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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JURNAL MEDIA INFORMATIKA BUDIDARMA IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Majalah Ilmiah Bijak Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Computer Science (CO-SCIENCE) J-SAKTI (Jurnal Sains Komputer dan Informatika) EXPLORER CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat Bangsa Jurnal Pengabdian Sosial Jurnal Pengabdian Kepada Masyarakat Radisi Jurnal KALANDRA Aksi Kita: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Science Artificial Intelligence and Communications Etanic : Journal Of English Language Teaching And Applied Linguistics Jurnal Ilmu Komputer dan Teknik Informatika Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies MURAKOM
Muhammad Noor Hasan Siregar
Universitas Graha Nusantara, Padangsidimpuan, Sumatera Utara - Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Design and Development of IdentifiKu: A Web-Based Diagnostic Model for Differentiated Learning Muhammad Noor Hasan Siregar; Yulia Rizki Ramadhani; Yusra Fadhillah; Yoviansyah Rizki Pratama
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.9762

Abstract

This study aims to develop and evaluate IdentifiKu, a web-based diagnostic assessment platform designed to support differentiated learning within the Kurikulum Merdeka framework in Indonesia. Specifically, the research seeks to bridge the gap in existing assessment platforms that predominantly focus on cognitive dimensions by integrating cognitive and non-cognitive domains—learning styles, personality traits, and multiple intelligences—into a unified scoring model. The platform was developed using a Design and Development Research (DDR) approach combined with the Waterfall Software Development Life Cycle (SDLC), encompassing requirements analysis, system design, implementation, testing, and deployment. The architecture adopts a three-tier client–server model, with a Laravel-based application layer and a MySQL database optimized to the third normal form. Performance evaluation involved functional testing and user feedback from twelve teachers across diverse subject areas. Quantitative results indicated that the system met or exceeded all operational benchmarks, including an average page load time of 2.4 seconds, 99.8% uptime, 100% scoring accuracy, and a System Usability Scale (SUS) score of 85.3. Teachers reported that the platform’s comprehensive learner profiles facilitated targeted instructional strategies, improved student engagement, and streamlined assessment processes. This research contributes a scalable, pedagogically aligned model for integrating multidimensional diagnostics into differentiated learning practices, which may be adapted to other educational contexts to enhance data-driven instruction.
Analisis Sentimen Pelanggan terhadap Produk UMKM pada Marketplace Menggunakan Algoritma Naïve Bayes Akhiruddin Pulungan; Muhammad Noor Hasan Siregar; Hery Dia Anata Batubara
MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika Vol. 2 No. 3 (2026): MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika
Publisher : CV MUARA EDUKASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64365/murakom.v2i3.484

Abstract

The rapid growth of online marketplaces has provided significant opportunities for Micro, Small, and Medium Enterprises (MSMEs) to market their products to a broader audience. Customer reviews posted on marketplaces contain valuable information regarding customer satisfaction, experiences, and perceptions of the products and services offered. However, the increasing volume of reviews makes manual analysis inefficient and time-consuming. Therefore, an automated method is needed to process and analyze review data effectively. This study aims to analyze the sentiment of MSME reviews on marketplaces using the Naïve Bayes algorithm and Natural Language Processing (NLP) techniques. This research employs a text mining approach consisting of data collection, preprocessing stages including case folding, tokenization, stopword removal, and stemming, followed by term weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The processed data are then classified into three sentiment categories: positive, negative, and neutral, using the Naïve Bayes algorithm. Model performance is evaluated using a confusion matrix as well as accuracy, precision, recall, and F1-score metrics. The results indicate that the Naïve Bayes model performs well in classifying sentiment from marketplace reviews. Based on the confusion matrix, the model correctly classified 176 out of 200 testing data instances. The evaluation results show an accuracy of 88.00%, precision of 87.25%, recall of 86.80%, and F1-score of 87.02%. The best performance was achieved in the positive sentiment class, with a precision of 92.96%, recall of 94.29%, and F1-score of 93.62%. These findings demonstrate that the Naïve Bayes method combined with NLP techniques is effective for conducting sentiment analysis of MSME reviews on marketplaces. This study is expected to assist MSME owners in understanding customer opinions more quickly and accurately, thereby providing valuable insights for improving product quality and service performance.
Perancangan Aplikasi Data Mining Untuk Menentukan Pasien Menderita Tifoid Dengan Metode Algoritma C4.5 Yusra Fadhillah; Muhammad Noor Hasan Siregar; Oberlin Siagian
Explorer Vol 1 No 2 (2021): Juli 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v1i2.92

Abstract

Data mining adalah suatu istilah yang digunakan untuk menguraikan penemuan pengetahuan didalam database. Proses data minig ini akan diimplementasikan dengan menggunakan algoritma C.45. Algoritma C.45 dapat digunakan dalam pembentukan pohon keputusan tetapi lebih mengarah pada perhitungan probabilitas dari tiap-tiap record terhadap kategori-kategori tersebut atau untuk mengklarifikasi record dengan mengelompokan ke dalam satu kelas. Setelah sebuah pohon keputusan dibangun maka dapat digunakan untuk mengklarifikasi record yang beulm ada kelasnya. Dimulai dari node root menggunakan tes terhadap atribut dari record yang belum ada kelasnya, kemudian mengikuti cabang sesuai dengan proses pohon keputusan yanitu mengubah bentuk data (table) menjadi pohon (tree) kemudian merubah model pohon tersebut menjadi aturan. Berdasarkan penelitian yang telah dilakukan yang telah dibangun dapat menentukan pasien yang menderita penyakit tipes.
Utilization of Sales Data Analysis for Product Recommendation Systems in E-Commerce Using the Apriori Algorithm Muhammad Noor Hasan Siregar; Furqan Khalidy; Rismayanti; Khairunnisa
Journal of Computer Science, Artificial Intelligence and Communications Vol 1 No 2 (2024): November 2024
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v1i2.17

Abstract

The rapid development of e-commerce has significantly increased the volume of sales transactions and customer interaction data. This presents an opportunity for businesses to leverage data mining techniques to extract valuable insights that support decision-making processes. One such application is the development of product recommendation systems, which play a crucial role in enhancing customer satisfaction and driving sales. This research focuses on utilizing sales transaction data to build a product recommendation system using the Apriori algorithm, a well-known method for association rule mining. The study begins with the collection and preprocessing of transaction data from an e-commerce platform. Through the application of the Apriori algorithm, frequent itemsets are identified, and association rules are generated based on specified support and confidence thresholds. These rules reveal purchasing patterns and relationships between products that are frequently bought together. The system then uses these patterns to recommend relevant products to users, aiming to improve cross-selling opportunities and personalize the shopping experience. The results demonstrate that the Apriori-based recommendation model is effective in identifying meaningful product combinations and can be implemented as a lightweight, interpretable alternative to more complex machine learning methods. Furthermore, the system helps e-commerce businesses optimize inventory management and marketing strategies by understanding customer buying behavior. This research concludes that the integration of the Apriori algorithm into recommendation systems provides tangible benefits for e-commerce platforms seeking data-driven personalization solutions.
Evaluating the Impact of Knowledge Management Systems on Organizational Performance: A Technology Company Case Amru Yasir; Deni Apriadi; Muhammad Noor Hasan Siregar; Divi Handoko; M. Arif Rahman
Journal of Computer Science, Artificial Intelligence and Communications Vol 2 No 1 (2025): May 2025
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v2i1.27

Abstract

This study aims to evaluate the impact of Knowledge Management Systems (KMS) on organizational performance within a technology company. In the digital era, knowledge has become a critical asset that drives innovation, efficiency, and competitive advantage. By leveraging a case study approach, the research examines how the implementation of KMS influences various performance indicators, including productivity, decision-making quality, employee collaboration, and knowledge retention. Data were collected through interviews, observations, and internal documents, and analyzed using a mixed-method approach. The findings suggest that effective use of KMS significantly improves organizational agility and innovation capabilities. However, the study also identifies challenges such as resistance to change, lack of user training, and insufficient integration with existing workflows. To maximize the benefits of KMS, organizations must foster a knowledge-sharing culture, provide ongoing support, and align KMS strategies with business objectives. The insights from this research are expected to contribute to the development of more effective knowledge management practices in technology-based organizations.
Kajian Teoritis Simulatif Mengenai Algoritma Huffman dalam Kompresi Data Teks Supiyandi; Chairul Rizal; Deni Apriadi; Muhammad Noor Hasan Siregar; Muhammad Iqbal
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 1 No. 1 (2025): Januari 2025
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v1i1.44

Abstract

Kompresi data merupakan salah satu teknik penting dalam pengelolaan informasi digital, terutama untuk efisiensi penyimpanan dan transmisi data. Algoritma Huffman dikenal sebagai salah satu metode kompresi lossless yang paling efisien dalam konteks data teks. Kajian ini bertujuan untuk menyajikan telaah teoritis yang dikombinasikan dengan pendekatan simulatif terhadap penerapan algoritma Huffman dalam proses kompresi data teks. Pembahasan diawali dengan pemaparan konsep dasar kompresi, prinsip kerja algoritma Huffman, dan analisis struktural terhadap pohon Huffman yang terbentuk dari distribusi frekuensi karakter dalam suatu dokumen teks. Simulasi dilakukan menggunakan sampel teks berbahasa Indonesia dan Inggris dengan variasi panjang dan kompleksitas karakter untuk mengamati dampak terhadap rasio kompresi, efisiensi encoding, serta performa algoritma secara keseluruhan. Hasil simulasi menunjukkan bahwa semakin tidak merata distribusi frekuensi karakter dalam data, semakin tinggi efisiensi kompresi yang dicapai. Selain itu, dibandingkan metode kompresi berbasis fixed-length encoding, algoritma Huffman mampu mengurangi ukuran file hingga lebih dari 40% dalam beberapa kasus uji, tanpa kehilangan informasi apa pun. Studi ini menegaskan pentingnya pemahaman algoritma Huffman tidak hanya dari sisi matematis, tetapi juga melalui pendekatan eksperimental untuk mengukur efektivitasnya dalam konteks data teks riil. Penulis merekomendasikan integrasi algoritma Huffman dalam sistem kompresi yang lebih luas, serta pengembangan varian algoritma untuk peningkatan performa pada data yang lebih heterogen. Kajian ini diharapkan dapat memberikan kontribusi bagi pengembangan teknologi kompresi data serta menjadi referensi awal bagi peneliti atau praktisi yang tertarik pada optimalisasi penyimpanan dan pengolahan informasi.
Analisis Keamanan Data pada Sistem Informasi Menggunakan Metode ISO/IEC 27001 Muhammad Noor Hasan Siregar; Mardiah
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.v1i2.52

Abstract

Keamanan data merupakan salah satu aspek krusial dalam pengelolaan sistem informasi, terutama di era digital yang semakin rentan terhadap ancaman siber. Permasalahan utama yang dihadapi adalah lemahnya penerapan standar keamanan yang dapat menyebabkan kebocoran data, akses ilegal, hingga hilangnya integritas informasi. Penelitian ini bertujuan untuk menganalisis tingkat keamanan data pada sistem informasi dengan menggunakan kerangka kerja ISO/IEC 27001, yang dikenal sebagai standar internasional untuk sistem manajemen keamanan informasi (Information Security Management System/ISMS). Metode penelitian dilakukan melalui studi literatur, observasi, serta evaluasi kepatuhan sistem informasi terhadap kontrol keamanan yang terdapat dalam ISO/IEC 27001, khususnya terkait aspek kerahasiaan, integritas, dan ketersediaan data. Hasil analisis awal menunjukkan bahwa tingkat kepatuhan sistem terhadap standar masih berada pada kategori sedang, dengan persentase implementasi kontrol keamanan sekitar 65%. Beberapa area yang masih lemah mencakup pengelolaan akses pengguna, kebijakan backup data, serta pelatihan keamanan informasi bagi pengguna sistem. Sebagai solusi, penelitian ini merekomendasikan penerapan manajemen risiko secara lebih ketat, penguatan prosedur keamanan teknis, serta penyusunan kebijakan keamanan data yang berkesinambungan. Dengan penerapan standar ISO/IEC 27001 secara menyeluruh, diharapkan sistem informasi dapat meningkatkan ketahanan terhadap ancaman, meminimalisasi risiko kebocoran, dan memastikan keberlangsungan operasional organisasi.
Co-Authors , Eka Putra AA Sudharmawan, AA Abdul Rahman Suleman Ade Ismail Abdul Kodir Agus Perdana Windarto Akhiruddin Pulungan Alfiarini Alkhairi, Putrama Amru Yasir Anata, Hery Dia Arief Wibowo Aswan, Novita Azhar Harahap Badawi, Masrof Chairul Rizal Deni Apriadi Deni Apriadi Divi Handoko Edi Suharto Eka Putra Fachri, Barany Fadhillah , Yusra Fadhillah, Yusra Ferawati Artauli Hasibuan Ferawati Artauli Hasibuan Furqan Khalidy Harahap, Angelia Utari Hasibuan, Ferawati Artauli Hendry Hendry . HERY DIA ANATA BATUBARA Indra Prasetia, Indra Indra Riyana Rahadjeng Indra Riyana Rahadjeng Irman Efendi Jumaria Nasution Kasmawati Khairunnisa, Khairunnisa Lubis, Fitri Romaito M. Arif Rahman Mardiah Mardiah, Mardiah Meiliana Friska Mokhamad Ramdhani Raharjo Muhammad Eka Muhammad Hasanuddin, Muhammad Muhammad Iqbal Muhammad Iqbal Muhammad Iqbal Muhammad Yasin Simargolang Muhammad Zen Munadi Munadi Nuranisah Nurkhasanah Rina Puspita Oberlin Siagian Putra, Randi Rian Ramadhani, Cerah Fitri Ramadhani, Yulia Rizki Rismayanti Rismayanti Rismayanti Rizki, Cindy Atika Rusmin Saragih, Rusmin Setiawan, Yudika Dwi Siti Hajar Siti Hardianti Wahyuni Siti Khodijah Suleman, Abdul Rahman Supiyandi Supiyandi Surya Handayani Toga Mahaji Yoviansyah Rizki Pratama Yuhandri Yuhandri, Yuhandri Yulia Rizki Ramadhani Yusra Fadhillah Yusra Fadhillah Yuswin Harputra