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All Journal JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) JURNAL MAHAJANA INFORMASI JTIK (Jurnal Teknik Informatika Kaputama) MEANS (Media Informasi Analisa dan Sistem) Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal Ilmu Komputer dan Sistem Informasi Jurnal ABDIMAS MUTIARA Pascal: Journal of Computer Science and Informatics JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
Simanjuntak, Magdalena
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Journal : Pascal: Journal of Computer Science and Informatics

 1 
Decision Support System for Choosing the Best Nurse Using the Multi Factor Evaluation Process (MFEP) Method at Djoelham Hospital, Binjai City Ningsih, Yulia; Simanjuntak, Magdalena; Saragih, Rusmin
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 01 (2024): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

Health workers are any person who is devoted to health and has knowledge and/or skills through education in the health field which for certain types requires the authority to carry out health efforts. A strategy is needed to increase the interest of health workers working in hospitals. The selection of exemplary health workers in hospitals is expected to be a motivation to increase the interest of health workers working in hospitals so that they can be a driver for the creation of health workers who have a nationalist, ethical and professional attitude, have a high spirit of service, are disciplined, creative, knowledgeable, skilled, virtuous and can uphold professional ethics. The purpose of this study is to evaluate the performance of nurses and reward the best nurses at Djoelham Hospital Binjai City. The use of the Multi Factor Evaluation Process (MFEP) method is relevant because it can help in integrating and evaluating various factors and criteria holistically. MFEP is a method that allows to evaluate various factors that affect decisions, as well as provide weight or value relative to each of these factors. The criteria used in this study are discipline, cooperation, loyalty, education, understanding of drug prescriptions, understanding of technology. The conclusion of this study is that the construction of this support system can help Djoelham Hospital in determining the best nurse and the use of the MFEP method in the decision support system to determine the best nurse increases accuracy in determining the best suitable nurse. This method is able to process various criteria that have been set, so that the results of decisions are more objective and fair compared to manual assessments.
Application of Decision Support System to Determine the Optimization of the Learning Plan Preparation Process in Schools Using the SAW Method Puspita Sari, Melani; Simanjuntak, Magdalena; Khadapi, Muammar
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 01 (2024): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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The preparation of an effective learning plan is one of the important factors in improving the quality of education. In the context of the 2024 Independent Curriculum, the flexibility and independence of schools in designing learning plans will be greater, but this also requires the right strategy in determining priorities and the resources needed. This research aims to develop a Decision Support System (DSS) that can help optimize the process of preparing lesson plans in schools using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of its ability to assess and compare various alternatives based on predetermined criteria, such as Relevance to the World of Work, Project-Based Learning, Special Competency Development, Technology Utilization, Soft Skills Development, Plan Flexibility, Inclusive Learning, Collaboration with Industry, Critical Thinking Skills, Time Management. The results of this study show that the DSS implemented is able to provide more effective and efficient recommendations in preparing learning plans that are in accordance with the principles of the 2024 Independent Curriculum. Thus, it is hoped that this system can be a tool for educators in developing more structured and targeted learning plans.
Decision Support System for Determining Effective Learning Strategies for Students Using the SMART Method Athaya, Fara; Simanjuntak, Magdalena; Sitompul, Melda Pita Uli
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 02 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Effective learning strategies are essential factors in improving students’ academic achievement. However, at SMP Negeri 2 Binjai, several challenges remain, including the low effectiveness of applied learning methods, the lack of adaptation to individual learning styles, and the limited use of academic data in supporting learning decisions. These issues were further exacerbated by the post-pandemic shift toward hybrid learning models, which has not been fully optimized. To address this problem, this study designed a Decision Support System (DSS) using the SMART (Simple Multi-Attribute Rating Technique) method to recommend suitable learning strategies for students. The system was developed through stages of requirement analysis, logical design of the SMART calculation, and the implementation of integrated multi-criteria processing. The results show that the system can provide objective and accurate learning strategy recommendations. From 32 students analyzed, 11 students (34.37%) were recommended to adopt E-learning, 7 students (21.87%) to use Blended Learning, and 14 students (43.75%) to apply Traditional Learning. The highest score of 1.00 was achieved by two students in the E-learning category, while the lowest score of 0.125 was recorded in the Traditional category. These findings confirm that the application of the SMART method in DSS is effective in helping teachers and students determine more adaptive and personalized learning strategies, thereby supporting the improvement of learning quality in schools.
A Decision Support System for the Selection and Distribution of Superior Durian Seedlings to the Community Using the Decision Tree Method Danisuwara, Ardiya Kansya; Manurung, Hotler; Simanjuntak, Magdalena
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 02 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The durian fruit is an agricultural commodity with high economic value and strong demand both domestically and internationally. However, the success rate of durian cultivation in Indonesia remains relatively low, at approximately 30.3%. This is partly due to the limited experience of farmers in managing durian plantations and the absence of an objective system for selecting eligible recipients of superior seedlings. Inaccurate selection of seedling recipients can lead to low productivity, suboptimal fruit quality, and an imbalance between market supply and demand. To address these issues, this study proposes the development of a Decision Support System (DSS) for the selection of superior durian seedling recipients using the Decision Tree algorithm. The study identifies several factors influencing eligibility, including age, land area, land ownership, farming experience, socioeconomic status, number of plants, water availability, membership in farmer groups, regional location, and education level. Data from 300 respondents were collected and processed through several preprocessing stages, including categorical data encoding, numerical data binning, normalization, and the division of training and testing datasets. The Decision Tree model was developed using the Scikit-learn library in the Python programming language, with the Gini index as the splitting criterion. The experimental results indicate that the model achieved an accuracy of 85%, a precision of 90%, and a recall of 95% for the "Eligible" class, demonstrating the system’s effectiveness in accurately identifying qualified recipients. The system was implemented as a GUI-based desktop application using Tkinter, equipped with features for data input, eligibility prediction, recipient data management, and statistical visualization. The implementation of this system is expected to enhance objectivity, efficiency, and accountability in the distribution of superior durian seedlings, thereby contributing to increased productivity among durian farmers and promoting better market equilibrium.
Co-Authors Achmad Fauzi Adek Maulidya Agus Sapitri, Liana Ambarita, Indah Ana, Putri Annatasia, Kristina Apriandi Alfa Reza Saragih Ariska, Dedek Arliana, Lina Athaya, Fara Buaton, Relita Danisuwara, Ardiya Kansya Defita Br. Manurung, Sella Edelwais, Nurul Fauzan Akbar, Ahmad Fauzi Ahmad Muda Fauzi, Achmad Ginting, Budi Serasi Ginting, Darmawan Hasibuan, Layla Mutiara Imran Lubis Kadim, Lina Arliana Nur Kahfi Lanang Khadapi, Muammar Khair, Husnul Khairunisa, Salsabila MANURUNG, HOTLER Maulidina, Nadia Melda Pita Uli Sitompul Mirah, Alta Muammar Khadafi Nopita, Siska Novriyenni Novriyenni, Novriyenni Nurhayati Nurhayati Pardede, Akim Manaor Hara Pasaribu, Tioria Pramana, I Gusti Puspita Sari, Melani Putra, Andika Cahya Rahayu, Nur Aprilia Rahimah Faizah Rahmadilla, Semiati Ramadhani, Sisca Ratih Puspadini Roynaldi, Muhammad Rusmin Saragih, Rusmin Sari, Indah Kelana Satria R, Dandi Sawitri Sawitri Selfira, Selfira Sembiring, Arnes Sihombing, Anton Sihombing, Marto Sinaga, Ayu Puspita Sari Syahputra, Siswan Tarigan, Kiki Dea Ananda Yani Maulita Yulia Ningsih Yunika , Vica