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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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Efektifitas Teknologi Tata Kelola Informasi Dalam Sistem Cerdas Antrian Pelayanan Rumah Sakit Efarina Etaham Pematang Siantar Menggunakan Cobit 2019 Sella Monika Br Tarigan; Muhammad Syahputra Novelan; Muhammad Amin
Bahasa Indonesia Vol 18 No 03 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i03.510

Abstract

Rumah Sakit Efarina Etaham Pematang Siantar telah mengimplementasikan Sistem Cerdas Antrean Pelayanan untuk mengurangi penumpukan pasien dan meningkatkan efisiensi layanan. Namun, optimalisasi pemanfaatan teknologi tersebut masih menghadapi kendala berupa fluktuasi kinerja server pada jam sibuk serta belum terstandarisasinya tata kelola teknologi informasi (TI) secara menyeluruh. Penelitian ini bertujuan untuk mengevaluasi tingkat kapabilitas tata kelola sistem antrean menggunakan kerangka kerja COBIT 2019 serta merumuskan rekomendasi perbaikan yang selaras dengan tujuan strategis operasional rumah sakit. Metode penelitian yang digunakan adalah mixed methods dengan tahapan pemetaan design factors untuk menentukan fokus audit pada proses TI kritis. Pengumpulan data dilakukan melalui observasi, wawancara mendalam, dan penyebaran kuesioner berbasis matriks RACI kepada pihak terkait. Evaluasi difokuskan pada domain BAI04 (Managed Availability and Capacity), DSS02 (Managed Service Requests and Incidents), dan MEA01 (Managed Performance and Conformance Monitoring). Hasil penelitian menunjukkan bahwa tingkat kapabilitas tata kelola sistem antrean berada pada level 2 (Managed), yang menandakan proses telah berjalan namun belum memenuhi standar dokumentasi dan evaluasi berkala. Analisis kesenjangan menunjukkan domain BAI04 memiliki gap tertinggi sebesar 1,88 akibat bottleneck server pada jam sibuk, sedangkan MEA01 memiliki gap 1,45 karena belum tersedianya monitoring real-time. Domain DSS02 mencatat gap terendah sebesar 0,52. Penelitian ini merekomendasikan penerapan auto-scaling server, penyusunan Service Level Agreement (SLA) internal, serta pengembangan dashboard monitoring layanan guna meningkatkan tata kelola TI yang lebih adaptif, terukur, dan berorientasi pada kenyamanan pasien.
Penerapan Big Data dan Algoritma Machine Learning untuk Meningkatkan Efisiensi Proses Pelayanan Izin Usaha di DPMPTSP Kabupaten Tapanuli Selatan Rezkinah Rambe; Muhammad Syahputra Novelan; Zulham Sitorus
Bahasa Indonesia Vol 18 No 4 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i4.513

Abstract

Layanan perizinan usaha merupakan aspek penting dalam meningkatkan kualitas layanan publik dan mendukung iklim investasi daerah. Namun, proses perizinan usaha di DPMPTSP Kabupaten Tapanuli Selatan masih menghadapi berbagai tantangan, seperti waktu pemrosesan yang lama, kesalahan verifikasi, dan pemanfaatan data yang tersedia yang belum optimal. Penelitian ini bertujuan untuk menerapkan teknologi big data dan algoritma machine learning guna meningkatkan efisiensi proses perizinan usaha. Metode yang digunakan adalah pendekatan kuantitatif dengan teknik eksperimental, memanfaatkan dataset layanan perizinan usaha yang terdiri dari berbagai atribut seperti jenis izin, lokasi, waktu pemrosesan, dan status permohonan. Algoritma yang digunakan dalam penelitian ini adalah Random Forest, Naïve Bayes, dan kombinasi antara Random Forest dan Naïve Bayes, dengan evaluasi menggunakan matriks kebingungan serta metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest menunjukkan kinerja terbaik, dengan akurasi 98,5%, presisi 98,57%, recall 98,5%, dan F1-score 98,51%. Sementara itu, Naïve Bayes menunjukkan kinerja terendah dengan akurasi sebesar 51%, dan kombinasi Random Forest dan Naïve Bayes menghasilkan akurasi sebesar 73%. Hal ini menunjukkan bahwa Random Forest lebih mampu menangani data yang kompleks dan menghasilkan prediksi yang lebih akurat dibandingkan dengan metode lainnya.
Penerapan Data Mining dan Machine Learning dalam Menentukan Strategi Bisnis UMKM Berdasarkan Tren Penjualan dan Perilaku Konsumen M. Dico TriyadI; Muhammad Syahputra Novelan; Rian Farta Wijaya
Bahasa Indonesia Vol 18 No 5 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i5.518

Abstract

This study aims to utilize Big Data and Machine Learning technology to assist the strategic decision-making process for MSMEs at the Deli Serdang Regency Cooperatives and SMEs Office. The methods applied include ARIMA and SARIMA to forecast sales trends, K-Nearest Neighbor (KNN) for consumer behavior classification and segmentation, and Rough Set for feature selection. The data used includes sales transaction data and consumer behavior for the period 2020–2023. The study shows that the Rough Set method can reduce data dimensionality by 50% without reducing model accuracy. The KNN model shows excellent performance with a high evaluation value (accuracy reaching 0.99), and can group consumers into loyal, potential, and non-loyal categories. On the other hand, the ARIMA and SARIMA models show unsatisfactory performance with high error rates, making them less appropriate for fluctuating data. Overall, the combination of Data Mining and Machine Learning has proven efficient in generating strategic information that can support MSMEs in formulating data-driven business strategies.
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; Abdul Muin Nasution; Muhammad Syahputra Novelan
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.
Analisis Algoritma Genetika dan Algoritma Monroe pada Penjadwalan Tenaga Kesehatan di Rumah Sakit H. Amri Tambunan Deli Serdang Ade Guna Suteja; Muhammad Iqbal; Muhammad Syahputra Novelan
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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

Abstract

The study aims to analyze the influence of multimedia web interface design on the user retention rate on the Google Classroom e-learning platform at Universitas Pembangunan Panca Budi (UNPAB). Given the transition of digital learning systems within the campus environment, evaluating user experience becomes crucial for determining future platform standards. The research method used is quantitative with a causal associative approach. The research sample consists of 118 students from the Information Technology Study Program, class of 2022, selected using purposive sampling techniques. Data were collected through questionnairs with a Likert Scale and analyzed using simple linear regression via statistical software. The results showed that the interface design variable has a positive and significant effect on user retention with a t count value of 12,653 and a significance value of 0,000 (<0,05). The coefficient of determination (R2) indicates that interface design contributes 58,0% to user retention rate, while the remaining 42,0% is influenced by other factors outside the study. These findings confirm that intuitive and functional multimedia interfaces play a vital role in maintaining the sustainability of user interaction within digital learning management systems.
Transaction Fraud Detection in Savings and Loan Cooperatives Using Xgboost with Shap Explanations (Shapley Additive Explanations) Indra Marto Silaban; Muhammad Syahputra Novelan; Muhammad Irfan Sarif
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.535

Abstract

Savings and loan cooperatives play a crucial role in supporting the community's economy. With the motto "From Members, By Members, and For Members," cooperatives focus on developing member funds and providing returns in the form of dividends. However, cooperative operations are not free from the risk of fraud, especially by internal parties (employees or administrators). This study aims to develop a machine learning-based transaction fraud detection model using the Extreme Gradient Boosting (XGBoost) algorithm and to increase model transparency through an Explainable Artificial Intelligence (XAI) approach with the SHAP (SHapley Additive exPlanations) method. This study uses user activity log data and financial transactions that can be described as operator/employee behavior in the savings and loan cooperative system. The model will be trained to classify whether transactions are fraudulent or non-fraudulent. The results will then be evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the XGBoost model has good performance with an accuracy value of 0.81 and an AUC of 0.912. SHAP analysis shows that features such as transaction amount, transaction frequency, transaction time, and changes in user and member data are key factors in fraud detection. This study demonstrates that the integration of XGBoost and SHAP can improve fraud detection accuracy and provide transparency in model decision-making. Therefore, the results of this study can support a more effective supervisory system for savings and loan cooperative financial institutions.
Application of Convolutional Neural Network (CNN) in Facial Expression Detection for Classifying the Level of Learning Concentration of Senior High School Students (SMA) Ramlan Marbun; Muhammad Irfan Sarif; Muhammad Syahputra Novelan
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.542

Abstract

Student learning concentration is one of the key factors influencing academic achievement and the effectiveness of the learning process. However, monitoring students' concentration levels manually is often subjective and challenging, especially in classrooms with a large number of students. This study aims to implement a Convolutional Neural Network (CNN) model for facial expression detection to classify the learning concentration levels of Senior High School (SMA) students. The research employs a quantitative experimental approach using facial image datasets collected during classroom learning activities. The dataset undergoes several preprocessing stages, including face detection, cropping, image resizing, and pixel normalization before being used for model training. The CNN architecture is designed to automatically extract facial features and classify students' concentration levels into three categories: high, medium, and low concentration. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results indicate that the CNN model is capable of recognizing facial expression patterns related to learning concentration effectively and achieving high classification performance. Furthermore, the developed system provides a more objective and efficient approach for monitoring student concentration compared to conventional observation methods. Therefore, the implementation of CNN-based facial expression recognition has significant potential to support intelligent educational systems and improve learning evaluation processes in school environments.  
Integration of FP-Growth Algorithm with XGBoost and SHAP to Predict Consumer Product Sales Patterns at CV. Mitra Ridge Syaiful Rahman Lubis; Muhammad Syahputra Novelan; Muhammad Iqbal
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.545

Abstract

This study aims to develop an integrative model based on data mining and machine learning to analyze purchasing patterns and predict consumer product sales at CV. Mitra Ridge. The approach used combines the FP-Growth algorithm to discover product association patterns (frequent itemsets and association rules), XGBoost as a gradient boosting-based sales prediction model, and SHAP (SHapley Additive Explanations) to provide transparent interpretability of the model's prediction results. The data used is sales transaction data from October 2024 to September 2025, which includes 500 transactions with various types of consumer products. The results show that the integration of association pattern features from FP-Growth as additional input to the XGBoost model can improve prediction accuracy compared to a single XGBoost model without integration. SHAP analysis revealed that purchase frequency, product category, and product combination patterns are the most influential factors in sales prediction. This integrative model is proven to be superior in performance and provides deeper business insights, so it can be used as an operational decision support system in stock management, bundling strategies, and data-driven marketing planning in consumer retail companies.  
Optimizing the AMIK Medicom Exam Schedule Using Simulated Annealing and Linear Programming Yohannes France Limbong; Muhammad Irfan Sarif; Muhammad Syahputra Novelan
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.549

Abstract

Examination timetabling in vocational higher education is a combinatorial optimization problem involving courses, students, invigilators, classrooms, laboratories, and time slots. At AMIK Medicom, the schedule preparation process is still semi-manual, which may cause student conflicts, room conflicts, unbalanced invigilator assignments, and poor exam distribution. This study aims to design an examination timetabling optimization model by integrating Linear Programming (LP) and Simulated Annealing (SA). LP is used to formulate hard constraints to ensure schedule feasibility, while SA is applied to improve schedule quality based on soft constraints, such as exam spread and room utilization. The dataset consists of 29 lecturers, student data from academic years 2021/2022 to 2024/2025, and curricula from three study programs with 154 courses in total. The proposed design shows that the model can transform the examination timetabling problem into structured decision variables, objective functions, and mathematical constraints. In the prototype testing scenario, the LP-SA approach is directed to produce a timetable with zero hard-constraint conflicts and lower soft-constraint penalties compared with semi-manual preparation. The model can serve as a basis for developing a web-based automatic examination scheduling system to improve academic administrative efficiency at AMIK Medicom.  
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