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All Journal InComTech: Jurnal Telekomunikasi dan Komputer ARZUSIN: Jurnal Manajemen dan Pendidikan Dasar
Agus Suwondo
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Religion Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences

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Desain dan Perancangan Sistem Bimbingan Skripsi dengan Model Total Architecture System (TAS) Studi Kasus pada Jurusan Akuntansi Politeknik Negeri Semarang Afiat Sadida; Sarana Sarana; Agus Suwondo; Prima Ayundyayasti
ARZUSIN Vol 5 No 4 (2025): AGUSTUS
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/arzusin.v5i4.7149

Abstract

The absence of an integrated thesis supervision system in the Accounting Department of Politeknik Negeri Semarang presents a significant issue, given the importance of monitoring the supervision process to support timely student graduation. This study aims to design an effective thesis supervision management system that facilitates the monitoring and management of the supervision process. The research employs a system development approach based on Unified Modelling Language (UML) within the Total Architecture Synthesis (TAS) framework. The database utilized is MySQL, supported by the XAMPP web server. System requirements were gathered through observations and interviews with academic supervisors and department administrators. The outcome of the study is a system design comprising database architecture, user interface prototypes, and a structured supervision workflow. The system is designed to assist lecturers and students in tracking supervision progress, minimizing administrative barriers, and supporting the achievement of higher education Key Performance Indicators (KPIs). Practically, this study delivers a prototype system that can be implemented in the Accounting Department or adapted by other programs with similar needs. Theoretically, it contributes to the development of information technology-based supervision management models in vocational education settings.
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 Afiat Sadida Eko Supriyanto Ilham Rizky Harijanto Prima Ayundyayasti Sarana Sarana Sindi Tri Raharjo Yudantoro Tulus Pramuji Wahyu Sulistyo Zulfa Nurma Novita Sari