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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal Basic Science and Technology
Yusuf Sutanto
Universitas Dharma AUB
Biochemistry, Genetics & Molecular Biology Computer Science & IT Electrical & Electronics Engineering Materials Science & Nanotechnology Mechanical Engineering

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Traffic light regulating system simulation at Surakarta city using decision table Heribertus Ary Setyadi; Yusuf Sutanto
Journal Basic Science and Technology Vol 12 No 1 (2023): February: Basic Science and Technology
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jbst.v12i1.3746

Abstract

The effectiveness of a transportation system depends on its ability to support the reliable movement of people, goods, and vehicles from one place to another. An urban traffic system is an important yet complex transportation system composed of vehicles, pedestrians, traffic lights, and a traffic network structure. Existing traffic lights are usually configured with a fixed cycle, and do not take into account specific traffic conditions. Such a configuration is very inefficient. When a large event is held during rush hours, it inevitably gives rise to traffic congestions. The purpose of this research is to produce a system for simulating traffic lights base on predetermined four criterias in order to reduce coongestion. The four criterias are intersections amount, vehicle density in a certain period of time, road width and one way street. Output of this system are engineering simulation and traffic light regulation at the intersection. The problem discussed and worked on in this research is set the time of traffic lights or the time of the red lights and green lights which are at the three intersection and four intersection. The analysis phase is to analyze the process at Surakarta Transportation and Information Agency in traffic lights. The next step is to analyze the weaknesses of the system and analyze the needs of the system being developed. The developed system can produce traffic light simulations and traffic engineering at intersections that includes setting the time the lights are red and green, the green lights are on at the same time and the rules for turn left may go.
LEVERAGING CONTINUAL FINE-TUNING FOR EMOTION CLASSIFICATION IN PRODUCT REVIEWS ON MSME SUSTAINABILITY SUPPORT Galih Setiawan Nurohim; Heribertus Ary Setyadi; Pudji Widodo; Yusuf Sutanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7729

Abstract

Automatic analysis of consumer product reviews is essential for understanding granular customer perceptions beyond basic sentiment. While transformer-based models are prevalent in Indonesian sentiment analysis, their adaptation for multi-emotion classification shifting from broad polarities to specific affective states remains underexplored. This study addresses this gap by proposing a Continual Fine-Tuning (CFT) approach to adapt a pre-trained IndoBERTweet model from three sentiment categories into five distinct emotion classes: Happiness, Sadness, Fear, Love, and Anger. The novelty lies in the strategic repurposing of sentiment-oriented weights to capture nuanced emotional representations in Indonesian e-commerce discourse. Experimental results on the PRDECT-ID dataset demonstrate that the proposed CFT model achieves an accuracy of 0.8157 and a weighted F1-score of 0.8118, significantly outperforming traditional neural networks and multilingual baselines. The CFT model demonstrates a 2.13% improvement in accuracy compared to the base IndoBERTweet without continual tuning and a substantial 59.54% lead over the multilingual BERT (mBERT) baseline. Despite limitations concerning the dataset scale (5,400 samples) and inherent subjectivity in emotion labeling, this research provides a robust conceptual framework for model adaptation in the Indonesian NLP ecosystem. These findings suggest that CFT is an efficient strategy for enhancing the emotional intelligence of transformer models, especially in domain-specific tasks where high-quality labeled data is constrained.
Co-Authors Galih Setiawan Nurohim Heribertus Ary Setyadi Pudji Widodo