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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Zero : Jurnal Sains, Matematika, dan Terapan ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JOURNAL OF SCIENCE AND SOCIAL RESEARCH JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Ensiklopedia Education Review Jurnal Mantik Journal of Applied Engineering and Technological Science (JAETS) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Brahmana : Jurnal Penerapan Kecerdasan Buatan Instal : Jurnal Komputer Jurnal Sains Teknologi dan Sistem Informasi Jurnal Info Sains : Informatika dan Sains Journal of Research in Social Science and Humanities International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Minfo Polgan (JMP) pendidikan, science, teknologi, dan ekonomi Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Nasional Teknologi Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Artificial Intelligence and Digital Business Indonesian Journal of Education And Computer Science Journal of Technology and Computer (JOTECHCOM) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) International Journal of Industrial Innovation and Mechanical Engineering Jurnal Bisantara Informatika Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Jurnal Pengabdian Masyarakat Variasi Jurnal Publikasi Ilmu Komputer dan Multimedia
Muhammad Syahputra Novelan
Universitas Pembangunan Pancabudi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences Other

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Predictive Analysis of Flood Risk Factors Based on a Machine Learning Approach: Comparative Study of SVM and XGBoost Algorithms Darma, Surya; Al Fayed, Ahmad Jihad; P Pardede, Surya Maruli; Aqsha, Muhammad Hizbul; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Flood events in Indonesia continue to increase in frequency and impact due to high rainfall variability, land-use change, and complex hydrological conditions. Accurate predictive modeling is therefore essential to support flood risk assessment and mitigation planning. This study evaluates the predictive performance of two supervised machine learning algorithms, Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), for flood risk classification. The analysis is conducted using a publicly available dataset comprising 500 samples that represent multiple environmental and spatial factors related to flood occurrence. Data preprocessing includes cleaning, normalization, and feature consistency adjustment prior to model implementation. Both algorithms are trained and tested using the same dataset configuration to ensure objective comparison. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that XGBoost achieves higher accuracy and precision, demonstrating stronger capability in reducing false-positive predictions, while SVM shows relatively higher recall, reflecting better sensitivity in identifying flood-prone cases. Overall, XGBoost provides more reliable predictive performance for flood risk modeling on the dataset used. The findings confirm the effectiveness of machine learning-based approaches for flood risk prediction and highlight the importance of algorithm selection in disaster risk analysis.
Penerapan Metode Rapid Application Development (RAD) Pada Sistem Absensi Karyawan Berbasis GPS Di CV. Bambang Tetuko Hermanto; Muhammad Syahputra Novelan; Afif Badawi
Jurnal Nasional Teknologi Komputer Vol 6 No 2 (2026): April 2026
Publisher : CV. Hawari

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Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan terhadap efisiensi proses bisnis dalam berbagai sektor industri. Salah satu penerapan teknologi yang berperan penting adalah sistem absensi dan penggajian berbasis lokasi (GPS) yang dapat meningkatkan akurasi data kehadiran karyawan. Penelitian ini bertujuan untuk merancang dan membangun sistem absensi dan penggajian karyawan berbasis GPS pada CV. Bambang Tetuko dengan menerapkan metode Rapid Application Development (RAD). Metode RAD dipilih karena mampu menghasilkan aplikasi dalam waktu pengembangan yang lebih singkat melalui pendekatan iteratif, kolaboratif, dan prototyping yang intensif antara pengembang dan pengguna. Tahapan penelitian dimulai dari analisis kebutuhan, perancangan sistem, pengembangan prototipe, hingga pengujian sistem berdasarkan feedback pengguna secara langsung. Sistem ini mengintegrasikan fitur pencatatan kehadiran melalui lokasi GPS secara real-time, validasi posisi karyawan sesuai titik lokasi kerja, serta perhitungan penggajian otomatis berdasarkan jumlah kehadiran dan jam kerja. Pengujian dilakukan menggunakan metode black box untuk memastikan fungsionalitas sistem berjalan sesuai kebutuhan operasional perusahaan. Hasil implementasi menunjukkan bahwa sistem absensi dan penggajian berbasis GPS dapat meningkatkan transparansi, akurasi data kehadiran, serta meminimalkan potensi kecurangan absensi manual. Selain itu, penerapan metode RAD mampu mempercepat proses pengembangan dan memastikan fungsionalitas sistem sesuai harapan pengguna. Dengan adanya sistem ini, proses administrasi kehadiran dan penggajian di CV. Bambang Tetuko menjadi lebih efektif, efisien, dan terstruktur secara digital.
Fault Detection And Recovery System On 20 Kv Distribution Network Using Real-Time Analysis With Support Vector Machine Algorithm Afrizal, Henri; Zulham Sitorus; Muhammad Syahputra Novelan
Bahasa Indonesia Vol 18 No 02 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i02.494

Abstract

The 20 kV distribution network is crucial for ensuring the uninterrupted supply of power to users in the PT. PLN UP2D North Sumatra Region. Disruptions in this network, including short circuits, overloads, and transient disturbances, can diminish system reliability and prolong outage durations if not promptly identified and rectified. This study seeks to develop a disturbance detection and recovery system for a 20 kV distribution network utilizing real-time analysis through the Support Vector Machine (SVM) algorithm. The system is designed by utilizing real-time electrical parameter data, including current, voltage, and network operational conditions, sourced from monitoring devices. The data undergoes preprocessing, feature extraction, and classification stages utilizing SVM to differentiate between normal and fault circumstances. The classification outcomes serve as the foundation for decision-making in isolating the fault zone and restoring supply to the unaffected segments of the network. The system's performance is assessed according to detection accuracy, response speed, and its capacity to facilitate the disturbance recovery process both automatically and semi-automatically. This research aims to enhance the dependability of the 20 kV distribution network, expedite fault resolution, and facilitate the advancement of a more intelligent, efficient, and responsive electrical distribution system.
Application of the K-Nearest Neighbor Algorithm in the Data Mining Process to Predict Drug Sales at Pratama Haji Medan-Pancing Clinic Indra Nasution; Rezkinah Rambe; Khairil Putra; Muhammad Dafa; Muhammad Syahputra Novelan
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 4 No. 2 (2025): Mei: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v4i2.4081

Abstract

Pratama Haji Medan-Pancing Clinic is a healthcare facility that routinely sells medications to patients. However, the current manual drug inventory management process poses risks such as delayed procurement and overstocking. To address this issue, this study aims to implement a data mining approach using the K-Nearest Neighbor (KNN) algorithm to predict drug sales at Klinik Pratama Haji Medan-Pancing. A quantitative research method was employed, utilizing historical drug sales data from the past two to three years. The data underwent a thorough process of assessment, cleaning, and transformation before being processed using the K-Neighbor Classifier from the scikit-learn library. The results demonstrated that the KNN method achieved a prediction accuracy rate of 88.9%, indicating its effectiveness in forecasting drug sales. By implementing this predictive system, Klinik Pratama Haji Medan-Pancing can improve the efficiency of inventory management, reduce the risk of stock shortages or surpluses, and support faster, data-driven decision-making. In conclusion, the KNN algorithm proves to be a feasible predictive solution for drug sales systems in clinics and holds potential for further development in intelligent and integrated pharmacy management.
The Influence of Shopee Free Shipping Vouchers on User Purchase Decisions: A Case Study of Rengas Pulau Subdistrict Muhammad Wahyudi; Muhammad Syahputra Novelan
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.339

Abstract

The rapid development of e-commerce has made promotional strategies increasingly vital, with free shipping vouchers standing out as one of the most effective tools to enhance consumer engagement. Shopee, as a leading player in the digital marketplace, has effectively utilized this strategy to boost user transactions by consistently offering free shipping vouchers. This study aims to analyze the influence of such vouchers on consumer purchasing decisions, focusing on users in Kelurahan Rengas Pulau. A quantitative research approach was adopted using a descriptive associative method. Data collection was carried out through the distribution of questionnaires to 200 respondents, selected using purposive sampling techniques. The data were then analyzed using regression analysis to determine the relationship between the use of free shipping vouchers and consumer purchasing behavior. The results of the regression analysis showed a strong and statistically significant relationship, with a regression coefficient of 0.620 and a p-value of 0.000. This suggests that the presence of free shipping vouchers substantially increases the likelihood of consumers making purchases. These findings confirm that free shipping promotions are a key factor in shaping consumer behavior, especially in suburban areas like Kelurahan Rengas Pulau. In conclusion, free shipping vouchers not only attract potential customers but also contribute to an increase in sales transactions. Businesses and e-commerce platforms should consider incorporating similar promotional tactics to maintain competitiveness and enhance customer satisfaction. This study affirms the positive and significant effect of Shopee’s free shipping incentives on user purchasing decisions, highlighting their strategic importance in today’s competitive digital marketplace
Application of Apriori Algorithm in Data Mining to Find Consumer Purchasing Patterns in Supermarkets Purwa Hasan Putra; Desilia Selvida; Muhammad Syahputra Novelan
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.392

Abstract

The development of information technology has encouraged the use of transaction data in the retail world to gain deeper business insights. One method used in data mining is the Apriori algorithm, which is able to identify consumer purchasing patterns through association analysis between products. This study aims to apply the Apriori algorithm in finding product combination patterns that are often purchased together by consumers in supermarkets. The data used are sales transactions that have gone through a preprocessing process, including product category classification and transformation into a basket format. The results of the analysis show that products such as biscuits, detergents, and household appliances have the highest support values ​​individually, while product combinations such as (milk, drinks, soap & shampoo, cosmetics) also appear consistently in transactions. The application of the Apriori algorithm with a certain minimum support threshold is able to produce frequent itemsets that represent consumer shopping habits. These findings can be used to develop promotional strategies, product arrangement, and category-based recommendation systems. Thus, this study proves that the Apriori algorithm can be used effectively in the context of data mining to support business decision making in the retail sector, especially supermarkets.
Perancangan Aplikasi Stok Barang Dengan Metode Waterfall Berbasis Web Patrialman Haryadi; Chairul Rizal; Muhammad Syahputra Novelan
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14743

Abstract

Teknologi dan inovasi terus berkembang tanpa henti. Perancangan aplikasi stok barang ini bertujuan untuk meningkatkan efisiensi pemantauan dan mempercepat proses dalam pengelolaan stok barang pada Toko Dunia Fotocopy, toko ini masi menggunakan cara yang manual yang ditulis pada sebuah buku,yang menyebabkan masalah dalam pemantauan, pemantuan yang sangat lambat dalam proses pengelolaan stok barang sehingga menjadi tidak efisien. Untuk Mengatasi masalah ini maka dibuat sebuah aplikasi stok barang berbasis web menggunakan metode Waterfall. Proses ini ditandai oleh tahapan pengerjaan yang dilakukan secara berurutan. Artinya, kita tidak dapat melanjutkan ke tahapan berikutnya jika tahapan pertama belum selesai. Setiap tahap dalam proses ini saling berhubungan, di mana hasil dari tahap pertama akan menjadi masukan untuk tahap selanjutnya. Oleh karena itu, sangat penting untuk menyelesaikan setiap tahapan dengan baik agar keseluruhan proses dapat berjalan dengan lancar. Diharapakan aplikasi yang dibuat berhasil mengatasi permasalahan yang terjadi dalam pengelolaan stok barang.
Analysis Of The Decision Tree (C4.5) And Random Forest Algorithms To Determine Student Eligibility For Final Project Assignments Based On Academic Requirements Eisyaniah Desvazulinda; Muhammad Iqbal; Muhammad Syahputra Novelan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9947

Abstract

Determining student eligibility for undertaking a final project is an important process in higher education, which is often still conducted manually and subjectively. This study aims to develop a classification model based on machine learning to determine the eligibility of students at Batam University using Decision Tree (C4.5) and Random Forest algorithms. The data used includes Grade Point Average (GPA), total completed credits (SKS), prerequisite course grades, and academic records. This research employs a quantitative approach with stages including data collection, data preprocessing, model development, and performance evaluation using accuracy, precision, and recall metrics. The results show that both algorithms are capable of classifying student eligibility effectively. The Decision Tree (C4.5) algorithm produces an interpretable model in the form of decision rules, while Random Forest demonstrates superior performance in terms of accuracy and prediction stability. The comparison indicates that Random Forest is more effective in handling complex data, whereas C4.5 provides better model transparency. In conclusion, the implementation of Decision Tree (C4.5) and Random Forest algorithms can serve as an effective solution to support objective and data-driven academic decision-making. The resulting model has the potential to be developed into a decision support system to improve the efficiency and quality of determining student eligibility for final project enrollment.
Evaluation of Information Technology Governance Related to Digital Transformation on The Alignment of Company Business Strategy Using Cobit 2019 And Fuzzy C-Means ( Case Study: Perumda Tirtanadi, North Sumatra Province) Rahmat Rezki; Rian Farta Wijaya; Muhammad Syahputra Novelan
Journal of Research in Social Science and Humanities Vol 5, No 4 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i4.616

Abstract

Digital transformation has become a strategic necessity for public sector companies to improve efficiency and service quality. Perumda Tirtanadi of North Sumatra Province has implemented a meter reading system as part of its digital transformation initiative to improve the accuracy of water consumption data. The success of this system is strongly influenced by the effectiveness of Information Technology (IT) governance and its alignment with the company's business strategy. This research aims to evaluate IT governance of the meter reading system using the COBIT 2019 framework and the Fuzzy C-Means method. The evaluation was conducted through design factor analysis, assessment of process capability levels, capability gap analysis, and clustering of user satisfaction levels. The results indicate that the relevant objectives include EDM01, EDM03, APO12, APO13, DSS05, and MEA03, with capability levels at level 2 and level 3. The capability gap analysis reveals gaps of two to three levels, particularly in strategic domains. The Fuzzy C-Means clustering results show that most users belong to the “satisfied” cluster, indicating that the system adequately supports operational needs despite existing capability gaps. This study provides recommendations and a governance improvement roadmap to support digital transformation and business strategy alignment
Empowering Teachers and Students through Machine Learning Training and Education on Personal Data Protection and Intellectual Property Rights in the Era of Artificial Intelligence MUHAMMAD SYAHPUTRA NOVELAN; ZULHAM SITORUS; AYUMI KARTIKA SARI
Jurnal Pengabdian Masyarakat Variasi Vol. 3 No. 2 (2026)
Publisher : LPPM STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/0dd5nt87

Abstract

The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), has significantly transformed the educational sector by enabling the use of intelligent applications to support teaching and learning activities. However, the widespread adoption of AI also introduces new challenges related to personal data protection and intellectual property rights. This community service program aimed to improve the knowledge and competencies of teachers and students regarding the fundamentals of Machine Learning while enhancing awareness of ethical and lawful AI utilization. The program was conducted in the school's computer laboratory and involved teachers and students as participants. The implementation employed the Participatory Learning and Action (PLA) approach, including needs assessment, educational seminars, hands-on training, AI application demonstrations, practical exercises, interactive discussions, and evaluation through pre-tests and post-tests. The training materials covered Machine Learning concepts, AI applications in education, personal data protection, and intellectual property rights. The results demonstrated a significant improvement in participants' understanding of Machine Learning applications in education, the importance of protecting personal data, and respecting copyright and academic integrity. Participants also showed high enthusiasm throughout the training, indicating that the program successfully enhanced both digital literacy and legal awareness. This community service initiative is expected to serve as a sustainable educational model for promoting innovative, secure, ethical, and legally compliant AI adoption within educational institutions.
Co-Authors ', Khairunnisa , Arpan Abdul Muin Nasution Ade Guna Suteja Ade Iskandar Adi Putra Adli Abdillah Nababan Adli Abdillah Nababan Afif Badawi Afif Yasri Afrizal, Henri Ahmad Deni Setiawan Al Fayed, Ahmad Jihad Albin Setiawan Alfarizi, Nauval Amin, Muhammad Aminuddin Indra Permana Andri Gunawan Andri Saputra Andysah Putera Utama Siahaan Annisa Khumairoh Antoni, Robin Anugrah, Maisya Fitri Aprilia, Katharina Tyas Aqsha, Muhammad Hizbul Aradi Sebayang Ardiansyah Ardiansyah Aria Dhanu Tirta Arpan Aulia Ukhti Fathia Aurelia, Cindy Aisha Ayumi Kartika Sari Ayumi Kartika Sari Bayu Angga Wijaya Chairul Rizal Cindy Aisha Aurelia Dani Mestika Daniel Panjaitan Darmeli Nasution Datin, Maha Valne Dedy Rahman Harahap Defri Abdul Majid Nasution Dian Kurnia Dika Donas Putra Eisyaniah Desvazulinda Fachri, Barany Fajri Razak Fathia, Aulia Ukhti Febby Sittah Gunawan Fitri Anugrah, Maisya Gunawan, Andri Harahap, Nur Azizah Hardinata, Rio Septian Harefa, Ade May Luky Heri Eko Rahmadi Putra Hermanto Ibnu Gunawan Ilka Zufria Indra Marto Silaban Indra Nasution Indra Nasution IQBAL , MUHAMMAD Irhami, Zahara Reva Islam, Muhammad Remanul Jacky Lius Juliyandri Saragih Khairil Putra Khumairoh, Annisa Limbong, Yohannes France Lubis, Syaiful Rahman Lydia, Prima M. Azhari Rizko M. Dico TriyadI Maisya Fitri Anugrah Mestika, Dani Mufida Padilla, Eva Muhammad Akbar Firdaus Muhammad Dafa Muhammad Fuad Hafiz Muhammad Iqbal Muhammad Iqbal Muhammad Iqbal Muhammad Irfan Sarif Muhammad Rasyid Ridha Muhammad Rizki Muhammad Wahyudi Muhammad Wahyudi Muhammad Zainal Arifin Pohan Muhammad Zen Muhammad Zen, Muhammad Muhardi Saputra Nabila Putri Br Sitepu Nasution, Indra P Pardede, Surya Maruli Padilla, Eva Mufida Patrialman Haryadi Prayogi, Dhimas Putra, Purwa Hasan Putri, Ranti Eka Rahmat Idhami Rahmat Rezki Raja Nasrul Fuad Rambe, Siska Mayasari Ramlan Marbun Ramlan Marbun Ranti Eka Putri Rendy Rabensi Sembiring Rezkinah Rambe Rezkinah Rambe Rian Farta Wijaya Rido Favorit Saronitehe Waruwu Rio Septian Hardinata Rio Septian Hardinata Rizko, M. Azhari Rizky Putro Nugroho Dwi Cahyo Robet Silaban Safii, Aidul Safi’i, Aidul Sari Harahap, Nurlina Sella Monika Br Tarigan Sella Monika Br Tarigan Selvida, Desilia Septiansyah, Yudha Setiawan, Ahmad Deni Setiawan, Albin Simanullang, Rahma Yuni Sinurat, Satria Siregar, Andree Rizky Yuliansyah Sitepu, Andri Ismail Sitepu, Nabila Putri Br Siti Aisyah Sitorus , Zulham Sitorus, Irwansyah Putera Sitorus, Zulham Solly Aryza Sri Hidayati Suhendar - Sulis Sutiono Surya Darma Suteja, Ade Guna Sutiono, Sulis Syafitri, Febry Dwi Syahputri, Maulisa Syahri, Rahma Syaiful Rahman Lubis Taufa Fadly Tengku Didi Ferdillah Toni Prabowo Uc Mariance Utari Utari Wanny, Puspita Wijaya, Rian Farta Wiwik Handayani Yohannes France Limbong Yudha Septiansyah Zulfahmi Syahputra