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All Journal Proceeding International Conference on Information Technology and Business International conference on Information Technology and Business (ICITB) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Jurnal Pengabdian kepada Masyarakat Nusantara ABDI MOESTOPO: Jurnal Pengabdian pada Masyarakat Journal of Information Systems and Technology Research Sewagati: Jurnal Pengabdian Masyarakat Indonesia Sejahtera: Jurnal Inspirasi Mengabdi Untuk Negeri Cerdika: Jurnal Ilmiah Indonesia Jurnal Teknologi dan Informatika (JEDA) BEGAWE: Jurnal Pengabdian Masyarakat JPEMAS: Jurnal Pengabdian Kepada Masyarakat
Resy Anggun Sari
Universitas Mitra Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Public Health Social Sciences Other

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Journal : Journal of Information Systems and Technology Research

 1 
Application of Bio-Inspired Particle Swarm Optimization Algorithm for Production Scheduling Optimization Yodhi Yuniarthe; Rosyana Fitria Purnomo; Resy Anggun Sari; Fadhilah Dirayati; M Budi Hartanto
Journal of Information Systems and Technology Research Vol. 4 No. 2 (2025): May 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i02.1132

Abstract

Production scheduling is a fundamental aspect of manufacturing systems that significantly affects operational efficiency, resource allocation, and delivery performance. Traditional scheduling methods often struggle to solve complex, dynamic scheduling problems, resulting in suboptimal job sequencing and increased makespan. This research aims to develop a hybrid optimization algorithm by integrating Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to address inefficiencies in job shop scheduling. The proposed hybrid PSO-GA method leverages the global exploration ability of PSO and the local refinement strength of GA. The algorithm was tested on several benchmark datasets using performance metrics such as makespan, tardiness, and machine utilization. Experimental results demonstrate that the hybrid approach achieved a 12.7% improvement over standard PSO and a 15.4% improvement over GA in terms of makespan. The convergence curve also showed stable and faster optimization. These findings confirm that the hybrid PSO-GA model provides a more effective and robust solution for complex production scheduling and has strong potential for real-time application in Industry 4.0 environments
Hybrid Intelligent Framework for Adaptive Decision-Making Systems dirayati, fadhilah; Anggun Sari, Resy; Fitria Purnomo, Rosyana; Jih-Fu Tu, Jih-Fu Tu
Journal of Information Systems and Technology Research Vol. 5 No. 1 (2026): January 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i1.1462

Abstract

This study proposes a Hybrid Intelligent Framework that integrates Neural Networks (NN), Fuzzy Logic Systems (FLS), and Evolutionary Computation (EC) to improve adaptive decision-making in dynamic, uncertain, and data-driven environments. The framework combines data-driven pattern learning using a multilayer perceptron, interpretable fuzzy reasoning through Mamdani inference and centroid defuzzification, and evolutionary optimization to tune network weights, membership parameters, and fuzzy rule structures. Two dataset categories were used to assess robustness: simulated decision scenarios and industrial datasets with dynamic operational variables. Data were normalized via min–max scaling and fuzzified using Gaussian membership functions before being processed by the NN–FLS pipeline. EC then minimized a weighted objective that balances prediction error and rule complexity, enabling accurate yet explainable decisions. Performance was evaluated using accuracy, MAE, RMSE, and F1-score, and compared against standalone NN and standalone FLS baselines. The hybrid model achieved the best results, reaching 92.3% accuracy and 0.93 F1-score while reducing MAE to 0.32 and RMSE to 0.48. These findings indicate that hybridizing learning, reasoning, and optimization yields faster adaptation and lower error rates than single-model approaches, supporting scalable deployment in real-world decision-support systems. Confusion-matrix inspection also showed fewer critical misclassifications under changing conditions, supporting suitability for online updates in practice.
Co-Authors Armalia Reny WA Dewi Silvia Dina Haryati Dirayati, Fadhilah Dwi Nurahman Fadhilah Dirayati Fawaati, Teuku Muhammad Hartanto, M Budi Helmita Helmita Hendri, Romi Hendro Pramono, Doni Eko ikhwan, Ahmad Ino Susanti Jih-Fu Tu, Jih-Fu Tu Khozainuz Zuhri Luh Putu Ratna Sundari M Budi Hartanto Mei ratnasari Mohammad Renandi Ekatama Muhammad Fawaati HS, Teuku Naeni, Ovi Nisa, Via Khoirun Novalita Novalita Nur Salma Ovi Naeni Putra, Fajri Aditya Ratnasari, Mei Rosyana Fitria Purnomo Sriyanto - Sulastri Sulastri Sulastri Sulastri Via Khoirun Nisa Yodhi Yuniarthe Yodhi Yuniarthe Yudhinanto, Cahyo Nugroho Yulistina Yulistina