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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Rudi Kurniawan
Universitas Bina Insan, Lubuklinggau
Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Tingkat Kematangan Buah Sawit Berbasis Deep Learning dengan Menggunakan Arsitektur Yolov5 Rudi Kurniawan; Ahmad Taqwa Martadinata; Sandy Dwi Cahyo
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4408

Abstract

Object identification and recognition in the field of computer vision is undergoing rapid development and is applied to various fields, ranging from industry to the health sector. This is reflected in the amount of research conducted, including a focus on the application and personalization of machine learning, as well as the development of new models to solve specific problems and challenges. In the palm oil industry, fruit maturity is divided into two categories, namely immature and ripe. Traditionally, fruit maturity is determined visually by experienced workers based on the number of fruits falling from the bunch or the color of the bunch. However, this technique has disadvantages such as the reduced amount of oil when many fruits fall from the bunch and the subjective assessment of fruit color. Therefore, the purpose of this research is to create an oil palm fruit maturity classification system based on YOLO v5. The dataset used consists of 1500 photos and the annotation data is created with roboflow. The final result is divided into three categories, namely ripe, immature, and rotten. The YOLOv5s algorithm was used to train the dataset. Based on the model estimation results, mAP reached 92%, accuracy reached 97%, and recall reached 96%. The last step is real-time system testing.
Klasfikasi Tingkat Kematangan Roasting Biji Kopi Berbasis Deep Learning dengan Arsitektur MobileNet Tegar Firmansyah; Rudi Kurniawan; Asep Toyib Hidayat
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6811

Abstract

Coffee is one of the most widely consumed beverage ingredients in Indonesia and has high economic value to improve the community's economy and as a source of foreign exchange. The roasting process is an important stage in coffee processing because it affects the aroma and flavor of coffee. What is often encountered is that visually determining the level of coffee roasting is often inaccurate and prone to human error. To overcome this problem, this study uses a deep learning approach with a transfer learning method based on MobileNet architecture to classify the level of coffee roasting maturity based on digital images. MobileNet was chosen due to its lightweight and fast architecture, suitable for implementation on mobile devices. This research aims to compare the performance of the model in detecting coffee roasting level automatically, efficiently, and objectively. With this approach, it is expected that coffee enthusiasts and producers can easily recognize the type of coffee roasting, support product quality consistency, and reduce dependence on experts in the roasting process. This study analyzed the performance of the classification model with the results showing excellent performance. The model achieved a total accuracy of 99.50%, with consistently high precision, recall, and f1-score values across all classes, including several classes with perfect scores (1,000). Evaluation using ROC curves and AUC also demonstrated the model's ability to distinguish between the two classes.
Co-Authors Asep Toyib Hidayat Martadinata, A. Taqwa Sandy Dwi Cahyo Tegar Firmansyah