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Journal of Information System Exploration and Research
Published by shm publisher
ISSN : 29641160     EISSN : 29636361     DOI : https://doi.org/10.52465/joiser.xxxx
Core Subject :
Journal of Information System Exploration and Research is a journal that publishes and disseminates scientific research papers on information systems to a wide audience particularly within the information system society
Arjuna Subject : -
Articles 25 Documents
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Smart Expert System for Tuberculosis Diagnosis Using the Naïve Bayes Method Ulumuddin Ulumuddin; Siti Harlina; Warjiyono; Amin Nur Rais; Arham Arifin; Abdul Ibrahim; James Adam Seo
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.93

Abstract

Tuberculosis (TB) remains one of the major infectious diseases worldwide, requiring accurate early diagnosis to reduce transmission and improve treatment outcomes. Although numerous machine learning-based diagnostic systems have been developed, most previous studies have primarily focused on improving classification accuracy without integrating an expert system to support clinical decision-making. This study aims to develop a Smart Expert System for the early diagnosis of tuberculosis using the Naïve Bayes algorithm. The novelty of this research lies in the integration of the Naïve Bayes algorithm with a web-based expert system that not only generates diagnostic predictions but also provides recommendation-based decision support. The proposed system was developed using 200 tuberculosis patient records consisting of demographic data and clinical symptoms. The research process included data preprocessing, model training, system implementation, and performance evaluation using Accuracy, Precision, Recall, and F1-score metrics. The experimental results achieved an Accuracy of 87.00%, Precision of 85.00%, Recall of 88.00%, and an F1-score of 86.00%, indicating reliable classification performance. The developed system assists healthcare professionals in conducting preliminary TB screening, accelerating clinical decision-making, and improving public access to early tuberculosis diagnostic information.
Predicting Student Academic Performance Using the Naïve Bayes Algorithm (Case Study: SMK Pelayaran “AKPELNI” Semarang) Aditya Desta Saputra; Herny Februariyanti
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.95

Abstract

Predicting student academic performance can help schools identify students who may require early academic support. This study evaluates a Naïve Bayes classification approach using attendance and parental socioeconomic factors to predict academic performance at SMK Pelayaran “AKPELNI” Semarang. The study used 210 student records from the 2024/2025 academic year. Academic performance was determined from the average scores in Indonesian Language, Mathematics, English, and vocational subjects and classified into Low, Medium, and High categories. These subject scores were excluded as predictors to maintain independence between the predictors and the target label. The predictors consisted of total attendance, father’s education and income, and mother’s education and income. Missing parental education values were handled using mode imputation. The data were divided using a stratified 80:20 train-test split. Categorical Naïve Bayes with Laplace smoothing (α=1) and uniform class priors was applied. On the 42-record test set, the model achieved 66.67% accuracy, 39.74% balanced accuracy, and 33.88% macro F1. The Medium class achieved an F1 score of 79.41%, the High class 22.22%, while the Low class was not detected. These findings indicate that attendance and parental socioeconomic factors alone are insufficient for reliable minority-class prediction and should not yet be used as a standalone early warning system.
Analysis of the Sentiment of Tourist Reviews on Google Maps Towards Tourist Villages in Lampung Province Using the Naïve Bayes Method Achmad Jundi Al Falah; Auliya Rahman Isnain Isnain
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.101

Abstract

Background: Tourist reviews provide information for evaluating visitor experiences and tourism village development. Objective: This study analyzes tourist sentiment toward tourism villages in Lampung Province using Google Maps reviews and Naive Bayes classification. Methods: A total of 2,847 reviews were collected from nine developed tourism villages registered in the Tourism Village Network. Data were preprocessed, transformed into textual features, and classified using Naive Bayes with 80% training and 20% testing data. Results: Positive sentiment accounted for 73.38% of reviews, neutral 17.98%, and negative 8.64%. The Naive Bayes model achieved 81.56% accuracy with good precision, recall, and F1-score, particularly for the positive class. Tourist satisfaction factors included natural beauty (28.46%), community friendliness (22.38%), affordable prices (18.92%), cleanliness (15.67%), and adequate facilities (14.57%). Dissatisfaction concerned accessibility and road conditions (31.71%), inadequate facilities (26.83%), site cleanliness (19.51%), and entrance ticket prices (14.23%). Conclusion: Text-mining-based sentiment analysis effectively captures tourist perceptions and identifies priorities for tourism village improvement, particularly infrastructure, facilities, cleanliness, and community service training for satisfying and sustainable tourism experiences.
Student Sentiment Analysis on the Use of Generative Artificial Intelligence Platforms to Support Academic Activities Riva Ariefta; Heny Pratiwi; Ahmad Fahrijal Pukeng
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.102

Abstract

The development of Generative Artificial Intelligence (Generative AI) has significantly supported students' academic activities, including information retrieval, assignment completion, and learning. This study aims to analyze student sentiment toward the use of Generative AI platforms in academic contexts using the Naïve Bayes algorithm. Unlike previous studies that primarily examined social media data or focused on a single Generative AI platform, this research utilizes primary textual responses collected directly from students regarding their experiences with multiple Generative AI platforms, providing a broader understanding of student perceptions in higher education. A quantitative approach was employed using questionnaire data from 102 students. After data screening, two incomplete responses were excluded, resulting in 100 valid responses for sentiment analysis. The data underwent text preprocessing, including case folding, cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF weighting before classification. The results indicate that 73% of responses expressed positive sentiment, 22% were neutral, and 5% were negative. Model evaluation achieved an accuracy of 75%, precision of 56%, recall of 75%, and an F1-score of 64%. Overall, the findings demonstrate that students generally perceive Generative AI platforms positively and consider them beneficial in supporting academic activities, highlighting their growing role in higher education learning environments.
Usability Evaluation of the Portal Bali Malajah Virtual Class Application Using Performance Measurement, System Usability Scale, and Heuristic evaluation Methods I Made Aris Satia Widiatmika; I Made Candiasa; I Made Gede Sunarya
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.110

Abstract

The Portal Bali Malajah Virtual Classroom application is an innovative educational service that functions as a Learning Management System (LMS) to facilitate the teaching and learning process in Bali Province. However, since the application was first used, users have encountered obstacles, and a usability evaluation has not been conducted. This study evaluated the application using performance measurement to measure effectiveness and efficiency, a System Usability Scale (SUS) to measure user satisfaction, and a heuristic evaluation to identify interface issues. Data collection using performance measurement and heuristic methods was conducted through direct observation of respondents. Heuristic testing involved three experts to assess the application interface. The SUS questionnaire was distributed online to the entire study population. The performance measurement test showed a very effective category with an effectiveness of 98%, and an efficiency of 98.99%. The SUS score resulted in a user satisfaction level of 80.9. The heuristic evaluation identified interface issues with cosmetic and minor usability problems. The Bali Melajah Portal Virtual Class has met the aspects of effectiveness, efficiency, and user satisfaction well, but needs some improvements to the user interface. Improvement recommendations are compiled based on heuristic aspects to produce more targeted recommendations.

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