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All Journal Bulletin of Information Technology (BIT) Indonesian Journal of Applied Technology
Budi Santoso
Universitas Bina Insan Lubuklinggau
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pengembangan Model Deteksi Autism Spectrum Disorder (ASD) Dengan Algoritma Facenet Vggface Dan Insightface Marsha Falen Fransisca; Lukman Sunardi; Harma Oktafia LW; Budi Santoso
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2697

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication, social interaction, and behavior. Conventional ASD diagnosis relies on clinical observation, which is time-consuming and subjective. Therefore, an automated approach using artificial intelligence is required to support early detection. This study proposes an ASD detection model based on facial image analysis using deep learning approaches, namely FaceNet, VGGFace2, and InsightFace as facial feature extraction methods. The dataset consists of 3,620 facial images categorized into ASD and non-ASD classes. The research process includes preprocessing, feature extraction, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that all models achieved good classification performance, with FaceNet achieving the highest accuracy of 98%, followed by InsightFace with 96%, and VGGFace2 with 95%. These findings demonstrate that face embedding-based models provide superior feature extraction capabilities for ASD detection.
Penerapan Metode Transfer Learning untuk Klasifikasi Penyakit Tanaman Kembang Kol dengan Arsitektur Inception V3 Randy Marsevin; Tri Hasanah Bimastari Aviani; A. Taqwa Martadinata; Budi Santoso
Indonesian Journal of Applied Technology Vol. 2 No. 4 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/ijat.v2i4.5167

Abstract

Indonesia sebagai negara agraris menghadapi tantangan dalam produksi pertanian, khususnya akibat serangan hama dan penyakit yang menurunkan kualitas hasil panen kembang kol (Brassica oleracea var. botrytis L). Penelitian ini mengembangkan model klasifikasi penyakit tanaman kembang kol menggunakan metode transfer learning berbasis arsitektur Inception V3. Dataset terdiri dari empat kelas: Bacterial Spot Rot, Black Rot, Downy Mildew, dan No Disease, diperoleh dari Kaggle. Proses pengembangan mengikuti tahapan CRISP-DM, mulai dari pra-pemrosesan data, pelatihan model, hingga evaluasi. Model dilatih dengan memanfaatkan bobot awal dari ImageNet, diikuti dengan penyesuaian beberapa lapisan klasifikasi dan penggunaan teknik fine-tuning serta augmentasi data. Evaluasi performa dilakukan dengan metrik akurasi, precision, recall, dan F1-score. Hasil akhir menunjukkan akurasi validasi sebesar 93,75% dan akurasi pengujian mencapai 99%, dengan nilai precision dan recall yang seimbang (93,75%). Model terbukti efektif dalam mengklasifikasi penyakit tanaman dan memiliki potensi untuk diterapkan pada sistem deteksi otomatis berbasis citra guna mendukung pertanian presisi secara real-time.
Co-Authors A. Taqwa Martadinata Harma Oktafia LW Marsha Falen Fransisca Randy Marsevin Sunardi, Lukman Tri Hasanah Bimastari Aviani