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Dede Kurniadi
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Journal of Intelligent Systems Technology and Informatics
Published by Aliansi Peneliti Informatika
ISSN : -     EISSN : 3109757X     DOI : https://doi.org/10.64878/jistics
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
The Journal of Intelligent Systems Technology and Informatics (JISTICS) is an international peer-reviewed open-access journal that publishes high-quality research in the fields of Artificial Intelligence, Intelligent Systems, Information Technology, Computer Science, and Informatics. JISTICS aims to foster global scientific exchange by providing a platform for researchers, practitioners, and academics to disseminate original findings, critical reviews, and innovative applications. The journal is published three times a year (March, July, November) and may also publish special issues on emerging topics.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
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Search results for , issue "Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025" : 5 Documents clear
Detection of Pneumonia Disease on Chest X-ray Images Using Convolutional Neural Network Salsabila, Rosa; Lea Saumi
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.18

Abstract

Pneumonia is a critical lung infection and a leading cause of morbidity and mortality worldwide. Early and accurate diagnosis is essential to ensure effective treatment and improved patient outcomes. Chest X-ray imaging, as a widely accessible diagnostic tool, presents challenges in manual interpretation due to overlapping anatomical structures and inter-observer variability. To address this, this study investigates the application of Convolutional Neural Networks (CNN) for automated pneumonia detection from chest X-ray images. The dataset used in this research consists of 5,863 labeled grayscale pictures obtained from the Kaggle repository, comprising 4,273 pneumonia and 1,583 normal cases. Preprocessing steps included image resizing, normalization, and class balancing through augmentation. The CNN model was trained using the augmented dataset and evaluated using various performance metrics. The proposed model achieved an overall accuracy of 79% on the test set, with a precision of 0.84, a recall of 0.79, and an F1-score of 0.78. The class-wise analysis revealed strong performance in detecting normal cases (F1-score = 0.82) but lower recall in pneumonia cases (Recall = 0.60), indicating a need for further improvement. In conclusion, CNN-based approaches demonstrate promising potential for aiding pneumonia diagnosis in clinical settings. However, additional work is necessary to enhance model reliability, particularly in detecting complex patterns of pneumonia. Future research may explore ensemble models and attention mechanisms to improve classification performance.
Customer Comment Clustering for Kahf Face Wash at Kahf Official Shop Using K-Means Method Arbiansyah, Gilang; Haq, Faizal
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.23

Abstract

The advancement of information technology has encouraged people to shop more confidently, including for men's skincare products. Although data indicate that men's interest in skincare remains relatively low, sales of Kahf Face Wash show high figures. In this context, consumer reviews on e-commerce platforms serve as a valuable source of information for understanding customer satisfaction and experience. This study aims to group consumer comments on Kahf Face Wash products from the Kahf Official Shop using the K-Means clustering method. A total of 4,966 consumer comments were collected automatically through web crawling techniques. These comments then underwent several text processing stages, including case folding, cleaning, tokenization, normalization, removal of stop words, and stemming. After the cleaning process, 2,431 comments remained for analysis. The textual data was transformed into numerical representations using the TF-IDF method, and the optimal number of clusters was determined using the Elbow method, which indicated the optimal value at k = 3. The clustering results categorized the comments into three groups: purchase experience (1,506 comments), product effectiveness (474 comments), and delivery and service (451 comments). Visualization was conducted using PCA and bar charts to better illustrate the distribution and proportion of comments in each cluster. Evaluation of the clustering results using inertia and the Davies–Bouldin Index revealed that the model effectively grouped the comments with a reasonably high quality. This study makes a significant contribution by helping companies analyze customer behavior through an unsupervised learning approach. This method enables companies to efficiently extract structured insights from unstructured reviews, which can be utilized to enhance service quality, marketing strategies, and future product development.
Classification of Thyroid Disease Risk Using the XGBoost Method Amelia, Melina; Fitriyani, Dila
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.26

Abstract

Thyroid disease is one of the essential health threats and requires early detection to enable more effective medical intervention. This study aims to develop a classification model using the XGBoost algorithm to categorize patient clinical data from the Kaggle platform into three levels of thyroid cancer risk: low, moderate, and high. The data processing process follows the stages of the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, with main techniques such as label coding, stratified 5-fold cross-validation, and hyperparameter tuning being applied. Performance evaluation was conducted using accuracy metrics, including F1-score and AUC-ROC. The results show that the model exhibits excellent performance in detecting low-risk cases (AUC = 1.00), but it still faces challenges in classifying moderate and high-risk categories. After adjusting the hyperparameters, the validation accuracy increased to 96.24%, although the final accuracy on the test data remained at 69.85%. These findings suggest that XGBoost is a promising approach for the early assessment of thyroid disease risk, particularly in detecting low-risk cases. However, further model development is needed to enhance generalizability across risk levels and support informed clinical decision-making.
Skin Tone Classification in Digital Images Using CNN For Make-Up and Color Recommendation Nurapipah, Nida; Yuliana, Siti Sarah
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.29

Abstract

Human skin tone variation is an obstacle in the development of a digital beauty product recommendation system. The purpose of this study is to categorize skin tone into three groups (Black, Brown, and White). Using a Convolutional Neural Network (CNN) based on the refined EfficientNetB0 architecture on a balanced dataset of 1,500 facial images, each class consisting of 500 images. All images in the dataset have been resized to 224 × 224 pixels to match the model input and ensure data uniformity and compatibility with the EfficientNetB0 model architecture used. The dataset used was obtained from the Kaggle platform and processed through the normalization and augmentation stages. It was then evaluated through the validation process using the 5-fold cross-validation method. This model achieved a total accuracy level of 88.67%, with the white category demonstrating precision (0.93), recall (0.95), and F1-score (0.94), as well as the highest AUC of 0.99, indicating very satisfactory performance. Additionally, this system can offer personalized beauty product recommendations, including foundation shades, lipstick colors, and clothing color palettes, tailored to specific skin tones. This method enhances the user experience by providing accurate recommendations that adapt to various lighting conditions, making it suitable for use on digital beauty platforms.
Automatic Sentiment Annotation Using Grok AI for Opinion Mining in a University Learning Management System Julianto, Indri Tri; Sidqi, Muhammad Affan Al
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS Vol. 1 No. 3 November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.42

Abstract

Sentiment analysis has become an essential tool in evaluating user feedback on digital learning platforms. Understanding student sentiments toward Learning Management Systems (LMS) in higher education can offer critical insights for system development and service improvement. This study aims to evaluate the effectiveness of AI-assisted sentiment labeling using Grok AI and ChatGPT compared to manual labeling for sentiment classification of student opinions on LMS at Institut Teknologi Garut. The research involved distributing an online questionnaire to 96 students across four academic levels, collecting open-ended responses regarding their LMS usage experiences. These responses were preprocessed through case folding, cleaning, tokenization, stopword removal, and stemming. The sentiment labels were assigned using Grok AI, ChatGPT, and manual annotation, and the resulting datasets were used to build classification models using the Naïve Bayes algorithm in Altair RapidMiner with 10-Fold Cross Validation. The performance evaluation shows that manual labeling yielded the highest accuracy (52.22%) and Cohen's Kappa (0.137), followed by ChatGPT (50.11%, 0.119) and Grok AI (48.00%, 0.087). Word cloud visualizations further revealed the dominant themes within each sentiment class, indicating that positive opinions emphasized helpfulness and ease of use, while negative ones focused on access issues and system lags. This research suggests that AI-assisted labeling methods can be viable alternatives, although manual labeling still offers slightly better accuracy.

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