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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi
Wicaksono, Alfan Farizki
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Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Estimating Passenger Density in Trains through Crowd Counting Modeling Tjandra, Bryan; Jodrian, Oey Joshua; Handoko, Nyoo Steven Christopher; Wicaksono, Alfan Farizki
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i1.1314

Abstract

The Greater Jakarta Commuter Rail, also known as the KRL Commuter Line, is one of the primary transportation choices for many people due to its comfort and efficiency. However, the level of user dissatisfaction is still relatively high, particularly regarding the frequent and unpredictable overcrowding of trains. To address this issue, our research develops an Artificial Intelligence-based model to predict train passenger density through crowd counting. By utilizing the proposed k-F1 metric and a constructed dataset of train density, we compare three object detection approaches: bounding box prediction (YOLOv5), density map (CSRNet), and proposal point (P2PNet). Our results show that P2PNet excels in estimating the number of people and predicting their locations in crowded situations. However, for situations that have fewer people and larger object sizes, YOLOv5 demonstrates the best performance. To estimate the density of space, we propose a method that takes into account the region of interest, image perspective transformation, and masking. The proportion between the masked area and the total area provides an estimation of the density level within the train. This method can be applied to real-time image-based CCTV systems in predicting train congestion and facilitating transportation management decisions aligned with Indonesia's sustainable development goals.
Classification of customer complaints on social media for e-commerce in Indonesia Aditama, Achmad Rizki; Wicaksono, Alfan Farizki
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2977-2985

Abstract

The e-commerce industry in Indonesia has experienced rapid growth, especially during the COVID-19 pandemic, which accelerated the shift to online platforms. The market is expected to grow by 105.5% from 2025 to 2030 due to increased internet and smartphone use. As e-commerce expands, companies must improve how they handle customer complaints to build trust and loyalty. Social media is a crucial channel for customer interactions, but it also includes non-complaint messages like positive comments, general questions, and spams that need to be filtered out. This research proposes a machine learning model to automatically classify social media interactions into complaints and non-complaints, focusing on Indonesian-language content. The modeling process utilized 10,600 data points collected from social media X. The best model, a bidirectional encoder representation from transformers (BERT) based classifier, achieved an F1-score of 98.3%. The McNemar test revealed significant performance differences between several models, with the BERT-based model outperforming others. This demonstrates that it is highly effective in distinguishing between complaints and non-complaints, making it a valuable tool for enhancing customer service in Indonesia's e-commerce sector.
Co-Authors Aditama, Achmad Rizki Handoko, Nyoo Steven Christopher Jodrian, Oey Joshua Tjandra, Bryan