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Forecasting Roof Tiles Production with Comparison of SMA and DMA Methods Based on n-th Ordo 2 and 4 Yel, Mesra Betty; Tundo, Tundo; Arinal, Veri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22225

This research aims to predict roof tile production trends at one of the roof tile companies in Kebumen to assist company management in determining and providing management recommendations for the tile production that occurs. A comparison of Single Moving Average (SMA) and Double Moving Average (DMA) Forecasting methods was used to better accommodate trends in roof tile production data optimally. Where the forecast is presented for several steps ahead, and is equipped with a value measuring the accuracy of the forecast using Mean Absolute Percentage Error (MAPE), on roof tile production transaction data over 60 months, namely January-December 2019 to January-December 2023 to produce a monthly forecast for predicting roof tile production with n-th ordo 2 and 4. The total sample of training data processed was 1,415,987 records which were roof tile production transaction data, as well as data in January 2024 as test data (to test the accuracy of the forecast). The results of testing the forecast results produced a MAPE calculation of 6.6% for SMA with n-th ordo 2, while for n-th ordo 4 it was 7.2%. The MAPE value for DMA is 6.3% for n-th ordo 2, while for n-th ordo 4 it is 8.2%, which means the accuracy level is very good, namely above 90%. Based on the MAPE results obtained, the DMA method with n-th ordo 2 is a suitable method for carrying out periodic forecasting for roof tile companies in carrying out the production process to maintain stability and avoid unexpected events.

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

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.

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

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

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.

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

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.

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

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.

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

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

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.

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

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.

OPTIMALISASI KEAMANAN FIREWALL PADA INFRASTRUKTUR JARINGAN SMK IDN BOGOR Yel, Mesra Betty; Mulyana, Dadang Iskandar; F, Joe Renaldy; Nurfaishal, Muhammad Dzaky; B, Muhamad Hasbi Toharudin
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 1 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i1.1393

IDN Bogor Vocational High School, which was established in 2016, is known as one of the many schools that are involved in the academic, religious and IT-focused world, as its vision is "Wonder of IT Smart of Praying". Given the large number of IT activities both in terms of learning and administratively office data collection (Administration), of course the application of a Firewall on the network is one of the crucial things needed to facilitate the security and authenticity of data from teachers and students. The purpose of preparing this KKP report is none other than to help optimize Firewall configurations, both on Firewall devices and PCs/Laptops along with counseling strategies as preventive measures to avoid physical/social engineering hacking at SMK IDN Bogor. As we all know, cybersecurity issues cannot be taken lightly. Therefore, for the sake of the continuity and smooth running of school education activities, Optimization/Standardization of all segments or parts that use the network within SMK IDN Bogor is required.

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