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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) The IJICS (International Journal of Informatics and Computer Science) JURNAL MAHAJANA INFORMASI Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Indonesian Journal of Education And Computer Science Jurnal Ilmiah Multidisiplin Indonesia International Journal of Industrial Innovation and Mechanical Engineering International Journal of Information Engineering and Science Bulletin of Engineering Science, Technology and Industry
Darmeli Nasution
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Physics Social Sciences

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SISTEM PENJUALAN ONLINE MATERIAL BANGUNAN BERBASIS WEBSITE PADA UD PINCURAN JAYA Khumairoh, Annisa; Irwan; Darmeli Nasution
Jurnal Mahajana Informasi Vol 10 No 1 (2025): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v10i1.6095

Abstract

Latar belakang: Sistem informasi penjualan sangat penting untuk membantu bisnis dalam mengelola transaksi dan data penjualan secara lebih efektif dan efisien. Dengan kemajuan teknologi, penjualan berbasis web memungkinkan usaha menjangkau lebih banyak pelanggan dengan cara yang lebih mudah. Saat ini, UD Pincuran Jaya hanya melayani pembeli di sekitar toko, sehingga pengembangan sistem penjualan online berbasis web sangat dibutuhkan agar usaha ini bisa menjangkau konsumen lebih luas dan mengikuti perkembangan zaman. Tujuan: Mengembangkan sistem penjualan online berbasis web pada UD Pincuran Jaya untuk mempermudah transaksi, memperluas pasar, meningkatkan efisiensi, dan kenyamanan pelanggan. Metode: Menggunakan model waterfall dengan tahapan analisis, perancangan, pengkodean, pengujian, dan pemeliharaan yang diselesaikan secara berurutan. Hasil: Sistem berjalan sesuai fungsi, memungkinkan pelanggan melihat katalog, memeriksa stok, dan memesan secara online. Sistem meningkatkan efisiensi, mempercepat transaksi, dan memberi kenyamanan berbelanja. Kesimpulan: Sistem penjualan online berhasil dibangun dan memudahkan pelanggan serta admin dalam transaksi dan pengelolaan. Penerapan sistem digital ini meningkatkan pelayanan dan mendukung transformasi digital UMKM.
Comparative Analysis of Sequencing Methods and Markov Models for Predicting High-Achieving Students at Budi Darma University Sinambela, Sugi Hartono; Iqbal, Muhammad; Khairul, Khairul; Darmeli Nasution; Zulham Sitorus
The IJICS (International Journal of Informatics and Computer Science) Vol. 9 No. 2 (2025): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v9i2.8964

Abstract

The prediction of high-achieving students is a strategic step in supporting the development of academic quality within higher education institutions. This study aims to compare two data mining approaches, namely the Sequencing method and the Markov Model, in predicting high-achieving students at Universitas Budi Darma Medan. The Sequencing method is used to identify patterns in the sequence of academic grades and non-academic activities of students from semester to semester, while the Markov Model is used to calculate the probability of transitions in students' academic status based on historical data. The research adopts a quantitative approach involving 100 active students with complete academic and non-academic data. The data analyzed include semester GPA, participation in organizations, seminars, and achievements in competitions. Both methods were evaluated using metrics such as accuracy, precision, recall, and F1-score. The evaluation results show that the Sequencing method achieved an accuracy of 87%, precision of 85%, recall of 88%, and an F1-score of 86%, while the Markov Model recorded an accuracy of 81%, precision of 79%, recall of 83%, and an F1-score of 81%. Based on these results, the Sequencing method is considered superior in detecting patterns and providing more accurate predictions of students’ achievement potential. The comparison of these two methods provides a foundation for institutions to develop more accurate, objective, and comprehensive student achievement prediction systems. Thus, universities can implement early and well-targeted interventions and guidance.
EVALUATION OF INFORMATION TECHNOLOGY GOVERNANCE E-KINERJA SYSTEMS IN ASSESSING EMPLOYEE PERFORMANCE USING THE MODEL COBIT 2019 AT THE DISTRICT COMMINFO OFFICE WAS REALLY FUN Eswin Syahputra; Khairul; Muhammad Iqbal; Rian Farta Wijaya; Darmeli Nasution
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 3 (2024): September
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i3.60

Abstract

The Department of Communication and Information Technology (Kominfo) in developing an e-performance information system needs to carry out a governance evaluation. The aim of this research is to evaluate information technology governance in the E-Kinerja system in assessing employee performance at the Bener Meriah Regency Communication and Information Service using the COBIT 2019 model using the Action Research method. Data collection techniques use two sources of primary data and secondary data. The results of this research are three main domains in COBIT 2019, namely EDM02 (Ensured Benefits Delivery), APO10 (Managed Vendors), and BAI11 (Managed Projects). This evaluation is carried out to measure the targeted capability level (to-be), the current capability level (as-is), as well as the gaps (GAP) that exist between the two. So the capability level in the EDM02 domain is at level 4, the APO10 domain is at level 2 and the BAI11 domain is at level 2. These findings provide an overview of areas that require further improvement and development to achieve more effective and efficient information technology governance. Thus, it is hoped that this research can contribute to improving the quality of information technology governance at the Bener Meriah Regency Communication and Information Service, as well as becoming a reference for other government agencies in implementing the COBIT 2019 model for evaluating information technology systems.
Comparative Analysis of the C4.5 and Random Forest Algorithms for the Prediction of Diarrheal Disease Sipra Barutu; Muhammad Iqbal; Khairul, Khairul; Darmeli Nasution
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 4 No. 7 (2025): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), August 2025
Publisher : Sean Institute

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

Abstract

Diarrhea remains one of the leading causes of death among infants in Indonesia, especially in areas with limited access to healthcare. Environmental pollution and unhealthy lifestyles are the main causes of its spread. This study aims to compare the performance of the C4.5 and Random Forest algorithms in predicting diarrhea cases among infants in the working area of the Parlilitan Subdistrict Health Center, Humbahas Regency, North Sumatra Province. Secondary data were obtained from medical records and health center reports, which were then analyzed using Python. Model performance evaluation was conducted using the metrics Accuracy, Precision, Recall, F1-Score, Specificity, False Positive Rate (FPR), and True Positive Rate (TPR). The test results showed that the C4.5 algorithm had superior performance with an Accuracy of 0.92; Precision, Recall, and F1-Score of 0.875 each; Specificity of 0.9412; and FPR of 0.0588. Meanwhile, Random Forest obtained an Accuracy of 0.88; Precision of 0.7778; Recall of 0.875; F1-Score of 0.8235; Specificity of 0.8824; and FPR of 0.1176. These findings indicate that C4.5 is more effective in maintaining a balance between prediction accuracy and detection capability, and is better at minimizing classification errors for negative classes.
Aweb-Based Library Application Design At SMP Negeri 18 Medan Titin Mega Andini Siahaan; Darmeli Nasution; Eko Hariyanto
Jurnal Scientia Vol. 13 No. 03 (2024): Education and Sosial science, June - August 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/scientia.v13i03.2466

Abstract

The school library serves as a vital facility in educational institutions, providing a wide range of materials including books and non-books for teaching and learning purposes. SMP Negeri 18 Medan, located in North Sumatra, Indonesia, is equipped with its own school library. However, the management of these libraries is currently manual, with borrowing and returning recorded transactions using traditional methods, resulting in time-consuming processes for both recording and retrieving book data.
Klasifikasi Penyakit Tanaman Daun Tomat Menggunakan Transfer Learning Andriani, Tuti; Iqbal, Muhammad; Darmeli Nasution
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8948

Abstract

Leaf diseases in tomato plants can significantly reduce crop yields and threaten agricultural sustainability. This study proposes a multi-class classification approach for tomato leaf diseases using transfer learning with pre-trained CNN architectures, specifically DenseNet121 and DenseNet169. The dataset used is a subset of PlantVillage, consisting of six disease classes and healthy leaves, with preprocessing steps including image augmentation and resizing. The training strategy involves two phases: feature extraction and fine-tuning, optimized using the Adam algorithm and categorical cross-entropy loss function. Evaluation metrics such as accuracy, precision, recall, and F1-score show that the DenseNet121 model achieves the best performance, reaching an accuracy of 96.23%, followed by MobileNetV2 with 92.89%. Loss curves and confusion matrix analysis confirm that the model performs classification tasks with stability and high precision, despite some misclassifications between visually similar disease classes. This study demonstrates that transfer learning with DenseNet—particularly DenseNet121 is effective for automatic and efficient classification of various tomato leaf diseases, offering potential for real-world implementation as a computer vision-based plant disease diagnosis system.
Co-Authors Amrizal Lubis Asri Santosa Bela Firmantoyo Eko Hariyanto Eswin Syahputra IQBAL , MUHAMMAD Irwan Jesica Uli Panggabean Juliyandri Saragih Khairul Khairul Khairul, Khairul Khumairoh, Annisa Ludfia Anggi Safitri Sinaga Muhammad Iqbal Muhammad Iqbal Muhammad Syahputra Novelan Muhammad Wahyudi Mutiara Widasari Sitopu Rian Farta Wijaya Sinambela, Sugi Hartono Sipra Barutu Sirait, Donna Nurhaida Masdiana Siregar, Andree Rizky Yuliansyah Sitorus , Zulham Sitorus, Zulham Sugeng Pranoto Tiara Sylvia Titin Mega Andini Siahaan Tuti Andriani Yoga Yuniadi Zhafirah Rizki Fadilah Lbs Zuhri Ramadhan