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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) J-SAKTI (Jurnal Sains Komputer dan Informatika) BEES: Bulletin of Electrical and Electronics Engineering Journal of Computer Science and Information Technology Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Aldisa, Rima Tamara
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Social Sciences

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Perbandingan Algoritma K-Means dan K-Medoids Dalam Pengelompokan Kelas Untuk Mahasiswa Baru Program Magister Faran, Jhiro; Aldisa, Rima Tamara
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.v5i2.4753

Abstract

This research discusses a comparison of two grouping algorithms, namely K-Means and K-Medoids, in the context of class grouping for new master's program students. Choosing the right clustering algorithm can help universities optimize resource allocation and maximize student learning experiences. K-Means is a popular clustering algorithm, which works by dividing data into a number of homogeneous groups based on the distance between data points and the cluster center. Meanwhile, K-Medoids is a variation of K-Means that uses actual data points as a cluster representation, which makes it more resistant to outliers. This research involves a dataset of new master's program students which includes various attributes, such as entrance exam scores, educational background, and major preferences. The comparison results between K-Means and K-Medoids were carried out by considering clustering evaluation metrics such as SSE (Sum of Squared Errors) and Silhouette Score. Experimental results show that the performance of K-Means and K-Medoids differs depending on the characteristics of the dataset. K-Means tends to produce more homogeneous groups, but is more sensitive to outliers. In contrast, K-Medoids tend to be more stable in dealing with outliers, but may produce less homogeneous groups. Therefore, the selection of an appropriate clustering algorithm should be based on the specific goals and characteristics of the new master's program student population. This research provides valuable insight for colleges in planning the allocation of classes, mentors, and other resources for new students. The right decisions in class grouping can increase student retention, learning satisfaction, and academic success. In addition, this research also stimulates further discussion in combining different clustering methods to achieve more optimal results in grouping classes of new master's program students.
Penerapan Metode Multi Objective Optimization on The Basis of Ratio Analysis (MOORA) dalam Penentuan Pembimbing Skripsi Terbaik Nasution, Aisah Amini; Aldisa, Rima Tamara; Mesran, Mesran; Fadillah, Rizkah
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.v5i2.4803

Abstract

The first step in thesis preparation involves titling and selecting a supervisor. Choosing the best supervisors aims to enhance both lecturers' enthusiasm and the quality of student guidance. To identify the most suitable lecturer, thorough consideration and a decision-support system are essential. In this research, the authors employed the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method as a technique integrated into the decision support system. This method aids in determining attribute weight values and the ranking process, ultimately selecting the most favorable supervisor from various available options. From the results of the application of the MOORA method in knowing the best thesis supervisor, namely on alternative A8 with a value of 0.3288 on behalf of Marigan Sianturi, SE, and M.kom,
Sistem Pendukung Keputusan Penerimaan Dosen Tetap Menggunakan Metode MOORA dan MOSRA Mesran, Mesran; Aldisa, Rima Tamara; Rangkuti, Wanda Tofani Devi; Sari, Cindy Nanda
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7140

Abstract

Lecturers are the forerunners and places to gain knowledge for the nation's children, good lecturers will produce good students too, and good students will become successors to the progress of the nation to be even better, the large number of lecturers at Budi Darma University results in a density of lecturers, it is important to do acceptance of permanent lecturers to provide rewards to lecturers who have worked diligently and earnestly, each lecturer has their own quality but permanent lecturers are lecturers who have a safer position and are trusted by the campus, the importance of selecting permanent lecturers using a system decision support to prevent fraud in the election process. In this study, the MOORA (Multi-Objective Optimization on the Basis Of Ratio Analysis) and MOOSRA (Multi-objective Optimization on the basis of Simple Ratio Analysis) methods are used to assist the selection process in a logical, systemic manner and can produce a decision value on the ranking value. which are different from each formula or algorithm, but these values are equally real and fair without any cheating. In this study the authors also used the ROC (Rank Order Centroid) value to obtain an effective and correct weighting value to perform calculations on the criteria values that had been set by the campus or college of the Budi Darma Medan University. The results in this study based on the calculation of the MOORA method, the highest result was achieved by A1, which is worth 0.4742 and in the MOOSRA method, the highest alternative result was achieved by A1, which is worth 28.1366.
Penerapan Data Mining Untuk Clustering Kualitas Udara Rifqi, Ahmad; Aldisa, Rima Tamara
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7145

Abstract

Human health at this time is the key to the continuity of life. Human health is very necessary in the process of development of human life. Environmental health is related to the circumstances or conditions that exist in the surrounding area where you live, whether in a small environment or a large environment. Air quality is the condition of the surrounding air. Air quality is very important for human life because air is what helps humans to live by breathing. With the availability of good air quality, it will certainly be an important factor for an area, not only for health but also for other sectors that interact directly in open areas. The important role of air quality for humans means that more attention needs to be paid and special treatment is given to areas exposed to bad air. The above is a very important problem that must be resolved immediately, if the problem is not resolved immediately it will have an impact on health. The process of solving problems requires a way to resolve them. Where the process of measuring air quality can be seen based on certain conditions or criteria that occur in an area. Data mining is a method used to carry out the problem solving process by processing data. In the process carried out in data mining, there are various ways of solving it. One thing that can be used is clustering. In clustering itself there are various kinds of algorithms such as DBSCAN, K-Means and K-Medoids. In this research, the solution process will use the three algorithms K-Means, K-Medoids and DBSCAN. The purpose of using these three algorithms is to compare the results obtained. In the process carried out in completing data mining, clustering techniques are used using 3 (three) algorithms, namely K-Means, K-Medoids and DBSCAN. The results obtained were that the K-Means algorithm had the highest accuracy value obtained at K=4 with a value of 0.843, for the K-Medoids algorithm the highest value was obtained at K=5 with a value of 0.896 and for the DBSCAN algorithm the highest value was obtained at K=2 with a value of 0.885.
Co-Authors Abdullah, Mohammad Aldinugroho Abdullah, Syamsun Nahar Ade Davy Wiranata Ahmad Rifqi Aldinugroho, Mohammad Alfarisi, Sechan Andrianingsih Azizah Azizah Fadhilah, Raihan Fajar Fadillah, Rizkah Faran, Jhiro Fauziah Fauziah Ghirrid, Aria Andros Iskandar Fitri, Iskandar Kaka, Siprianus Rendi Karel, Fhizyel Nazareta Maulana, Pandu Mesran, Mesran Nasution, Aisah Amini Putra, Gifari Yudiya Putra, Ricky Syah Rafli, R Rangkuti, Wanda Tofani Devi Ratih Titi Komalasari Rifqi, Muhammad Naufal Romadhon, Raihansa Sari, Cindy Nanda Sembiring, Rinawati Septi Andryana Sholeha, Puspa Ayu Sholihati, Ira Diana Wahyuddin, Mohammad Iwan Zikrullah, Alhadi Putra