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Penerapan Algoritma Naive Bayes Dalam Mengetahui Pola Pengguna Keluarga Berencana Pada Tempat Praktek Mandiri Bidan (TPMB) Lilik Faiqoh Sugiono, Sugiono; Marliani, Tiara; Sarimole, Frencis Matheos; Tundo, Tundo
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 2 (2024): July 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i2.61406

Seiring kemajuan teknologi dan informasi yang semakin berkembang, dan menjadikan masyarakat paham akan pentingnya segala informasi, termasuk tentang Keluarga Berencana atau KB. Berdasarkan observasi dan wawancara dengan bidan Lilik Faiqoh bahwa yang menjadi masalah kurangnya penyuluhan terhadap masyarakat, supaya masyarakat paham apa saja alat kontrasepsi yang ada di TPMB Lilik Faiqoh Jakarta Timur. Untuk mengatasi masalah tersebut, maka Algoritma Naive Bayes merupakan salah satu algoritma machine learning yang dapat digunakan untuk mengklasifikasikan data. Tujuan dari penelitian ini adalah untuk menentukan penerapan Algoritma Naive Bayes dalam mengetahui pola pengguna Keluarga Berencana pada TPMB Lilik Faiqoh dengan mencakup identifikasi jenis kontrasepsi (KB) yang paling sering digunakan. Kemudian untuk data Keluarga Berencana ini akan dilakukan dengan proses penerapan metode CRISP-DM. Penelitian ini diharapkan dapat meningkatkan layanan TPMB Lilik Faiqoh dan memberikan manfaat yang lebih besar bagi masyarakat setempat dalam hal penyediaan layanan kesehatan.

Sistem Data Mining Penentuan Prioritas terhadap Penerima Bantuan Bencana Banjir dengan Metode Naive Bayes dan Klusterisasi K-Means (Studi Kasus: Wilayah Cengkareng 2025) Sarimole, Frencis Matheos; Nurmayanti, Laily
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jpni.v6i3.1609

This research develops a ranking system for flood aid recipients in Jakarta, focusing on Cengkareng, by utilizing K-Means and Naïve Bayes algorithms. Data were obtained from Satu Data Jakarta (2025), comprising 158 records with attributes including region, sub-district, village, average water level, affected RWs, families, individuals, and flood events. The analytical workflow encompasses data cleaning and normalization, risk level clustering using K-Means (three categories: high, medium, low), and predictive classification with Naïve Bayes. Model evaluation at training-testing splits of 70:30, 80:20, and 90:10 reveals that the combined K-Means and Naïve Bayes approach achieves the highest accuracy of 98.18%, significantly outperforming conventional Naïve Bayes which reached only 43.47%. This improvement demonstrates the effectiveness of combining both algorithms for complex data classification. The developed system expedites the prioritization process, facilitates local teams in verifying recipient lists, and enhances the precision of aid distribution and evacuation. Field simulations with community members were conducted to assess the system’s practical implementation and ensure direct access to flood risk information. Future development will focus on integrating external variables such as real-time rainfall data and expanding field testing to other regions.

Pengenalan dan Edukasi Motif Batik Untuk Sekolah Dasar Negeri Pondok Bahar 06 Menggunakan Metode Convolution Neural Network (CNN) Bili, Yudisman Ferdian; Sarimole, Frencis Matheos
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1573

Batik is a cultural heritage of Indonesia rich in philosophical values and diverse motifs. However, a deep understanding of its meaning remains limited among elementary school students. This study aims to develop an educational application based on Convolutional Neural Networks (CNN) to introduce and classify batik motifs such as Kawung, Parang, Megamendung, and Truntum in an interactive manner. The batik image dataset was obtained from various online sources and underwent preprocessing, augmentation, training, and testing stages using the CNN model. The developed application was then tested with students from SD Negeri Pondok Bahar 06 using a pre-test and post-test method. Test results indicated that the CNN model was able to recognize batik motifs with adequate accuracy. Moreover, there was a significant improvement in students’ understanding of the philosophical meanings behind the motifs after using the application. Thus, integrating CNN technology into cultural learning proves to be effective in enhancing student interest and comprehension. This research is expected to serve as a reference for developing AI-based educational media to preserve local culture in the digital era.

Security Analysis of Midtrans Payment Gateway API against DDoS Attack and Rate Limiting Technique Using Node.js Widianto Putro, Faris; Matheos Sarimole, Frencis
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.v4i2.308

The development of digital transaction services has led to the widespread use of APIs in payment systems, including payment gateway services such as Midtrans. However, the open access to APIs also increases the risk of cyber attacks, one of which is Distributed Denial of Service (DDoS) which can destabilize the system and reduce user confidence. This research aims to analyze the potential DDoS threats to the Midtrans API and explore the application of rate limiting techniques using Node.js as one of the mitigation measures. The methodology used is a waterfall approach, which includes requirements analysis, system design, implementation, testing, and evaluation. The test design is done through simulating DDoS attacks on API endpoints, both before and after the application of rate limiting, by measuring parameters such as the number of requests, response time, and request success rate. It is hoped that this research can provide a clear picture of the importance of API protection in digital payment systems, and produce a technical approach that can be used as a reference in developing a secure and reliable system. This research is also expected to make practical and theoretical contributions in the field of API security and digital service traffic management.

Decision Tree-Based Predictive Model Development for RumahNet Customer Satisfaction Analysis in West Jakarta Yuliantoro, Dita Tri; Sarimole, Frencis Matheos
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.v4i2.310

The rapid growth of information technology has amplified the demand for fast and reliable internet services, particularly in urban centers such as West Jakarta. This study aims to design a predictive model of customer satisfaction for RumahNet’s Fiber to the Home (FTTH) services by applying the Decision Tree (C4.5) algorithm. A survey of 250 active subscribers was conducted using a Likert-scale questionnaire distributed through Google Forms, capturing perceptions of internet speed, connection stability, pricing, and technical support. The dataset was processed and analyzed using RapidMiner Studio within the Knowledge Discovery in Databases (KDD) framework. Results show that the model achieved an accuracy of 85.33%, precision of 91.93%, recall of 90.47%, and an F1-score of 91.18%. The decision tree revealed that internet speed and connection stability were the most critical determinants of satisfaction, followed by pricing and responsiveness of customer service. These findings suggest that prioritizing technical reliability while maintaining affordability and responsive support is essential for strengthening loyalty and reducing churn. The research demonstrates that Decision Tree modeling not only provides high predictive accuracy but also offers clear interpretability, making it a valuable tool for data-driven decision-making in the ISP sector.

IMPELEMENTASI METODE HARRIS BENEDICT PADA SISTEM INFORMASI PENGHITUNGAN GIZI REMAJA BERBASIS WEBSITE Guntara, Arya; Frencis Matheos Sarimole
Jurnal Nasional Teknologi Komputer Vol 1 No 1 (2021): Volume 1 Nomor 1 Oktober 2021
Publisher : CV. Hawari

The teenage period is the peak stage of the growth period of a person's weight and height. This growth process requires sufficient nutritional support. Teenagers who have adequate nutritional intake will have a healthy body condition, rarely experience pain so that activities at home and at school will run smoothly. Lack of adolescent knowledge to know the nutritional needs in the body, can result in inhibition of the growth process. There needs to be a nutrition education for teenagers. Adolescents need knowledge about the calculation of nutritional adequacy. Utilization of information technology is widely used as a tool of convenience and aids in daily activities. With the above problems, an information system is needed in providing knowledge to adolescents about nutritional needs in their bodies. Information systems are built with PHP and Mysql and counting nutritional needs using harris benedict methods. The Harris Benedict method is a way to count the number of calories a person needs. The programming languages used for the creation of this application are PHP and MySQL for databases. Based on the results of the study, this application can help parents to find out the nutritional needs of their children who are teenagers online.

Security Analysis of Midtrans Payment Gateway API against DDoS Attack and Rate Limiting Technique Using Node.js Widianto Putro, Faris; Matheos Sarimole, Frencis
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.v4i2.308

The development of digital transaction services has led to the widespread use of APIs in payment systems, including payment gateway services such as Midtrans. However, the open access to APIs also increases the risk of cyber attacks, one of which is Distributed Denial of Service (DDoS) which can destabilize the system and reduce user confidence. This research aims to analyze the potential DDoS threats to the Midtrans API and explore the application of rate limiting techniques using Node.js as one of the mitigation measures. The methodology used is a waterfall approach, which includes requirements analysis, system design, implementation, testing, and evaluation. The test design is done through simulating DDoS attacks on API endpoints, both before and after the application of rate limiting, by measuring parameters such as the number of requests, response time, and request success rate. It is hoped that this research can provide a clear picture of the importance of API protection in digital payment systems, and produce a technical approach that can be used as a reference in developing a secure and reliable system. This research is also expected to make practical and theoretical contributions in the field of API security and digital service traffic management.

Decision Tree-Based Predictive Model Development for RumahNet Customer Satisfaction Analysis in West Jakarta Yuliantoro, Dita Tri; Sarimole, Frencis Matheos
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.v4i2.310

The rapid growth of information technology has amplified the demand for fast and reliable internet services, particularly in urban centers such as West Jakarta. This study aims to design a predictive model of customer satisfaction for RumahNet’s Fiber to the Home (FTTH) services by applying the Decision Tree (C4.5) algorithm. A survey of 250 active subscribers was conducted using a Likert-scale questionnaire distributed through Google Forms, capturing perceptions of internet speed, connection stability, pricing, and technical support. The dataset was processed and analyzed using RapidMiner Studio within the Knowledge Discovery in Databases (KDD) framework. Results show that the model achieved an accuracy of 85.33%, precision of 91.93%, recall of 90.47%, and an F1-score of 91.18%. The decision tree revealed that internet speed and connection stability were the most critical determinants of satisfaction, followed by pricing and responsiveness of customer service. These findings suggest that prioritizing technical reliability while maintaining affordability and responsive support is essential for strengthening loyalty and reducing churn. The research demonstrates that Decision Tree modeling not only provides high predictive accuracy but also offers clear interpretability, making it a valuable tool for data-driven decision-making in the ISP sector.

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