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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.
Pengembangan Mixtilinear Excircle pada Segiempat Siklik Z, Tomi
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2023: SNTIKI 15
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Teorema mixtilinear incircle dibangun dari segitiga yang menyinggung lingkaran luar (circumcircle) dari dalam dan mixtilinear excircle dibangun dari segitiga yang menyinggung lingkaran luar (circumcircle) dari luar. Sebagian besar pengembangan yang banyak dilakukan oleh penulis adalah mixtilinear incircle yang pada umumnya berfokus pada perhitungan jari-jari mixtilinear incircle. Jika dikonstruksi segiempat siklik yang dapat garis diagonal membentuk empat segitiga, maka pada tulisan ini akan dikembangkan hubungan berbagai mixtilinear excircle jika dikonstruksi pada segiempat siklik. Metode penelitian yang digunakan adalah mengkonstruksi berbagai mixtilinear excircle yang menyinggung perpanjangan dua sisi dan berbagai mixtilinear excircle menyinggung perpanjangan dua diagonal segiempat siklik, melakukan berbagai simulasi jari-jari mixtilinear excircle yang bisa dibuat dan melakukan pembuktian secara matematis. Hasil penelitian membuktikan bahwa konsep mixtilinear excircle dapat dikembangkan pada segiempat siklik dan terdapat hubungan perkalian rasio jari-jari mixitilinear mixtilinear excircle dengan rasio panjang diagonal segiempat siklik.    Kata kunci: mixtilinear excircle, rasio, segiempat siklik