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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Excellence Humanities and Religiosity Jurnal MediaTIK Jurnal Pengabdian kepada Masyarakat (PEMAS) Scientific: Journal of Computer Science and Informatics Journix: Journal of Informatics and Computing
Teguh Ansyor Lorosae
Universitas Muhammadiyah Bima
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Journix: Journal of Informatics and Computing

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Genetic Algorithm Optimization for Solving the Traveling Salesman Problem in the Indonesian Business Environment Siti Mutmainah; Teguh Ansyor Lorosae; Erin Eka Citra
Journix: Journal of Informatics and Computing Vol. 1 No. 2 (2025): August
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i2.14

Abstract

The Traveling Salesman Problem (TSP) is one of the combinatorial optimization problems that is highly relevant in distribution and logistics route planning. This study aims to optimize the Genetic Algorithm (GA) for solving TSP in the Indonesian business environment, which has complex geographical characteristics and diverse logistics infrastructure. The proposed approach combines dynamic parameter adaptation and regional clustering to improve convergence efficiency and solution quality. Experiments were conducted on the distribution route data of an Indonesian logistics company with three scenarios: conventional GA, adaptive GA, and clustering-based GA. Performance evaluation was based on total travel distance, computation time, solution stability, and convergence rate. The results show that adaptive AG produces the best performance, with a reduction in total travel distance of up to 20% more efficient, faster convergence time (95 iterations compared to 120 iterations in conventional AG), and solution stability reaching 90.6%. These findings indicate that parameter adaptation in AG can significantly improve the effectiveness of TSP optimization in the Indonesian business context. The contribution of this research not only strengthens the development of adaptive metaheuristic algorithms but also provides practical benefits for the logistics industry in designing more efficient, cost-effective, and sustainable distribution routes.
Integration of Fuzzy Logic and Neural Networks for Explainable Early Diagnosis of Rice Plant Diseases Teguh Ansyor Lorosae; Miftahul Jannah; Siti Mutmainah; Fathir; Hilyatul Mustafidah
Journix: Journal of Informatics and Computing Vol. 1 No. 3 (2025): December
Publisher : Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/journix.v1i3.21

Abstract

Early diagnosis of rice leaf diseases remains challenging due to subtle symptom manifestation, uncontrolled illumination, heterogeneous backgrounds, and the limited interpretability of purely data-driven models. This study proposes an explainable hybrid framework integrating a Mamdani Fuzzy Inference System (FIS) with an Artificial Neural Network (ANN) for early rice leaf disease diagnosis under real-field conditions. The framework combines engineered symptom descriptors extracted from segmented leaf regions (GLCM texture and HSV color features), acquisition-time environmental measurements, and a fuzzy-derived disease severity cue to mitigate symptom ambiguity while preserving rule-based interpretability. Experiments were conducted on 8,000 field-acquired rice leaf images collected from multiple locations, covering Healthy, bacterial leaf blight, brown spot, and leaf smut classes. Evaluation followed a leakage-controlled, location-disjoint protocol. Across five independent runs, the proposed FIS–ANN achieved an average accuracy of 91.3 ± 0.6% and a macro-F1 score of 90.8 ± 0.7%, significantly outperforming a feature-based ANN and a fine-tuned ResNet-18 baseline (paired McNemar test, p < 0.05). Per-class analysis shows consistent recall improvements for visually overlapping diseases, and additional evaluation on mild-severity samples confirms maintained sensitivity at early disease stages. Field deployment experiments using smartphone-acquired images from unseen locations further demonstrate robust generalization with low on-device inference latency. These results indicate that integrating fuzzy severity reasoning into a lightweight neural classifier provides a practical balance between performance, interpretability, and computational efficiency, supporting early disease screening and mobile decision-support applications in precision agriculture.
Co-Authors -, Nursani Alan Nuari, Alan Amilia Umi Astagina Arfianita Arfianita Arfianita, Arfianita Citra, Erin Eka Ema Utami Erin Eka Citra Erlangga , Ahmad Sagif Fachruddin Edi Nugroho Saputro Fardan, Muhammad Farid Fitriadi Fathir Fathir Fendi Sumanto Ferdian , Muhammad Gufran Hardinandar, Fajrin Hilyatul Mustafidah Irwansya Ismail Juniar, Eka Khairunnas Khairunnisa Khairunnisa Khairunnisa Khairunnisa Khas Sukma Mulya Martina Endah Pratiwi Miftahul Jannah Muh Fitrah, Muh Muh. Alimin Muhammad Fardan Munir Rahmad Ardhani Ramadhan, Sahrul Randitha Missouri Rizky Amelia Sahrul Ramadhan Sahrul Ramadhan Siti Mutmainah Siti Mutmainah Sutriawan Sutriawan Ummu Rofikah Yohanes Aryo Bismo Raharjo Zumhur Alamin