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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 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
Muhammad Syahputra Novelan
Universitas Pembangunan Pancabudi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities 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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APPLICATION OF INTELLIGENT SYSTEMS (DEEP LEARNING) TOWARDS THE USE OF AI APPLICATIONS IN DAILY LIFE (PANCABUDI UNIVERSITY MEDAN) Ardiansyah; Aldy Agustian; Perianus lombu; Kiki Wulandari; Muhammad Syahputra Novelan
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 11 (2025): OCTOBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v5i1.1566

Abstract

As technology advances in this digital era, technology has become a very important thing for human life and has a dependency, marked by the use of digital machines that cause very rapid, significant changes to all sectors of human life, making it easier for humans to carry out activities and have dependencies. Artificial intelligence or better known as AI (Artificial Intelligence) is a major supporter in the development of intelligent systems (Intelligence Systems) that increase efficiency and innovation in various sectors of life. Therefore, the latest advances in AI's predictive capabilities can create a productive work environment. Although AI offers great potential to encourage innovation and better decision-making, there are also challenges in the use of AI such as ethical issues, data security, and infrastructure limitations that must be overcome to ensure responsible use.
IMPLEMENTATION OF AN INTELLIGENT SYSTEM TO PREDICT PRODUCT DEMAND WITH THE BACKPROPAGATION NEURAL NETWORK ALGORITHM Rahmat Idhami; Andri Saputra; Taufa Fadly; Robet Silaban; Muhammad Syahputra Novelan
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 11 (2025): OCTOBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v5i1.1591

Abstract

Accurate production prediction is essential in product sales efforts, especially food products whose raw materials have a short shelf life. This paper aims to present a system application model based on the Neural Network algorithm to predict the number of Siomay sales in the future, as a reference for preparing raw materials appropriately. The prediction uses historical data as system training data. The Neural Network trial used 357 historical sales data, 7 initial data used as references, 315 data as training data, and 35 latest data as test data. The neural network input variables were the average sales of the previous 7 days, sales value 1 to 3 days before, the end of the month, identification of discount/benefit days, and weekends. This research methodology includes data collection, pre-processing through data normalization to a scale of [0, 1], and designing a neural network architecture consisting of an input layer, a hidden layer, and an output layer. The Backpropagation algorithm was used to train the network by iteratively updating weights to minimize error values ​​using the Mean Squared Error (MSE). Test results show that the BPNN model is capable of recognizing demand patterns with a high degree of accuracy. Optimal parameters such as learning rate, number of epochs, and number of neurons in the hidden layer significantly influence convergence speed and prediction accuracy. This system is expected to be a management tool for making more accurate and efficient inventory procurement decisions.
Implementasi Kecerdasan Buatan dalam Deteksi Cybercrime: Komparasi Model Naive Bayes dan SVM pada Pola Komentar Judi Online Ardiansyah, Ardiansyah; Nasution, Abdul Muin; Novelan, Muhammad Syahputra
Indonesian Journal of Education And Computer Science Vol. 3 No. 3 (2025): INDOTECH - December 2025
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v3i3.1762

Abstract

Maraknya promosi judi online di platform media sosial seperti YouTube telah menjadi ancaman serius dalam kategori cybercrime di Indonesia. Pola komentar yang bervariasi dan penggunaan bahasa non-formal menyulitkan identifikasi konten secara manual. Penelitian ini bertujuan untuk mengimplementasikan teknologi Kecerdasan Buatan (AI) melalui pendekatan Machine Learning untuk mendeteksi secara otomatis pola komentar judi online. Dua algoritma populer, yaitu Naive Bayes (NB) dan Support Vector Machine (SVM), digunakan dan dibandingkan kinerjanya untuk menentukan model klasifikasi terbaik. Data penelitian diekstraksi dari komentar YouTube berbahasa Indonesia, yang kemudian melewati tahap pra-pemrosesan teks meliputi case folding, tokenization, stopword removal, dan stemming. Fitur teks ditransformasikan menjadi bentuk numerik menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Hasil penelitian menunjukkan perbandingan kinerja kedua algoritma berdasarkan metrik akurasi, precision, recall, dan f1-score. Temuan ini diharapkan dapat memberikan kontribusi bagi pengembangan sistem filtrasi konten negatif otomatis guna memperkuat keamanan siber di ekosistem digital Indonesia.
Pelatihan Penerapan Sistem Informasi Berbasis Komputer Untuk Pengelolaan Arsip Hukum Pada Kantor Desa Ayumi Kartika Sari; Muhammad Syahputra Novelan; Adli Abdillah Nababan
Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Vol 2 No 1 (2025): Januari
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

Pengelolaan arsip hukum yang baik merupakan salah satu aspek penting dalam mendukung transparansi, akuntabilitas, dan efisiensi administrasi di lingkungan pemerintahan desa. Namun, pengelolaan arsip hukum secara manual sering kali menghadapi berbagai kendala, seperti keterbatasan ruang penyimpanan, risiko kehilangan data, dan kesulitan dalam aksesibilitas. Dalam rangka mengatasi permasalahan tersebut, dilakukan kegiatan pengabdian kepada masyarakat berupa pelatihan penerapan sistem informasi berbasis komputer untuk pengelolaan arsip hukum pada Kantor Desa Kelambir V Kebun, Kecamatan Hamparan Perak. Kegiatan ini bertujuan untuk meningkatkan kapasitas aparatur desa dalam mengelola arsip hukum secara digital menggunakan sistem informasi berbasis komputer. Metode pelatihan meliputi sesi teori mengenai pentingnya digitalisasi arsip hukum, pengenalan perangkat lunak sistem informasi arsip, serta praktik langsung dalam menginput, menyimpan, dan mengakses data arsip hukum secara efektif. Hasil dari kegiatan ini menunjukkan bahwa peserta pelatihan mampu memahami dan mengoperasikan sistem informasi arsip dengan baik. Selain itu, implementasi sistem ini juga diharapkan dapat meningkatkan efisiensi administrasi, meminimalisasi risiko kehilangan data, dan mempermudah pencarian arsip hukum. Kegiatan ini memberikan kontribusi signifikan dalam mendorong tata kelola arsip hukum yang modern dan efisien di tingkat desa. Ke depan, pengembangan fitur tambahan dan dukungan berkelanjutan diharapkan dapat dilakukan untuk memastikan keberlanjutan sistem informasi ini.
Sosialisasi Perlindungan Hukum Transaksi Digital dan Penerapan Fintech untuk Pembayaran Layanan Kesehatan di Puskesmas Hamparan Perak Ayumi Kartika Sari; Raja Nasrul Fuad; Muhammad Syahputra Novelan
Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi Vol 2 No 3 (2025): Juli
Publisher : CV. ADMITECH SOLUTIONS

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Abstract

Kegiatan pengabdian masyarakat ini dilaksanakan dengan tujuan untuk meningkatkan pemahaman masyarakat dan tenaga kesehatan di Puskesmas Hamparan Perak mengenai perlindungan hukum dalam transaksi digital serta pemanfaatan teknologi finansial (fintech) dalam pembayaran layanan kesehatan. Perkembangan fintech yang pesat telah mendorong perubahan sistem pembayaran konvensional menjadi digital, termasuk dalam sektor pelayanan kesehatan. Namun, rendahnya literasi hukum dan teknologi sering kali menimbulkan risiko, seperti penyalahgunaan data pribadi dan ketidaktahuan terhadap hak-hak konsumen digital. Kegiatan ini meliputi sosialisasi regulasi perlindungan konsumen digital, khususnya yang terkait dengan Undang-Undang Perlindungan Data Pribadi dan regulasi Otoritas Jasa Keuangan (OJK) terkait layanan fintech. Selain itu, pelatihan juga diberikan mengenai penggunaan metode pembayaran digital seperti QRIS dan dompet digital yang aman dan sah secara hukum. Hasil kegiatan menunjukkan peningkatan pemahaman peserta mengenai risiko dan perlindungan hukum dalam transaksi digital, serta kesiapan awal Puskesmas dalam menerapkan sistem pembayaran berbasis fintech. Diharapkan kegiatan ini dapat menjadi langkah awal dalam mendukung transformasi digital layanan kesehatan yang aman, inklusif, dan berbasis hukum.
Comparative Machine Learning Analysis for Sentiment Classification of Sumatra Disaster 2025 Alfarizi, Nauval; Lydia, Prima; Novelan, Muhammad Syahputra; Putra, Adi; Sinurat, Satria
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Indonesia is highly vulnerable to natural disasters due to its geological position, resulting in extensive disaster-related news coverage that shapes public sentiment. This study presents a comparative machine learning analysis for sentiment classification of online news related to natural disasters in Sumatra during December 2025. The dataset was collected through web scraping from two major Indonesian news portals, like CNN Indonesia and Detik, and categorized into three sentiment classes: negative, neutral, and positive. Sentiment classification was conducted using Naive Bayes, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) algorithms. The results demonstrate that Naive Bayes achieved accuracy values of 0.57 on the CNN Indonesia dataset and 0.61 on the Detik dataset. However, its performance was highly biased toward the dominant negative class, as indicated by low macro-average F1-scores of (0.24) and (0.39). In contrast, SVM showed the most balanced performance by reducing class bias, achieving accuracies of (0.68) and (0.67) with macro-average F1-scores of (0.51) and (0.59), respectively. KNN demonstrated moderate performance, with accuracy values of 0.60 and 0.59, but remained less effective than SVM in handling minority sentiment classes.
Comparison of Random Forest and Naïve Bayes Classifier Methods for Monkeypox Classification Aprilia, Katharina Tyas; Sitorus, Irwansyah Putera; Ridha, Muhammad Rasyid; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Monkey Pox is a disease caused by a virus with the genus orthopoxvirus that can infect humans. The initial symptoms of this disease are the appearance of lumps due to swollen lymph nodes, muscle pain, fever, feeling tired and weak. Although similar to Chickenpox, Monkey Pox is clinically difficult to distinguish from other smallpox diseases. This study aims to classify Monkey Pox disease using the "Monkey-Pox PATIENTS Dataset". Classification of Monkey Pox disease is done using Random Forest and Naïve Bayes methods. Random Forest produces higher accuracy than Naïve Bayes in classifying Monkey Pox disease, which is 69.24% with a k-fold value of 5 and the number of trees 64 using an unbalanced dataset with 6 attributes. While Naïve Bayes produces an accuracy of 68.56% using a dataset without balancing with 8 attributes (k-fold=5, kernel=Gaussian) and 9 attributes (k-fold=3 and 10, kernel=Gaussian).
Predictive Analysis of Flood Risk Factors Based on a Machine Learning Approach: Comparative Study of SVM and XGBoost Algorithms Darma, Surya; Al Fayed, Ahmad Jihad; P Pardede, Surya Maruli; Aqsha, Muhammad Hizbul; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

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

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Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan terhadap efisiensi proses bisnis dalam berbagai sektor industri. Salah satu penerapan teknologi yang berperan penting adalah sistem absensi dan penggajian berbasis lokasi (GPS) yang dapat meningkatkan akurasi data kehadiran karyawan. Penelitian ini bertujuan untuk merancang dan membangun sistem absensi dan penggajian karyawan berbasis GPS pada CV. Bambang Tetuko dengan menerapkan metode Rapid Application Development (RAD). Metode RAD dipilih karena mampu menghasilkan aplikasi dalam waktu pengembangan yang lebih singkat melalui pendekatan iteratif, kolaboratif, dan prototyping yang intensif antara pengembang dan pengguna. Tahapan penelitian dimulai dari analisis kebutuhan, perancangan sistem, pengembangan prototipe, hingga pengujian sistem berdasarkan feedback pengguna secara langsung. Sistem ini mengintegrasikan fitur pencatatan kehadiran melalui lokasi GPS secara real-time, validasi posisi karyawan sesuai titik lokasi kerja, serta perhitungan penggajian otomatis berdasarkan jumlah kehadiran dan jam kerja. Pengujian dilakukan menggunakan metode black box untuk memastikan fungsionalitas sistem berjalan sesuai kebutuhan operasional perusahaan. Hasil implementasi menunjukkan bahwa sistem absensi dan penggajian berbasis GPS dapat meningkatkan transparansi, akurasi data kehadiran, serta meminimalkan potensi kecurangan absensi manual. Selain itu, penerapan metode RAD mampu mempercepat proses pengembangan dan memastikan fungsionalitas sistem sesuai harapan pengguna. Dengan adanya sistem ini, proses administrasi kehadiran dan penggajian di CV. Bambang Tetuko menjadi lebih efektif, efisien, dan terstruktur secara digital.
Co-Authors ', Khairunnisa , Arpan Adi Putra Adli Abdillah Nababan Adli Abdillah Nababan Afif Badawi Afif Yasri Al Fayed, Ahmad Jihad Aldy Agustian Alfarizi, Nauval Amin, Muhammad Aminuddin Indra Permana Andri Saputra Andysah Putera Utama Siahaan Antoni, Robin Anugrah, Maisya Fitri Aprilia, Katharina Tyas Aqsha, Muhammad Hizbul Ardiansyah ARDIANSYAH ARDIANSYAH Aria Dhanu Tirta Arpan Aurelia, Cindy Aisha Ayumi Kartika Sari Ayumi Kartika Sari Bayu Angga Wijaya Daniel Panjaitan Darmeli Nasution Datin, Maha Valne Defri Abdul Majid Nasution Dian Kurnia 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 Haryadi, Patrialman Heri Eko Rahmadi Putra Hermanto Ibnu Gunawan Ilka Zufria IQBAL , MUHAMMAD Irhami, Zahara Reva Islam, Muhammad Remanul Jacky Lius Juliyandri Saragih Khumairoh, Annisa KIKI WULANDARI Lewika Tampubolon Limbong, Yohannes France Lubis, Syaiful Rahman Lydia, Prima Mestika, Dani Mufida Padilla, Eva Muhammad Iqbal Muhammad Rasyid Ridha Muhammad Rizki Muhammad Wahyudi Muhammad Zen, Muhammad Muhardi Saputra Nasution, Abdul Muin Nasution, Indra Norita Tampubolon P Pardede, Surya Maruli Padilla, Eva Mufida Penggabean Siahaan Perianus lombu Prayogi, Dhimas Putra, Purwa Hasan Putri, Ranti Eka Rahmat Idhami Raja Nasrul Fuad Rambe, Siska Mayasari Ramlan Marbun Rido Favorit Saronitehe Waruwu Rio Septian Hardinata Rizal, Chairul Rizko, M. Azhari Rizky Putro Nugroho Dwi Cahyo Robet Silaban Safii, Aidul Safi’i, Aidul Sari Harahap, Nurlina 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 Solly Aryza Suhendar - Surya Darma Suteja, Ade Guna Sutiono, Sulis Syafitri, Febry Dwi Syahputra, Zulfahmi Syahputri, Maulisa Syahri, Rahma Taufa Fadly Uc Mariance Utari Utari Wanny, Puspita Wijaya, Rian Farta Wiwik Handayani Zailani Sinabariba Zulfahmi Syahputra Zulfahmi Syahputra Zulfahmi Syahputra