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All Journal SEWAGATI: Jurnal Pengabdian Kepada Masyarakat Journix: Journal of Informatics and Computing
Mustafidah, Hilyatul
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health

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Promoting Equitable Scholarship Literacy through an Integrated Webinar Series on the Tatra Academy Digital Platform Khairunnas, Khairunnas; Alamsyah, Syahrul; Mustafidah, Hilyatul; Mahmudah, Husnatul
SEWAGATI: Jurnal Pengabdian kepada Masyarakat Vol 4 No 3 (2025): SEWAGATI: Jurnal Pengabdian kepada Masyarakat
Publisher : Sarau Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61461/sjpm.v4i3.143

Abstract

Unequal access to international scholarship information remains a significant barrier in Indonesia. This community service program aimed to promote equitable scholarship literacy through a digital mentoring approach via the Tatra Academy platform. The program followed four stages: needs identification via Instagram, resource planning, online implementation via Zoom and YouTube, and demographic evaluation. Analysis of 1,102 cleaned registrants revealed a broad nationwide reach, including participants from frontier and underdeveloped (3T) regions. The program demonstrated high inclusivity across generations, engaging 369 participants under age 20 and 34 participants over age 40, supporting lifelong learning. The initial session on Hungary and Poland scholarships recorded the highest enthusiasm, followed by the AAS/MANAKI series and technical guides for essays and CVs. Findings show that digital platform integration effectively overcomes geographic barriers, democratizes information access, and supports inclusive human resource development.
Integration of Fuzzy Logic and Neural Networks for Explainable Early Diagnosis of Rice Plant Diseases Lorosae, Teguh Ansyor; Jannah, Miftahul; Mutmainah, Siti; Fathir; Mustafidah, Hilyatul
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 Alamsyah, Syahrul Fathir Husnatul Mahmudah Khairunnas, Khairunnas Miftahul Jannah Siti Mutmainah Teguh Ansyor Lorosae