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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Information Technology Education Journal
Andi Ahmad Taufiq
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Computer Science & IT Education

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Analisis Pengaruh Kepercayaan dan Pemanfaatan Teknologi Terhadap Penggunaan Chat-GPT Irwansyah Suwahyu; Sofyan Taurid Ode Madi; Andi Ahmad Taufiq; Andi Faiz Nabiel Rasyid
Information Technology Education Journal Vol. 3, No. 2, Mei (2024)
Publisher : Jurusan Teknik Informatika dan Komputer

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Abstract

Chat-GPT adalah teknologi kecerdasan buatan yang dapat memberikan jawaban dan solusi atas pertanyaan dan masalah yang diajukan oleh pengguna. Namun, pengguna juga menghadapi isu-isu keamanan dan privasi. Untuk meningkatkan kepercayaan pengguna, perlu dilakukan penelitian tentang pengaruh kepercayaan dan pemanfaatan teknologi terhadap penggunaan Chat-GPT. Penelitian ini menggunakan metode kuantitatif untuk menganalisis data survei yang dilakukan terhadap 23 pengguna Chat-GPT. Data survei tersebut mencakup pertanyaan tentang tingkat kepercayaan pengguna, kualitas jawaban, serta masalah keamanan dan privasi. Temuan penelitian menunjukkan bahwa tingkat kepercayaan pengguna terhadap Chat-GPT masih rendah. Hal ini disebabkan oleh adanya masalah keamanan dan privasi, serta kualitas jawaban yang belum memuaskan. Untuk meningkatkan kepercayaan pengguna, perlu dilakukan upaya-upaya seperti memberikan informasi yang akurat dan relevan kepada pengguna, menjaga privasi dan keamanan data pengguna dengan mengimplementasikan standar keamanan yang ketat, serta memberikan pelayanan yang baik dan responsif kepada 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 Baso Kaswar Andi Baso Kaswar Andi Faiz Nabiel Rasyid Andi Tenriola Hanum Zalsabilah Idham Irwansyah Suwahyu Muh Fuad Zahran Firman Muhammad Fajar B Sofyan Taurid Ode Madi