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All Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi AHKAM : Jurnal Hukum Islam dan Humaniora
Azzahra, Dini
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Humanities Computer Science & IT Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis Ramadhani, Siti; Azzahra, Dini; Z, Tomi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.9292

Abstract

The thesis is one of the scientific works based on the conclusions of field research or observations compiled and developed by students as well as research carried out according to the topic containing the study program which is carried out as a final project compiled in the last stage of formal study. A large number of theses, of course, will be sought in looking for categories of thesis topics, or the titles raised have different relevance. However, the student thesis can be by topics that are almost relevant to other topics so that it can make it easier to find topics that are relevant to the group. One of the uses of techniques in machine learning is to find text processing (Text Mining). In-text mining, there is a method that can be used, namely the Clustering method. Clustering processing techniques can group objects into the number of clusters formed. In addition, there are several methods used in clustering processing. This study aims to compare 2 cluster algorithms, namely the K-Means and K-Medoids algorithms to obtain an appropriate evaluation in the case of thesis grouping so that the relevant topics in the formed groups have better accuracy. The evaluation stage used is the Davies Bouldin Index (DBI) evaluation which is one of the testing techniques on the cluster. In addition, another indicator for comparison is the computation time of the two algorithms. According to the DBI value test carried out on algorithm 2, the K-Medoids algorithm is superior to K-Means, where the average DBI value produced by K-Medoids is 1,56 while K-Means is 2,79. In addition, the computational time required in classifying documents is also a reference. In testing the computational time required to group 50 documents, K-Means is superior to K-Medoids. K-Means has an average computation time for grouping documents, which is 1 second, while K-Medoids provide a computation time of 26,7778 seconds.
Metode dalam Islam dan Implementasinya di SMP Annur Prima Arlina, Arlina; Azzahra, Dini; As-Salim, Malikul Sholeh; Agustin, Rika Nurmida
AHKAM Vol 4 No 1 (2025): MARET
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ahkam.v4i1.4741

Abstract

Learning methods are ways that teachers use to convey learning materials to students in achieving goals. Teachers are also expected to be objective in epistemological aspects and apply holistic and flexible methods, strategies and approaches. Choosing a method must be accurate in accordance with various factors, so that the results will be satisfactory. There are 8 methods of learning in Islam: the method of targib, tarhib, exemplary, story, habituation, tamsil, memorization, warning and giving motivation. The method we use in this research is a phenomenalogical qualitative method by collecting some field data, in the form of observation, interviews and documentation. The results showed that Islamic learning methods at An-nur Prima Junior High School have been implemented in the classes so that learning runs effectively and can also shape the character of students who are more religious.
Co-Authors Agustin, Rika Nurmida Arlina Arlina, Arlina As-Salim, Malikul Sholeh Ramadhani, Siti Z, Tomi