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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Syntax Literate: Jurnal Ilmiah Indonesia
Alfan Farizki Wicaksono
Faculty Of Computer Science - Universitas Indonesia
Humanities Computer Science & IT Education Electrical & Electronics Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Detecting Controversial Articles on Citizen Journalism Alfan Farizki Wicaksono; Sharon Raissa Herdiyana; Mirna Adriani
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.521 KB) | DOI: 10.21609/jiki.v11i1.478

Abstract

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.
Detection of Negative Content (Hoax) On Microblog Data That Contains Covid-19 Information Putra Tresna Linge; Alfan Farizki Wicaksono
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (311.882 KB) | DOI: 10.36418/syntax-literate.v7i6.8279

Abstract

Over the past few years, the amount of information dissemination has increased, especially since the advent of social media. Among the information circulating, there is information that includes negative content or hoax that have a bad impact such as the emergence of divisions due to incorrect information. Based on the 2018 Kominfo performance report, Twitter social media is the largest contributor to the spread of hoax. To reduce the impact of the spread of hoax, a method is needed to detect hoaxes on Twitter so that prevention can be done such as taking down tweets that are hoax. The purpose of this research is to develop a model that can detect negative content (hoax) automatically and also see the correlation between hoax content and sentiment orientation. The results of this study are a machine learning-based model using a decision tree algorithm with an accuracy of 97.2% with a precision value of 85.4, recall of 81.4, and f1-score 93 and the model. In addition, the results of the analysis show that tweets that are hoax as a result of model identification are dominated by positive sentiment orientation, which is 52.64% of the total data identified as hoax
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 Mirna Adriani Putra Tresna Linge Sharon Raissa Herdiyana Tjandra, Bryan