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All Journal Teknika CESS (Journal of Computer Engineering, System and Science) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan INTECOMS: Journal of Information Technology and Computer Science JURNAL PENDIDIKAN TAMBUSAI Jurnal Sains dan Teknologi Journal of Applied Engineering and Technological Science (JAETS) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Pengabdian Nasional (JPN) Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) AJAD : Jurnal Pengabdian kepada Masyarakat Jurnal Pendidikan Sains dan Komputer International Journal Software Engineering and Computer Science (IJSECS) International Journal Education and Computer Studies Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Nasional Teknologi Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business CKI On Spot Sistem Komputer & Teknologi Intelegensi Artifisial (SIKOMTIA) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia Journal Innovations Computer Science International Journal of Applied Mathematics and Computing. International Journal of Computer Technology and Science International Journal of Information Engineering and Science International Journal of Mechanical, Electrical and Civil Engineering Journal of Engineering, Electrical and Informatics
Sarimole, Frencis Matheos
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
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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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (653.795 KB) | DOI: 10.61306/jnastek.v1i1.3

Abstract

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

Abstract

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

Abstract

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.
Application of Decision Tree Method for Sales Prediction at PT. Cipta Naga Semesta (Mayora Group) North Jakarta for 2023 Richardviki Beay; Frencis Matheos Sarimole
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.2999

Abstract

The purpose of this study is to forecast sales of PT. Cipta Naga Semesta, one of the companies owned by Mayora Group headquartered in North Jakarta using the Decision Tree method during 2023. Decision Tree was chosen because this model identifies key attributes that greatly affect sales in the data and has the ability to predict outcomes by recognizing patterns in historical data. The database used in this analysis includes monthly records of sales, promotions, prices, and other economic characteristics. The findings of the study indicate that the Decision Tree method is very effective in providing accurate sales predictions with a low margin of error. The forecast provides valuable perspectives for company management, which can help them design tighter sales strategies and make better inventory decisions, thereby maximizing operational efficiency and profitability. In addition, the exploration of sales prediction models is one of the future works proposed in this study, which recommends practitioners to explore alternative methods to improve forecast accuracy and robustness.
Analysis of Scooter Spare Parts Sales at Harapan Indah Scooter Using the K-Means Algorithm Frencis Matheos Sarimole; Tracy Olivera Lingga
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3026

Abstract

: K-means clustering algorithm has been used in this study to analyze the sales performance of scooter spare parts at Harapan Indah Scooter. By using the K-means method, researchers can classify products into 3 categories according to their sales volume. The purpose of this analysis is to identify patterns in sales data and compare the characteristics of each product group. Researchers can see the output from the previous step shows three clusters: Low, Medium, and High Sales. Associating products with these categories Empowers improved tracking of sales movements and fluctuation trends in product options. The findings of this study can be useful in the field of inventory management and to develop marketing strategies to increase product sales. Companies can find out which products fall into which categories and therefore can make better decisions on how to manage stock and promotional efforts. These findings are the first step to maintain and improve sales performance and optimize Harapan Indah Scooter business
Vehicle License Plate Object Detection for Vehicle Registration Using Fuzzy Logic Fiky Alannuari; Frencis Matheos Sarimole; Dadang Iskandar Mulyana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3055

Abstract

Object detection of vehicle license plates plays a role in the efficiency of vehicle data collection systems. There are many factors that make the accuracy and speed of detection on vehicle license plates less than optimal, causing errors in the detection process. The factors that affect the accuracy of object detection of vehicle license plates include clarity, lighting, shadows, color, font type, weather, and others. Based on the advantages of the Fuzzy Logic approach in handling various vague factors and uncertain data, it is hoped that this method can help the detection process to be more accurate and faster. This research aims to develop a method for detecting vehicle license plate objects using the Fuzzy Logic approach so that it can be applied in diverse environments to produce data with consistent accuracy. This research involves the development of software integrated with computers and cameras for vehicle license plate recognition, and also takes some data sources and code from libraries already available in the programming language used. The results of the tests conducted, detection using this Fuzzy Logic approach has an accuracy rate of up to 93.33% and the accuracy of reading the text stored in the database reaches 63.66%.
Co-Authors Abdillah, Junindo Abdulloh Achmad Syaeful Aditya Zakaria Hidayat Ahas Eko Septianto Ahmad Baidowi Ahmad Ramdani Akbar, Firman Aulia Akbar, Yuma Alannuari, Fiky Alwi Renaldhy Amelia, Ika Andrian Nur Ihsan Anita Rosiana Apriyanto, Kevin Jonathan Ari Ramadhan Arinal, Veri Aryanti, Putri Gea Awang Hariman, Aloisius Azis, Abd Barronzoeputra, Gaoeng Qalbun Beay, Richardviki Bere Tae, Chelvyn Erikson Betty Yel, Mesra Betty Yel, Mesra Bili, Yudisman Ferdian Bimantoro, Dava Sevtiandra Brian - Pangestu Candra Milad Ridha Eislam Dadang Iskandar Mulyana` Dava Septya Arroufu Dedi Gunawan Diadi, Randitia Ridad Fadhil Khanifan Achmad Fahmi Chairulloh Fahmi, Hakon Feni Putriani Fentri Boy Pasaribu Fiktor Kurnia Tafonao Fiky Alannuari Ginting, Yafet Nikolas Guntara, Arya Hakim, Lukamanul Haryati Heri Rizky Firdaus Ikhwanul Kurnia Rahman Iqhlima, Salabila Listania Karim, Lutfi Kiki Setiawan Kudrat, Kudrat Kurnia, Mega Tri Lingga, Tracy Olivera Lutfi Karim Marjuki Marliani, Tiara Meilisa Miftahul Huda Muhammad Ilham Fadillah Novianto, Firza Nufaisa Almazar Nugraha, Pramudya Nur Arif Khairudin Nur Aswan Multazam Nurmayanti, Laily Nurmaylina, Vivi Oky Tria Saputra7 Praja Raymond , Samuel Pramudya Nugraha Purwandono, Eddy Purwanto, Helmi Purwasih, Intan Rahmah, Shafira Azzahra Nurul Raihan, Farid Raihanah, Syifa Randitia Ridad Diadi Rasiban Richardviki Beay Rindy Julianda Rizky Adawiyah Roid Adip Akmal Romadan, Diva Putra Saepudin Satria Wira Yudha Septian, Wahyu Septiansyah, Muhamad Aqil Septianto, Ahas Eko Setiawan, Kiki Siahaan, Bangun Sidiq, Bagas Maulana Siti Nur Hidayati SOPAN ADRIANTO Sugeng Sugeng Sugiono Sugiono Sugiyono Sugiyono Sugiyono Surapati, Untung Sutisna Sutisna Sutisna Syaeful, Achmad Tanjung, Cici Yolanda Tasya Aisyah Amini Tracy Olivera Lingga Tundo, Tundo Untung Wahyudi Wibawa, Andri Putra Wida Lestari Widianto Putro, Faris Wijayanto, Willy Yakob, Galih Satria Yuliantoro, Dita Tri