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All Journal Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Arcitech: Journal of Computer Science and Artificial Intelligence
Murlena Murlena
Universitas Pat Petulai
Computer Science & IT Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice

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Penerapan Screen Reader dalam Pembelajaran Pendidikan Agama Islam di SLB-N Rejang Lebong Wandi Syahindra; Nia Dahniarti; Nofita Sari; Murlena Murlena
Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2023): November 2023
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/kontribusi.v4i1.296

Abstract

Tujuan penelitian ini adalah penerapan suatu model pembelajaran PAI khususnya untuk siswa disabilitas dalam pendidikan menggunakan software screen reader. Terdapat kendala yang dihadapi oleh siswa tunanetra yang belum begitu mengenal teknologi terutama pada penggunaan sistem operasi android yang ada di smartphone untuk kebutuhan proses belajar yang seharusnya menjadi pendorong siswa untuk belajar lebih mudah. Metode riset yang dipakai pada riset ini merupakan riset kualitatif dengan pendekatan deskriptif. Masih banyak anak didik tunanetra yang belum paham serta menguasai teknologi. Baik itu kegunaan teknologi, ataupun penggunaannya itu sendiri. Minimnya informasi yang didapat siswa-siswa SLB, membuat screen reader ini menjadi hampir tidak dikenali. Hasil penerapan model menggunakan software screen reader 60% cukup efektif untuk siswa disabilitas sehingga dapat membantu mereka dalam pembelajaran PAI.
Model Klasifikasi Tingkat Kematangan Sayur Hijau Menggunakan Ekstraksi Fitur Warna dan Convolutional Neural Network Rini Widyastuti; Firna Yenila; Eko Syaputra; Wandi Syahindra; Murlena Murlena
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17403

Abstract

The maturity level of green vegetables is an important factor affecting product quality, market value, and shelf life. Maturity identification is generally performed visually based on leaf color changes, making the assessment subjective and potentially inconsistent. This study aims to develop a classification model for green vegetable maturity levels using a combination of color feature extraction and a Convolutional Neural Network (CNN) to provide a more objective and accurate system. The research began with image acquisition of green vegetables categorized into three maturity levels: immature, mature, and overripe. Preprocessing included image resizing, normalization, and segmentation. Color feature extraction was performed using RGB and HSV color spaces to represent maturity conditions. The dataset was divided into training and testing sets with a 90:10 ratio and processed using a CNN architecture. Model performance was evaluated using accuracy, precision, recall, and F1-score. Results showed that the proposed model achieved 95.2% accuracy, 94.8% precision, 95.6% recall, and 95.1% F1-score. These findings indicate that combining color features and CNN effectively supports automated vegetable sorting and quality control systems.
Co-Authors Eko Syaputra Firna Yenila Nia Dahniarti Nofita Sari Rini Widyastuti Wandi Syahindra