cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Muhammad Wali
Contact Email
muhammadwali487@gmail.com
Phone
+6285277777449
Journal Mail Official
journal@kawanad.com
Editorial Address
Jl. Teuku Nyak Arief Number: 5 Lamnyong, Kota Banda Aceh
Location
Kota banda aceh,
Aceh
INDONESIA
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is a peer-reviewed, twice-annually published international journal that focuses on innovative, original, previously unpublished, experimental or theoretical research concepts.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
 5   6   7   8   9 
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.
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.
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.
Clustering and Classification of Retail Sales Data: A Big Data and Data Mining Analysis Almagribi, Ahmad Bilal; Redjeki, Sri
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.v4i2.303

Abstract

In the evolving retail landscape, data-driven decision-making has become essential for understanding customer behavior and predicting sales trends. This study integrates clustering and classification techniques to analyze retail sales data comprising 1,000 transactions obtained from Kaggle. Using the K-Means algorithm, three optimal customer clusters were identified through the Elbow Method, achieving an average within-centroid distance of 25,272.635 and a Davies–Bouldin Index of 0.443, indicating clear cluster separation. The subsequent classification phase compared the predictive performance of three algorithms—Naïve Bayes, Decision Tree, and Random Forest—on 70:30 training-to-testing data partitions. The Naïve Bayes algorithm attained 94.67% accuracy, while both Decision Tree and Random Forest achieved perfect classification accuracy of 100%. These findings highlight the robustness and adaptability of tree-based models for complex retail datasets, outperforming probabilistic methods in terms of accuracy and generalization. The results suggest that the integration of clustering and classification provides retailers with a powerful analytical framework for identifying high-value customer segments, optimizing marketing strategies, and enhancing inventory management. Despite achieving strong outcomes, the study acknowledges dataset limitations and recommends future research involving larger and more diverse datasets, as well as additional features, to expand model scalability and predictive precision.
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 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.
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
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.
Mobile-Based Real-Time Ornamental Rose Classification System Using YOLOv8 Algorithm on Digital Imagery Achmad Fahrezi, Irgy; Poerwandono, Edhy
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.v4i2.339

Abstract

This research introduces a mobile-based system for real-time identification of ornamental rose varieties using the YOLOv8 deep learning algorithm. Motivated by the growing interest in ornamental plants during the COVID-19 pandemic and the high penetration of smartphone users in Indonesia, the study aims to create an efficient and accessible flower recognition tool. A dataset of 813 labeled rose images—red, white, yellow, orange, and pink—was collected from the Roboflow platform and processed using data augmentation techniques to improve model generalization. The YOLOv8 model was trained with 100 epochs, a batch size of 16, and the SGD optimizer, then converted to TensorFlow Lite for mobile deployment through the Flutter framework. Experimental results achieved a mean average precision (mAP50–95) of 0.581, with strong detection performance across most classes. The system successfully operated offline, delivering real-time classification accuracy despite dataset imbalance, particularly in the orange rose class. These findings demonstrate that YOLOv8 can be effectively adapted for mobile horticultural applications, offering practical benefits for flower sorting, crop management, and educational use. Future studies are recommended to expand dataset diversity, enhance environmental testing, and explore cloud-based integration for scalable deployment.
Analysis of Enterprise Network Performance Using the SNMP (Simple Network Management Protocol) Method Alwanto, Hilmi; 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.v4i2.346

Abstract

This study examines the implementation of the Simple Network Management Protocol (SNMP) integrated with the Cacti monitoring platform to evaluate enterprise network performance within a simulated environment using PNETLab. A quantitative approach was applied through continuous data collection and measurement of key performance indicators such as throughput, packet loss, delay, and availability. The experiment utilized virtual Mikrotik routers connected to an Ubuntu-based Cacti server configured for SNMP polling and RRDTool data storage. Real-time visualization enabled efficient tracking of network behavior and early detection of anomalies. The results showed that under normal conditions, the network achieved stable performance with throughput between 70–90% of link capacity, zero packet loss, latency below 150 milliseconds, and availability above 99%, meeting ITU-T/TIPHON Quality of Service (QoS) standards. When faults were simulated, the system accurately detected and displayed traffic interruptions, allowing rapid identification and resolution of network issues. Compared with other monitoring tools such as Zabbix and Nagios, the SNMP–Cacti integration proved simpler to configure while maintaining analytical precision and reliability. These findings confirm that Cacti, supported by SNMP, provides an efficient, scalable, and low-overhead solution for enterprise network monitoring. Future development may incorporate SNMPv3 for enhanced security and automated alert systems or predictive analytics to improve responsiveness and proactive maintenance in larger infrastructures.

Page 7 of 14 | Total Record : 137

 5   6   7   8   9 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 2 (2025): November Vol. 4 No. 1 (2025): May Vol. 3 No. 1 (2024): May 2024 Vol. 3 No. 2 (2024): November Vol. 2 No. 2 (2023): November 2023 Vol. 2 No. 1 (2023): May 2023 Vol. 1 No. 2 (2022): November 2022 Vol. 1 No. 1 (2022): May 2022