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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,338 Documents
 923   924   925   926   927 
Topic modeling in tourism research: a bibliometric study Valentinus Roby Hananto; Vivine Nurcahyawati; Tutut Wurijanto; Titik Lusiani; Mate Kovacs
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp485-497

Abstract

The application of topic modeling in the tourism domain has become a popular research topic in the last decade. This study aims to provide a comprehensive bibliometric analysis of topic modeling research in tourism over the period 2010 to 2023. The data for this study were sourced from the Scopus database, a widely recognized repository of peer-reviewed literature. The search was restricted to publications published from January 1, 2010, to December 31, 2023, to capture the evolution and current state of this rapidly growing field. Using VOSviewer and SciMAT software to analyze articles in the Scopus database, the study identified key trends, influential authors, and future research directions. This study indicates the growth and development in topic modeling for tourism research, with more than 100 Scopus-indexed papers published annually in 2023 alone. The results of this study show that topic modeling has a wide range of applications in tourism, demonstrating its utility in various contexts to understand tourist behavior and enhance smart tourism initiatives.
Harmonization of regulations and innovation: VR/AR teacher readiness model in Indonesian education under public policy based on Pancasila Helga Charolina Antonia Silubun; Dadan Rosana; Samsul Hadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp767-773

Abstract

The adoption of immersive technologies such as virtual reality (VR) and augmented reality (AR) faces the challenge of significant infrastructure disparities between developed countries (HIC) and developing countries (LMIC). In Indonesia, this implementation is hampered by an acute digital divide and the absence of an adaptive regulatory framework. This research proposes Pancasila-driven VR/AR educational architecture (PD-VAREA), a multidimensional framework that integrates principles of social justice into technical optimization through edge computing and adaptive rendering. The novelty of this research lies in the formalization of the teacher readiness index (Tready) using an integral calculus approach to predict systemic readiness. Numerical simulation results show that the PD-VAREA model produces a Tready value of 2.4 (High Readiness), far exceeding the conventional market-driven model, which reaches 0.6. These findings prove that the integration of cost-effective technology (frugal technology) with public policies based on the Fifth Principle of Pancasila is able to emphasize network latency below 20 ms while ensuring equitable access. This article provides a contribution in the form of a predictive model and strategic recommendations for policymakers in LMIC to mitigate the risks of digital inequality in the global education transformation.
Sensor-based prediction of ALS progression: exploring PHI and feature engineering Chibuzor Chukwuemeka Okere; Edwin Thuma; Gontlafetse Mosweunyane
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp835-845

Abstract

Amyotrophic lateral sclerosis (ALS) is a serious disease that affects nerve and muscle function, with no known cure. Early and accurate monitoring is essen tial to help physicians provide better care. Although machine learning has been applied to predict the progression of ALS, many models struggle with issues such as poor data quality and missing information, which affect accuracy. In this paper, our aim is to improve existing models by introducing better features to enhance prediction performance. A key contribution is the development of a new feature called the physical health index (PHI), which combines four im portant patient attributes: body mass index (BMI), weight, forced vital capacity (FVC), and basal calories. This feature provides a clearer view of the physical health of the patient, enabling the model to learn more effectively. We used the IDPP CLEF 2024 BTO dataset and performed three experiments: using 50 raw features, 29 engineered features, and 25 further engineered features including PHI. The results showed that the R-squared of the XGBoost model improved from 0.9573 to 0.9663 and finally 0.9828, while RMSE decreased from 0.2317 to 0.1801 and then 0.1182 with PHI. This study highlights how targeted feature engineering can improve the prediction of ALS using machine learning.
Natural language processing for report consolidation and matching based on latent semantic analysis and cosine similarity Jeleen M. Mangubat; Ryndel Ventura Amorado; Lovely Rose T. Hernandez; Jennifer L. Marasigan
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp609-618

Abstract

Consolidation of reports and matching of documents pose several challenges especially when dealing with large amounts of textual data. Thus, organizations are in need of intelligent systems that are capable of automating these processes, ensuring faster, more accurate analysis and retrieval of relevant information. This study applies Latent Semantic Indexing (LSI) and Cosine Similarity to automate the matching of gender related issues, activities, and programs submitted by university offices. An intelligent web-based system was developed using Python and Django to implement these algorithms for report consolidation. Performance evaluation using accuracy, precision, recall, and F1-score demonstrated that the model correctly classified 90% of entries. A threshold sweep experiment further revealed that a similarity value of 0.51 provides the optimal decision boundary for identifying semantically similar instances. The findings confirm that LSI remains effective for low-resource institutional text analysis, enabling more efficient and accurate report consolidation.
A multi-class classification approach for feminist sentiment analysis in Bangla social media using TF-IDF and ensemble learning Zaid Bin Sajid; Md. Mijanur Rahman; Md. Sumon Hosen; Sarara Jaman Riya; Yeamin Akon; S. M. Fahad Bin Jim; Ornab Biswass
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp584-595

Abstract

Social media has emerged as an important part of societal discourse on feminism and gender equality, especially in Bangladesh. Nevertheless, any feminist debate on social media in Bengali polarizes reactions, highlighting the need for automated sentiment analysis. This paper introduces one of the earliest multi-class feminist sentiment classification schemes of the Bengali social media with a manually annotated dataset of 6,830 comments categorized as positive, neutral, or negative. The framework uses term frequency-inverse document frequency (TF-IDF) based n-gram feature representations utilizing traditional machine learning algorithms, with a majority voting ensemble to determine optimal robust models. The data was divided into 80% and 20% for training and testing, respectively. Models were evaluated on the basis of accuracy, precision, recall, and macro-F1 to correct on imbalance of classes. Multinomial naive bayes (MNB) has the best accuracy of 84.74% and macro-F1 of 84.66, which is 4-7 times higher than other models. The ensemble method improved feature strength. Such results indicate that lightweight machine learning models based on TF-IDF features and ensemble models can be useful to detect feminist sentiment in Bangla social media and serve as a guideline in the field of domain-specific sentiment analysis in low-resource languages and help monitor online feminist discourse.
A structured process model to optimize detection capabilities in security operations centers (SOCs) Adi Nugroho; Charles Lim; Heru Purnomo Ipung
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp518-530

Abstract

The security operations center (SOC) is essential for protecting organizational assets and maintaining operational continuity against rapidly changing cyber threats. Despite its significance, numerous SOCs establish detection capabilities lacking of a systematic framework, frequently culminating in inefficiencies and constrained efficacy. This paper presents a process model aimed at improving SOC detection capabilities by aligning them with business objectives, pertinent risks, and the evolving character of contemporary threats. The study includes an evaluation of current detection methodologies, utilizing the MITRE ATT&CK architecture and threat intelligence data to pinpoint relevant risks and detection deficiencies. A case study was performed at the XYZ Organization to evaluate current detection capabilities and implement the recommended process model. The model was validated through interviews with experts in the SOC field, verifying the findings' credibility. The findings demonstrate that the model efficiently helps SOC in synchronizing detection methods with organizational objectives, prioritizing pertinent threats, and promoting the enhancement of more targeted and adaptable detection capabilities. This research provides theoretical insights into SOC detection modeling and practical assistance for enterprises aiming to enhance their cybersecurity operations.
Enhanced detection of chronic obstructive pulmonary disease via exhaled breath analysis: internet of things and electronic nose system Nur Hidayah Naimah Harahap; Budi Yanti; Muhammad Ilham; Muhammad Suhaili; Dzakiroh Mufidah Hasibuan; Farah Narizki
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp875-883

Abstract

Chronic obstructive pulmonary disease (COPD) remains a major global health burden, highlighting the need for accessible, non-invasive screening tools. This study aims to develop a portable, real-time internet of things (IoT)-integrated electronic nose (e-nose) system for COPD detection using exhaled volatile organic compounds (VOCs). Breath samples from 44 participants (healthy, smokers, and COPD) were analyzed using a MOS based e-nose, and four machine-learning classifiers were evaluated. Data were processed through cloud-based pipelines enabling real-time acquisition and automated analysis. The random forest (RF) model achieved the highest performance (accuracy 86%) in distinguishing COPD-related VOC patterns. This approach overcomes limitations of earlier offline Tedlar-bag methods by enabling direct, real-time breath analysis. The prototype dashboard provides immediate visualization for potential remote monitoring. Key limitations include the small sample size and non-standardized breath sampling, which may affect VOC variability. Overall, this work contributes a cost-effective, portable, IoT-enabled framework demonstrating the feasibility of real-time VOC analysis for early COPD screening and future integration into telehealth and community-based diagnostics.
E-APPS: a digital platform for application processing and records management in private educational institutions with data visualization Eusebio Laureta Mique Jr.; Alvin Reyes Malicdem; Liezl Padilla Mique; Marylen De Guia Rodriguez; Marydel Carrera Estira
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp414-425

Abstract

Online application and processing systems are digital platforms that allow applications to be submitted, reviewed, and processed electronically. These systems enhance efficiency by streamlining workflows, reducing paperwork, and enabling faster and more accurate decision-making for both applicants and administrators. This paper presents the development, implementation, and evaluation of E-APPS: an electronic application processing system for private schools and educational institutions, and records management (E-APPS). Specifically, it determined the process and forms involved in private school applications for approval, developed an E-APPS for private schools and records management using the extreme programming model, assessed the software quality of the E-APPS, and evaluated its technical performance. Based on the result, the existing application process includes submission, validation of documents, inspection, endorsement to the regional Office, approval, and issuance of permits. The E-APPS was developed and evaluated to ensure it meets quality requirements. The developed system was also tested and proven to work reliably even when many users use it simultaneously. It can accommodate hundreds of users without delays or system errors.
Improved interactivity and automated response for visual question answering Nguyen Ha Manh Khang; Nguyen Tuan Anh; Nguyen Minh Hoang; Bui Thanh Hung
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp742-752

Abstract

Visual question answering (VQA) systems have made substantial progress, yet they still face limitations in handling complex or ambiguous queries and supporting real-time interaction due to reliance on large, computationally expensive models that increase latency and restrict practical deployment, particularly in educational contexts. This study aims to develop an efficient and interactive VQA system that enhances answer accuracy while enabling natural two-way communication with users. To achieve this goal, we propose a lightweight multimodal framework based on pre-trained vision language models such as BLIP and fine-tuning T5, combined with prompt engineering to improve question understanding and answer generation. The system further incorporates conversational context memory and a feedback mechanism that generates clarification questions when user inputs are ambiguous, thereby strengthening interaction capabilities. Experiments are conducted on public benchmark dataset Flickr8k, using single-GPU computational settings to evaluate accuracy, response latency, and interaction effectiveness. The experimental results demonstrate that the proposed approach achieves competitive or superior accuracy compared to heavier baseline models, while significantly reducing inference time and enabling real-time interaction. The main contributions of this work include a lightweight, prompt-driven VQA architecture, an interactive strategy for resolving ambiguous queries, and empirical evidence that efficient models can support accurate and conversational VQA for education and other real world applications.
On exploring text mining approaches to sentiment analysis based on the combination of word-based and ontology-based approaches Suthira Plansangket; Supaporn Kansomkeat; Supasit Kajkamhaeng
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp827-834

Abstract

Currently, sentiment analysis plays an important role in business. Entrepreneurs try to understand customer needs for products and services. If they know about the needs, they can create the marketing plans or strategy plans in their business that help improve products and services. Therefore, this study explores two novel approaches to improve the classification accuracy of sentiment analysis data using a combination of a word-based approach (TF-IDF or CSDF) and an ontology-based approach (ontoSen) to provide two new methods, called ontoTF IDF and ontoCSDF. The experimental results show that CSDF method had the best classification accuracy among all the methods in this study: ontoCSDF did not improve further the classification accuracy of sentiment analysis data. Furthermore, ontoTFIDF method improved the classification by IBk algorithm significantly (p

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