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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JURIKOM (Jurnal Riset Komputer) JSI (Jurnal sistem Informasi) Universitas Suryadarma Indonesian Journal of Education and Mathematical Science IT (INFORMATIC TECHNIQUE) JOURNAL Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Blend Sains Jurnal Teknik Jutech: Jurnal Teknologi Informasi Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal Komprehenshif Hanif Journal of Information Systems Jurnal Kecerdasan Buatan dan Teknologi Informasi Portal Riset dan Inovasi Sistem Perangkat Lunak Al'Adzkiya International of Computer Science and Information Technology Journal Tsabit Inventa: Journal of Science, Technology, and Innovation
Mulkan Azhari
Universitas Muhammadiyah Sumatera Utara
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Implementation of The Sales and Purchase Program Application Using The Rapid Application Development Model Web – Based Ichsan, Aulia; Al-Khowarizmi, Al-Khowarizmi; Azhari, Mulkan
Tsabit Journal of Computer Science Vol. 1 No. 1 (2024): June Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/tsabit21

Abstract

The development of information technology is currently developing rapidly and rapidly, which is supported by one of the means, namely computers. Of course, computers that are equipped with certain applications are used to help make human work easier in managing data for an organization or company so that they get accurate results that meet their needs. The results of observations that have been made show that sales and purchasing activities still use manual systems, one of which is in clothing stores. Starting from processing goods data, difficulties checking stock, purchasing transactions, sales transactions, as well as storing other data related to all types of activities, which can cause losses for shop owners, errors in recording and inaccurate reports being made. Judging from the large number of transactions carried out at clothing stores, a faster and more accurate information system is needed. Therefore, the author created a computerized program design using the Microsoft Visual Basic.net programming language and MySQL database, so that information and activities that occur can be carried out quickly and accurately. The method used in designing this program uses the Rapid Application Development (RAD) model. This RAD model is an adaptation of the high-speed version of the waterfall model for the development of each software component. The results achieved from discussing this theme are in the form of ready-to-use sales and purchasing program applications. In this case, the use of program applications is the best solution to solve existing problems, and by using program applications an effective and efficient activity can be achieved in supporting activities, especially for handling sales and purchasing problems.
The Aplikasi Model Text Area Based Image Selective Encryption Menggunakan YoloV3, Arnold's Catmap dan AES Pada Pengamanan Konten Teks Pada Citra Digital Riza, Ferdy; Azhari, Mulkan; Zulherry, Andi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9261

Abstract

sensitive content in digital images. This research proposes a selective encryption model based on text area detection in digital images, integrating object detection using You Only Look Once version 3 (YOLOv3), Arnold's Cat Map transformation, and the Advanced Encryption Standard (AES) algorithm. The model automatically identifies and selects areas containing text in the image using YOLOv3, applies Arnold's Cat Map for spatial disorganization, and then encrypts the transformed result with AES to ensure data security. System performance is evaluated through visual quality analysis using PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) parameters, as well as encryption and decryption processing time. The test results show that this approach can maintain the integrity of non-text areas while providing strong protection for sensitive text areas without compromising efficiency or overall visual quality. This model has the potential to be applied in the context of securing digital documents, visual identities, and other sensitive data in images.
CATATAN ELEKTRONIK GAWATDARURAT BENCANA (CEGAB) UNTUK OPTIMALISASI MANAJEMEN BENCANA Kipa Jundapri; Ade Irma Khairani; Mulkan Azhari; Nadia Febrina; Anggy Wulandari
Devote: Jurnal Pengabdian Masyarakat Global Vol. 4 No. 3 (2025): Devote : Jurnal Pengabdian Masyarakat Global, 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/devote.v4i3.4569

Abstract

Electronic Disaster Emergency (CEGaB) is a platform that can be used to record data on patients/victims undergoing treatment in disaster conditions so that it can help in recording patient/victim data, as well as to get an overview in preparing for future disaster mitigation, so that preparations in facing disasters such as preparing human resources in this case the number and profession of health workers needed, the epidemiology of diseases that appear when a disaster occurs, as well as the need for consumables such as medicines needed if a disaster occurs in the future.
Komparasi Algoritma KNN Dan SVM Untuk Klasifikasi Kesehatan Mental Pada Usia Remaja Rizky Hidayat Hasibuan; Mulkan Azhari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.28151

Abstract

Mental health among adolescents is a critical issue that continues to increase and is often not detected early due to the limitations of assessment methods that remain largely subjective. This study aims to classify adolescent mental health levels based on stress levels using a machine learning approach and to compare the performance of the KNN and SVM algorithms. The dataset used consists of 1,100 adolescent records obtained from GitHub, comprising 11 predictor attributes and one target attribute, namely stress_level, which is classified into three categories: low, moderate, and high. The research stages include data preprocessing, EDA, feature selection, handling class imbalance using the SMOTE, modeling, and evaluation. Model testing was conducted using several training–testing split ratios. Model performance was evaluated using confusion matrix. The results indicate that the SVM algorithm achieved the best performance with an accuracy of 89.55% and an F1-Score of 89.58% using an 80:20 data split prior to the application of SMOTE. Overall, SVM demonstrated higher stability and accuracy compared to KNN in classifying adolescent mental health levels, indicating its strong potential as a data-driven early detection tool for adolescent mental health issues. Keywords: Adolescent Mental Health, Stress Level Classification, K-Nearest Neighbor, Support Vector Machine
Analisis Perbandingan Metode LSTM dan BiLSTM untuk Prediksi Harga Saham Menggunakan Alpha Vantage Rifqi Yafik; Mulkan Azhari
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.650

Abstract

Pasar saham Indonesia mengalami pertumbuhan signifikan, namun fluktuasi harga yang tinggi tetap menjadi tantangan utama bagi investor. Penelitian ini bertujuan membandingkan performa dua model deep learning, Long Short-Term Memory (LSTM) dan Bidirectional LSTM (BiLSTM), untuk memprediksi harga penutupan harian dua saham blue chip, PT Bank Central Asia Tbk (BBCA) dan PT Telkom Indonesia Tbk (TLKM). Data historis dari Januari 2019 hingga Desember 2023 diperoleh melalui Alpha Vantage API. Proses penelitian mencakup normalisasi data dengan MinMaxScaler dan pembentukan sliding window untuk pemodelan deret waktu. Model LSTM dan BiLSTM dilatih menggunakan TensorFlow-Keras, dengan optimasi hyperparameter melalui metode Grid Search yang menguji kombinasi units, batch size, epochs, dan dropout rate. Hasil eksperimen menunjukkan bahwa model BiLSTM memberikan akurasi prediksi yang lebih tinggi dibandingkan LSTM pada kedua saham. Untuk saham BBCA, BiLSTM mencatat RMSE sebesar 0.0178, lebih baik dari LSTM yang mencatat RMSE 0.0180. Begitu pula pada saham TLKM, BiLSTM mencapai RMSE 0.0172, mengungguli LSTM dengan RMSE 0.0199. Keunggulan BiLSTM disebabkan kemampuannya memproses data secara dua arah, yang memungkinkan model menangkap pola kontekstual dan titik pembalikan tren dengan lebih baik. Penelitian ini berkontribusi pada pengembangan model prediksi saham yang lebih akurat dan sistematis bagi peneliti dan praktisi di pasar modal.
Clustering Jenis Sayuran Di Daerah Desa Sempa Jaya Dengan Algoritma Gausian Mixture Model Hafizan Syafik; Mulkan Azhari
Portal Riset dan Inovasi Sistem Perangkat Lunak Vol. 3 No. 4 (2025): Artikel Penelitian
Publisher : SoraTekno Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/prinsip.v3i4.188

Abstract

Sektor pertanian di Desa Sempajaya, Kabupaten Karo, memiliki potensi besar dalam menghasilkan berbagai jenis sayuran yang menjadi sumber utama pemenuhan kebutuhan gizi masyarakat sekaligus penyokong perekonomian lokal. Namun, pengelolaan lahan dan pemetaan jenis sayuran unggulan masih menghadapi kendala karena belum adanya sistem pengelompokan data yang akurat. Penelitian ini bertujuan untuk mengelompokkan jenis sayuran di Desa Sempajaya menggunakan algoritma Gaussian Mixture Model (GMM) sebagai metode clustering yang mampu menangani distribusi data yang kompleks. Metode penelitian dilakukan melalui beberapa tahap, yaitu pengumpulan data (observasi, wawancara, dan dokumentasi), pra-pemrosesan data, analisis faktor menggunakan diagram Fishbone untuk menentukan atribut relevan, serta implementasi algoritma GMM dengan pendekatan Expectation-Maximization (EM). Data yang digunakan mencakup enam komoditas utama, yaitu cabai, tomat, sawi, wortel, terung, dan buncis, dengan variabel meliputi ukuran, berat, warna, serta luas lahan. Hasil penelitian menunjukkan bahwa algoritma GMM berhasil mengelompokkan data sayuran ke dalam tiga kategori kluster produksi, yaitu rendah, sedang, dan tinggi, dengan visualisasi hasil clustering yang lebih representatif dibandingkan metode konvensional. Sistem ini mampu memberikan informasi potensi sayuran unggulan di setiap wilayah Desa Sempajaya, yang dapat dimanfaatkan oleh petani, masyarakat, maupun pemerintah daerah dalam pengambilan keputusan strategis terkait diversifikasi, distribusi, dan pengembangan pertanian berkelanjutan.
Implementasi Metode Fuzzy Time Series Untuk Prediksi Permintaan Produck Coca Cola Di Bukit Sinar Mart Dimas Wijayanto; Mulkan Azhari
Portal Riset dan Inovasi Sistem Perangkat Lunak Vol. 3 No. 4 (2025): Artikel Penelitian
Publisher : SoraTekno Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59696/prinsip.v3i4.190

Abstract

Penelitian ini membahas tentang implementasi metode Fuzzy Time Series untuk memprediksi permintaan produk Coca Cola di Bukit Sinar Mart. Latar belakang penelitian ini berangkat dari permasalahan fluktuasi permintaan produk yang menyebabkan ketidakpastian dalam manajemen stok, sehingga sering terjadi overstock maupun stockout. Untuk mengatasi permasalahan tersebut, metode Fuzzy Time Series dipilih karena mampu menanganiketidakpastian data historis penjualan serta menghasilkan prediksi yang lebih akurat dibandingkan metode konvensional.Penelitian ini menggunakan data permintaan Coca Cola selama 2 tahun terakhir di Bukit Sinar Mart. Proses penelitian meliputi pengumpulan data, fuzzifikasi, pembentukan relasi fuzzy (Fuzzy Logical Relationship/FLR), pembentukan kelompok relasi (Fuzzy Logical Relationship Group/FLRG), hingga tahap defuzzifikasi untuk menghasilkan prediksi numerik. Sistem prediksi kemudian diimplementasikan dalam bentuk aplikasi berbasis web dengan PHP dan MySQL, sehingga dapat digunakan secara langsung oleh pihak Bukit Sinar Mart.Hasil penelitian menunjukkan bahwa metode Fuzzy Time Series mampu memberikan hasil prediksi yang mendekati data aktual dengan tingkat akurasi yang baik. Prediksi untuk periode September 2025 menghasilkan estimasi permintaan sebesar11.569 pcs. Implementasi aplikasi prediksi ini membantu pihak Bukit Sinar Mart dalam merencanakan persediaan secara lebih efisien, mengurangi risiko kerugian, serta meningkatkan kepuasan pelanggan.
Application of Data Mining to Determine the Performance of Family Planning Field Officers (PLKB) Using the C4.5 Algorithm Perdinal Nasution; Mulkan Azhari
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.52

Abstract

The effectiveness of family planning programs is closely related to the performance of Family Planning Field Officers (PLKB). Conventional performance evaluation methods often rely on manual assessments, which may lead to subjectivity and inconsistency. To overcome this issue, data mining techniques can be applied to analyze performance data systematically and objectively. This study employs the C4.5 decision tree algorithm to classify and evaluate the performance of PLKB. The dataset used in this research includes several indicators, such as service coverage, counseling frequency, reporting accuracy, and community participation. Prior to model construction, data preprocessing was performed to handle missing values and normalize attributes. The model performance was evaluated using accuracy, precision, recall, and F-measure. The findings indicate that the C4.5 algorithm successfully classified PLKB performance into three categories: high, medium, and low. The model achieved an accuracy of [insert % if available], demonstrating its effectiveness in identifying key determinants of officer performance. Moreover, the decision tree generated interpretable rules that highlight the most influential attributes affecting PLKB performance. The application of data mining using the C4.5 algorithm provides an objective and efficient method for evaluating PLKB performance. This approach not only enhances decision-making for supervision and training but also contributes to the improvement of family planning program implementation. Future research is suggested to compare the C4.5 algorithm with other classification methods to achieve higher accuracy and generalizability.
Development of Virtual Reality-Based Computer Assembly Simulation Learning Media Ilham Prastia; Mulkan Azhari
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.66

Abstract

The development of Virtual Reality (VR) technology provides great opportunities in creating interactive and immersive learning media that can simulate hands-on practice more realistically. In computer assembly learning at vocational schools, limited availability of laboratory equipment often becomes a major obstacle, resulting in students not gaining optimal direct practice experience. This study aims to develop a Virtual Reality-based computer assembly learning simulation as an interactive, safe, and engaging alternative learning tool. The research employed a Research and Development (R&D) method using the ADDIE model, consisting of the stages of analysis, design, development, implementation, and evaluation. Computer component assets were modeled using Blender 3D and then integrated into Unity to build an interactive VR-based simulation. The testing phase involved Black Box Testing and Application Testing with 10 respondents, consisting of 7 vocational students and 3 alumni from the Computer and Network Engineering major. The results show that all interactive features performed according to the expected scenarios, and the feasibility assessment through Application Testing achieved a score of 87.2%, indicating that the simulation is suitable, easy to use, and effective in improving students’ understanding of computer assembly procedures. Additionally, the VR media was considered to provide a more realistic learning experience, reduce the potential for errors during real practice, and increase student engagement throughout the learning process. Therefore, this VR-based learning media can serve as a solution to laboratory limitations and a foundation for further development of VR-based practical learning materials in the field of Computer and Network Engineering.
Machine Learning-Based Phishing Email Detection: A Comparative Study of Support Vector Machine and Random Forest nurkumalalubis; Mulkan Azhari
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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

Abstract

Information and communication technology has now developed very rapidly, bringing significant changes to our daily lives. With the advancement of information and communication technology, access to information has become very easy and fast. However, this convenience also brings its own challenges, especially in terms of personal data security. As technology users, we are required to be wise and vigilant in safeguarding our personal data so that it is not misused by irresponsible parties. One example of cybercrime that often occurs is phishing emails. In this attack, the perpetrator uses a link containing a virus to encrypt the user's data or device, then asks for a ransom to restore access to the data. Phishing emails usually look like official emails from trusted sources, so recipients are often unaware of the dangers lurking. Therefore, to minimize the losses that can occur, we can also take advantage of technology so that we can automatically classify phishing emails. Therefore, this research will carry out the process of building a machine learning model which can automatically classify phishing emails. So that with the model built in this research, it is hoped that it can help in anticipating phishing emails. In this research, the construction of machine learning models will use data with a total of 18650 data which consists of 11322 non-phishing email data and 7328 phishing email data. The model that will be built in this research is a model using the Support Vector Machine and Random. Forest algorithms. In the model building process, to find the optimal parameters, the hyperparameter tuning process is carried out using CV gridsearch, so as to produce optimal parameters. After testing the model to classify phishing emails, the results show that using the Support Vector Machine algorithm produces a test accuracy of 97.27%, while using the Random Forest algorithm produces an accuracy of 96.51%.
Co-Authors Ade Irma Khairani - Al Khowarizmi Aldryan Bhara Putra Andi Zulherry Anggy Wulandari dimas arya prayoga Dimas Wijayanto Efendi, Syahril Eka Syarif Maulana Ferdy Riza Hafizan Syafik Halim Maulana Husaini, Abdillah Ichsan, Aulia Ilham Ilham Ilham Prastia Indah Purnama Sari Jannah, Aulia Kipa Jundapri Manurung, Asrar Aspia Maulia Rahman Nadia Febrina nurkumalalubis Oris Krianto Sulaiman Perdinal Nasution Purwanto Purwanto Rifqi Yafik Rika Rosnelly, Rika Rizky Hidayat Hasibuan Siti Khairun Nisa Situmorang, Zakaria Taufik Rifki Al Hafiz Lubis Zahra Apriyani Hakim Nasution