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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Tatyantoro Andrasto
Universitas Negeri Semarang, Semarang
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Analisis dan Optimasi Jumlah Dataset pada YOLOv8 untuk Inspeksi Stamping Otomatis Khairul Ma’mur; Tatyantoro Andrasto; Arief Arfriandi
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10302

Abstract

Quality inspection of stamping products in the manufacturing industry is generally performed manually, which may lead to errors caused by operator fatigue, inconsistent observations, and low inspection efficiency. This study aims to implement the You Only Look Once version 8 (YOLOv8) algorithm to automatically detect and classify stamping products into Good and Not Good (NG) categories. The research stages included dataset collection, data preprocessing, model training, validation, and real-time testing. To analyze the effect of dataset size on model performance, three training scenarios were conducted using 100, 1,134, and 1,552 images with identical training parameters. Model performance was evaluated using Precision, Recall, mean Average Precision at 50% Intersection over Union (mAP50), mean Average Precision at 50%–95% Intersection over Union (mAP50–95), and a confusion matrix. The results indicate that increasing the number of datasets improves the performance of the YOLOv8 model. The model trained using 1,552 images achieved the best performance, with a Precision of 99.8%, Recall of 100%, mAP50 of 99.5%, and mAP50–95 of 96.6%, representing an improvement of 11.7 percentage points in mAP50–95 compared with the model trained using 100 images, which achieved an mAP50–95 of 84.9%. These findings indicate that increasing the dataset size enhances the model's generalization capability in recognizing variations in stamping quality. The best-performing model was subsequently implemented in real-time testing using a laptop camera and was able to consistently detect and classify stamping products under various lighting conditions, achieving a testing accuracy of 90%.
Implementasi dan Analisis Algoritma FIFO, FEFO, dan LIFO pada Sistem Automated Storage and Retrieval System (ASRS) Berbasis Internet of Things untuk Produk Minuman Kemasan Muhammad Daffa Fauzan; Tatyantoro Andrasto; Mario Norman Syah
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10316

Abstract

Warehouse management for packaged beverage products faces critical risks of expired products and human error in stock rotation. This research implements an IoT-based Automated Storage and Retrieval System (ASRS) integrating three stock rotation algorithms, FIFO, FEFO, and LIFO, in a single backend control platform using PHP, MySQL, ESP32 microcontroller, and QR Code scanner. Research and Development (R&D) method with Waterfall model was employed. Testing was conducted at the Electrical Engineering Laboratory of Semarang State University using 100 ml UHT milk products across 60 trials (20 per algorithm). Results show all three algorithms achieved 100% accuracy, and QR Code identification yielded 93.3% accuracy with an average response time of 287 ms. Edge case testing proved deterministic handling of identical expiry dates, simultaneous timestamps, empty stock, and full slots. FEFO proved most suitable for packaged beverage management by consistently prioritizing products with the nearest expiry date, minimizing spoilage risk. The main contributions of this research simultaneous integration of three stock rotation algorithms (FIFO, FEFO, LIFO) within a single IoT-based backend control platform, a deterministic SQL-based tiebreaker mechanism to eliminate ambiguity in identical-data conditions, and empirical validation of FEFO's superiority over FIFO and LIFO for packaged beverage products through comparative residual stock analysis.
Co-Authors Arief Arfriandi Khairul Ma’mur Mario Norman Syah Muhammad Daffa Fauzan