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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics CommIT (Communication & Information Technology) JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization Emerging Science Journal Syntax Literate: Jurnal Ilmiah Indonesia Gema Wiralodra Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Engineering, Mathematics and Computer Science Journal (EMACS) Parameter: Journal of Statistics Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Penelitian Pendidikan Indonesia Teknologi : Jurnal Ilmiah Sistem Informasi
Abba Suganda Girsang, Abba Suganda
Bina Nusantara University, Jakarta, Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : International Journal of Informatics and Communication Technology (IJ-ICT)

 1 
Indonesian generative chatbot model for student services using GPT Priccilia, Shania; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp50-56

Abstract

The accessibility of academic information greatly impacts the satisfaction and loyalty of university students. However, limited university resources often hinder students from conveniently accessing information services. To address this challenge, this research proposes the digitization of the question-answering process between students and student service staff through the implementation of generative chatbot. A generative chatbot can provide students with human-like responses to academic inquiries at their convenience. This research developed generative chatbot using pre-trained GPT-2 architecture in three different sizes, specifically designed for addressing practicum-related questions in a private university in Indonesia. The experiment utilized 1288 question-answer pairs in Indonesian and demonstrated the best performance with a BLEU score of 0.753, signifying good performance accuracy in generating text despite dataset limitations.
Traffic accident classification using IndoBERT Naufal, Muhammad Alwan; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i1.pp42-49

Abstract

Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.
Autism detection based on autism spectrum quotient using weighted average ensemble method Lawysen, Lawysen; Anggara, Nelsen; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp188-196

Abstract

Autism spectrum disorder (ASD) is a condition that occurs in an individual, wherein it is accompanied by various symptoms such as difficulties in socializing with others. Early detection of ASD patients can assist in preventing various symptoms caused by ASD. The focus of this research is to automate the diagnosis of ASD in an individual based on the results of the autism spectrum quotient (AQ) using weighted average ensemble method. Initially, preprocessing is carried out on the dataset to ensure optimal performance of the resulting model. In the preprocessing step, the filling of missing values and feature selection occurs, where the feature selection method being utilized is p-value. The model in this research uses the weighted average ensemble method, which is the model that combines three machine learning classification algorithms. Eight classification algorithms are tested to identify the three algorithms with the best performance, namely gaussian Naïve Bayes (NB), logistic regression (LR), and random forest (RF). Following the testing, the model constructed using the weighted average ensemble method exhibits the highest performance compared to the model built using a single classification algorithm. The performance matrix used to measure the model’s performance is area under the curve (AUC)/receiver operating characteristic (ROC), with the developed model achieving an AUC/ROC value of 0.912.
Automated multi-document summarization using extractive-abstractive approaches Nasari, Maulin; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp400-409

Abstract

This study presents a multi-document text summarizing system that employs a hybrid approach, including both extractive and abstractive methods. The goal of document summarizing is to create a coherent and comprehensive summary that captures the essential information contained in the document. The difficulty in multi-document text summarization lies in the lengthy nature of the input material and the potential for redundant information. This study utilises a combination of methods to address this issue. This study uses the TextRank algorithm as an extractor for each document to condense the input sequence. This extractor is designed to retrieve crucial sentences from each document, which are then aggregated and utilised as input for the abstractor. This study uses bidirectional and auto-regressive transformers (BART) as an abstractor. This abstractor serves to condense the primary sentences in each document into a more cohesive summary. The evaluation of this text summarizing system was conducted using the ROUGE measure. The research yields ROUGE R1 and R2 scores of 41.95 and 14.81, respectively.
Transformer-based abstractive indonesian text summarization Aurelia, Miracle; Monica, Sheila; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp388-399

Abstract

The volume of data created, captured, copied, and consumed worldwide has increased from 2 zettabytes in 2010 to over 97 zettabytes in 2020, with an estimation of 181 zettabytes in 2025. Automatic text summarization (ATS) will ease giving points of information and will increase efficiency at the time consumed to understand the information. Therefore, improving ATS performance in summarizing news articles is the goal of this paper. This work will fine-tune the BART model using IndoSum, Liputan6, and Liputan6 augmented dataset for abstractive summarization. Data augmentation for Liputan6 will be augmented with the ChatGPT method. This work will also use r ecall-oriented understudy of gisting evaluation (ROUGE) as an evaluation metric. The data augmentation with ChatGPT used 10% of the clean news article from the Liputan6 training dataset and ChatGPT generated the abstractive summary based on that input, culminating in over 36 thousand data for the model’s fine-tuning. BART model that was finetuned using Indosum, Liputan6, and augmented Liputan6 dataset has the best ROUGE-2 score, outperforming ORACLE’s model although ORACLE still has the best ROUGE-1 and ROUGE-L score. This concludes that fine-tuning the BART model with multiple datasets will increase the performance of the model to do abstractive summarization tasks.
Human detection in CCTV screenshot using fine-tuning VGG-19 Dewangga, Firdaus Angga; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp645-652

Abstract

Closed-circuit television (CCTV) systems have generated a vast amount of visual data crucial for security and surveillance purposes. Effectively categorizing security level types is vital for maintaining asset security effectively. This study proposes a practical approach for classifying CCTV screenshot images using visual geometry group (VGG-19) transfer learning, a convolutional neural network (CNN) classification model that works really well in image classification. The task in classification compromise of categorizing screenshots into two classes: “humans present” and “no humans present.” Fine-tuning VGG-19 model attained 98% training accuracy, 98% validation accuracy, and 85% test accuracy for this classification. To evaluate its performance, we compared fine-tuning VGG-19 model with another method. The VGG-19-based fine-tuning model demonstrates effectiveness in handling image screenshots, presenting a valuable tool for CCTV image classification and contributing to the enhancement of asset security strategies.
IndoBART optimization for question answer generation system with longformer attention Andrew, Peter; Girsang, Abba Suganda
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i2.pp478-487

Abstract

The Incorporation of Question Answering system holds immense potential for addressing Indonesia’s educational disparities between the abundance of high school students and the limited number of teachers in Indonesia. These studies aim to enhance the Question Answering System model tailored for the Indonesian language dataset through enhancements to the Indonesian IndoBART model. Improvement was done by incorporating Longformer’s sliding windows attention mechanism into the IndoBART model, it would increase model proficiency in managing extended sequence tasks such as question answering. The dataset used in this research was TyDiQA multilingual dataset and translated the SQuADv2 dataset. The evaluation indicates that the Longformer-IndoBART model outperforms its predecessor on the TyDiQA dataset, showcasing an average 26% enhancement across F1, Exact Match, BLEU, and ROUGE metrics. Nevertheless, it experienced a minor setback on the SQuAD v2 dataset, leading to an average decrease of 0.6% across all metrics.
Co-Authors Achmad Abimanyu Adi Setya Agung Nur Aprianto Agus Susanto Ali, Ichsan Andrew, Peter Andri Wijaya Anggara, Nelsen Anita Sindar Ros Maryana Sinaga Ardhito, Danar Aurelia, Miracle Budi Juarto Burhanudin Burhanudin Chu-Sing Yang Chun-Wei Tsai Danar Ardhito Dela Nurlaila Dewangga, Firdaus Angga Febryan Grady Gagas Pandusarani, Gagas I Dewa Bagus Gde Khrisna Jayanta Nugraha Imran, Bahtiar Indrabudhi Lokaadinugroho Jimmy Jimmy Joel Rizky Wahidiyat Ko-Wei Huang Lawijaya, Viriyaputra Lawysen, Lawysen Monica, Sheila Muhammad Imam Taufiq Nasari, Maulin Naufal, Muhammad Alwan Novika, Andien Dwi Nursandi, Bambang Priccilia, Shania Putra, Anugrah Dwiatmaja Ricky Eka Putra Rifqi Ramadhani Almassar Stefani Agusta Suharjito Suharjito, Suharjito Syamsul Arifin Taufiq, Muhammad Imam Tedjasulaksana, Jeffrey Junior Tjeng Wawan Cenggoro Widjaya Kumala Putra Yefferson, Danny Yongky Yulianto Nugroho