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All Journal Jurnal Komtekinfo Bulletin of Engineering Science, Technology and Industry Moral : Jurnal Kajian Pendidikan Islam
M. Rasyid
Unknown Affiliation
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences

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Sistem Pakar Identifikasi Penyakit Kulit Melanoma dengan Metode Teorema Bayes Nopi Purnomo; Riko Muhammad Suri; Devi Yuliana; M. Rasyid
Jurnal KomtekInfo Vol. 10 No. 2 (2023): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v10i2.368

Abstract

Melanoma is a skin cancer disease that occurs due to abnormal growth of melanocytes, which are cells that can produce pigment in the skin. Melanoma is one of the most vicious skin cancers and can easily spread to other organs in the human body, such as the lungs and brain. This expert system for diagnosing melanoma skin disease is a computer-based system that is used as a tool for diagnosing melanoma skin disease based on a dynamic knowledge base. The purpose of this research is to build an expert system that can identify Melanoma Skin Disease. An expert system built using the Bayes theorem method in a diagnostic process based on expert knowledge or experience. The results of this study present an expert system capable of diagnosing melanoma skin disease quickly, precisely and accurately. These results are presented in a system built in the form of a website using PHP programming with a MySQL database. Overall this expert system can be useful for patients or the general public to be able to know clearly about melanoma skin disease from the symptoms and can provide solutions in handling it.
Menelusuri Pengaruh Kepemimpinan Situasional dan Dinamika Kelompok terhadap Produktivitas Tim di Pondok Pesantren Darul Arifin Jambi Suci Karlina Aziz; M. Rasyid; Jamrizal Jamrizal; Samsu Samsu
Moral : Jurnal kajian Pendidikan Islam Vol. 2 No. 1 (2025): Maret : Moral : Jurnal kajian Pendidikan Islam
Publisher : Asosiasi Riset Ilmu Pendidikan Agama dan Filsafat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/moral.v2i1.499

Abstract

The results of the research show that there is a significant influence between situational leadership and group dynamics on team productivity at the Darul Arifin Islamic Boarding School in Jambi. Situational leadership: Mentors and administrators at the Darul Arifin Islamic Boarding School in Jambi demonstrate good adaptability in providing guidance and direction to the team. Group dynamics: There is a positive relationship between effective communication, good cooperation and team productivity. Teams that are able to establish open communication and support each other show higher productivity. Team Productivity: Teams that have good situational leadership and positive group dynamics show increased productivity in carrying out tasks and achieving targets. This is reflected in the level of satisfaction of students, improving the quality of education, and the active role of students in Islamic boarding school activities.
MACHINE LEARNING ANALYSIS IN IMPROVING THE EFFICIENCY OF THE STUDENT ADMISSION DECISION MAKING PROCESS NEW AT PANCA BUDI MEDAN DEVELOPMENT UNIVERSITY M. Rasyid; Zulham Sitorus; Rian Farta Wijaya; Muhammad Iqbal; Khairul
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.v2i3.62

Abstract

The decision-making process in admitting new students is a crucial aspect that can influence the quality and efficiency of academic administration in higher education. This research aims to analyze the role of Machine Learning methods, especially Support Vector Machines (SVM), in increasing the efficiency of the decision-making process for new student admissions at the Panca Budi Development University, Medan. The data used in this research includes information from the student admissions process for the odd semester of the 2022/2023 academic year, which includes various variables such as Registration Number, School of Origin, Registration Payment, and others. The data is divided into a training set (70%) and a testing set (30%). The Support Vector Machine (SVM) model that was built was evaluated using metrics such as accuracy, precision, recall, and F1-Score. The research results show that the SVM model achieves an accuracy of 100%, with high precision and recall for both classes. Precision for both classes reached 1.00, while recall for the minority class (class 1) reached 0.91, indicating excellent model performance in classification. The conclusion of this research is that the Support Vector Machine (SVM) model can significantly increase efficiency and accuracy in the decision-making process for new student admissions at the Panca Budi Development University in Medan compared to conventional methods. These findings indicate that the application of Machine Learning methods can provide substantial benefits in the context of academic administration.
Co-Authors Devi Yuliana Jamrizal Khairul Muhammad Iqbal Purnomo, Nopi Rian Farta Wijaya Riko Muhammad Suri Samsu Samsu Sitorus, Zulham Suci Karlina Aziz