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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Abdi Insani
Indrajaya, Denny
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Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales Arini Parhusip, Hanna; Trihandaru, Suryasatriya; Indrajaya, Denny; Labadin, Jane
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3291-3305

Abstract

You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number of data points and the small number of epochs have proven reliable algorithms to implement on store products instead of using QR codes in a digital manner. Five models are examined, i.e., YOLOv8-seg, YOLOv8s-seg, YOLOv8m-seg, YOLOv8l-seg, and YOLOv8x-seg, with a data distribution of 64% for the training dataset, 16% for the validation dataset, and 20% for the testing dataset. The best model, YOLOv8l-seg, was obtained with the highest mean average precision (mAP) box value of 99.372% and a mAPmask value of 99.372% from testing the testing dataset. However, the YOLOv8mseg model can be the best alternative model with a mAPbox value of 99.330% since the number of parameters and the computational speed are the best compared to other models.
PENGUJIAN NESS-APP UNTUK DETEKSI SARANG BURUNG WALET TESTING OF NESS-APP FOR DETECTING SWIFTLET NESTS Parhusip, Hanna Arini; Trihandaru, Suryasatriya; Indrajaya, Denny; Hartomo, Kristoko Dwi; Lewerissa, Karina Bianca; Mahastanti, Linda Ariany
Jurnal Abdi Insani Vol 11 No 4 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i4.1786

Abstract

This article discusses the development and testing of the Ness-App application, designed to detect and assess the quality of swallow nests effectively and efficiently. The main issue addressed is the difficulty in determining the quality of swallow nests through photos or videos in buying and selling transactions. The purpose of this research is to develop an Android application using object detection technology to assist PT. Waleta Asia Jaya in assessing the quality of swallow nests. The method used involves creating an object detection model using Convolutional Neural Network (CNN) and SSD MobileNet architecture. The results indicate that the Ness-App application can improve transaction efficiency and quality, providing a better understanding of swallow nest conditions for collectors and farmers. In conclusion, Ness-App supports digitalization and technological advancement in the swallow nest industry by providing an effective tool for quality assessment and accelerating the transaction process.
Co-Authors Hanna Arini Parhusip Jane Labadin Karina Bianca Lewerissa Kristoko Dwi Hartomo Linda Ariany Mahastanti Suryasatriya Trihandaru