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All Journal The IJICS (International Journal of Informatics and Computer Science) Jatilima : Jurnal Multimedia Dan Teknologi Informasi INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Ulead : Jurnal E-pengabdian Pengabdian Kepada Masyarakat Indonesia SEAN (ABDIMAS SEAN) Marsipature Hutanabe: Jurnal Pengabdian Kepada Masyarakat Literacy: Journal of Education and Social Science Pascal: Journal of Computer Science and Informatics "Journal of Data Science
Panjaitan, Muhammad Iqbal
Unknown Affiliation
Religion Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Penerapan Algoritma Genetika Pada Optimasi Penjadwalan Matakuliah Pada Perguruan Tinggi STMIK Mulia Darma Sihombing, Monang Juanda Tua; M.Rajagukguk, Denni; Panjaitan, Muhammad Iqbal; Manalu, Mamed Rofendi; Simangunsong, Pandi Barita Nauli; Sridewi, Nurmala
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 1 (2024): September 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i1.8457

Abstract

This research aims to produce an optimal course schedule at STMIK Mulia Darma, with the aim of reducing the number of conflicting courses, equalizing the student burden, and maximizing the use of classrooms. The optimization process is carried out through determining the course schedule using a genetic algorithm. Genetic algorithms were chosen because of their ability to solve large-scale and complex problems, making them suitable for handling complex course scheduling problems that involve many variables and constraints. It is hoped that the results of this study will produce an optimal course schedule, taking into account course clashes, student loads, and classroom use efficiency. After research, the optimal course schedule was obtained.
Penerapan Algoritma Genetika Pada Optimasi Penjadwalan Matakuliah Pada Perguruan Tinggi STMIK Mulia Darma Sihombing, Monang Juanda Tua; M.Rajagukguk, Denni; Panjaitan, Muhammad Iqbal; Manalu, Mamed Rofendi; Simangunsong, Pandi Barita Nauli; Sridewi, Nurmala
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 1 (2024): September 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i1.8457

Abstract

This research aims to produce an optimal course schedule at STMIK Mulia Darma, with the aim of reducing the number of conflicting courses, equalizing the student burden, and maximizing the use of classrooms. The optimization process is carried out through determining the course schedule using a genetic algorithm. Genetic algorithms were chosen because of their ability to solve large-scale and complex problems, making them suitable for handling complex course scheduling problems that involve many variables and constraints. It is hoped that the results of this study will produce an optimal course schedule, taking into account course clashes, student loads, and classroom use efficiency. After research, the optimal course schedule was obtained.
Penerapan Hybrid Naïve Bayes dan Decision Tree dalam Sistem Pakar Diagnosis Penyakit Mulut Berbasis Android Panjaitan, Muhammad Iqbal; Nadeak, Berto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 1 (2025): September 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i1.9055

Abstract

Oral diseases such as stomatitis, gingivitis, and candidiasis are often undetected at an early stage due to limited public knowledge and restricted access to healthcare services, leading to delays in treatment. This study aims to develop an Android-based expert system for diagnosing oral diseases using a hybrid approach that combines the Naïve Bayes and Decision Tree algorithms. The Naïve Bayes method is applied to calculate the probability of symptoms associated with potential diseases, while the Decision Tree generates diagnostic rules that are more transparent and interpretable. The research stages include a literature review, data collection and validation of disease symptoms from medical experts, development of the hybrid model, implementation into an Android application, and system testing using cross-validation and a confusion matrix. The expected outcomes include a prototype Android application for oral disease diagnosis with a minimum accuracy of 85%, a scientific article published in a nationally accredited journal indexed in Sinta, and additional outputs such as copyright registration of the application and publication of a book. This study is expected to improve public access to early diagnosis of oral diseases, support early detection, and contribute to the advancement of digital health systems based on artificial intelligence in Indonesia.
Co-Authors Batubara, Muhammad Iqbal Cosmas Samuel Daeli Denni M Rajagukguk Fachry Abda El Rahman Hulu, Firdama Ika Yusnita Sari Khairunisa, Khairunisa Kristian Siregar Lumbanbatu, Maristella J. M.Rajagukguk, Denni Manalu, Mamed Rofendi Maulana, Fajar Monang Juanda Tua Sihombing Rahmi, Elvika Rajagukguk , Denni M. Rajagukguk, Denni M Rajagukguk Rajagukguk, Denni M. Siagian, Edward Robinson Siddik Karo-Karo Sihombing , Monang Juanda Tua Sihombing, Marjones H.H. Sihombing, Marjones Hardy H Sihombing, Monang JT Simangunsong, Pandi Barita Nauli Tamba, RL. Harmady TONNI LIMBONG