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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) 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 Buletin Ilmiah Informatika Teknologi DUNIA KEPERAWATAN: JURNAL KEPERAWATAN DAN KESEHATAN Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer Jurnal INFOTEL 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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Implementation of the Multi-Objective Optimization Method on the Basic of Ratio Analysis (MOORA) and Entropy Weighting in New Employee Recruitment Karim, Abdul
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4859

Abstract

The problems faced by the company in managing the selection of new employees must be done in the right way so that it can help a good work cycle. The employee recruitment process still uses manual methods so the Human Resource Development (HDR) division has to sort, select and select applicants one by one. The large number of applicants means that the Human Resource Development division often experiences difficulty in selecting prospective employees and there is subjectivity when deciding which employees fit the established criteria. To overcome the problem of making employee recruitment decisions, we will use the Multi-Objective Optimization method based on ratio analysis (MOORA) and weighting using Entropy. In research, data is collected based on the position of prospective employees. The results obtained in this research determine each position that will be accepted by 3 prospective employees, namely Sales Position, Position, Graphic Design, Accounting Staff, IT Support position, Sales Project.
Optimasi Prediksi Harga Sawit Menggunakan Teknik Stacking Algoritma Machine Learning dan Deep Learning dengan SMOTE Karim, Abdul; Bangun, Budianto; Prayetno, Sugeng; Afrendi, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7239

Abstract

The prediction of palm oil prices plays a strategic role in decision-making within the agribusiness sector, particularly in addressing market volatility and imbalanced historical data distribution. This study aims to optimize the accuracy of palm oil price prediction by applying a stacking approach that combines machine learning and deep learning algorithms, while integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance issues. Three main models were employed in this study: Random Forest, Long Short-Term Memory (LSTM), and a model enhanced with SMOTE. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics, supported by confusion matrix analysis. The results indicate that the model integrated with SMOTE outperforms the others, achieving an accuracy of 0.5447, precision of 0.5512, recall of 0.5447, and F1-score of 0.5462. This model also demonstrates a more balanced classification performance compared to the LSTM and Random Forest models. These findings confirm that the application of oversampling techniques such as SMOTE, when combined with appropriate algorithms, can significantly enhance predictive performance in imbalanced datasets. The study contributes to the development of predictive models for commodity prices based on historical data and opens opportunities for further exploration of more adaptive hybrid methods in future research.
Studi Perbandingan Metode Dempster-Shafer dan Teorema Bayes dalam Sistem Pakar Diagnosa Penyakit Sistem Pernapasan Kusmanto, Kusmanto; Esabella, Shinta; Karim, Abdul; Bobbi Kurniawan Nasution, Muhammad; Hidayatullah, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7317

Abstract

Respiratory system disease diagnosis often faces challenges in ensuring the accuracy of results due to the complexity of overlapping symptoms. In particular, a method is needed that is able to handle data uncertainty and utilize existing evidence optimally. This study aims to compare two methods, namely Bayes' Theorem and Dempster-Shafer, in diagnosing three types of respiratory diseases: Asthma, Tuberculosis, and Bronchitis. The solution is done by analyzing the percentage of confidence produced by each method based on symptom data. The results show that Bayes' Theorem produces the highest confidence for Tuberculosis (74.92%), while Dempster-Shafer provides the highest confidence for Bronchitis (80%). This comparison indicates that the selection of methods must be adjusted to the characteristics of the data and the needs of the analysis. This study contributes to providing insight into the advantages and disadvantages of each method, which can be used as a reference in developing a more accurate disease diagnosis decision support system.
Komparasi Kinerja Algoritma Random Forest dan C4.5 untuk Klasifikasi Harga Mobil Ernawati, Andi; Karim, Abdul
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 1: September 2024
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i1.95

Abstract

Determining car prices is a crucial aspect of the automotive industry that requires accurate data analysis for strategic decision-making. This study aims to compare the performance of the Random Forest and C4.5 algorithms in classifying car prices based on specific features, such as technical specifications, production year, and market conditions. The dataset used in this study consists of [mention the size and source of the dataset if available], analyzed using a cross-validation approach to ensure the accuracy of the results. The performance of both algorithms is evaluated based on several metrics, including accuracy, precision, recall, and F1-score. The results show that the Random Forest algorithm consistently outperforms the C4.5 algorithm across most evaluation metrics, achieving an accuracy of [best Random Forest accuracy] compared to [best C4.5 accuracy]. These findings indicate that the Random Forest algorithm is more effective in handling multivariate data complexity and providing more reliable predictions. The conclusions of this study highlight the potential of Random Forest as the primary method for car price classification, especially in scenarios requiring high accuracy levels. This research also contributes to a comparative understanding of decision-tree-based algorithms for applications in the automotive industry and opens opportunities for further research into developing more adaptive and efficient models.
Analisis Perbandingan Metode SAW dan MAUT Dalam Pemilihan Dokter Terbaik di RSU Setio Husodo Kisaran Karim, Abdul
Jurnal Teknologi Ilmu Komputer Vol. 2 No. 2: Juni 2024
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jtik.v2i2.235

Abstract

Dokter merupakan bagian yang paling penting yang dimiliki sebuah rumah sakit dalam mempertahakan kelangsungan hidup serta kemampuan dalam bersaing. Dalam hal ini seorang dokter harus memiliki skill bahkan keterampilan untuk meningkatkan akreditas suatu rumah sakit. Supaya kinerja dokter menjadi lebih baik, maka dibutuhkan suatu sistem dalam membantu pemilihan dokter terbaik, dibutuhkan suatu Sistem Pendukung Keputusan Metode SAW dan MAUT Untuk Pemilihan dokter terbaik di rsu setio husodo kisaran. Untuk metode SAW untuk pemilihan dokter terbaik di rsu setio husodo kisaran, untuk urutan peringkat pertama dengan nama Dasya memperoleh nilai 0,8400. Urutan kedua dengan nama Wilona memperoleh nilai 0.8333. dan untuk urutan ketiga dengan nama Sinta memperoleh nilai 0,8133. Sedangkan untuk perhitungan metode MAUT dokter terbaik dengan nama Sinta memperoleh nilai 0,262, untuk urutan kedua dengan nama Wilona memperoleh nilai 0,248, dan untuk urutan ketiga dengan nama putri memperoleh nilai 0.195  
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.
Co-Authors Afrendi, Mohammad 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 Cheylani Lukito, Salwa Christiorenfa Br Haloho, Agatha Daulay, Nelly Khairani Dayu Sari, Arini Dhea Ananda, Tasya Dito Putro Utomo Efendi Hutagalung, Jhonson Efendi, Safri Fadli, Muhammad Bagus Fadlina Fahmi Rizal Febriani, Budi Fifto Nugroho Garuda 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 Iwan Purnama 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 Purba, Elvitrianim Purba, Elvitrianim Putra Juledi, Angga Putri, Nathania Rahman, Ben Rizal, Chairul Rohani Rohani 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 Tengku Mohd Diansyah, Tengku Mohd Triana, Dewi Trianovie, Sri Trianovie, Sri Unung Verawardina Uswatun Hasanah Vita S. Siregar, Siony Wilson, Eric Yessica Siagian Yulizar, Isma Ahmad Zebua, Yuniman Zulham Sitorus Zulkifli Zulkifli Zuly Budiarso