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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknik Elektro dan Komputer TRIAC JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Repositor INJECT Interdisciplinary Journal of Communication
Christian Sri Kusuma Aditya
Universitas Muhammadiyah Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Implementasi Automatic Speech Recognition Bacaan Al-Qur’an Menggunakan Metode Wav2Vec 2.0 dan OpenAI-Whisper Danny Ferdiansyah; Christian Sri Kusuma Aditya
Jurnal Teknik Elektro dan Komputer TRIAC Vol 11, No 1 (2024): Mei 2024
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v11i1.24332

Abstract

Implementasi Pengenalan Ucapan Otomatis untuk memprediksi bacaan sering digunakan dalam kehidupan sehari-hari. Salah satu tujuan yang dilakukan penelitian ini adalah untuk mengurangi angka buta mengaji Al-Qur'an pada umat Islam dengan mengimplementasikan ASR sebagai prediksi huruf hijaiyah dan membaca dengan teks ayat-ayat suci Al-Qur'an sebagai target. Data diambil dari platform YouTube dengan suara-suara murottal dari Syeikh Mahmoud Al-Hussary. Ada banyak metode deep learning ASR yang dapat digunakan untuk memprediksi kata ( transcribing ), contohnya adalah Wav2vec 2.0 dan OpenAI-Whisper . Hasil dari metode Wav2vec 2.0 menunjukkan nilai Character Error Rate (CER) dalam memprediksi ayat suci Al-Qur'an dari jarak 0.226 (23%) ~ 0.677 (68%). Hasil dari metode OpenAI-Whisper menunjukkan performa yang lebih bagus daripada Wav2vec 2.0 dengan nilai Character Error Rate (CER) dari rentang 0.064 (6%) ~ 0.172 (17%). Hasil dari kedua metode yang telah diusulkan mengimplikasikan bahwa nilai error yang rendah menjadi metode yang terbaik dengan kesalahan yang minimal.
STUNTING CLASSIFICATION IN CHILDREN USING VIOLA-JONES AND MULTI-FEATURE FUSION WITH PRE-TRAINED MODELS Maylani Kusuma Wardhani; Garin Muhammad Akbar; Christian Sri Kusuma Aditya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7890

Abstract

Stunting remains a critical public health issue, particularly in developing countries, where early detection plays a vital role in prevention and intervention. Previous studies have generally relied on single-feature approaches, either using handcrafted descriptors or convolutional neural networks (CNNs) alone, which often fail to capture subtle craniofacial differences associated with stunting. This study proposes an image-based classification system for detecting stunting in children using facial analysis. The proposed method integrates Viola–Jones face detection with facial landmarks, Gray Level Co-occurrence Matrix (GLCM), Color Co-occurrence Matrix (CCM), and local descriptors such as SIFT–FAST/ORB, combined with deep features extracted from a pre-trained EfficientNet model. Feature fusion was performed by concatenating handcrafted and deep features before classification using a fully connected layer with Softmax activation. Experimental results demonstrated that the proposed fusion model achieved superior performance compared to single-feature baselines, reaching 98% accuracy, 0.98 precision, 0.97 recall, and an F1-score of 0.98. These findings indicate that the integration of geometric, texture, color, and deep semantic cues effectively enhances sensitivity toward the stunting class and improves model interpretability. The novelty of this study lies in the combination of classical computer vision and deep learning techniques for robust, interpretable, and clinically relevant stunting detection. This approach offers strong potential for developing digital health tools that enable early, non-invasive stunting screening in children.
Co-Authors Abdul Hadiy Dyo Fatra Adam Novrisal Ahmad Akbar Maulana Ahmad Rizky Habibullah Chandranegara, Didih Rizki Danny Ferdiansyah Dati Nafa Alfiana Dzur Rifqi Aziz Evi Dwi Wahyuni Fatahillah Arsyad Fatimah Defina Setiti Alhamdani Garin Muhammad Akbar Gian Bagus Sadewa Gita Indah Marthasari Hamdan Nafiatur Rosyida Hardianto Wibowo Hasbi Atsqalani Hilman Abi Sarwan Heryawan Indra Puja Laksana Maylani Kusuma Wardhani Moch Fuad Nasvian Muhammad Abizar Muhammad Ferry Septian Ihzanor Syahputra Muhammad Robby Fathuda Nisrina Arintia Maghfiroh Nur Hayatin Nuryasin, Ilyas Qori Raditya Damayanti Raffi Ainul Afif Rakha Pradana Susilo Putra Roni Hadi Wijaya S, Vinna Rahmayanti Sabrila, Trifebi Shina Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Vera Suryani Wicaksono, Galih Wasis Wildan Suharso Yufis Azhar