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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal Information System Development ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Sistem Informasi Kaputama (JSIK) Building of Informatics, Technology and Science Majalah Ilmiah Warta Dharmawangsa JTIK (Jurnal Teknik Informatika Kaputama) JUKI : Jurnal Komputer dan Informatika Jurnal Manajemen Informatika Jayakarta Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Journal of Vision and Ideas (VISA) Jurnal Pengabdian Masyarakat IPTEK EXPLORER Bulletin of Multi-Disciplinary Science and Applied Technology Journal Of Human And Education (JAHE) Journal of Information Systems and Technology Research Sci-Tech Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Ulead : Jurnal E-pengabdian Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) JOURNAL OF ICT APLICATIONS AND SYSTEM Jurnal Teknik, Komputer, Agroteknologi dan Sains Zadama: Jurnal Pengabdian Masyarakat TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi International Journal of Health, Engineering and Technology Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Indonesian Journal of Science, Technology, and Humanities Pengabdian Pendidikan Indonesia (PPI) Jurnal Ilmu Komputer dan Sistem Informasi Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi KETIK : Jurnal Informatika Ulil Albab Pascal: Journal of Computer Science and Informatics Journal of Computer Science Artificial Intelligence and Communications Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak Global Science: Journal of Information Technology and Computer Science Journal of Data Science and Informatics Engineering

Rusmin Saragih, Rusmin

STMIK Kaputama

Author-ID : 2547035

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Implementasi Association Rule pada Sistem Rekomendasi Peningkatan Hasil Pertanian Menggunakan Metode Apriori: Studi Kasus: Dinas Pertanian dan Pangan Kab. Langkat Yekolya Anatesya; Achmad Fauzi; Rusmin Saragih
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): November: Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.245

The rapid development of technology increases the need for effective and efficient information. Information that is not managed properly loses value, especially when large amounts of data are available, making conventional methods no longer adequate to analyze the potential of the data. Therefore, a system capable of analyzing, summarizing, and extracting data into useful information is required. The Department of Agriculture and Food Security, as an agency that handles food security, agriculture, animal husbandry, animal health, and fisheries, is responsible for supporting the increase in agricultural yields to meet the food needs of the population and encourage economic growth. To achieve this goal, the agency needs to utilize technology to process agricultural data quickly and accurately. The system built using the apriori method can analyze data efficiently and provide recommendations for increasing agricultural yields. Based on the test results, a support value of 9% and a confidence of 68% were obtained, with the rule If the crop is Cassava, then the production yield is 6000-8000 tons.

JARINGAN SARAF TIRUAN MEMPREDIKSI PENJUALAN MAKANAN DAN MINUMAN DENGAN MENGGUNAKAN METODE BACKPROPAGATION (STUDI KASUS : PONDOK JATI RESTO BINJAI) Satria R, Dandi; Simanjuntak, Magdalena; Saragih, Rusmin
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 5 No. 1 (2021): Volume 5, Nomor 1, Januari 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v5i1.590

Pondok Jati Resto (PJR) is a cafe that provides a variety of foods and beverages that are sold to attract customers or potential customers. The number of food and beverages that have been sold, of course, PJR has data on sales of food and beverages. So far, sales data have only been seen from sales reports. It is of course very unfortunate that other data, for example, such as ordered food and beverage menus, can be used as an evaluation material for food and beverage needs that are often in demand. Food and drink is one of the most needed needs by humans. There are many types of food and drink that are made to fulfill the desire to try a food and drink. Apart from being at home, food and beverages can also be obtained at shops, stalls, restaurants, cafes and so on. The increasing number of population levels and the increasing popularity of the food and beverage business, of course, there are more and more food and beverage sellers circulating in several areas, one of which is Cafe Pondok Jati Resto. The application of artificial neural networks to predict the amount of food and beverages using Matlab software using the Backpropagation method can be applied in predicting the number of food and beverage sales. Based on the analysis process that has been carried out under the artificial neural network system using the Backpropagation method, it can identify data on the number of food and beverage sales, with test results or predictions of the average number of foods per year 20, 5 drinks and 19 snacks.

KWh Meter Monitoring System for Boarding House Payments using The BLYNK and ESP32 Applications Ryan Hidayat; Rusmin Saragih; Husnul Khair
International Journal of Health Engineering and Technology Vol. 1 No. 3 (2022): IJHET-SEPTEMBER 2022
Publisher : CV. AFDIFAL MAJU BERKAH

The growth of electrical energy continues to increase from time to time in line with increasing economic activities and community welfare. The increasing growth of electrical energy will deplete existing non-renewable energy sources if their utilization is not effective and efficient. In the utilization of electrical energy it is not known how much energy has been used so that it tends to waste electrical energy. It is necessary to measure the use of electrical energy which aims to manage electrical energy which is very important so that the process of saving and efficiency can be obtained easily. This device is designed to replace the manual electrical energy measurement system because the data obtained cannot be done all the time and the results take too long to come out. This device consists of 4 (four) parts, namely sensors, microcontroller, display and network. The sensor used is the PZEM-004T sensor which is used to measure AC voltage and current, the microcontroller is used NodeMCU which will process the sensor results, the display uses a Liquid Crystal Display (LCD). ) type to display real time output data. The last part is the network as a place for permanent storage and further data processing.

Diagnosing Bilirubin Disease in Infants Using the Certainty Factor Method Br Sitepu, Dinda Isabella; Saragih, Rusmin; Ramadani, Suci
International Journal of Informatics, Economics, Management and Science Vol 3 No 1 (2024): IJIEMS (January 2024)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/ijiems.v3i1.1224

Poor health and an unhealthy lifestyle are vulnerable to making a person more at risk of experiencing certain health problems or diseases. Bilirubin which is related to health is a metabolite in the form of a yellow pigment derived from the breakdown of heme in hemoglobin. Increased Bilirubin in newborns is a common problem that causes a normal or physiological transition during pregnancy. Al Fuadi General Hospital (RSU Al Fuadi) is a hospital located in Binjai that serves general public health. However, the large number of queues of patients waiting for examination results in a lengthy consultation process and treatment that will be received by Bilirubin sufferers. Based on the existing problems, an expert system is needed that can store information from an expert who can help the hospital. The Certainty Factor method is a method that can prove whether a fact is certain or uncertain in the form of metrics that are usually used in expert systems. Based on the results of trials conducted on this system it can be seen that the level belief from results diagnosis to disease i.e. 93% diagnosed with High Bilirubin disease.

Pengaruh Penerapan IT Governance Terhadap Efektivitas Pengelolaan Sistem Informasi Manajemen Apriadi, Deni; Saragih, Rusmin
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 1 No. 2 (2025): Juli 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v1i2.49

Penerapan IT Governance menjadi salah satu faktor kunci dalam meningkatkan efektivitas pengelolaan Sistem Informasi Manajemen (SIM) di berbagai organisasi. Dalam era digital saat ini, kebutuhan akan tata kelola teknologi informasi yang baik semakin penting untuk menjamin keberlangsungan operasional, peningkatan kualitas layanan, serta pencapaian tujuan strategis organisasi. Penelitian ini bertujuan untuk menganalisis sejauh mana penerapan IT Governance berpengaruh terhadap efektivitas pengelolaan SIM. Metode penelitian yang digunakan adalah studi literatur dan survei terhadap beberapa organisasi yang telah menerapkan IT Governance dengan mengacu pada kerangka kerja seperti COBIT dan ITIL. Hasil penelitian menunjukkan bahwa adanya penerapan IT Governance yang terstruktur mampu meningkatkan efisiensi proses bisnis, kualitas pengambilan keputusan berbasis data, serta meningkatkan kepercayaan pengguna terhadap sistem yang digunakan. Selain itu, IT Governance juga terbukti dapat meminimalisir risiko kegagalan sistem dan meningkatkan kepatuhan terhadap standar maupun regulasi yang berlaku. Dengan demikian, dapat disimpulkan bahwa penerapan IT Governance memiliki pengaruh signifikan terhadap efektivitas pengelolaan SIM, khususnya dalam mendukung tercapainya visi, misi, dan tujuan organisasi secara lebih optimal.

Pemanfaatan Aplikasi Kasir Digital Berbasis Android untuk UMKM di Desa Cinta Rakyat Saragih, Rusmin; Gultom, Imeldawaty; Supiyandi; Khalidy, Furqan
Jurnal Pengabdian Masyarakat Berdampak Vol. 1 No. 1 (2025): Januari 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jupemba.v1i1.34

Pemanfaatan teknologi digital dalam pengelolaan usaha mikro, kecil, dan menengah (UMKM) menjadi strategi penting untuk meningkatkan efisiensi operasional dan daya saing. Penelitian ini bertujuan untuk mengkaji implementasi aplikasi kasir digital berbasis Android dalam mendukung aktivitas transaksi dan pencatatan keuangan UMKM di Desa Cinta Rakyat. Metode yang digunakan adalah pendekatan deskriptif kualitatif melalui observasi, wawancara, dan pelatihan langsung kepada pelaku UMKM. Hasil kegiatan menunjukkan bahwa mayoritas pelaku UMKM sebelumnya masih menggunakan pencatatan manual yang rentan terhadap kesalahan dan kehilangan data. Dengan diperkenalkannya aplikasi kasir digital seperti Kasir Pintar dan Moka POS, pelaku usaha dapat mencatat transaksi secara real-time, memantau stok barang, serta menghasilkan laporan keuangan sederhana. Selain itu, penggunaan aplikasi ini meningkatkan pemahaman pelaku UMKM terhadap pentingnya digitalisasi dalam manajemen usaha. Kendala yang ditemui mencakup keterbatasan literasi digital dan akses terhadap perangkat yang memadai, namun dapat diatasi melalui pelatihan berkelanjutan dan pendampingan teknis. Penelitian ini menyimpulkan bahwa aplikasi kasir digital berbasis Android berperan signifikan dalam mendukung tata kelola keuangan UMKM secara lebih akurat dan efisien. Rekomendasi diarahkan pada perlunya dukungan dari pemerintah desa dan pihak terkait untuk menyediakan infrastruktur pendukung dan memperluas program literasi digital guna mempercepat adopsi teknologi oleh pelaku UMKM di pedesaan.

Benchmarking Machine Learning Models for Large-Scale Loan Default Prediction Using Real Data Devianto, Yudo; Saragih, Rusmin; Cahyana, Yana
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 1 (2026): March: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i1.181

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

Pengembangan Sistem Pembelajaran Berbasis Kecerdasan Buatan untuk Pendidikan Jarak Jauh Rusmin Saragih
Journal of Data Science and Informatics Engineering Vol. 1 No. 1 (2025): Desember 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jodsie.v1i1.15

Perkembangan pendidikan jarak jauh menuntut adanya inovasi teknologi yang mampu meningkatkan kualitas dan efektivitas pembelajaran. Kecerdasan buatan (Artificial Intelligence/AI) menjadi salah satu solusi potensial dalam menjawab tantangan tersebut melalui pembelajaran yang adaptif, personal, dan berbasis data. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem pembelajaran berbasis AI yang dirancang untuk mendukung pendidikan jarak jauh secara efektif dan beretika. Metode penelitian yang digunakan adalah penelitian dan pengembangan (Research and Development) dengan pendekatan mixed methods, yang meliputi analisis kebutuhan, perancangan sistem, pengembangan prototipe, implementasi terbatas, serta evaluasi sistem. Hasil penelitian menunjukkan bahwa sistem pembelajaran berbasis AI mampu meningkatkan personalisasi pembelajaran, keterlibatan peserta didik, serta kualitas umpan balik pembelajaran. Selain itu, penelitian ini mengidentifikasi pentingnya penerapan prinsip etika, transparansi, dan perlindungan data dalam penggunaan AI di bidang pendidikan. Dengan demikian, sistem pembelajaran berbasis AI berpotensi menjadi solusi strategis dalam meningkatkan kualitas pendidikan jarak jauh apabila diterapkan secara bertanggung jawab dan terintegrasi dengan kebijakan institusional yang tepat

Reduksi Noise Pada Citra Menggunakan Metode Contrast Limited Adaptive Histogram Equalization Darmawan Ginting; Magdalena Simanjuntak; Rusmin Saragih
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 1 No. 1 (2022): Mei 2022
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v1i1.1

Technological developments have changed computers so that they can process various kinds of data such as sound, images, and so on. Image is a combination of points, lines, fields, and colors to create an imitation of an object, usually a physical object or a human. Even though an image is rich in information, often the image that is owned has decreased in quality, for example, contains defects or denoises. Decreasing image quality due to noise can reduce the information contained in an image. Noise is an acoustic, electrical, or electronic interference signal that is present in a system in the form of interference which is not the desired signal. Image processing that can be done by a computer consists of several types. Image quality improvement (image enhancement) is one of the fields of image processing that is quite popular. The application of image enhancement can improve the quality of the image which was initially blurred or not in accordance with the wishes of the owner for the better. One of the image enhancement methods that can be used is Contrast Limited Adaptive Histogram Equalization (CLAHE). The use of the CLAHE method can improve poor image quality by reducing noise in the image. The system is designed with the MATLAB R2014a programming application, after carrying out the testing process on several Google Maps images, it is found that the inputted "Certificate 1.jpg" image shows that changes in the image are good with reduced noise, so the resulting image has good quality. using the 2nd kernel weight (0, -1, 0; -1, 5, -1; 0, -1, 0).

Context Sensitive Artificial Intelligence for Dynamic User Behavior Modeling in Next Generation Smart Information Platforms Rusmin Saragih; Enda Ribka Meganta P; Tiwuk Widiastuti; Ahmad Jurnaidi Wahidin; Erlita Sulistiati; Muhamad Furqon
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.194

This study explores the development and implementation of a context sensitive artificial intelligence (AI) model designed to predict and personalize user behavior in smart information platforms. Traditional user behavior models often fail to adapt to dynamic and evolving user needs, especially in diverse environments where contextual factors such as time of day, location, and device type play a critical role in shaping user preferences. To address these limitations, the proposed context sensitive AI model integrates real time contextual data alongside traditional behavioral data, enabling it to make more accurate predictions and provide personalized, relevant content. The model utilizes advanced machine learning techniques, such as deep learning and reinforcement learning, to continuously update and refine user behavior models based on contextual shifts. Through the integration of contextual parameters, the model demonstrates improved prediction accuracy, system responsiveness, and overall user satisfaction compared to static, context agnostic models. Furthermore, the study discusses the key advantages of context aware AI, such as its ability to dynamically adjust to real time changes in user behavior, providing more adaptive, personalized services. Challenges encountered during the model's development, including issues related to data privacy, scalability, and the integration of multiple contextual data sources, are also addressed. The findings suggest that context sensitive AI can significantly enhance the effectiveness of smart platforms by improving user engagement and content relevance. Finally, the study provides recommendations for further research to explore deep learning methods for context detection and to improve the discoverability and integration of AI driven features in user interfaces.

Co-Authors , Eka Putra Abdul Azan Abdul Azan Abdullah Hamid, Abdullah Abdullah Husein Achmad Fauzi ACHMAD FAUZI Ahmad Jurnaidi Wahidin Alfina Damayanti Ambarita, Indah Andini Andini Andre Adrian Andrean Samuel Siahaan Aprilianda, Dinda Arianta Bangun Arnes Sembiring Asih, Munjiat Setiani Barany Fachri Boyke Gunawan Manurung Br Sitepu, Dinda Isabella Buaton, Relita Chairul Rizal Charles Jhony Mantho Sianturi, Charles Jhony Mantho Cindy Primadona Siahaan Damayanti, Fera Dandi Satria R Darmawan Ginting Deni Apriadi Dewantara, Nowell Dimas Prayogi Dinda Firdawati Simamora Divi Handoko Eka Pandu Cynthia Eka, Muhammad Enda Ribka Meganta P Erbin Sitorus Erlita Sulistiati Fany Juliawati Fatimah Fatmaira, Zira Fauzi, Achmad frans ikorasaki Fuzy Yustika Manik Fuzy Yustika Manik, Fuzy Yustika Gea, Fide Evianti Gultom, Imeldawaty Handoko, Divi Herdiansyah Harahap Herdiansyah Harahap Hesty Vitara I Gusti Prahmana Ikhsan Arif Indra Prasetia, Indra Irfan Yusuf Ismi Asmita Jesayas Sembiring Khair, Husnul Khalidy, Furqan Lestari, Yuyun Dwi Lili Musarofah Lili Musarofah M. Yogi Riyantama Isjoni Magdalena Simanjuntak Mardiah Marto Sihombing Marto Sihombing Meisaroh Melda Pita Uli Sitompul Mhd Ferdiansyah Putra Mili Alfhi Syari Muhamad Furqon Muhammad Danil Syahputra Muhammad Danil Syahputra Muhammad Eka Muhammad Eka Muhammad Noor Hasan Siregar Muhammad Reza Habibi Muhammad Zen, Muhammad Munadi Munadi Nadia Nurhafiza Nasril Hidayat Nico Kurniawan Purba Nikous Soter Sihombing Novriyenni Novriyenni Novriyenni Novriyenni, Novriyenni Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurlaila Nurlaila Nuryahati - Pakpahan, emma martina Pakpahan, Victor Maruli Pardede, Akim Manaor Hara Pasaribu, Tioria Petrus Loo Rafli Fitriawan Rahayu Utami Rahmadani Rahmadani Rahmawati Rahmawati, Rahmawati Raihan, Muhammad Ramadani, Suci Ramli Ramli Ramos Parulian Ambarita Ratih Puspadini Rianty Zabitha Siregar Ricky Ramadhan Harahap Rivalri Kristianto Hondro Rizki Kurniawan Ryan Hidayat Sari Suwandi, Ema Saripurna, Darjat Satria R, Dandi SELVY ANGGRAINI, SELVY Sihombing, Anton Sihombing, Marto Simanjuntak, Magdalena Simanjuntak, Magdalena Sinaga, Ayu Puspita Sari Sirait, Win Gomgom Parsaulian Siswan Syahputra Sitepu, Ruine Buana Br Sonadi Perangin Angin Suci Pratiwi, Kiki Supiyandi Supiyandi Syahputra, Siswan Syari, Milli Alfhi Tantia Azzahra Tata Mustika Dewi tata, tatamustikadewi Theodora MV Nainggolan Tiwuk Widiastuti Ulandari, Seri Wati, Sri Kesuma Yana Cahyana Yani Maulita Yekolya Anatesya Yessi Fitri Annisah Lubis Yudo Devianto Yulia Ningsih Yusuf Afani Yuyun Dwi Lestari

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