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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Information Technology Education Journal
Hanum Zalsabilah Idham
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Computer Science & IT Education

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Analisis Komponen Antecedents E-Learning, Kesiapan Digital, dan Perilaku Penggunaan Terhadap Kinerja E-Learning Irwansyah Suwahyu; Muh. Agung Sidiq; Muh. Reyhansyah Syahrir; Hanum Zalsabilah Idham
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

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Abstract

Berkembangnya teknologi dan internet pada dunia pendidikan membuat penggunaan E-Learning dijadikan sebagai alternatif pembelajaran di berbagai lembaga pendidikan.Penelitian ini bertujuan untuk mengkaji pengaruh faktor-faktor antecedents E-Learning, kesiapan digital, dan perilaku pengguna dalam konteks pengembangan sistem E-Learning. Penelitian ini menggunakan pendekatan kuantitatif dengan metodepenelitian deskriptif.Populasi yang digunakan pada penelitian ini sebanyak 41 orang. Pengumpulan data dilakukan dengan teknik penyebaran kuesioner menggunakan platform Google form. Analisis data dilakukan menggunakan SkalaLikert dengan tujuan untuk memberikan skor dalam bentuk skala pada setiap pernyataan dalam kuesioner. Hasil Penelitian ini menunjukkan bahwa menerapkan e-learning memiliki efek positif yang signifikan. Perlu dilakukan evaluasi berkala terhadap sistem E-Learning untuk meningkatkan kualitas berdasarkan umpan balik dari pengguna.
CLASSIFICATION OF PAPAYA NUTRITION BASED ON MATURITY WITH DIGITAL IMAGE AND ARTIFICIAL NEURAL NETWORK Andi Ahmad Taufiq; Hanum Zalsabilah Idham; Muh Fuad Zahran Firman; Andi Baso Kaswar; Dyah Darma Andayani; Muhammad Fajar B; Abdul Muis Mappalotteng; Andi Tenriola
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7070

Abstract

Papaya is a tropical fruit with high nutritional content and significant health benefits. Nutritional components such as sugars, vitamin C, and fibre are strongly influenced by ripeness level. Identifying these nutrients usually requires laboratory tests that are time-consuming and rely on sophisticated equipment. Previous studies have focused on classifying ripeness levels, yet none have specifically addressed the classification of nutritional content. This study proposes a classification system for papaya nutrition based on ripeness using digital image processing and artificial neural networks (ANN). The method consists of six stages: image acquisition, preprocessing, segmentation, morphology, feature extraction, and classification with a trained ANN model. Experiments were conducted to evaluate feature combinations, including colour and texture features. The combination of LAB colour features and texture features-contrast, correlation, energy, and homogeneity-produced the best results. Testing on 75 images achieved an average precision of 97.22%, recall of 96.67%, F1-Score of 96.80%, and accuracy of 97.33%, with an average computation time of 0.02 seconds per image. These findings indicate that the proposed method provides fast and highly accurate classification of papaya’s nutritional content, offering a practical alternative to laboratory testing. Nevertheless, the study is limited by the relatively small dataset and controlled acquisition environment. Future research should extend the dataset, incorporate deep learning approaches, and validate performance under real-world conditions to enhance robustness and generalization
Co-Authors Abdul Muis Mappalotteng Andi Ahmad Taufiq Andi Baso Kaswar Andi Baso Kaswar Andi Tenriola Irwansyah Suwahyu Muh Fuad Zahran Firman Muh. Agung Sidiq Muh. Reyhansyah Syahrir Muhammad Fajar B