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All Journal Information Technology Education 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): May 2021
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Comparison of Project-Based Learning and Lecture-Based Learning in Machine Learning Courses on Model Implementation Skills A. Sultan Agung; Hamzah Pagarra; Nur Asima; Nur Azizah; Nurhikma; Nurilmi Amalia Marda; Nurlisah
Information Technology Education Journal Vol. 4, No. 4, November (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i4.11192

Abstract

The aim of this study was to compare the effectiveness of Project-Based Learning (PBL) and Lecture-Based Learning (LBL) in enhancing students’ ability to implement machine learning models. While both teaching methodologies are widely used, the impact of PBL on practical machine learning skills has not been sufficiently explored. This study investigates whether a hands-on, project-based approach leads to better performance in real-world machine learning applications compared to a traditional lecture-based approach. This experimental study involved 60 undergraduate students from a machine learning course, randomly divided into two groups: PBL and LBL. The PBL group worked on real-world machine learning projects, while the LBL group followed traditional lectures and individual assignments. Data were collected using pre- and post-test questionnaires, project performance rubrics, and observational notes. Statistical analyses were conducted to compare the two groups’ performance on machine learning tasks. The results revealed that the PBL group outperformed the LBL group in model accuracy, code quality, problem-solving, and debugging skills. The PBL group also demonstrated greater motivation and engagement, with statistically significant differences in performance (p = 0.001 for model optimization and p = 0.027 for problem-solving). The LBL group showed improvements, but the gains were less substantial. The findings suggest that PBL is more effective for developing practical skills in machine learning. However, the study's limitations include a small sample size and short duration, which may limit the generalizability of the results. This study provides novel insights into the benefits of PBL in machine learning education, offering valuable implications for curriculum design. Future research could explore long-term outcomes and the potential of hybrid teaching methods that combine PBL and LBL
Analysis of the Acceptance of Generative AI Use in Academic Tasks Using the UTAUT Model Nurhikma; Rabiatul Adawiah; Rachmat Hidayat Bachtiar; Rahma Agustini Putri; Rahmadinar Kadir; Rahmat Hidayat
Information Technology Education Journal Vol. 4, No. 1, February (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i1.2503

Abstract

This study aims to examine students’ acceptance of Generative Artificial Intelligence (AI) in academic tasks using the Unified Theory of Acceptance and Use of Technology (UTAUT). The rapid integration of Generative AI tools in higher education raises important questions regarding the determinants of students’ behavioral intention and actual usage. This study argues that performance-related perceptions are the primary drivers of adoption. Design/methods/approach – A quantitative explanatory design was employed using a survey of 210 undergraduate students who had experience using Generative AI for academic purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Measurement evaluation included outer loadings, Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE), while structural relationships were tested using bootstrapping with 5000 resamples. Findings – Performance Expectancy significantly influenced Behavioral Intention (β = 0.41, p < 0.001), followed by Effort Expectancy (β = 0.27, p < 0.001) and Social Influence (β = 0.18, p = 0.003). Behavioral Intention strongly affected Use Behavior (β = 0.53, p < 0.001), and Facilitating Conditions also had a significant direct effect (β = 0.29, p < 0.001). The model explained 62% of the variance in Behavioral Intention and 58% in Use Behavior. Research implications/limitations – The study was limited to a single institution and relied on self-reported cross-sectional data, which may restrict generalizability and causal inference. Originality/value – This study extends UTAUT to the context of Generative AI in academic assignments and provides empirical evidence of its predictive power in emerging AI-based educational technologies.
Co-Authors A. Sultan Agung Andi Baso Kaswar Andi Baso Kaswar Aril Hamzah Pagarra Herman Muh. Yusril Anam Mushaf Nur Asima Nur Azizah Nurilmi Amalia Marda Nurlisah Nurul Isra Humaira B Rabiatul Adawiah Rachmat Hidayat Bachtiar Rahma Agustini Putri Rahmadinar Kadir Rahmat Hidayat