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All Journal Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Studi Islam Lintas Negara (Journal of Cross-Border Islamic Studies) International Journal of Basic and Applied Science Jurnal Mandiri IT TRIMAS: Jurnal Inovasi dan Pengabdian Kepada Masyarakat Sahabat Sosial: Jurnal Pengabdian Masyarakat West Science Business and Management International Journal of Health Sciences Jurnal Sistem Informasi Galuh Jurnal Ilmiah Research Student Jurnal TNI Angkatan Udara
Hondor Saragih
Universitas Pertahanan Republik Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Development of android-based weapon monitoring application in the armory Sadewa, Rio Bintang; Saragih, Hondor; Lediwara, Nadiza
Jurnal Mandiri IT Vol. 13 No. 2 (2024): CALL FOR PAPER !!! Scope Computer Science
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i2.334

Abstract

Information and communication technologies (ICT) are of vital importance in the context of modern military operations, with Android-based solutions becoming increasingly relevant for the improvement of military weapons management systems. This study addresses the inefficiencies of current manual weapon management practices, which are susceptible to data errors and operational delays. The objective of this research is to enhance the accuracy, efficiency, and security of weapon management through the development of an Android-based monitoring application. The Rapid Application Development (RAD) methodology was employed to streamline the development process, which entailed the planning of requirements, the design of the user interface, the construction of the application, and its implementation. The application was constructed using Kotlin for the front end and Node.js with Express.js for the back end. It integrates real-time data updates, automated notifications, and efficient data management. The system’s design incorporates a comprehensive class diagram for database structure, user interface mockups, and various backend components, including models, migrations, controllers, routing, and middleware. The results demonstrate that the application markedly enhances weapon management by reducing manual errors, increasing operational efficiency, and providing enhanced security and accountability through accurate, real-time digital record-keeping.
Customer segmentation analysis using DBSCAN method in marketing research of retail company Saragih, Hondor; Manurung, Jonson
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.906.pp321-328

Abstract

Customer segmentation is an important aspect of an effective marketing strategy, yet many traditional methods are unable to capture the complexity of diverse customer behaviors. This research aims to apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method for customer segmentation in retail companies, focusing on identifying patterns of purchasing behavior and product preferences. Data was collected through a questionnaire distributed to 500 respondents, then analyzed using the DBSCAN method. The results showed that DBSCAN successfully identified several customer segments with unique characteristics, and provided an average Silhouette Score of 0.67 and Davies-Bouldin Index of 0.45, indicating good cluster quality. The findings imply that a density-based approach can improve a company's understanding of customer dynamics, and enable the development of more targeted and effective marketing strategies. This research makes an important contribution to the marketing literature, while opening up opportunities for further exploration of the use of machine learning methods in customer segmentation.
Leveraging the BERT Model for Enhanced Sentiment Analysis in Multicontextual Social Media Content Saragih, Hondor; Manurung, Jonson
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.766.pp82-89

Abstract

The increasing prevalence of social media platforms has led to a surge in user-generated content, necessitating advanced techniques for accurate sentiment analysis. This study investigates the application of the BERT model for sentiment analysis on multicontextual social media content, aiming to enhance sentiment classification accuracy by leveraging contextual embeddings. The research objectives include examining the effectiveness of BERT in capturing sentiments across diverse social media posts and evaluating its performance in comparison to traditional methods. The methodology involves tokenizing text content, converting tokens into contextual embeddings using BERT, and integrating multimedia features for a comprehensive sentiment analysis framework. The results from a numerical example demonstrate that the BERT model achieves a high probability of correctly classifying sentiments, with a notable improvement in accuracy and a low cross-entropy loss. These findings underscore the model's capability to understand contextual nuances and its potential to optimize social media monitoring and analysis processes. The study also highlights limitations such as the need for larger and more diverse datasets and the inclusion of multimedia content to enhance generalizability. Future research should explore hybrid models and address ethical considerations to ensure data privacy and mitigate biases. This work contributes to advancing theoretical frameworks and offers practical implications for businesses and marketers seeking to leverage sentiment analysis for informed decision-making and improved customer engagement strategies.
Performance Comparison of Naive Bayes and Support Vector Machine Algorithms in Spambot Classification in Emails Manurung, Jonson; Saragih, Hondor
International Journal of Basic and Applied Science Vol. 13 No. 3 (2024): Dec: Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v13i3.522

Abstract

In the ever-growing digital era, email spam is a serious threat that affects user productivity and information security. This study aims to analyze the comparative effectiveness of Naive Bayes and SVM algorithms with radial basis function (RBF) kernels in classifying spambots in emails. The methodology used includes collecting email datasets, applying both algorithms for classification, and evaluating performance using accuracy, precision, recall, and f1-score metrics. The results showed that SVM RBF performed better than Gaussian Naive Bayes, with significant improvements in all evaluation metrics. These findings provide important insights for the development of more accurate and efficient spam detection systems, and highlight the importance of selecting appropriate algorithms in the face of complex data classification challenges.
The development of a data lakehouse system for the integration and management of cyber threat intelligence data in XYZ unit Chan, Ricky; Dhaifullah, Rendi Hanif; Saragih, Hondor; Lediwara, Nadiza; Adha, Rochedi Idul
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 1 (2025): March: Intelligent Decision Support System
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i1.293

Abstract

Cybersecurity systems are evolving to deal with increasingly complex digital threats. One of the main challenges in this field is integrating and managing Cyber Threat Intelligence (CTI) efficiently. This research aims to design and implement Data Lakehouse as a solution to manage CTI data in XYZ Unit. The system was built using Apache Spark, MinIO, Dremio, Nessie, and Apache Iceberg with a containerization approach using Docker to ensure flexibility and ease of implementation. The implementation results show that the system successfully integrates various CTI data sources and improves efficiency in data storage, processing, and analysis. MinIO is used as the primary storage, Apache Spark processes data at scale, Dremio enables real-time data analysis, and Nessie manages data version control to maintain its integrity. Blackbox testing proves that the system can work optimally, with results showing improved data integration and efficiency in managing cyber threat information. Thus, the developed Data Lakehouse can be an effective solution in supporting threat detection and strategic decision-making in XYZ Unit.
Heart disease prediction using machine learning models Vernando, Deden; Manurung, Jonson; Saragih, Hondor
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.291

Abstract

Heart disease remains one of the leading causes of death globally, with mortality rates continuing to rise each year. Early detection is critical to reducing the burden of this disease; however, conventional diagnostic methods are often costly, time-consuming, and reliant on specialist expertise. This study aims to evaluate the effectiveness of four machine learning (ML) algorithms—Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—in predicting heart disease using clinical datasets. The methodology involves data preprocessing, feature selection using the Random Forest algorithm, and performance evaluation through metrics such as accuracy, precision, recall, F1-score, and support. Experimental results indicate that KNN achieved the highest accuracy after feature selection, while SVM demonstrated the highest recall despite lower precision. RF offered the most balanced performance, making it a reliable model for real-world medical applications. These findings highlight the importance of selecting appropriate algorithms and features to improve the performance of predictive models. The study suggests that future research should incorporate larger datasets, apply systematic hyperparameter tuning, and explore deep learning techniques to further enhance prediction accuracy.
Evaluation of free nutritious food program distribution in Tanah Sareal sub-district with ANOVA Simarmata, Dicky Daniel; Hidayati , Ajeng; Saragih, Hondor
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.402

Abstract

This study aims to evaluate The Makan Bergizi Gratis (MBG) or Free Nutritious Meal Program in Tanah Sareal Sub-district in the first quarter of 2025 by using one-way ANOVA test. The results of the analysis show that the F-statistic values for January, February, and March are 0.00044 which are all smaller than the F-critical value of 3.490, so the null hypothesis (H₀) stating that there is no significant difference in the distribution of food portions is accepted. The sample size of this study consisted of 5 schools, with data collected over a three-month period (January, February, and March). The calculated p-value for the F-statistic was 0.9996, indicating that the observed difference in meal distribution was not statistically significant. These findings suggest that the distribution of nutritious food portions in Kecamatan Tanah Sareal was relatively stable during the first quarter of 2025, with small fluctuations, but no significant variations. Although the program shows stability in distribution, further improvements in logistical aspects and coordination of distribution are recommended to ensure more equitable and timely distribution. Periodic evaluation of the program is needed to assess its efficiency and ensure that all areas in need are well served.
Design and construction of telegram bot-based data breach preprocessing application for cyber threat intelligence in institution x Gandhara, Seto; Satria, Tegar Pandu; Saragih, Hondor
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.413

Abstract

Data breaches pose a significant threat in today's digital landscape, especially for organizations handling sensitive information, such as government institutions. These incidents can result in serious consequences, including risks to national security, loss of public trust, and financial harm. Institution X, an Indonesian organization dedicated to cyber threat prevention, faces challenges due to the high volume of unstructured and "dirty" leaked data, often shared via hidden platforms like the dark web and Telegram. To address this issue, a Telegram bot-based application was designed and developed using the Rapid Application Development (RAD) method. The application automates data collection, cleaning, and preprocessing, with features such as keyword-based search and CSV file conversion. It was built using Python and deployed through the Replit cloud platform, utilizing the Telebot library to interact with Telegram APIs. Internal testing covered six usage scenarios, including keyword processing, multi-file handling, and unauthorized access control, with all scenarios producing successful outcomes. The application significantly improves the CSIRT team's effectiveness and efficiency in responding to cyber threats. The results confirm the system’s readiness for operational deployment and its potential contribution to enhancing cyber threat intelligence for Institution X and other government agencies.
Evaluasi Kinerja Algoritma Kriptografi dalam Pengamanan Video: Studi Perbandingan AES, DES dan Blowfish Zefanya Seto Gandhara; Tegar Pandu Satria; Hondor Saragih; Muhammad Naufal Nafian Abror
JURNAL ILMIAH RESEARCH STUDENT Vol. 2 No. 2 (2025): September
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jirs.v2i2.5908

Abstract

Keamanan data digital semakin menjadi perhatian utama di era modern, terutama dalam pengamanan file video yang berisi informasi penting. Salah satu metode yang umum digunakan untuk melindungi data video adalah kriptografi yang bertujuan untuk mengubah data asli menjadi bentuk terenkripsi agar tidak dapat diakses oleh pihak yang tidak berwenang. Dilakukan perbandingan antara tiga algoritma kriptografi populer, yaitu Advanced Encryption Standard (AES), Data Encryption Standard (DES), dan Blowfish, dalam proses enkripsi dan dekripsi video. Penelitian ini menganalisis performa ketiga algoritma berdasarkan waktu pemrosesan, konsumsi sumber daya, keamanan hasil enkripsi dan efisiensi penyimpanan. Hasil evaluasi menunjukkan bahwa AES memiliki tingkat keamanan tinggi tetapi membutuhkan waktu pemrosesan lebih lama dibandingkan dengan Blowfish. DES menunjukkan kelemahan dalam aspek keamanan karena ukuran kuncinya yang lebih kecil, sehingga lebih rentan terhadap serangan kriptografi. Blowfish menawarkan keseimbangan antara kecepatan pemrosesan dan keamanan, menjadikannya pilihan yang efisien untuk pengamanan video dengan performa yang lebih baik dibandingkan DES. Diharapkan dapat memberikan rekomendasi mengenai algoritma yang paling optimal untuk digunakan dalam pengamanan video digital baik dari segi efisiensi waktu, konsumsi sumber daya maupun tingkat keamanan.
Sistem Manajemen Laboratorium Komputer Berbasis Website Naufal Rizki, Muhammad; Christanto, Febrian Wahyu; Saufik Suasana, Iman; Ahmad Firdaus, Eryan; Saragih, Hondor; Maulani, Shanti
Jurnal Sistem Informasi Galuh Vol 3 No 2 (2025): Journal of Galuh Information Systems
Publisher : Fakultas Teknik Jurusan Sistem Informasi Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jsig.v3i2.4959

Abstract

Computer laboratory is an important aspect of supporting the learning process at the Politeknik Kesehatan Kemenkes Semarang. However, manual laboratory management is currently inefficient and prone to errors that can reduce the effectiveness of laboratory use by up to 30% and have a direct impact on the quality of learning. Primary data collected includes laboratory specifications such as the availability of 165 computer units, adequate printers for printing needs, internet networks, and additional tools to support practicums. The laboratory is also supported by technical and admin staff who are responsible for the operation and maintenance of equipment and are equipped with a data management system to help smooth activities. To overcome these management problems, this study implemented a website-based laboratory management system using the Prototype method which is designed to simplify the administration process with centralized management, increase management efficiency, and reduce administrative errors. The system implementation test using the Black Box method obtained 100% functional system results running well. The results of the User Acceptance Testing (UAT) analysis obtained a value of 90.6% which indicates that the developed system can overcome existing problems. Further development is expected to improve the quality of laboratory services and expand the use of information technology in education management at the Politeknik Kesehatan Kemenkes Semarang.
Co-Authors Adha, Rochedi Idul Ajeng Hidayati Anindito Anisa Purnamasari Anjani, Prasashti Alya Arif Rachman Asmi, Syamsinar Aulia Khamas Heikmakhtiar Chan, Ricky Cut Maisyarah Karyati Dewi Parwati Dhaifullah, Rendi Hanif Erika Erika Eryan Ahmad Firdaus Febrian Wahyu Christanto Fitriana Fitriana Gandhara, Seto Harfika, Meiana Heikhmakhtiar, Aulia Khamas Hendra Manurung, Hendra Hidayati , Ajeng Hidayati, Ajeng Hutabarat, Erny Iman Saufik Suasana Lediwara, Nadiza Mainassy, Meilisa Carlen Makualaina, Fenska Narly Manurung, Jonson Mardamsyah, Adam Marunung, Hendra Meillisa Carlen Mainassy Muhadi Sugiono Muhamad Risal Tawil Muhammad Naufal Nafian Abror Mulat, Trimaya Cahya Naufal Rizki, Muhammad Nurul Aisyiyah Puspitarini P, Nur Triningtias Pannyiwi, Rahmat Prabukusumo, Muhammad Azhar Putri, Nanda Selviana Putri, Noviyanti Rahardjo Rahmat, Rezqiqah Aulia Rezasyah, Teuku Ricky Chan Sadewa, Rio Bintang Saputra, M. Khalid Fredy Satria, Tegar Pandu Sembada Denrineksa Bimorogo Shanti Maulani Simarmata, Dicky Daniel Sipahutar, Pongki Sofiana Sofiana, Sofiana Tegar Pandu Satria Treasa, Adisty Dwi Vernando, Deden Zefanya Seto Gandhara