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ANALISIS SENTIMEN PDI PERJUANGAN PASCA PILPRES 2024 DI JAKARTA TIMUR DENGAN NAÏVE BAYES Marsan, Alvin Cahya Pratama; Yel, Mesra Betty
KNOWLEDGE: Jurnal Inovasi Hasil Penelitian dan Pengembangan Vol. 5 No. 3 (2025)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/knowledge.v5i3.6782

This study aims to determine the level of trust of the people of East Jakarta towards PDI Perjuangan after the 2024 Presidential General Election (Pilpres) and identify the tendency of sentiment formed, both positive, negative, and neutral. The background of this research is based on the complex dynamics of national politics, including the controversy over the candidacy of Gibran Rakabuming Raka as vice president, which caused pros and cons among the public and had the potential to influence public perception of PDI Perjuangan as the main supporting party. This study uses a quantitative approach with data collection techniques through a Likert scale questionnaire survey and essay questions distributed to respondents in the East Jakarta area. The data obtained is then processed through the text preprocessing stage, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and analyzed using the Naïve Bayes algorithm with the help of the RapidMiner application. The results of the study show that the majority of public sentiment tends to be positive, followed by neutral and negative sentiments. This shows that PDI Perjuangan still has a strong support base in East Jakarta despite the controversial national political dynamics. The findings of this study not only provide a comprehensive picture of local political perceptions, but can also be used as a strategic reference in the preparation of political communication patterns, strengthening the party's image, and planning the legislative campaign of PDI Perjuangan in the 2029 elections. ABSTRAK Penelitian ini bertujuan untuk mengetahui tingkat kepercayaan masyarakat Jakarta Timur terhadap PDI Perjuangan pasca Pemilihan Umum Presiden (Pilpres) 2024 serta mengidentifikasi kecenderungan sentimen yang terbentuk, baik positif, negatif, maupun netral. Latar belakang penelitian ini didasari oleh dinamika politik nasional yang cukup kompleks, termasuk kontroversi pencalonan Gibran Rakabuming Raka sebagai wakil presiden, yang menimbulkan pro dan kontra di kalangan publik serta berpotensi memengaruhi persepsi masyarakat terhadap PDI Perjuangan sebagai partai pengusung utama. Penelitian ini menggunakan pendekatan kuantitatif dengan teknik pengumpulan data melalui survei kuesioner skala Likert dan pertanyaan esai yang disebarkan kepada responden di wilayah Jakarta Timur. Data yang diperoleh kemudian diproses melalui tahapan preprocessing teks, ekstraksi fitur dengan metode Term Frequency–Inverse Document Frequency (TF-IDF), serta dianalisis menggunakan algoritma Naïve Bayes dengan bantuan aplikasi RapidMiner. Hasil penelitian memperlihatkan bahwa mayoritas sentimen masyarakat cenderung positif, diikuti dengan sentimen netral dan negatif. Hal ini menunjukkan bahwa PDI Perjuangan masih memiliki basis dukungan yang cukup kuat di Jakarta Timur meskipun terdapat dinamika politik nasional yang kontroversial. Temuan penelitian ini tidak hanya memberikan gambaran komprehensif mengenai persepsi politik lokal, tetapi juga dapat dijadikan sebagai acuan strategis dalam penyusunan pola komunikasi politik, penguatan citra partai, serta perencanaan kampanye legislatif PDI Perjuangan pada Pemilu 2029.

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.

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