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IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal on Information and Communication Technology (IJoICT) is a peer-reviewed journal in the field of computing that published twice a year; scheduled in December and June.
Arjuna Subject : -
Articles 140 Documents
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Buzzer Account Detection in Political Hate Tweets: Case Study of the Indonesian Presidential Election 2024 Herman, Fizio Ramadhan; Ade Romadhony
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1012

Abstract

The Indonesian Presidential Election of 2024 has seen a widespread use of social media such as Twitter for political campaigning and discussion. However, this has also enabled the spread of hate speech from buzzer accounts that are created to influence public opinions. This study implements a machine learning approach to classify buzzer accounts that are spreading hate speeches during the presidential election period. By utilizing IndoBERT for hate speech classification and a traditional machine learning model to classify buzzer accounts. This study analyzes 62,341 tweets for hate speech classification and 961 accounts for buzzer account classification. Our implementation of IndoBERT achieved a strong performance with 91.12% of precision and recall, and 91.19\% accuracy and F1-score in hate speech classification. While for buzzer account classification, we compared Decision Tree, Random Forest, and XGBoost, with Decision Tree achieving the highest performance of 64% precision, recall, accuracy, and F1-Score. Our results demonstrate the effectiveness of combining deep learning for hate speech classification with traditional machine learning for buzzer account classification, contributing to the development of more effective content filtering for election discourse on social media.
FlowForge: A Prototype for Generating User Stories and Gherkin Test Cases from BPMN with DMN Integration and Pattern Matching Riskiana, Rosa Reska; Ryan Oktaviandi Susilo Wibowo; Arpriansah Yonathan
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1015

Abstract

Business Process Model and Notation (BPMN) is a powerful tool for modeling complex business workflows, but its potential for automation in testing remains underexplored. Manual testing of BPMN models is time-intensive and error-prone, necessitating automated approaches to generate test cases directly from the models. This paper presents FlowForge, a tool that transforms BPMN models into User Stories and Gherkin test cases, leveraging Decision Model and Notation (DMN) to enhance test completeness. By addressing challenges related to complex pathways, exception handling, and evolving process structures, FlowForge bridges gaps in existing prototypes, enabling automated test case generation with 100% path coverage within BPMN models. The study demonstrates the successful mapping of BPMN elements to detailed Gherkin syntax while identifying limitations, such as incomplete cross-pool verification and restricted pattern libraries. These findings highlight the tool's potential for improving the efficiency and reliability of BPMN-based testing and offer insights for future development to expand its applicability to more diverse business processes.
Influences of Interaction Styles and Its Relation to Bloom’s Taxonomy on Level 3 Thinking Skills for Children at age 8-9 in Educational Application (Case Studies: Math Subject) Veronikha Effendy; Mira Kania Sabariah; Junaedi, Danang; Arbi Baruni
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1023

Abstract

Educational application is one of the supplementary learning processes that help children to develop their thinking skills. One of the thinking skills is called application or third level (C3 level) in Bloom's Taxonomy for children aged 8 to 9 years. To help those children develop thinking skills, an educational application needs interaction styles to support and bear children in the learning process. The method to determine which interaction styles best suit children's C3 level thinking skills is by comparing two styles. The conclusion from the tests that have been carried out and the data generated for children aged 8-9 years is that the interaction style is form-filling. This interaction style had higher results and preferences in all three tests than the Direct Manipulation interaction style. Children had shorter average processing times, higher average scores, and preferred questions with the form-filling interaction style
Classification Prediction of Dengue Fever Spread Using Decision Tree with Time-Based Feature Expansion Hawa, Iqlima Putri; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1026

Abstract

In Indonesia, dengue hemorrhagic fever (DHF) has become a serious community health concern due to fluctuating incidence rates influenced by several factors. It requires comprehensive control strategies to prevent the rise of the incidence. This study seeks to classify the future spread of DHF in Bandung City, accompanied by optimal factors that influence the increase in its spread. This study proposes using Decision Tree to predict a classification of dengue hemorrhagic fever (DHF) spread with implementation of spatial time-based feature expansion. The developed scenario is to build a target class classification prediction model based on the previous time period. From the developed scenario, the selected model has optimal performance to form a classification prediction model in the future. The results obtained show that the performance of Decision Tree using time-based feature expansion is more than 90%. The contribution of this study is to inform the public and health institution regarding DHF spread for the future and influential factor so that the government can provide policies as early as possible to prevent DHF spread.
JSON-Based RSS as an Alternative to XML-Based RSS on AJAX-Based Aggregator Websites Qori Utama, Dody; Firdaus, Yanuar; Sulistiyo Kusumo, Dana
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1030

Abstract

The rapid development of Internet technology makes information can be exchanged very quickly. RSS was born as one of the technologies that makes it easy to exchange information on the Internet. With RSS, exchanging news on the Internet is even easier than subscribing to a newspaper. RSS was created by utilizing the XML data format which is easy to exchange. Over time, a new data type called JSON was born. JSON is an alternative to XML. Everything XML can do can be done by JSON. JSON has a promising performance compared to XML because JSON has a smaller size than XML. In this final project, the author tries to create a new technology for sharing information by utilizing the JSON data format. In this final project, the author compares user-made technology with existing technology from three things, namely the speed of information creation, data size and data reading speed. The author compares these three things in the author's final project. In the end, the information sharing technology proposed by the author in this final project finally has advantages over existing technologies. This technology is ultimately worthy of continued development as an alternative to existing technologies.
Analysis and Implementation of a Fast Corner Detector on Image Stitching in the Formation of Aerial Photogrammetry Images Lukmana Sardi, Indra; Yulianto, Fazmah Arif; Sthevanie, Febryanti
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1031

Abstract

Aerial Photogrammetry is one of the products from the fields of geography in taking the object, area, or phenomenon on the surface of the earth. Using kamera with a photographic recording process and by the help of a detector in the form of film. In the application, required proper technique in merging images of aerial photographs in order to gain a broader perspective. In this final project, stitching method used in order to merging the images. Image stitching is a method for combining multiple images with overlapping fields of view to produce a panoramic image or picture, the image has a wider viewing angle. Image stitching takes some of the features of the image as a reference in merging overlapping areas, it's named FAST corner detector. The results suggest that the FAST corner detector with a threshold n = 9 appropriate if used in image stitching process that resulted in three classes based on the panoramic image of the cross correlation results, which is the upper class (cc>0.9) on the image with stitching horizontal, diagonal and vertical, the medium class (0.8<cc<0.9) on the image with stitching involves rotation and the lower class (cc <0.8) on a stitched image with scale.
Geospatial Sentiment Analysis Using Twitter Data on Natural Disasters in Indonesia with Support Vector Machine (SVM) Algorithm Muhamad Agung Nulhakim; Yuliant Sibaroni; Ku Muhammad Naim Ku Khalif
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1032

Abstract

Twitter serves as a crucial platform for expressing public sentiment during natural disasters. This study conducts geospatial sentiment analysis on 988 labeled tweets related to the eruption of Mount Marapi, categorized into four aspects which are Basic Needs, Impact and Damage, Response and Action, and Weather and Nature. The preprocessing stage includes data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Feature extraction uses TF-IDF, while class imbalance is addressed with SMOTE. Each aspect is modeled separately using Support Vector Machine (SVM) with linear, polynomial, and RBF kernels, evaluated through 10-fold cross-validation. Results show that the linear kernel performed best across most aspects, achieving 92.42% accuracy for Impact and Damage, 80.38% for Response and Action, and 94.22% for Weather and Nature. Meanwhile, the RBF kernel showed competitive performance with 89.54% accuracy for Basic Needs. Geospatial visualization highlights regional sentiment distribution patterns, offering insights into public responses across Indonesian regions. This study demonstrates the effectiveness of the linear kernel in SVM for sentiment classification and emphasizes the role of geospatial analysis in understanding public sentiment during natural disasters.
Prediction of Classification of Air Quality Distribution in Java Island using ANN with Time-Based Feature Expansion and Spatial Analysis Gutama, Soni Andika; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1035

Abstract

Air pollution is a major concern that significantly impacts human health and the environment, especially in densely populated and economically active areas like Java, Indonesia. Air pollution is primarily caused by motor vehicles and industrial activities, leading to higher concentrations of harmful pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM10). In this study, an Artificial Neural Network (ANN) model is employed to forecast air quality classifications across Java Island, utilizing time-based features and spatial analysis. The model achieves an impressive accuracy and an F1-score of 92.19%, demonstrating its capability in capturing the intricate dynamics of air quality. These results highlight the potential of the ANN model in supporting effective policy-making, crisis management, and the development of environmentally sustainable infrastructure.
SAFE NUSANTARA: A semi-automatic framework for engineering and populating a Nusantara Food Ontology Wiharja, Kemas Rahmat Saleh; Barawi, Mohamad Hardyman; Romadhony, Ade; Atastina, Imelda; Dharayani, Ramanti; Othman, Mohd Kamal
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1042

Abstract

Constructing a comprehensive food ontology, particularly for culturally diverse cuisines like Southeast East Asian (Nusantara), is hindered by the variability of online recipes and the scarcity of structured data. This research introduces SAFE Nusantara, a novel semi-automated system designed to build and populate a Nusantara food ontology by extracting relevant terms from diverse online sources in Indonesian and Malaysian languages. By leveraging a combination of techniques, including topic modelling, natural language processing, and knowledge graph techniques, SAFE Nusantara addresses the challenges of data format diversity and language specificity. The system has demonstrated significant improvements in the accuracy of food classification and has the potential to enhance food recommendation systems and cultural heritage preservation efforts.
Image Color Enhancement Methods: An Experiment-Based Review younis, zainab; Mohd Shafry Mohd Rahim; Farhan Bin Mohamed
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1044

Abstract

Image color enhancement is a vital aspect in the field of image processing. It is a technique to enhance and improve the image's visual quality. Color enhancement is applied in different applications such as photography, medicine, and computer vision. This research reviews eight methods-based color enhancement methods according to their methodology, complexity, pros, and cons. Then, three evaluation metrics used Colorfulness (CF), average saturation measure (ASM), and average chroma measure (ACM) to assess each method. The result showed that fuzzy enhancement (FE) exceeded other methods and scored the highest records. This study provides a beneficial resource for researchers involved in image enhancement, as it presents a complete review and detailed analysis of various academic studies published in reputable journals. The study evaluates each research work's findings, proposed algorithm, and accuracy using many assessment metrics. Furthermore, it emphasizes the strengths and limitations of each method, giving a performance analysis. Additionally, the study discusses future recommendations for improving the effectiveness of these algorithms. Finally, this research is a rich and reliable reference for scholars aiming to develop novel algorithms in this domain.

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