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All Journal Journal of Applied Information, Communication and Technology (JAICT) InComTech: Jurnal Telekomunikasi dan Komputer J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Review Pendidikan dan Pengajaran (JRPP) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Teknik dan Sains (JPTS)
Tri Raharjo Yudantoro
Politeknik Negeri Semarang
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Hybrid Deep Learning–Machine Learning for Bird’s Eye Chili Quality Classification Tri Raharjo Yudantoro; Zulfa Nurma Novita Sari; Tulus Pramuji; Eko Supriyanto; Wahyu Sulistyo; Agus Suwondo; Sindi; Ilham Rizky Harijanto
InComTech : Jurnal Telekomunikasi dan Komputer Vol. 16 No. 2 (2026)
Publisher : Department of Electrical Engineering

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Abstract

The manual inspection of dried bird’s eye chili (Capsicum frutescens L.) is prevalent yet susceptible to subjectivity, inter-rater variability, and low efficiency. This research introduces a hybrid deep learning and machine learning pipeline for the classification of images into three categories: fresh, medium, and dried. The method encompasses staged image acquisition throughout the drying process, preprocessing (including HEIC to PNG conversion, background elimination, scaling, and normalizing), and real-time augmentation to enhance robustness. Feature embeddings are obtained from MobileNetV2 by transfer learning utilizing a Global Average Pooling head and are evaluated against EfficientNetB0, NASNetMobile, ResNet50, and DenseNet121. The embeddings are categorized using various algorithms: Random Forest (RF), Support Vector Machine, and a shallow Artificial Neural Network, with RF selected for its consistent performance. Evaluation employs an 80/20 split, focusing on accuracy, precision, recall, F1-score, and confusion matrix analysis. Results indicate that MobileNetV2 produces the most distinctive features, whereas RF provides the most reliable downstream predictions: the system achieves 94% validation accuracy during feature extraction and 91% at the final classification stage, alongside high precision-recall and minimal misclassification. The chosen MobileNetV2+RF model is implemented in an Android application for real-time inference from smartphone photos, providing class labels and a moisture-level signal based on mass-loss measurements to aid postharvest decisions. The contributions consist of an objective and efficient quality-assessment pipeline, an empirical model comparison, and a deployable mobile implementation. Future endeavors will focus on extensive datasets, multimodal signals, and cross-variety generalization. 
Co-Authors Abu Hasan Afandi Nur Aziz Thohari Agus Suwondo Angga Wahyu Wibowo Dwi Desy Nur Hani'ah Eko Supriyanto Eko Supriyanto Hanny Nurrani Ilham Rizky Harijanto Irwan Yanwari Kurnianingsih, - Kuwat Santoso Liliek Triyono Mardiyono Mardiyono Moh. Djaeni Moudina Risma Slodia Muhammad Irwan Yanwari Muttabik Fathul Lathief Nurseno Bayu Aji Prahara, Tahan Prayitno R Rizal Isnanto Risnadona Putra Milandika Samuel BETA K. K Sindi Sukamto Sukamto Tulus Pramuji Tulus Pramuji Wahyu Sulistiyo Wahyu Sulistyo Widhi Bagus Nugroho Wiktasari Wiktasari Z. N. Novita Sari Zulfa Nurma Novita Sari