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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika dan Teknik Elektro Terapan International Conference on Coastal and Delta Areas Al-Ta'dib: Jurnal Kajian Ilmu Kependidikan Jurnal Keperawatan Komprehensif (Comprehensive Nursing Journal) JURNAL KESEHATAN BHAKTI HUSADA Jurnal Ilmiah Teknologi Informasi dan Robotika Keris: Journal of Community Engagement Jurnal Pendidikan Islam Al-Affan International Journal of Community Engagement Payungi Ocean Engineering: Jurnal Ilmu Teknik dan Teknologi Maritim Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Media Akademik (JMA) International Journal of Geography, Social, and Multicultural Education (IJGSME) Jurnal Online Skripsi Manajemen Rekayasa Konstruksi (JOS-MRK)
Abdul Khamid
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Religion Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Klasifikasi Jenis Sampah Organik Dan Anorganik Menggunakan Convutional Neural Network Berbasis Citra Digital Nova Eliza; Bambang Irawan; Abdul Khamid
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3309

Abstract

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.
KLASIFIKASI PENYAKIT KULIT WAJAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK EFFICIENTNET-B3: Riko Angga Bayu Kusuma; Bambang Irawan; Abdul Khamid
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8721

Abstract

Facial skin diseases are a common health issue that significantly affect an individual's quality of life. Early detection through image processing is a crucial step for timely treatment. This study applies Convolutional Neural Network with EfficientNet-B3 architecture to classify five types of facial skin diseases, namely acne, actinic keratosis, basal cell carcinoma, eczema, and rosacea. The model was developed through fine-tuning on an augmented image dataset, with training and testing data splits. Evaluation results show a testing accuracy of 96.61 percent, accompanied by average precision, recall, and F1-score values of 0.97. The confusion matrix indicates high classification performance with minimal errors between classes. This approach proves effective in improving detection accuracy, thus potentially supporting medical personnel in early diagnosis.
Co-Authors Abdul Latif N Abul A’la Al Ma’adudi ACHMAD FAUZI Afif Fudin Ahmad Alfi Ridlo Aida Dwi Rahmawati Aida Dwi Rahmawati Aminkun Imam Rafii Arifah Rakhmawati Bambang Irawan Bambang Laras Media Chusnul Chotimal Dandung Novianto Eli Indawati Enggarbela Ogi Intan Pratiwi Firyal Nabila Ulya H.M Fitria Hidayanti Ilma Nur Rizqi Irhamudin M. Umar Faqieh Moch. Dimas Egi Asyam Al Dzakwan Moch. Sholeh Moh. Lutfi Izaki Moh. Mu’amarul Ma’aruf Mohammad Lucky P Muammar Muchamad Chairul Umam Niki Herdian Permana Nova Eliza Nur Ali Nur Ariesanto Ramdhan Reni Anggraeni Aprillia Riko Angga Bayu Kusuma rini nurdini Rofiqotul Munifah SALAMUN ROHMAN NUDIN Sarmini Siswahyudianto Sukma Perdana Prasetya Suryawahyuni Latief Wahudin Diantoro Yulia Feriska