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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,106 Documents
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Analysis of Gamification Implementation on Student Motivation and Learning Outcomes Burhan, Rifa'atul Mahmudah; Jamilatul Badriyah
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4995

Abstract

This study aims to analyze the effect of applying gamification methods on student motivation and learning outcomes in computer architecture and organization courses. The study was conducted using a quantitative approach with an experimental design to obtain valid data. Based on the research conducted, it can be concluded that the application of gamification methods has a significant positive impact on student motivation and understanding. Specifically, the use of gamification increased the average motivation score by 27.3, compared to an increase of 6.1 in conditions without gamification. Similarly, the aspect of understanding experienced an average increase of 27.1 with the application of gamification, while without gamification it only reached 5.4. In general, the results of this study indicate that the use of gamification in the learning process has a greater impact on increasing student motivation and understanding compared to conventional learning methods without gamification.
A Conceptual Framework for Integrating SUS into ITIL: Enhancing IT Service Management Through Usability Evaluation Rakhmadi, Aris; Rochmadi, Tri; Azis, Abdul; Ayuningtyas, Astika; Sarmini; Wahyusari, Retno
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4842

Abstract

Effective IT service management must combine operational excellence with seamless user experience in today's digital era. This paper introduces the Deployment and Integration Framework for Assessment (DIFA), a conceptual model that integrates the System Usability Scale (SUS) within the IT Infrastructure Library (ITIL) framework. ITIL offers a structured approach to aligning IT services with business objectives, while SUS provides reliable usability measurements from the user's perspective. By embedding SUS assessments throughout ITIL's lifecycle—spanning service strategy, design, transition, operation, and continual improvement—DIFA enables organizations to evaluate and enhance IT services' usability systematically. This integration bridges the gap between process efficiency and user satisfaction, supporting informed decision-making, improved service adoption, and better alignment with user needs. The findings highlight the strategic value of combining usability evaluation with ITIL's best practices, offering a sustainable and scalable pathway for organizations to deliver IT services that are both technically robust and intuitively user-friendly.
Comparative Analysis of Naïve Bayes and K-NN Methods on Social Media Boycott Issue X Case Study: McDonald’s Azzahra, Morra Fatya Gisna Nourielda; Vitianingsih, Anik Vega; Cahyono, Dwi; Maukar, Anastasia Lidya; Badri, Fawaidul
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4956

Abstract

The boycott movement against McDonald’s, triggered by its alleged support for Israel during the conflict in Gaza, has generated significant public discourse, particularly on the social media platform X (formerly Twitter). This study investigates public sentiment regarding the boycott campaign by analyzing comments and reactions to related content. A total of 1,585 tweets were collected using techniques for web scraping and underwent a comprehensive pre-processing phase, encompassing cleaning, tokenization, filtering, and stemming. Sentiment categories, namely positive, neutral, and negative, are automatically assigned using a lexicon-based technique customized for the Indonesian language. Text data was transformed into numerical form through the Term Frequency-Inverse Document Frequency (TF-IDF) technique, followed by sentiment classification using two supervised machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Evaluation of both models was conducted using a confusion matrix and classification metrics. The results show that the dataset is highly imbalanced, with 93.5% of the tweets labelled as negative, 6.1% as neutral, and only 0.3% as positive. The K-NN model achieved better performance than Naïve Bayes (NB), with an accuracy of 93%, a precision of 31%, a recall of 33%, and an F1-score of 32%. On the other hand, the Naïve Bayes algorithm reached 39% accuracy, 33% precision, 29% recall, and an F1-score of 22%. These findings highlight the dominance of negative sentiment toward McDonald’s and demonstrate the efficacy of the K-NN algorithm in sentiment classification in unbalanced datasets. The insights from this study can inform public relations strategies and corporate reputation management in the face of socio-political controversies.
Implementasi Sistem Informasi Reservasi Event Berbasis Web Wahyuni, Ayu Dewi Sri Wahyuni; Adli, Idham Abdi Al; Sahrul, Nizar; Nurajizah, Siti; Syabaniah, Rifa Nurafifah; Yulianti, Ita
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4960

Abstract

The hospitality industry currently experiencing rapid development in line with advances is information technology, which drive operational efficiency and enhance customer satisfaction. Amaris Hotel Tasikmalaya faces challenges in its event reservation process, which is still conducted manually, often resulting in delays, recording errors, and suboptimal data management. This study aims to develop and implement a web basesd Event Reservation Information System to address these issues. The methodology used is the waterfall model, covering requirements analysis, design, implementation, testing, and maintenance. The developed system provides features for event data management, scheduling, reservations, and reporting, accessible online by both hotel and customers. Internal testing results indicate that the system can accelerate the reservation process, reduce, data entry errors, and improve data management efficiency. The system also provides ease of access and information transparency for customers and supports hotel management decision making. With the implementation of this system, service quality and customer satisfaction are expected to improve.
Evaluating the Impact of Artificial Intelligence Enhanced Augmented Reality Tools on Social Interaction in Learners with Autism Spectrum Disorder Elegbeleye, Femi; Olusegun Oguntona; Ife Elegbeleye; Jose Lukose
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4969

Abstract

Autism Spectrum Disorder (ASD) is a cognitive developmental condition characterized by persistent deficits in social communication and interaction, alongside restricted and repetitive patterns of behavior. The global prevalence of ASD is estimated at approximately 1% in the general population, with higher rates observed in specific demographic groups. Individuals with ASD often experience challenges in interpreting social cues, initiating interactions, and participating in group settings, which can impede their academic and social development. This study examines how Augmented Reality (AR) and Artificial Intelligence (AI)-based interventions can complement or improve the social communication skills and behavioral patterns of individuals with ASD. A systematic literature review (SLR) was conducted, focusing on peer-reviewed studies published between 2019 and 2024, to assess the efficacy and practicality of these technologies in educational environments. The analysis covers engagement of visual boards, smartphones, tablets, and AR glasses, which are increasingly integrated into pedagogical strategies to enhance the learning experiences of students with ASD. The results demonstrate that AI-enhanced AR-based interventions significantly outperformed traditional teaching methods, with notable improvements in social interaction (70% vs. 50%), emotional recognition (60% vs. 40%), engagement (80% vs. 55%), communication skills (75% vs. 45%), and behavioral outcomes (65% vs. 50%). These technologies appear to support the development of social skills by providing interactive, personalized, and visually enriched learning environments. The outcomes of this research highlight the potential of AI-enhanced AR to complement traditional teaching methods, offering valuable insights for educators, therapists, and policymakers seeking practical approaches to support learners with ASD. Further empirical research is recommended to validate these findings across diverse educational settings
Developing a generative AI conceptual framework for higher education Chukwuere, Joshua Ebere
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.4997

Abstract

The overall aim of this research is to develop a whole-of-institution framework that supports the use of generative AI chatbots across higher education institutions (HEIs). The Generative AI Chatbots Acceptance Model (GAICAM) was developed from a blend of models that influence acceptance (TAM, UTAUT2, TPB, and more). optimism, innovativeness, discomfort, insecurity, and others; hence, including variables relevant to GAICAM. It is also critical to analyse and identify the implications for higher education of generative AI Chatbots. A research design was employed based on an integrative literature review and an active analysis of numerous studies from diverse databases, including IEEE, ACM, ScienceDirect, and Google Scholar. Part of the goal was to make sense of the implications for higher education created by AI Chatbots, which also necessitated identifying the prominent considerations relevant to implementation challenges and success. The search criteria were limited to peer-reviewed, English-language publications covering the use of AI chatbots in higher education that were published between 2020 and 2023. The findings demonstrate the degree to which AI chatbots have the capacity to drive improved student engagement, enhance the educational process, and support administrative and research tasks. But there are also clear difficulties, such as unfavorable student sentiments, doubts about the veracity of material produced by AI, and unease and nervousness with new technologies.
K-Owl: Next-Gen Interactive Platform for Student Engagement and Feedback Nan Cenka, Baginda Anggun; Agnes Audya Tiara P; Ardanisa Rachma; Avelia Diva Zahra; Harry B. Santoso
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.5001

Abstract

In the digital era, the challenges faced by lecturers and students in learning are increasingly diverse, which makes students tend to be less involved in learning. A student response system called “K-Owl” is proposed to support the Know, Want to Know, Learn (KWL) strategy where lecturers can monitor students’ knowledge in real-time while the platform helps students stay actively engaged in class. User-Centered Design was used as a methodology to ensure that the proposed platform aligns with user needs, with stages including identifying user problems and needs, system design and development, and evaluation. This study involved 16 interview participants and 71 usability testing participants. Quantitative data were collected using the System Usability Scale (SUS) and the Maze application to usability testing and analysed using the SUS Score and Maze Score. Meanwhile, qualitative data were collected through user interviews and analysed using thematic analysis. This study resulted in three main features including courses management, KWL, and dashboard. The results of user acceptance testing and usability testing were with a score of 100% and a SUS score of 69 (Good category), respectively. The results of the study indicate that K-Owl is a promising platform to support active learning.
Teaching-Learning-Based Optimization Algorithm for Pressure Vessel Design Problem Hawar Bahzad Ahmad; Danial William Odeesho; Reving Masoud Abdulhakeem; Merdin Shamal Salih; Zebari, Dilovan
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.5004

Abstract

This paper presents the utilization of the Teaching-Learning-Based Optimization (TLBO) algorithm to tackle the intricate problem of pressure vessel design. Design optimization for pressure vessels holds a critical role in various engineering domains, demanding effective techniques for achieving designs that are both optimal and safe. The TLBO algorithm, drawing inspiration from the dynamics of teaching and learning, offers a unique approach by amalgamating exploration and exploitation strategies. In this research, we investigate the incorporation of TLBO within the realm of pressure vessel design, with the objective of improving design efficiency while strictly adhering to demanding safety and performance benchmarks. Through a comprehensive assessment, we analyze the performance of TLBO in generating optimal designs and draw comparisons with established optimization methods. Our findings underscore the proficiency of TLBO in effectively converging towards competitive solutions, thus highlighting its potential to bring about a paradigm shift in the domain of pressure vessel design optimization. This paper underscores the importance of the Teaching-Learning-Based Optimization algorithm as a transformative instrument, providing invaluable insights for researchers, practitioners, and experts involved in fields such as structural engineering, optimization, and related disciplines.
Diabetes prediction based on Ensemble Methods: A Review Mosa, Jihan; Mohsin Abdulazeez, Adnan
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.5006

Abstract

Diabetes is a global health crisis, and early prediction is critical to preventing serious complications. Recent research shows that ensemble machine learning methods and deep learning architectures significantly improve diabetes prediction accuracy. Ensemble methods such as random forest, XGBoost, bagging, boosting, and stacking utilize multiple algorithms to capture diverse data patterns and consistently outperform traditional single classifiers. In parallel, deep learning models, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and hybrid CNN-LSTM architectures, excel at identifying complex temporal and spatial relationships. These techniques are widely applied to benchmark datasets, such as the Pima Indian diabetes data and other repositories at the University of California, Irvine, and are evaluated through metrics including area under the curve (AUC-ROC), precision, and recall. Challenges remain—particularly computational cost and model interpretabilitybut both approaches deliver superior accuracy and reliability. By integrating current evidence, this overview highlights the potential of ensemble learning and deep learning methods to enable earlier and more accurate detection of diabetes and enhance personalized healthcare solutions.
The Use of ChatGPT in Higher Education: The Advantages and Disadvantages Chukwuere, Joshua Ebere
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.5009

Abstract

Higher education scholars are fascinated by an artificial intelligence (AI) technology, ChatGPT, developed by OpenAI. Debate exists among the experts on whether or not ChatGPT can support learning. This literature review paper sits within the broader scope of measuring ChatGPT within higher education as a means to understand and produce higher-order learning. The purpose of this paper is to examine essential literature with the goal of providing a balanced contribution around the merits and limitations of the use of ChatGPT in higher education contexts. However, it is also important to examine the potential outcomes, both positive and negative. For this rapid review, the researcher searched Google Scholar, Scopus, and others between January 2023 and July 2023 for prior studies from other publications, and these studies were reviewed. This study found that using ChatGPT in higher education is helpful for many reasons. For example, it provides individualized instruction, spontaneous feedback, access to learning opportunities, and student engagement. There may be some benefits to the learning ecosystem and enjoyment for academics and students. The negative aspects of ChatGPT exist, too. The negatives include the inability to decipher emotion, lack of social interaction, technological limitations, and the risk of becoming overly reliant on ChatGPT in higher education. Higher education should blend ChatGPT with other delivery methods to provide a holistic learning experience to students and lecturers. It is a serious consideration to consider the positives, negatives, and ethical issues when applying ChatGPT in the classroom.

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