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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 Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Sains Teknologi dan Sistem Informasi Jurnal Info Sains : Informatika dan Sains Jurnal Minfo Polgan (JMP) pendidikan, science, teknologi, dan ekonomi Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Artificial Intelligence and Digital Business International Journal of Industrial Innovation and Mechanical Engineering
Muhammad Syahputra Novelan
Universitas Pembangunan Pancabudi
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences

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Implementation of Apriori Algorithms to Analyze and Determine Consumer Purchase Patterns in Gadget Stores as Sales Increase Strategy Simanullang, Rahma Yuni; ', Khairunnisa; Wanny, Puspita; Utari, Utari; Novelan, Muhammad Syahputra
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7355

Abstract

This study aims to identify the pattern of product purchases that often occur simultaneously at a gadget store in order to develop a more effective sales strategy. The research problem focuses on how to find associations between products based on sales transaction data. The proposed solution is to apply data mining techniques, specifically a priori algorithms, to analyze transaction data and find significant association rules. The A priori algorithm is used through several stages, including the calculation of support for each item, the elimination of items with support below the minimum threshold, the formation of itemset combinations, and the calculation of confidence to generate association rules. The results showed two association rules that met the minimum confidence threshold (60%), namely: (1) If customers buy USB-C, they tend to buy Powerbank (confidence: 67%), and (2) If customers buy Smartwatches, they tend to buy Screen Protectors (confidence: 67%), and (3) If customers buy Screen Protectors, they tend to buy Smartwatches (confidence: 100%). These patterns can be used by the store for strategic product placement and bundling promotions.
DEEP Q-NETWORK ANALYSIS IN OPTIMIZING DATA PROCESSING FOR DECISION MAKING ON FUEL EXPENDITURE FINANCE Siregar, Andree Rizky Yuliansyah; Iqbal , Muhammad; Sitorus , Zulham; Novelan, Muhammad Syahputra; Darmeli Nasution
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 02 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i02.1381

Abstract

As we know, Diesel fuel or also called Solar is a fuel used for diesel-engined motor vehicles, which are generally used in public transportation vehicles or commercial vehicles. In addition, it is also used in diesel for industry. Solar energy is obtained from petroleum refining. In addition to being a fuel, diesel also functions as a lubricant in diesel engine components. In managing the fuel budget for companies or agencies that have high operational needs, decisions regarding the allocation of funds and fuel purchases are very important. Inefficient or unplanned fuel purchases can result in waste and reduce profitability. Therefore, an optimal decision-making system is needed at PT. Deztonindo, which can accurately predict fuel needs and adjust the budget according to the company with the right price and market demand. This study uses a literature review method with the Deep Q-Network (DQN) method. The number of samples in this study is 2559 data with 10 test data. With a reduction in idle time of up to 50%, idle fuel consumption is reduced by 18 liters, increasing efficiency from 0.88 km / liter to 1.28 km / liter, or an increase of 45.5%. After optimization, there was a decrease in average fuel consumption of 20%, which had a direct impact on saving operational costs in a year for the diesel fuel purchase budget. The existence of this decision system can overcome the obstacles to obtaining accurate results for the diesel fuel purchase budget, to minimize the level of conditions that occur
Analysis of the Monte Carlo Method in Simulation of Snake and Ladder Game Using R Programming Afif Yasri; Ramlan Marbun; Harefa, Ade May Luky; Muhammad Syahputra Novelan
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study applies the Monte Carlo method to simulate the classic board game "Snakes and Ladders" using the R programming language. The research aims to explore how randomness and probability influence the number of moves needed to complete the game and to provide a statistical overview of game outcomes. A simulation of 10,000 iterations was conducted, where each iteration represents one complete game play, starting from position 1 and ending exactly at position 100. The results show that players require an average of 51.41 moves to finish the game, with a minimum of 8 and a maximum of 394 moves. These results illustrate the highly variable nature of the game due to random dice rolls and the presence of snakes and ladders that can significantly alter a player's position. Visualization techniques such as histograms, density plots, boxplots, and line graphs were used to represent the distribution and variability of moves. The findings demonstrate the effectiveness of Monte Carlo simulations in analyzing stochastic systems, where outcomes are driven by random variables. This research contributes to the understanding of probabilistic modeling and can serve as a simple yet insightful example of applying computational methods to real-world scenarios.
Digital Transformation of it Governance at The Department of Community and Village Empowerment, Population and Civil Registration of North Sumatra Province Juliyandri Saragih; Andysah Putera Utama Siahaan; Muhammad Syahputra Novelan
International Journal of Industrial Innovation and Mechanical Engineering Vol. 2 No. 2 (2025): May : International Journal of Industrial Innovation and Mechanical Engineering
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijiime.v2i2.280

Abstract

Digital transformation in Information Technology (IT) governance has become a crucial aspect in improving the efficiency of public services, particularly within the Department of Community and Village Empowerment, Population, and Civil Registration of North Sumatra Province. This study aims to analyze the implementation of digital transformation in IT governance using the COBIT 2019 framework. The research method includes the analysis of regulations, the role of IT, procurement models, implementation methods, and technology adoption strategies applied by the department. The findings show that IT implementation is predominantly strategic in nature, supporting the digitization of population services and enhancing data transparency. The IT procurement model comprises a combination of outsourcing (30%), cloud computing (30%), and insourcing (40%) to balance efficiency and system control. Agile methodology is the most dominant implementation method (50%), followed by DevOps (35%) for maintenance and traditional approaches (15%) for more structured projects. The department primarily adopts a "follower" technology adoption strategy (75%), reflecting a selective approach to digital innovation. Based on COBIT 2019 evaluation, the BAI (Build, Acquire, and Implement) domain is the main focus, with high scores in solution identification and improvement management (90) and change management (100), indicating the department’s readiness to adopt digital systems. However, challenges remain in information security, inter-agency data integration, and human resource readiness. The digital transformation of IT governance at the department has been systematically implemented, supporting the improvement of population service efficiency. Enhancements in security, infrastructure, and the strengthening of IT governance policies are necessary to optimize and sustain digital transformation implementation.
A Data-Driven Framework for Integrating Decision-Making and Operational Efficiency in Multi-Product Retail: A Case Study with Experimental Evaluation Aryza, Solly; Novelan, Muhammad Syahputra; Islam, Muhammad Remanul
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24301

Abstract

In today’s highly competitive retail and industrial landscape, multiproduct retail systems face growing challenges due to complex operations, fluctuating demand, and market uncertainty. This paper presents a data-driven framework for optimizing integrated decision-making and enhancing operational efficiency. By utilizing historical transaction data and advanced analytical techniques, the model combines key operational functions—including demand forecasting, inventory management, and resource allocation—to support real- time, data-informed decisions. The approach employs predictive modeling and optimization algorithms to minimize operational costs while maintaining product availability and service level targets. The initial model features five interconnected components: inspection, distribution, disposal, recovery, and retail centers. However, it currently excludes forward logistics, fleet operations, and is limited to a single product and planning period. To address supplier uncertainty, a deterministic equivalent formulation is introduced, relying on the estimation of statistical moments from limited data. Since supplier selection is critical to effective sourcing strategies, improving this process directly enhances supply chain performance. The study highlights that accurately identifying and modeling operational uncertainties is essential for achieving robust and optimal outcomes in retail environments.
The Influence of Shopee Free Shipping Vouchers on User Purchase Decisions: A Case Study of Rengas Pulau Subdistrict Wahyudi, Muhammad; Novelan, Muhammad Syahputra
Jurnal Komputer Teknologi Informasi Sistem Informasi (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 Putra, Purwa Hasan; Selvida, Desilia; Novelan, Muhammad Syahputra
Jurnal Komputer Teknologi Informasi Sistem Informasi (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.
Enhanced Rainfall Forecasting Through Deep Learning Optimization Using Long Short-Term Memory Networks Harefa, Ade May Luky; Antoni, Robin; Sitepu, Andri Ismail; Limbong, Yohannes France; Novelan, Muhammad Syahputra
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.487

Abstract

This study aims to develop a rainfall prediction system using Deep Learning with the Long Short-Term Memory (LSTM) method to improve prediction accuracy and efficiency. The model was built using rainfall data from Gunung Sitoli, covering the period from October 16 to December 14, 2004. The dataset was divided into 90% for training and 10% for testing. The LSTM model was configured with 1 hidden layer and trained for 50 epochs. To evaluate its performance, the Mean Squared Error (MSE) metric was applied. The model achieved an MSE of 0.03 on the test data, indicating a low prediction error and good accuracy. This result shows that LSTM is capable of learning rainfall patterns over time and producing reliable forecasts. Furthermore, the model was integrated into a system to streamline the forecasting and evaluation process. This integration provides an efficient alternative to manual calculations, offering users faster and more accessible predictions. The implementation of this system is especially beneficial for early warning and decision-making processes in regions like Gunung Sitoli, where rainfall patterns can significantly impact on daily activities and disaster preparedness.
Analisis Pola Pembelian Konsumen Menggunakan Algoritma Apriori dan Hash-Based Prayogi, Dhimas; Novelan, Muhammad Syahputra; Lubis, Syaiful Rahman; Rizko, M. Azhari; Suteja, Ade Guna
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.497

Abstract

Penggunaan teknologi data mining telah menjadi aspek penting dalam meningkatkan efisiensi dan efektivitas pengelolaan data di berbagai sektor industri, termasuk di bidang kuliner seperti restoran. Penelitian ini bertujuan untuk mengimplementasikan algoritma Apriori dan teknik Hash-Based dalam proses pengolahan data transaksi penjualan.. Algoritma Apriori digunakan untuk menggali pola asosiasi dari data transaksi pelanggan, seperti kombinasi menu makanan dan minuman yang sering dibeli secara bersamaan. Sementara itu, teknik Hash-Based diterapkan untuk mengoptimalkan proses penyimpanan dan pencarian data agar lebih cepat dan hemat memori. Penelitian ini tidak hanya menjelaskan langkah-langkah implementasi dari kedua metode tersebut, tetapi juga mengevaluasi kinerjanya dari segi waktu proses dan kualitas aturan asosiasi yang dihasilkan. Dengan pengujian pada data transaksi nyata, hasil eksperimen menunjukkan bahwa pendekatan ini mampu meningkatkan efisiensi dalam pengolahan data serta menghasilkan informasi yang berguna dalam mendukung pengambilan keputusan strategis oleh manajemen rumah makan. Temuan ini diharapkan dapat memberikan kontribusi nyata dalam pengembangan sistem informasi yang cerdas dan adaptif di bidang kuliner, sekaligus menjadi referensi bagi penelitian lanjutan yang ingin menggabungkan algoritma data mining untuk kebutuhan industri kecil dan menengah di era digital.
Analisis Algoritma Certainty Factor dalam Menentukan Pembagian Warisan Hukum Perdata Menggunakan Metode RDR Muhammad Syahputra Novelan; Syahputri, Maulisa; Rido Favorit Saronitehe Waruwu; Sella Monika Br Tarigan; Heri Eko Rahmadi Putra
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 4 No. 4 (2025): EDISI JULI 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v4i4.11482

Abstract

Dalam surah Al-Jasiyah ayat 18 dijelaskan mengenai prosedur atau hukum yang telah ditetapkan Allah bagi hamba-Nya untuk diikuti, baik yang berkaitan dengan aqidah, ibadah, akhlak, maupun muamalah. Di antara hukum yang harus dipenuhi adalah hukum waris. Warisan dikenal dengan istilah ‘faraid’, yaitu bentuk peraturan yang mengatur pemindahan hak milik seseorang yang telah meninggal kepada ahli warisnya agar dapat digunakan untuk meningkatkan kesejahteraan dan mengubah kehidupan mereka yang ditinggalkan. Dalam proses pembagian warisan juga menggunakan perhitungan yang akurat dan adil guna menghindari potensi konflik di antara ahli waris. Selain hukum waris Islam, terdapat pula hukum waris yang diadopsi dari negara-negara Barat, yaitu hukum waris sipil. Hukum perdata menjelaskan bagian-bagian yang diperoleh berdasarkan pembagian kelompok. Dari penelitian yang dilakukan menggunakan algoritma Certainty Factor (CF) dan metode Ripple Down Rules untuk mendapatkan pembagian warisan kelompok pertama dengan nilai CF sebesar 0,424.
Co-Authors ', Khairunnisa , Arpan Adli Abdillah Nababan Afif Yasri Amin, Muhammad Aminuddin Indra Permana Andysah Putera Utama Siahaan Antoni, Robin Aria Dhanu Tirta Arpan Aurelia, Cindy Aisha Ayumi Kartika Sari Bayu Angga Wijaya Daniel Panjaitan Darmeli Nasution Defri Abdul Majid Nasution Dian Kurnia Fachri, Barany Fajri Razak Fathia, Aulia Ukhti Febby Sittah Gunawan Gunawan, Andri Hardinata, Rio Septian Harefa, Ade May Luky Haryadi, Patrialman Heri Eko Rahmadi Putra Ibnu Gunawan Ilka Zufria IQBAL , MUHAMMAD Irhami, Zahara Reva Islam, Muhammad Remanul Jacky Lius Juliyandri Saragih Khumairoh, Annisa Limbong, Yohannes France Lubis, Syaiful Rahman Mestika, Dani Muhammad Rizki Muhammad Wahyudi Muhammad Zen, Muhammad Muhardi Saputra Prayogi, Dhimas Putra, Purwa Hasan Putri, Ranti Eka Ramlan Marbun Rido Favorit Saronitehe Waruwu Rio Septian Hardinata Rizal, Chairul Rizko, M. Azhari Rizky Putro Nugroho Dwi Cahyo Sella Monika Br Tarigan Selvida, Desilia Septiansyah, Yudha Setiawan, Ahmad Deni Setiawan, Albin Simanullang, Rahma Yuni Siregar, Andree Rizky Yuliansyah Sitepu, Andri Ismail Sitepu, Nabila Putri Br Siti Aisyah Sitorus , Zulham Solly Aryza Suhendar - Suteja, Ade Guna Sutiono, Sulis Syafitri, Febry Dwi Syahputra, Zulfahmi Syahputri, Maulisa Uc Mariance Utari Utari Wanny, Puspita Wijaya, Rian Farta Wiwik Handayani Zulfahmi Syahputra Zulfahmi Syahputra Zulfahmi Syahputra