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Journal Innovations Computer Science
Published by Yayasan Kawanad
ISSN : 29619718     EISSN : 2961970X     DOI : https://doi.org/10.56347/jics
Core Subject : Science,
Computer Science & IT
Journal Innovations Computer Science (JICS) is a peer-reviewed, twice-annually published international journal that focuses on innovative, original, previously unpublished, experimental or theoretical research concepts. Journal Innovations Computer Science (JICS) covers all areas of computer & information science, applications & systems engineering in computer & information science. JICS core vision is to be an innovation platform in information technology and computer science. Articles of interdisciplinary nature are particularly welcome. All published article URLs will have a digital object identifier (DOI).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 137 Documents
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Social Media X-Based Public Opinion Analysis of Prabowo-Gibran Government's First 100 Days Using Naïve Bayes and K-Nearest Neighbor Classification Methods Afifah, Ridha; Sugiyono
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.316

Abstract

This study investigates public sentiment toward the first 100 days of the Prabowo–Gibran administration by analyzing opinions expressed on X (formerly Twitter) using machine learning approaches. A total of 431 valid tweets were collected, preprocessed, and manually labeled into positive and negative categories. The results reveal that 62% of public sentiment was negative, while 38% was positive, indicating widespread public criticism during the administration’s early period. Two algorithms, Naïve Bayes and K-Nearest Neighbor (KNN), were applied to classify sentiment. The Naïve Bayes model achieved superior performance, with an accuracy of 97.22%, compared to KNN’s 62.65%. The probabilistic nature of Naïve Bayes allowed it to manage high-dimensional, imbalanced textual data effectively, while KNN suffered from the “curse of dimensionality” and class bias. These findings demonstrate that Naïve Bayes remains a reliable and computationally efficient model for political sentiment analysis in the Indonesian digital context. Despite its strengths, this study acknowledges limitations in manual labeling and linguistic nuances such as sarcasm and irony. Future research is encouraged to integrate deep learning architectures like LSTM or BERT and adopt aspect-based sentiment analysis to capture more contextual insights from political discourse.
IoT-Based Integrated Monitoring System for Household Water Level and Usage Tracking Rivaldi, Muhammad Rizki; Said, Fadillah
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.317

Abstract

Conventional household water management often results in inefficiencies, including tank overflow, unexpected shortages, and a lack of awareness about daily consumption. Most existing solutions address these issues only partially—either by monitoring water levels, automating pump control, or recording usage data—without integrating the three into a unified system. To address this gap, this research developed and validated a low-cost Internet of Things (IoT) prototype that combines real-time water-level monitoring, daily consumption measurement, and automatic pump control within a smartphone-connected platform. The system is built on a NodeMCU ESP8266 microcontroller equipped with an HC-SR04 ultrasonic sensor, a YF-S201 flow sensor, and a relay-controlled pump, with data transmitted via Wi-Fi to the Blynk application. Using a Research and Development (R&D) methodology with a prototyping model, the study conducted functional, accuracy, and usability testing. Results show that the prototype achieved reliable performance, with an average error below 2% for both sensors and stable operation during 24-hour trials. Beyond technical validation, the system demonstrated its potential as an eco-feedback tool by providing clear consumption data that can encourage more sustainable water use at the household level.
Implementation of Defense In Depth and IAM Best Practices Based on Segmented VPC Architecture Using Amazon Web Services (AWS) for Small Business Network Security Asrori, Muhamad Umar Hassan; Said, Fadillah
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.318

Abstract

This study presents the design, implementation, and validation of a cloud security architecture on Amazon Web Services (AWS) that integrates Defense in Depth strategies with Identity and Access Management (IAM) Best Practices, tailored for small and medium-sized enterprises (SMEs). Using the AWS Free Tier, an experimental cloud infrastructure was constructed to evaluate the effectiveness of multi-layered protection encompassing network segmentation, least-privilege access control, and real-time monitoring. The architecture employed a segmented Virtual Private Cloud (VPC) with public and private subnets, controlled by Security Groups (SGs) and Network Access Control Lists (NACLs), while IAM policies and Multi-Factor Authentication (MFA) enforced identity-level security. Centralized monitoring through CloudTrail and CloudWatch enabled anomaly detection and event logging with high accuracy. Test results showed that unauthorized access was effectively blocked, suspicious activities were detected promptly, and all administrative actions were recorded reliably. The findings indicate that combining layered network defenses and IAM governance significantly enhances the resilience, visibility, and security posture of SMEs adopting AWS environments. Beyond its technical effectiveness, the model offers scalability, auditability, and cost-efficiency—demonstrating that enterprise-grade protection can be achieved even within the resource constraints of SMEs. Future work is encouraged to integrate automation and advanced AWS tools such as GuardDuty and Config to strengthen real-world adaptability and compliance.
Support Vector Machine-Based Sentiment Analysis of Customer Reviews for Android Smartphone Products on Shopee Marketplace Hutauruk, Lucas Namora; Lestari, Sri; Aula, Raisah Fajri
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.321

Abstract

The rapid expansion of e-commerce in Indonesia has resulted in a surge of unstructured online reviews, especially on platforms such as Shopee. These reviews offer valuable insights into customer satisfaction, product complaints, and purchasing behavior but remain largely underutilized due to their volume and informal language style. This study applies Support Vector Machine (SVM) with Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction to classify reviews of Android smartphones into positive, negative, and neutral categories. Using a dataset of 300 manually annotated reviews from Samsung, Xiaomi, and Oppo official stores, the model achieved an accuracy of 76.67% and demonstrated stable results through 5-fold cross-validation. The negative class showed the highest performance (F1 = 0.86), while the neutral class performed weakest (F1 = 0.62), reflecting challenges posed by mixed opinions and underrepresented samples. Compared with Naïve Bayes and Logistic Regression, the SVM model consistently outperformed both baselines, confirming its suitability for high-dimensional text data and informal Indonesian expressions. The findings highlight SVM’s potential to support automated sentiment monitoring in e-commerce, enabling businesses to identify emerging issues, improve customer service strategies, and leverage positive reviews for marketing. Future research should consider larger and more balanced datasets, techniques for handling imbalanced classes, and integration with deep learning models such as LSTM or BERT to improve performance and generalization.
Real-Time Face Recognition System with Enhanced Security Using Cryptographic Hash-Based Encrypted Embedding Matching Zaidan, Rodhi Shafia; Kastum; Mulyana, Dadang Iskandar
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.322

Abstract

This study presents the development and evaluation of a secure and efficient real-time face recognition system for school attendance, integrating cancelable biometrics with cryptographic hashing. A total of 115 face samples were collected from students and teachers under diverse lighting, pose, and expression conditions. Images were pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction, followed by feature extraction with ResNet-128D, key-based random projection, binarization into 128-bit templates, and SHA-256 hashing. Evaluation results demonstrated an accuracy of 86.09%, precision of 100%, recall of 86.09%, and F1-score of 92.52%, with an average latency of 281.71 ms, remaining well below the operational threshold of 500 ms. Offline pre-processing improved the F1-Score by 7.50% on large datasets and 7.28% on smaller datasets without sacrificing processing speed. From a security perspective, the system achieved zero false acceptances (FAR = 0%) and allowed template regeneration when compromised, reinforcing privacy preservation. These findings validate the feasibility of combining cancelable biometrics with cryptographic hashing to balance accuracy, speed, and security in practical attendance systems. The research underscores its broader applicability to access control and public security, while future work should emphasize adaptive pre-processing, diverse hardware validation, and hardware acceleration for robust real-time deployment.
Enhancing Student Learning Engagement Through Game-Based Learning Implementation Using Naive Bayes Algorithm at BQ Boarding School Junior High Yusuf, Musalim; Yel, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.323

Abstract

This study investigates the impact of Game-Based Learning (GBL) on students’ learning interest and examines the effectiveness of the Naive Bayes algorithm in predicting engagement levels among junior high school students. Using a quasi-experimental quantitative design, data were collected from fifty seventh-grade students at SMP BQ Boarding School through pre-test and post-test questionnaires administered before and after a four-week GBL intervention. Statistical analysis revealed a significant increase in learning interest, with mean scores rising from 2.85 to 4.10 (t(49) = –10.24, p < 0.001), confirming the positive influence of GBL in promoting motivation and participation. The Naive Bayes classification model achieved an accuracy rate of 90%, with precision and recall values of 0.92 and 0.95 for the high-interest category, respectively. These results demonstrate that GBL effectively transforms classroom dynamics into interactive learning experiences while the Naive Bayes model reliably identifies students’ motivational levels. The combination of pedagogical innovation and predictive analytics presents a practical framework for educators to design adaptive interventions and data-informed teaching strategies. This study underscores the importance of integrating artificial intelligence and game-based methods in education to enhance engagement, motivation, and learning outcomes in the digital era.
Customer Review Sentiment Analysis of Alisa Batik Solo E-Commerce on TikTok Using Naive Bayes Algorithm Maharani, Delia; Yell, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.334

Abstract

This study analyzes customer sentiment toward Alisa Batik Solo’s TikTok e-commerce using the Naïve Bayes algorithm. A total of 626 customer comments were collected through manual data crawling, cleaned, labeled, and processed using text preprocessing techniques including cleaning, case folding, tokenization, stopword removal, and stemming. The processed data were then transformed using TF-IDF feature weighting and classified with Naïve Bayes to determine the polarity of customer opinions. The evaluation results showed an accuracy of 90.85%, precision of 98.29% for positive sentiment, recall of 95.24%, and an F1-score of 96.72%, indicating that the model performs effectively in classifying Indonesian short-text reviews. The findings reveal that 75.6% of the comments expressed positive sentiment, while 24.4% reflected negative opinions, demonstrating a strong level of customer satisfaction and trust in Alisa Batik Solo’s products and online engagement strategy. This research confirms that the integration of Naïve Bayes with TF-IDF preprocessing provides reliable results in social media sentiment analysis and can serve as a strategic tool for e-commerce businesses to enhance marketing decisions and service quality
Public Sentiment Analysis on Instagram Regarding the Film "Pengepungan di Bukit Duri" Using Naïve Bayes Approach Azzizah, Putri Salfa Dhiyaa; Yel, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.335

Abstract

This study investigates public sentiment toward Joko Anwar’s 2025 film Pengepungan di Bukit Duri using computational text analysis on 583 Instagram comments. The research applies the Naïve Bayes algorithm combined with TF-IDF weighting to classify opinions into positive and negative sentiments. Data were collected through web scraping of public Instagram posts related to the film and processed through several stages including data cleaning, manual labeling, text preprocessing, and probabilistic classification. The results reveal that 72.9% of the comments express positive sentiment, while 27.1% are negative, indicating strong audience appreciation for the film’s narrative quality and social themes. The model achieved an accuracy of 83.67%, with a precision of 87.13%, recall of 91.04%, and F1-score of 89.04% for positive sentiment. These findings confirm that the Naïve Bayes approach is effective for analyzing short, informal Indonesian-language texts on social media. Practically, the results provide valuable insights for filmmakers and cultural analysts in understanding audience perceptions, managing digital reputation, and designing sentiment-based marketing strategies. Future research is recommended to employ hybrid models and multi-platform datasets to enhance sentiment detection, particularly for nuanced or negative expressions.
Implementation of Naïve Bayes for Public Sentiment Analysis on QRIS and GPN Digital Dominance through Instagram Nabilah, Laila; Setiawan, Kiki
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.337

Abstract

This study examines public sentiment toward the dominance of QRIS and GPN compared to Mastercard and Visa, using data collected from Instagram comments. Employing the Knowledge Discovery in Databases (KDD) methodology and the Naïve Bayes Classifier, the research analyzed 820 comments retrieved through automated scraping and processed using text mining techniques such as case folding, tokenization, stopword removal, stemming, and TF-IDF transformation. The model achieved an accuracy of 84.27%, a precision of 86.09%, a recall of 94.7%, and an F1-score of 90.21%, indicating strong reliability in identifying sentiment polarity. The analysis revealed that 76.5% of the comments expressed positive sentiment, highlighting users’ appreciation of QRIS and GPN for their convenience, speed, and accessibility across both micro and macro-scale transactions. Negative comments, representing 23.5%, centered on concerns about connectivity, data security, and trust in financial governance. These findings suggest that while QRIS and GPN have been widely embraced as efficient digital payment solutions, there remains a need for improved infrastructure, user education, and data protection. The study demonstrates the effectiveness of the Naïve Bayes algorithm for large-scale sentiment analysis in multilingual online environments and provides empirical insights for policymakers to strengthen Indonesia’s digital payment ecosystem.
Implementation of C4.5 Algorithm for Student Satisfaction Analysis on Scout Extracurricular Activities in the Framework of Scout Extracurricular Information System Development at SDN Pondok Bambu 10 & 11 Rifai, Hanna Sabilla; Yel, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.338

Abstract

This study investigates student satisfaction toward Scout extracurricular activities at SDN Pondok Bambu 10 and 11 by applying the C4.5 algorithm within the CRISP-DM framework. Data were collected from 210 students through questionnaires and interviews to evaluate perceptions of program quality, mentor support, and social interaction. The C4.5 model achieved an accuracy rate of 99.52%, effectively identifying key determinants of student satisfaction. Results indicate that program quality, mentor support, and peer interaction are the most influential factors shaping students’ experiences. The decision tree produced interpretable rules that help educators understand satisfaction patterns and make data-driven improvements to program design. Compared with other methods such as SVM and Random Forest, C4.5 provides clearer interpretability while maintaining high predictive precision. The study further recommends integrating the model into a web-based information system to enable continuous monitoring and evaluation of extracurricular activities. These findings highlight the potential of data mining techniques to strengthen decision-making in education and to create a more adaptive, student-centered approach to extracurricular management.

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