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All Journal Journal of Embedded Systems, Security and Intelligent Systems Artificial Intelligence in Educational Decision Sciences
Nurhikma
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences

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KLASIFIKASI TINGKAT KUALITAS DAN KEMATANGAN BUAH TOMAT BERDASARKAN FITUR WARNA MENGGUNAKAN JARINGAN SYARAF TIRUAN Nurul Isra Humaira B; Magfira Herman; Nurhikma; Andi Baso Kaswar
Journal of Embedded Systems, Security and Intelligent Systems Vol. 2 No. 1 (2021): Vol 2, No 1 (2021): May 2021
Publisher : Program Studi Teknik Komputer

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Abstract

Pada umumnya, manusia melakukan pemilahan hasil pertanian bergantung pada presepsi mereka terhadap komposisi warna yang dimiliki citra seperti buah-buahan. Masyarakat menilai kualitas dan kematangan tomat dengan cara manual dari tampaknya saja yaitu pada warnanya. Namun, identifkasi dengan cara manual memiliki kelemahan seperti waktu yang dibutuhkan relatih lama serta menghasilkan produk yang cukup beragam karena keterbatasan visual dan perbedaan persepsi manusia tentang buah tersebut. Oleh karena itu, penelitian ini mengusulkan metode yang dapat digunakan pada klasifikasi kualitas dan kematangan buah tomat yaitu Jaringan Saraf Tiruan. Metode ini dimulai dari tahap akuisisi citra dan preprocessing, kemudian segmentasi citra lalu operasi morfologi, kemudian ekstraksi fitur hingga tahap pelatihan menggunakan JST dan tahap pengujian klasifikasi berdasarkan fitur warna. Hasil pengujian klasifikassi kualitas dan kematangan buah tomat berdasarkan fitur warna menggunakan JST sebesar 90% dengan waktu proses 3.12 detik setiap citra. Dari penelitian tersebut, menunjukkan bahwa metode yang diusulkan memberikan waktu yang efisien terhadap klasifikasi citra tomat.
Computer Vision-Driven Classroom Analytics: Real-Time Attendance Verification and Student Focus Monitoring for Data-Informed Teaching Decisions Nurhikma; Aril; Mushaf; Muh. Yusril Anam
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.7

Abstract

Purpose – Student attendance and learning activity monitoring are essential for ensuring instructional quality and academic accountability. However, conventional attendance methods remain inefficient, error-prone, and vulnerable to manipulation, while existing Computer Vision-based solutions often require high computational resources and focus on attendance or engagement separately. This study aims to develop an integrated, lightweight Computer Vision-based system for automatic student attendance recording and real-time focus monitoring suitable for resource-limited educational environments.Methods – This study employs a classical Computer Vision approach integrating Haar Cascade for face detection, Local Binary Patterns Histogram (LBPH) for face recognition, and rule-based eye detection for focus classification. The system automatically records attendance, tracks focus duration, and generates real-time digital reports. System performance was evaluated under controlled classroom conditions using accuracy, precision, recall, and F1-score.Findings – Experimental results demonstrate that the proposed system achieves high recognition reliability, with face detection and recognition accuracy reaching 100% in small-scale testing. The system operates efficiently with low latency and minimal computational requirements, while successfully monitoring multiple students simultaneously and generating structured attendance and focus duration reports in real time. Research limitations – The evaluation was conducted on a limited number of students under controlled conditions, which may restrict generalisability. Further testing in larger, more diverse classroom settings is required to validate system robustness.Originality – This study presents a unified and resource-efficient solution that integrates attendance validation and real-time focus monitoring within a single platform, offering practical value for schools seeking scalable and affordable learning analytics systems.
Co-Authors Andi Baso Kaswar Andi Baso Kaswar Aril Herman Muh. Yusril Anam Mushaf Nurul Isra Humaira B