-based diabetes prediction system that provided fast and simple classification results for medical personnel and individual users. The system used the entropy-based subset selection (E-ss) method to choose the most relevant attributes and a classification tree to classify the risk. The dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, contained 768 patient records with attributes such as number of pregnancies, glucose level, blood pressure, and other risk factors. The E-ss process produced three attributes with the highest information scores, namely body mass index (BMI), blood pressure, and triceps skinfold thickness. These three attributes were then used as input to the classification tree model to generate diabetes risk predictions. Cross-validation testing showed an accuracy of up to 78.95%. These findings indicated that E-ss feature reduction helped maintain prediction performance while improving computational efficiency. This system was expected to serve as a practical and reliable diagnostic tool. 
Pengembangan Sistem Informasi Prediksi Risiko Dropout Mahasiswa Berbasis Web Menggunakan Algoritme CatBoost Tania Aurellia; Genrawan Hoendarto; Thommy Willay
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3656

Abstract

The problem addressed was the limited use of dropout prediction models, which had generally focused on algorithm performance and had not been integrated into an academic monitoring system design.This study aimed to develop a web-based prototype information system for predicting student dropout risk using the CatBoost algorithm to support academic monitoring. The development method used was Prototyping, while the prediction model was built from the Predict Students Dropout and Academic Success dataset, which was reduced to 3,399 records with two classes, namely dropout and graduate. The results showed that the developed prototype included manual prediction, CSV import, prediction monitoring, and role-based reporting features. The CatBoost model achieved 90.29% accuracy, 85.33% precision, 88.76% recall, and 87.01% F1-score. These findings indicated that the prototype had the potential to serve as a basis for developing an early detection system for students at risk of dropout. Keywords: Academic monitoring; CatBoost; Dropout prediction; System prototype; Web-based information system AbstrakPermasalahan yang diangkat adalah keterbatasan pemanfaatan model prediksi dropout yang umumnya masih berfokus pada performa algoritme dan belum terintegrasi ke dalam rancangan sistem pemantauan akademik. Penelitian ini bertujuan mengembangkan prototype sistem informasi prediksi risiko dropout mahasiswa berbasis web menggunakan algoritme CatBoost untuk mendukung pemantauan akademik. Metode pengembangan yang digunakan adalah Prototyping, sedangkan model prediksi dibangun dari dataset Predict Students Dropout and Academic Success yang diseleksi menjadi 3.399 data dengan dua kelas, yaitu dropout dan graduate. Hasil penelitian menunjukkan bahwa prototype yang dikembangkan memuat fitur prediksi manual, impor file CSV, monitoring hasil prediksi, dan laporan berbasis peran pengguna. Model CatBoost memperoleh accuracy 90,29%, precision 85,33%, recall 88,76%, dan F1-score 87,01%. Temuan ini menunjukkan bahwa prototype tersebut berpotensi menjadi dasar pengembangan sistem deteksi dini mahasiswa berisiko dropout. Kata kunci: Pemantauan akademik
Pengembangan Sistem Informasi Deteksi Dini Penyakit Ginjal Kronis Berbasis Web dengan TabNet Jessen Hero Pratama; Genrawan Hoendarto
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3669

Abstract

Chronic kidney disease is a global health problem that requires early detection to prevent the progression of more serious conditions. This study aims to design and implement a web-based early detection information system for chronic kidney disease integrated with a prediction model. The system was developed using the Prototyping method with TabNet as the classification model and Class-Conditional Conformal Prediction (CCP) to provide prediction confidence information. The study used secondary dummy data representing clinical attributes and risk factors for chronic kidney disease. Data were processed using median imputation, soft class weighting, and train–validation–calibration–test splitting. Prediction labels were determined using a CKD probability threshold of 0.75. Evaluation results showed accuracy of 0.8614, precision of 0.9448, recall of 0.9013, and F1-score of 0.9226. CCP enables the system to display a prediction set, making early detection results more informative and structured.Keywords: Chronic kidney disease; Conformal prediction; Early detection; Information systems; TabNet algorithmAbstrakPenyakit ginjal kronis merupakan masalah kesehatan global yang memerlukan deteksi dini untuk mencegah perkembangan kondisi yang lebih serius. Penelitian ini bertujuan merancang dan mengimplementasikan sistem informasi deteksi dini penyakit ginjal kronis berbasis web yang terintegrasi dengan model prediksi. Sistem dikembangkan menggunakan metode Prototyping dengan TabNet sebagai model klasifikasi dan Class-Conditional Conformal Prediction (CCP) untuk menyajikan informasi keyakinan prediksi. Data penelitian menggunakan data sekunder berbentuk data dummy yang merepresentasikan atribut klinis dan faktor risiko penyakit ginjal kronis. Data diproses menggunakan median imputation, soft class weighting, dan pembagian train–validation–calibration–test. Label prediksi ditentukan berdasarkan threshold probabilitas CKD sebesar 0,75. Hasil pengujian menunjukkan accuracy 0,8614, precision 0,9448, recall 0,9013, dan F1-score 0,9226. Penerapan CCP memungkinkan sistem menampilkan prediction set, sehingga hasil deteksi dini menjadi lebih informatif dan terstruktur.Kata kunci: penyakit ginjal kronis; conformal prediction; deteksi dini; sistem informasi; algoritma TabNet
Co-Authors Antonius Antonius Armita, Angella Balbo, Florensius Bobby Reza Chang, Calvin Clesya, Clesya Didik Setiyadi Donata Yulvida Erick Fernando Felix Liando Ferdynand, Hosea Gunawan Helmi Wibowo Hoga Saragih Irlon Irlon Jessen Hero Pratama Jimmy Tjen Kartono , Krisna Widi Nugraha Kurniawan, Yogiek Indra Mursalim Mursalim Ng, Junira Merrylin Pavan Kumar Rian Ardianto Ricky I. Ndaumanu Rosyid Ridlo Al-Hakim Sigan, Agustinus Siti Shofiah Steven Joses Sudjalmo, Sugito Susana Susana Tania Aurellia Tendean, Sandi Tony Darmanto Venecia Venecia Vicni Iskandar Viktorius Ando Saputra Willay, Thommy