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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
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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.
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.
ANALISIS PENGELOMPOKAN JADWAL MENGAJAR GURU DI SMKN 1 STABAT MENGGUNAKAN METODE K-MEANS CLUSTERING Sutiono, Sulis; Wijaya, Rian Farta; Novelan, Muhammad Syahputra
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3550

Abstract

Abstract: This study aims to analyze the distribution of teaching load of teachers at SMKN 1 Stabat using the K-Means Clustering method. The main problem faced is the imbalance in the number of teaching hours between teachers which can affect the effectiveness of teaching and learning activities. The data used is the total weekly teaching hours of each teacher, which is taken from the document on the distribution of teaching tasks for the even semester of the 2024/2025 academic year. The K-Means method is used to group teachers into three categories of teaching load: light, medium, and heavy. The grouping process is carried out by determining the number of clusters (K = 3), initializing the centroid, calculating the distance of each data to the centroid, and updating the position of the centroid until the results are stable. The final results show that most teachers are included in the medium load cluster, while a small number are in the light and heavy categories. This shows that the distribution of the teaching load is not yet completely even. The application of K-Means has been proven to be able to provide an analytical picture of the distribution of teacher work, as well as support data-based decision making in education management. Keywords: K-Means Clustering, Teaching Load, Teacher Schedule, Data Clustering, SMKN 1 Stabat Abstrak: Penelitian ini bertujuan untuk menganalisis pembagian beban mengajar guru di SMKN 1 Stabat dengan menggunakan metode K-Means Clustering. Masalah utama yang dihadapi adalah ketidakseimbangan jumlah jam mengajar antar guru yang dapat memengaruhi efektivitas kegiatan belajar-mengajar. Data yang digunakan berupa total jam mengajar mingguan dari setiap guru, yang diambil dari dokumen pembagian tugas mengajar semester genap tahun ajaran 2024/2025. Metode K-Means digunakan untuk mengelompokkan guru ke dalam tiga kategori beban mengajar: ringan, sedang, dan berat. Proses pengelompokan dilakukan dengan menentukan jumlah klaster (K=3), menginisialisasi centroid, menghitung jarak masing-masing data ke centroid, serta memperbarui posisi centroid hingga hasil stabil. Hasil akhir menunjukkan bahwa sebagian besar guru tergolong dalam klaster beban sedang, sementara sebagian kecil masuk kategori ringan dan berat. Hal ini menunjukkan bahwa distribusi beban mengajar belum sepenuhnya merata. Penerapan K-Means terbukti mampu memberikan gambaran analitis terhadap distribusi kerja guru, serta mendukung pengambilan keputusan berbasis data dalam manajemen pendidikan. Kata kunci: K-Means Clustering, beban mengajar, jadwal guru, pengelompokan data, SMKN 1 Stabat
OPTIMALISASI SISTEM PEMBAYARAN SPP BERBASIS WEB DENGAN FRAMEWORK CODEIGNITER PADA SMK MULTI KARYA MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT Aurelia, Cindy Aisha; Novelan, Muhammad Syahputra
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3541

Abstract

Abstract: Payment of Education Development Contribution (SPP) is a crucial element in supporting school operations, but the payment system that is still done manually often faces various obstacles, such as late payments, recording errors, and lack of transparency in financial management. Therefore, this research aims to optimise the web-based SPP payment system using the CodeIgniter framework at SMK Multi Karya to improve efficiency and accuracy in the school financial administration process. The development of this system uses the Rapid Application Development (RAD) method which allows system design and implementation to be carried out more quickly and flexibly through an iterative approach and direct interaction with users. The developed system provides key features such as automatic payment recording, bill notifications, real-time transaction reports, and accessibility for students and parents to monitor payment status. The results of this study show that the implementation of a web-based system with CodeIgniter is able to increase efficiency in the administration process of tuition payments, reduce the level of recording errors, and accelerate the preparation of school financial reports. With this system, school financial management becomes more transparent, accurate, and structured, thus supporting the improvement of the quality of administrative services at SMK Multi Karya. Keywords: Tuition Payment, Information Systems, CodeIgniter, Rapid Application Development, Schools. Abstrak: Pembayaran Sumbangan Pembinaan Pendidikan (SPP) merupakan elemen krusial dalam mendukung operasional sekolah, tetapi sistem pembayaran yang masih dilakukan secara manual sering kali menghadapi berbagai kendala, seperti keterlambatan pembayaran, kesalahan pencatatan, serta minimnya transparansi dalam pengelolaan keuangan. Oleh karena itu, penelitian ini bertujuan untuk mengoptimalkan sistem pembayaran SPP berbasis web dengan menggunakan framework CodeIgniter pada SMK Multi Karya guna meningkatkan efisiensi dan akurasi dalam proses administrasi keuangan sekolah. Pengembangan sistem ini menggunakan metode Rapid Application Development (RAD) yang memungkinkan perancangan dan implementasi sistem dilakukan dengan lebih cepat dan fleksibel melalui pendekatan iteratif serta interaksi langsung dengan pengguna. Sistem yang dikembangkan menyediakan fitur utama seperti pencatatan pembayaran otomatis, notifikasi tagihan, laporan transaksi real-time, serta aksesibilitas bagi siswa dan orang tua dalam memantau status pembayaran. Hasil dari penelitian ini menunjukkan bahwa penerapan sistem berbasis web dengan CodeIgniter mampu meningkatkan efisiensi dalam proses administrasi pembayaran SPP, mengurangi tingkat kesalahan pencatatan, serta mempercepat penyusunan laporan keuangan sekolah. Dengan adanya sistem ini, pengelolaan keuangan sekolah menjadi lebih transparan, akurat, dan terstruktur, sehingga mendukung peningkatan kualitas layanan administrasi di SMK Multi Karya. Kata kunci: Pembayaran SPP, Sistem Informasi, CodeIgniter, Rapid Application Development, Sekolah.
PERBANDINGAN KINERJA K-NN DAN NAIVE BAYES DALAM TATA KELOLA TEKNOLOGI INFORMASI UNTUK MENGANALISA TINGKAT KEPUASAN PENGGUNA APLIKASI SHOPEE Wahyudi, Muhammad; Novelan, Muhammad Syahputra
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3547

Abstract

Abstract: The rapid growth of the e-commerce industry in Indonesia compels companies to actively evaluate customer satisfaction through tingkat kepuasant analysis of app reviews. This study aims to compare the effectiveness of the K-Nearest Neighbors (K-NN) and Naive Bayes algorithms in assessing user satisfaction levels of the Shopee app as a component of IT governance. The research data comprises 2,000 samples of Shopee user reviews collected from Google Play Store and App Store, classified into five rating categories: excellent, good, fair, poor, and bad, based on textual content analysis. The preprocessing stage involved text cleaning (removing stopwords and punctuation), feature extraction using TF-IDF, and an 80:20 split of the dataset (training and testing). The analysis revealed significant differences between the two algorithms. K-NN achieved an accuracy of 54% with the optimal parameter *K=5*, while Naive Bayes demonstrated superior performance with 98% accuracy. The low accuracy of K-NN is suspected to stem from its sensitivity to data imbalance and noise in text features, whereas Naive Bayes, with its probabilistic foundation, better handles the sparse characteristics of review data. These findings emphasize the criticality of selecting appropriate algorithms in IT governance for user satisfaction analysis. Naive Bayes is recommended as the optimal approach for text classification in e-commerce reviews, while K-NN requires refinement through techniques such as feature normalization or class imbalance handling. The study also highlights the need to integrate adaptive models, such as deep learning, to enhance accuracy in complex scenarios. For future research, expanding data scope or implementing multilingual analysis could serve as strategic steps to improve result generalization. Keywords: K-NN, Naive Bayes, E-commerce, user satisfaction, IT governance, Shopee. Abstrak: Perkembangan yang cepat dalam industri e-commerce di Indonesia mendorong perusahaan untuk secara aktif mengevaluasi kepuasan pelanggan melalui analisis kepuasan pelanggan dari ulasan aplikasi. Studi ini bertujuan untuk membandingkan efektivitas algoritma K-Nearest Neighbors (K-NN) dan Naive Bayes dalam menilai tingkat kepuasan pengguna aplikasi Shopee sebagai komponen tata kelola teknologi informasi. Data penelitian mencakup 2000 sampel ulasan pengguna Shopee yang diambil dari Google Play Store dan App Store, dengan klasifikasi penilaian 5 kategori yaitu sangat puas, puas, cukup puas, kurang puas dan buruk pada konten teks. Proses pra-pemrosesan data melibatkan pembersihan teks (menghilangkan kata penghubung, tanda baca), ekstraksi ciri menggunakan TF-IDF, serta pembagian dataset menjadi 80% pelatihan dan 20% pengujian. Hasil analisis mengungkapkan perbedaan yang signifikan antara kedua algoritma. K-NN mencapai akurasi 58,50% dengan parameter *K=5*, sementara Naive Bayes menunjukkan performa lebih unggul dengan akurasi 100%. Rendahnya akurasi K-NN diduga berasal dari kepekaan algoritma terhadap ketidakseimbangan data dan gangguan pada fitur teks, sedangkan Naive Bayes, yang berbasis probabilitas, lebih mampu mengatasi karakteristik data yang jarang (sparse) dalam ulasan. Temuan ini menekankan pentingnya pemilihan algoritma yang sesuai dalam tata kelola TI untuk analisis kepuasan pengguna. Naive Bayes direkomendasikan sebagai pendekatan optimal untuk klasifikasi teks pada ulasan e-commerce, sedangkan K-NN perlu disempurnakan melalui teknik seperti normalisasi fitur atau penanganan ketidakseimbangan kelas. Studi ini juga mengidentifikasi perlunya integrasi model adaptif, seperti deep learning, untuk meningkatkan akurasi dalam skenario yang lebih kompleks. Untuk penelitian mendatang, perluasan cakupan data atau penerapan analisis multibahasa dapat menjadi langkah strategis guna meningkatkan generalisasi hasil. Kata kunci: K-NN, Naive Bayes, E-commerce, Kepuasan Pengguna, Tata Kelola TI, Shopee.
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