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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) 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,
DIGITALISASI SISTEM E-COMMERCE KAIN TENUN TRADISIONAL DENGAN COLLABORATIVE FILLTERING PADA PENGUJIAN PELANGGAN Kaka, Siprianus Rendi; Aldisa, Rima Tamara
Journal of Computer Science and Information Technology Vol. 3 No. 2 (2026): Maret
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70248/jcsit.v3i2.1975

Abstract

Penelitian ini bertujuan untuk membangun sistem e-commerce kain tenun tradisional Sumba berbasis web dengan menerapkan algoritma Collaborative Filtering guna membantu proses promosi, penjualan, serta memberikan rekomendasi produk yang sesuai dengan preferensi pelanggan. Metode penelitian yang digunakan adalah Agile Software Development dengan tahapan pengumpulan data, perancangan sistem, analisis masalah, implementasi, pengujian, penyebaran, dan pemeliharaan sistem. Sistem dikembangkan menggunakan PHP, MySQL, UML, dan framework Bootstrap sebagai pendukung antarmuka website. Hasil penelitian menunjukkan bahwa sistem e-commerce yang dibangun mampu mempermudah pengelolaan produk, transaksi pemesanan, checkout, serta pengelolaan data pelanggan secara digital. Implementasi algoritma Collaborative Filtering menggunakan perhitungan Cosine Similarity berhasil memberikan rekomendasi produk yang relevan berdasarkan tingkat kemiripan preferensi antar pengguna dengan nilai similarity tertinggi sebesar 0,98. Selain itu, sistem juga memiliki tampilan antarmuka yang responsif dan mudah digunakan baik oleh pelanggan maupun admin. Simpulan penelitian ini adalah digitalisasi sistem e-commerce kain tenun tradisional Sumba berbasis web berhasil meningkatkan efektivitas promosi dan transaksi penjualan, membantu pelanggan dalam menemukan produk yang sesuai melalui fitur rekomendasi, serta mendukung pelestarian budaya lokal melalui pemanfaatan teknologi digital modern.  
Co-Authors Abdullah, Mohammad Aldinugroho Abdullah, Syamsun Nahar Ade Davy Wiranata 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 Ratih Titi Komalasari Rifqi, Muhammad Naufal Romadhon, Raihansa Sembiring, Rinawati Septi Andryana Sholeha, Puspa Ayu Sholihati, Ira Diana Wahyuddin, Mohammad Iwan Zikrullah, Alhadi Putra