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All Journal JOIV : International Journal on Informatics Visualization Jurnal Otomasi Kontrol dan Instrumentasi
Sidehabi, Sitti Wetenriajeng
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Comparative Performance Analysis of YOLO and Faster R-CNN in Detecting Species and Estimating the Weight of Grouper and Snapper Fish Using Digital Images Sidehabi, Sitti Wetenriajeng; Indrabayu, Indrabayu; Warni, Elly; Bake, Sabda Ansari
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3121

Abstract

Grouper and snapper fish are widely consumed species with high economic value in the global market. To determine their economic value, identifying the species and estimating the weight are essential in the pricing and quality determination of the traded fish. The commonly used manual methods are often time-consuming and labor-intensive. Based on this, a more effective computer-based method is needed for these repetitive tasks. This research aims to analyze the performance of two commonly used deep learning models, YOLO and Faster R-CNN, in detecting species and estimating the weight of specific grouper and snapper fish. The data used consisted of 2991 samples divided into 18 classes. This data was then augmented using rotate and flip features to create 6843 image samples. A threshold of 0.8 was used in the detection process, meaning objects detected with confidence below 0.8 would be ignored. Once trained, the performance of both models was tested using precision, recall, and accuracy parameters to assess how accurately the models predicted fish species from the input data and Mean Absolute Percentage Error (MAPE) to evaluate the estimation results of the models. There were differences in the quantitative evaluation results between the YOLO and Faster R-CNN models. The YOLO model achieved precision, recall, and accuracy rates of 0.98, 0.98, and 0.96, respectively, while the Faster R-CNN model had precision, recall, and accuracy rates of 0.97, 0.98, and 0.95, respectively. Additionally, the MAPE for weight estimation was 2.42% for image data and 3.66% for video data for the YOLO model. In contrast, for the Faster R-CNN model, the results were 14.62% for image data and 13.59% for video data. Thus, it can be concluded that the YOLO model provides better quantitative evaluation results compared to the Faster R-CNN model.
Implementasi Mesin Pengeruk Isi Buah Markisa berbasis Mikrokontroler dan Elektro Pneumatik Sidehabi, Sitti Wetenriajeng; Akbar, Zainal; Habibuddin, Julianti; Ramadhan, Muh. Alamsyah
Jurnal Otomasi Kontrol dan Instrumentasi Vol 15 No 2 (2023): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2023.15.2.1

Abstract

Passion fruit has a distinctive sweet and sour taste, which is quite popular in Indonesia, especially in South Sulawesi. Passion fruit is processed into syrup, and dodol is one of the typical souvenirs popular with tourists. Processing passion fruit syrup in the Micro, Small, and Medium Enterprises (MSMEs) Industry still uses very simple equipment. Hence, the quality and quantity of the product produced are not optimal. Passion fruit cutting uses a knife with a low capacity for cutting and shrinkage results, so this research aims to design a passion fruit-filled shaver machine based on a microcontroller and electro-pneumatics. This passion fruit shaver machine uses a microcontroller as the control center. First, the passion fruit passes through an infrared sensor, which detects the fruit and counts the number of incoming fruits. After that, Arduino Uno reads and activates the control relay from the DC motor. After 0.5 seconds, cylinder 1 is active, which pushes the DC motor down so that the passion fruit shrinks for 3 seconds. Then, the pneumatic push up to the normal position; after 0.5 seconds, the second cylinder actively pushes the passion fruit skin that has been shaved out (thrown away). This processes for 1 second and returns to its normal position. If there is passion fruit, it comes in again, and the tool functions similarly, and so on. The results of this study produced a passion fruit shrinker machine with dimensions of length 300 mm x width 300 mm x height 600 mm, a motor power of 240 Watt or 0.321 HP, and a production capacity of 54 kg/hour.
Co-Authors Akbar, Zainal Bake, Sabda Ansari Elly Warni Habibuddin, Julianti Indrabayu Indrabayu Ramadhan, Muh. Alamsyah