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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI)
Setya Perdana, Rizal
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : journal of information technology and computer science

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Multilabel Classification for Keyword Determination of Scientific Articles Rafif, Sulthan; Setya Perdana, Rizal; Pandu Adikara, Putra
Journal of Information Technology and Computer Science Vol. 8 No. 2: August 2023
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202382560

Abstract

In writing scientific articles, there are provisions regarding the structure or parts of writing that must be fulfilled. One part of the scientific article that must be included is keywords. The process of determining keywords manually can cause discrepancies with the specific themes discussed in the article. Thus, causing readers to be unable to reach the scientific article. The process of determining the keywords of scientific articles is determined automatically by the classification method. The classification process is carried out by determining the set of keywords owned by each scientific article data based on the abstract and title. Therefore, the classification process applied is multi-feature and multi-label. Classification is done by applying the Contextualized Word Embedding Method. The implementation of Contextualized Word Embedding Method is done by applying BERT Model. By applying the BERT Model, it is expected to provide good performance in determining the keywords of scientific articles. The evaluation results by applying the BERT Model to the case of multi-label classification on abstract data for keyword determination resulted in a loss value of Training Data is 0.514, loss value of Validation Data is 0.511, and an accuracy value of 0.71, a precision value of 0.71, a recall value of 0.71, an error value of 0.29 and f-1 score of 0.83. Based on the results of the evaluation, it shows that the BERT Classification Model can carry out a classification process to determine a set of keywords from each abstract data in scientific articles.
Combining Inference Results of YOLOv8 and Faster R-CNN using Weighted Boxes Fusion for Car’s Underbody Quality Inspection (Study Case On Automotive Company In Indonesia) Alfons Abilo, Nelson; Indriati; Setya Perdana, Rizal
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101918

Abstract

The automotive industry in Indonesia has gradually experienced rapid development over time, leading to intense competition among automotive companies. In such a competitive environment, the quality inspection process is a critical aspect of the automotive manufacturing industry. One significant issue in the quality inspection process is human error. The Fourth Industrial Revolution offers solutions through computer vision technology to improve production quality. YOLOv8 is a real-time object detection algorithm that offers fast inference and good background reduction, but it is less effective for small and low-contrast objects. Conversely, Faster R-CNN has high confidence scores but slow inference capabilities. Company XYZ has a visual inspection system using YOLOv8, with a mAP50@95 value of 74%. However, the confidence scores produced often do not meet the company's standard threshold, which requires a confidence score above 90%. Therefore, a study was conducted to combine the inference results of YOLOv8 and Faster R-CNN using the weighted boxes fusion method to enhance the inference results of YOLOv8. The study results showed an increase in mAP when combining the inferences of the two models, compared to when each model performed inference individually – there was a 3.8% increase compared to YOLOv8's performance and a 5.5% increase compared to Faster R-CNN's performance.  
Co-Authors Abdul Jabbar Afrialdy, Firman Alfons Abilo, Nelson Aryo Pinandito Candra Dewi Dian Eka Ratnawati Firdaus, Nada Hamzaturrazak, Multohadi Indriati Jibril Averroes, Muhammad Lailil Muflikhah Pandu Adikara, Putra Rafif, Sulthan RR. Ella Evrita Hestiandari Salsabila Sangdiva Laksono, Khansa Susanto, Dominicus Christian Bagus Syemael Siahaan, Arion