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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JPM: JURNAL PENGABDIAN MASYARAKAT Bulletin of Computer Science Research Journal of Social Responsibility Projects by Higher Education Forum KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) EXPLORER Jurnal Pengabdian Masyarakat Gemilang (JPMG) Jurnal Pengabdian Masyarakat Inovasi Jurnal Bangun Abdimas Jurnal Informatika dan Teknologi Informasi Journal of Computing and Informatics Research Buletin Ilmiah Informatika Teknologi DUNIA KEPERAWATAN: JURNAL KEPERAWATAN DAN KESEHATAN Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer Jurnal INFOTEL Management of Information System Journal Portal Riset dan Inovasi Sistem Perangkat Lunak Journal of Decision Support System Research Jurnal Mitra Pengabdian Farmasi
Abdul Karim
Universitas Labuhanbatu
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Decision Support System for Selecting the Best Head of Study Program Using the MOORA and MOOSRA Methods Karim, Abdul; Hidayatullah, Muhammad; Kurniawan Nasution, Muhammad Bobbi; Esabella, Shinta
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8928

Abstract

The Head of the Study Program is one of the most important parts of a university. The Head of the Study Program is also the highest leader within the study program structure. The role of the Head of the Study Program is as an organizational unit that is responsible for the administration of the study program they lead. The Head of the Study Program is tasked with coordinating all study program activities, as well as managing lecture schedules, practicum schedules, and lecture evaluation results. The selection of the Head of the Study Program requires precise accuracy to avoid errors in the selection process. The stability of a study program heavily depends on the role and reputation of its lecturers, especially the lecturer responsible for the core courses of that study program. Therefore, the participation of lecturers is highly necessary in the selection of the Head of the Study Program. Since the higher education management is also interested in the selection process, methodological assistance is needed to accommodate the aspirations of the lecturers and the interests of the university management. The reward system is a crucial element for motivation toward a better direction, aiming to further increase performance. This reward system is expected to encourage the performance of the Head of the Study Program to be more productive, so that the vision and mission for achieving the development of a university can be properly attained and implemented.
Implementation of MOORA and MOORSA Methods in Supporting Computer Lecturer Selection Decisions Zulham Sitorus; Abdul Karim; Asyahri Hadi Nasyuha; Moustafa H. Aly
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1184

Abstract

The selection of computer science lecturers is an important process for educational institutions, requiring a balanced assessment of various criteria to find the most suitable candidates. This paper examines the implementation of Multi-Objective Optimization based on Ratio Analysis (MOORA) and its variant, namely Multi-Objective Optimization based on Ratio Analysis with a Subjective Attitude (MOORSA), as a tool to support decision making. in this case. This selection process is often complex, requiring consideration of various criteria, such as academic qualifications, teaching experience, research capabilities, and others. This research was conducted to support the decision-making process. by developing a Decision Support System (DSS) using the Multi-Objective Optimization on The Basic of Ratio Analysis (MOORA) and MOORSA methods. Many methods are used, such as SAW, AHP, Topsis and others. based on the calculation of the MOORA method, the highest result has been achieved by A1 worth 0.651819 and similarly, in the MOOSRA method the highest alternative result is A1 worth 0.592177.
Pemanfaatan Teknologi AI untuk Mengembangkan Strategi Digital Marketing Berbasis Data bagi UMKM Desa Karim, Abdul; Syahrizal, Muhammad; Diansyah, Tengku Mohd.
Jurnal Pengabdian Masyarakat Inovasi Vol. 5 No. 1 (2026): February 2026
Publisher : Sekolah Tinggi Ilmu Manajemen Sukma Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35126/jpmi.v5i1.999

Abstract

This community service program aims to enhance the capacity of micro, small, and medium enterprises (MSMEs) in Aek Pamingke Village in utilizing artificial intelligence (AI) for data-driven digital marketing strategies. The activities were conducted through several stages, including needs analysis, intensive training, practical implementation, and evaluation. The initial analysis revealed that most MSME participants had limited knowledge and skills in digital marketing, with 60% categorized as having low understanding before the program. After the training, significant improvement was recorded, with 50% of participants reporting being very satisfied and 35% satisfied. The program’s impact was evident in the participants’ improved ability to design more effective data-driven marketing strategies. The main limitations of this program were the relatively small number of participants and the limited implementation time, indicating the need for extended programs with broader coverage in the future.
Peningkatan Pengarahan Beam dan Estimasi Sudut Kedatangan Berbasis CNN untuk Sistem Antena MIMO Cerdas Karim, Abdul; Purnama, Iwan; Ernawati, Andi
Explorer Vol 6 No 1 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v6i1.2592

Abstract

This study proposes a Convolutional Neural Network (CNN)–based approach to enhance the intelligence of MIMO antenna systems in Internet of Things (IoT) environments, particularly for modeling the relationship between wireless channel characteristics and achievable communication capacity. Modern MIMO systems face complex challenges due to dynamic channel conditions such as noise, path loss, and multipath fading, which significantly affect data transmission quality. In this research, channel-related features are processed through a structured preprocessing stage before being fed into a CNN model to learn nonlinear relationships among channel parameters. The developed model is designed to predict achievable channel capacity accurately as part of an adaptive and intelligent wireless communication framework. Experimental results show that the proposed CNN model achieves a Test Loss of 0.0317 and a Mean Absolute Error (MAE) of 0.1267 on unseen test data. Visualization of actual versus predicted values indicates that the model demonstrates good generalization across most data ranges, although some deviations remain at extremely high capacity values. Compared to conventional approaches, the CNN-based method shows superior capability in capturing complex correlations among MIMO channel parameters. Therefore, this approach contributes to the development of adaptive and efficient intelligent antenna systems, supporting the growing demands of next-generation IoT communication networks.
Sistem Pendukung Keputusan Pemilihan Kepala Desa Terbaik Menerapkan Metodethe Extended Promethee II (EXPROM II) Nurlela Nurlela; Muhammad Syahrizal; Fadlina Fadlina; Abdul Karim
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 1 No. 3 (2020): Mei 2020
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v1i3.2151

Abstract

Decision Support System is a system that can help management in making the right decision, which is needed at a management level. Likewise at the Lubuk Pakam Sub-District Office in selecting the best village head. So far, the Camat Office has never determined the best village head in Lubuk Pakam Subdistrict, so that it encounters obstacles in choosing the village head election. SPK is able to provide alternative solutions to semi / unstructured problems for individuals or groups and in a variety of decision making processes and styles, SPK uses data, databases and analyzes of decision models. Seeing this, researchers are interested in conducting research by applying the Extended Promethee II (EXPROM II) method to elect the best village head in a decision support system. It is expected that the results of the research can help the Lubuk Pakam sub-district
Comparison Of Machine Learning Algorithms For Rice Production Prediction Karim, Abdul; Away, Yuwaldi; Syahrial; Roslidar; Hutahaean, Jeperson; William Ramdhan; Siagian, Yessica
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7453

Abstract

Rice production forecasting plays an important role in supporting future agricultural planning, food supply management, and food security. Accurate yield prediction allows governments and farmers to estimate production outcomes and develop appropriate strategies to maintain stable food availability.This study addresses this gap by comparing four regression-based machine learning models: Random Forest, XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN). All models were trained and tested using the same dataset to ensure a fair evaluation. Model performance was measured using the coefficient of determination (R²). The results show that Random Forest achieved the best performance (R² = 0.963), followed by XGBoost (R² = 0.959). In contrast, SVR (R² = -0.064) and ANN (R² = -2.417) performed poorly, indicating limited predictive capability. Overall, these findings suggest that ensemble-based methods, particularly Random Forest and XGBoost, are more reliable and effective for rice production forecasting compared to SVR and ANN.
Estimasi Sudut Kedatangan yang Ditingkatkan dengan CNN pada Array Antena MIMO Menggunakan Data Sinyal IoT Dunia Nyata Karim, Abdul; Ernawati, Andi
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2564

Abstract

− This study proposes the application of a Convolutional Neural Network (CNN)–based approach to analyze signals in Internet of Things (IoT)–based MIMO antenna systems, with the aim of enhancing the understanding of system performance characteristics, particularly in predicting latency parameters. The CNN model is trained using real-world IoT signal data that have undergone comprehensive preprocessing stages, including data normalization, missing value handling, and feature engineering to ensure compatibility with the model input format. Experimental results on previously unseen test data demonstrate that the proposed model achieves a test loss of 1.4410, represented by the Mean Squared Error (MSE), and a Mean Absolute Error (MAE) of 0.9395. These results indicate that the model attains a relatively low prediction error and effectively captures the nonlinear relationships between signal features and system responses. Visualization of the testing results reveals a strong correlation between actual and predicted latency values, although some dispersion remains due to channel complexity and the inherent variability of IoT signals. The distribution of prediction errors is centered around zero, indicating the absence of significant systematic bias in the model. Overall, the findings confirm the potential of CNN as a reliable approach for modeling and performance analysis of IoT-based MIMO antenna systems, while also highlighting opportunities for further development in spatial parameter estimation and intelligent wireless communication system optimization.
Analisa Perbandingan Algoritma Shannon Fano Dan Algoritma Stout Code Pada Kompresi File Teks Putro Utomo, Dito; Karim, Abdul; Syahrizal, Muhammad
Management of Information System Journal Vol 4 No 2: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i2.2568

Abstract

The rapid development of technology today attracts a lot of attention from the wider community. The dynamic development of computers is accompanied by the ability to get information very quickly. Data compression is a technique to reduce the amount of data in the original data. Data compression is usually applied to computer machines. This happens because each symbol displayed on the computer has a different bit value. The large size of text files will be a problem for storage space. Because the need for text files is very important, we tend to collect data in the form of text files, and often without realizing it, we store it in large sizes. This causes the need for storage media to be large. To overcome this problem, text files that have a larger size are used by compressing text files. Large data will be compressed into a small size, which will reduce storage. After applying the comparison of the Shannon Fano Algorithm and the Stout Code algorithm, compressing the text file has proven that the text file has been successfully compressed. After performing the text file compression process, the author can conclude that the Shannon Fano algorithm is better at performing the compression process.
Machine Learning Decision Support System for Heart Disease Prediction with Optuna and Threshold Optimization William Ramdhan; Jeperson Hutahaean; Deny Jollyta; Abdul Karim
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5684

Abstract

Cardiovascular disease remains a major global health challenge, necessitating accurate and reliable decision support systems for early detection. This study proposes a machine learning–based decision support system that integrates ensemble learning, automated hyperparameter optimization using Optuna, and decision threshold tuning. The system was evaluated using several baseline machine learning models, including Logistic Regression, SVM, KNN, Decision Tree, and Random Forest, with the Random Forest model selected for optimization. Hyperparameter tuning with Optuna and decision threshold optimization led to a significant improvement in accuracy (95.0%) and ROC–AUC (0.977), with the optimized model outperforming all baseline models. This approach demonstrates improved sensitivity, reduced false negatives, and enhanced predictive performance, offering a clinically reliable tool for early heart disease detection. The results emphasize the importance of model optimization and decision threshold calibration in clinical decision support systems.
Co-Authors Afrendi, Mohammad Agus Perdana Windarto Agustina Sidabutar Agustina, Asri Widya Ahyuna Ahyuna, Ahyuna Aldiansyah, Ferry Alfarisi Pasaribu, Ahmad Ambiyar, Ambiyar Andi Ernawati Andi Ernawati Andriani, Titi Aritonang, Putri Armasari, Selly Arridha Zikra Syah Asyahri Hadi Nasyuha Awfa, Qifari Bangun, Budianto Bernadus Gunawan Sudarsono Bobbi Kurniawan Nasution, Muhammad Chairul Rizal Cheylani Lukito, Salwa Christiorenfa Br Haloho, Agatha Daulay, Nelly Khairani Dayu Sari, Arini Deny Jollyta Dhea Ananda, Tasya Dito Putro Utomo Dwika Asrani Dwika Assrani Efendi Hutagalung, Jhonson Efendi, Safri Erlin Windia Ambarsari Fadli, Muhammad Bagus Fadlina Fahmi Rizal Febriani, Budi Fifto Nugroho Garuda Ginting Guidio Leonarde Ginting Harahap, Armyka Pratama Hasibuan, Awaludin Heni Pujiastuti Hersatoto Listiyono Hidayatullah, Muhammad I Wayan Sugianta Nirawana Imam Saputra Indah Sari, Leni Indrayani, Puput Iwan Purnama Jahril Jeperson Hutahaean Jeperson Hutahaean Kraugusteeliana Kraugusteeliana Kurniawan Nasution, Muhammad Bobbi Kusmanto Kusmanto Kusmanto Kusmanto M. Rafi Mardinata, Erwin Marha As, Pawa Niassa Meryance Viorentina Siagian Mesran, Mesran Mhd Ali Hanafiah Mhd Bobbi Kurniawan Nasution Moustafa H. Aly Muhammad Bobbi Kurniawan Nasution Muhammad Hamka Muhammad Syahrizal Nababan, Dosmaida Nasution, Mhd Bobbi Kurniawan Nasution, Muhammad Bobbi Kurniawan Natalia Silalahi Nona Oktari Nurlela Nurlela Nurliadi Pane, Rahmadani Pane, Siddik Pohan, Tatang Hidayat Poningsih Pratama, Armyka Prayetno, Sugeng Prayetno, Sugeng Prayetno Purba, Elvitrianim Purba, Elvitrianim Putra Juledi, Angga Putri, Nathania Rahman, Ben Rohani Rohani Roslidar Saidi Ramadan Siregar Saludin Muis Sartika Br Siregar, Amanda Sempurna, Teguh Shinta Esabella Siagian, Yessica Siddik Siregar, Anwar Sinulingga, Raja Ingata Siregar, Feby Khairunnisya Siti Sahara Nasution Soeb Aripin Suha Alvita Suhada, Karya Sundari Retno Andani Supiyandi Supiyandi Suryadi, Sudi Sutrino Dwi Raharjo Syahputra Harahap, Hasmi Syahrial Tengku Mohd Diansyah, Tengku Mohd Triana, Dewi Trianovie, Sri Trianovie, Sri Unung Verawardina Uswatun Hasanah Vita S. Siregar, Siony William Ramdhan William Ramdhan Wilson, Eric Yessica Siagian Yulizar, Isma Ahmad Yuwaldi Away Yuwaldi Away Zebua, Yuniman Zulham Sitorus Zulkifli Zulkifli Zuly Budiarso