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All Journal Jurnal Teknologi dan Manajemen Informatika REKAYASA Jurnas Nasional Teknologi dan Sistem Informasi Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi JIKO (Jurnal Informatika dan Komputer) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal Linguistik Komputasional JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika International Journal of Business, Humanities, Education and Social Sciences (IJBHES) Indonesian Journal of Data and Science TIN: TERAPAN INFORMATIKA NUSANTARA Infotek : Jurnal Informatika dan Teknologi Teknika Science in Information Technology Letters Journal of Social Research Journal Of World Science Jurnal Informatika Teknologi dan Sains (Jinteks) NERO (Networking Engineering Research Operation) SmartComp Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues
Adityo Permana Wibowo
Universitas Teknologi Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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DETEKSI OTOMATIS PENYAKIT LAYU FUSARIUM PADA DAUN TOMAT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK BERBASIS EKSTRAKSI CITRA RGB Dwita Dhara Nanda; Adityo Permana Wibowo
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6918

Abstract

Tanaman tomat memainkan peranan krusial pada sektor pertanian, tetapi seringkali produksi tanaman tomat terancam oleh penyakit salah satunya adalah penyakit layu yang disebabkan oleh Fusarium. Deteksi yang cepat dan akurat sangat penting untuk mengurangi kerugian pada saat panen. Penelitian ini bertujuan untuk mengevaluasi kinerja model Convolutional Neural Network (CNN) dalam mendeteksi penyakit layu Fusarium pada daun tomat berbasis ekstraksi citra RGB. Dataset yang digunakan dalam penelitian ini terdiri dari gambar daun tomat yang sehat serta yang terinfeksi, yang dibagi dengan rasio 80% untuk pelatihan dan 20% untuk pengujian. Model CNN dilatih selama 20 epoch dengan memakai optimasi Adam pada learning rate sebesar 0.0001. Hasil pengujian menunjukkan performa model yang sangat memuaskan, dengan akurasi pada validasi mencapai 98,47% dan nilai loss serendah 0,0457. Temuan ini menunjukkan bahwa model CNN yang telah diuji sangat efektif dan dapat diandalkan dalam mendeteksi penyakit layu Fusarium pada daun tanaman tomat, sehingga dapat menjadi dasar yang berharga bagi petani dalam pengelolaan tanaman tomat dan pengambilan keputusan.
Comparative Study of Machine Learning Methods for Disease Classification Based on Natural Language Symptom Descriptions Ery Setiyawan Jullev Atmadji; Adityo Permana Wibowo; Edi Faizal
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.361

Abstract

The growing demand for remote healthcare solutions has increased the importance of efficient disease diagnosis based on textual symptom descriptions. This study explores the application of machine learning models Multinomial Naive Bayes, Random Forest, and Support Vector Machine (SVM) to classify 24 different diseases from natural language symptom inputs. Utilizing a dataset of 1,200 balanced samples and TF-IDF for feature extraction, we trained and evaluated the models using both accuracy and cross-validation metrics. Among the models, SVM achieved the highest test accuracy of 97.5% and demonstrated consistent performance across all disease categories. These findings underscore the potential of classical machine learning approaches in enhancing digital diagnostic tools, particularly for early screening in telemedicine applications. Future work could extend this study by integrating deep learning architectures and multilingual capabilities to accommodate broader and more diverse healthcare scenarios.
Co-Authors AA Sudharmawan, AA Achmad Safruddin Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Naswin Akmal Setiawan Wijaya Albertus Carloson Fallo Allam, Sayyidan Fahd Arief Hermawan Arif Amrulloh Barus, M Akmal Shievo Bella Primin Donny Avianto Dwita Dhara Nanda Edi Faizal Ery Setiyawan Jullev Atmadji Hamzah Fachrurozi Ifannur, Muhammad Taufik Ika Arfiani Imantoko Imantoko Kuncorojati, Fariz Wahyu Kustrianugraha, Afri Fajar Kusumastuti, Asriana Dyah Luh Putu Ratna Sundari M. Raihan Alghifari Medi Taruk Muhammad Fachrie Muhammad Habibi Muhammad Insan Kamil, Muhammad Insan Nararya Budiyono Nastiti, Kartika Rizqi Nugroho, Bagus Candra Nur Hidayati Pangesty, Shandika Sayyid Ammar Pratama, Khrisna Yudha Primin, Bella Putra, Elvanda Adi Putra, Muhammad Fakhri Despawida Aulia Radhitya, Made Leo Raihan Anmar Widyasmoro Rianto Setyadi, Dian Febry Sri Hartati Sri Wulandari Suryo Sudiro Sutarman Sutarman Tamam Huda, Muhammad Tarigan, ⁠⁠Thomas Edyson Tri Waluyo Wiwik Handayani