The Implementation of K-Means Clustering Method in Classifying Undergraduate Thesis Titles

2021

JRST

Self Cite

Data laporan judul kerja praktik (KP) biasanya hanya terkumpul di perpustakaan dan jarang dipubilkasikan ke mahasiswa, hal ini menyebabkan kesulitan bagi mahasiswa yang akan mengkasesnya. Berdasarkan permasalahan tersebut, maka dibuatlah suatu program pada penlitian ini untuk pengelompokkan Trend Topik. Metode yang digunakan dalam penelitian ini adalah Manhattan Distance Similariy dan Single Linkage. Sebelum masuk tahapan text mining, perlu dilakukan perancangan diantaranya perancangan basis data dan antar muka (interface). Tahapan dan text mining adalah mengumpulkan data (collect data), penguraian teks (text mining), penyaringan teks (text filtering), pembobotan kata (calculate term count), similarity, pengelompokan, dan pengujian. Hasil dari penelitian ini adalah program yang dapat mengolah data judul KP menjadi pola kelompok Trend Topik KP. Dari 905 data yang di dapatkan, terbentuk 7 kelompok yaitu Sistem Informasi, Multimedia, Jaringan, Web, Kewirausahaan, Magang, dan Pelatihan. Tetapi dari hasil pengujian Purity Test didapatkan nilai sebesar 0,267, yang artinya Manhattan Distance Similarity dan Single Linkage kurang cocok untuk mengelompokkan Judul KP.

Section: Pendahuluanunclassified

Penerapan Metode Single Linkage dengan Manhattan Distance Similarity dalam Mengelompokkan Trens Topik Kerja Praktik

Nita

2021

JRST

Self Cite

“…Diantara beberapa teknik yang ada dalam data mining ini adalah Teknik data mining yang lain adalah pengelompokkan, dimana dalam pengelompokkan ini digunakan untuk mencari kelompok data yang memiliki karakteristik tertentu. Pengelompokkan karakteristik data rumah tangga [1], pemetaan kualifikasi pendidikan di Indonesia [2], pengelompokkan karakteristik pelanggan telepon [3] dan pengelompokkan dokumen berbasis teks [4]. Penelitian sebelumnya adalah analisis dua metode dimana hasil pengelompokkan dengan metode K-means lebih baik dari pengelompokkan menggunakan metode Agglomerative Hierarchichal Clustering (AHC).…”

Section: Pendahuluanunclassified

“…Analisis ini dilakukan pada pengelompokkan data penumpang bus trans jogja [5]. Metode K-Means juga efektif dan efisien dalam mengelompokkan dalam mengelompokkan data berbentuk text [4].…”

Section: Pendahuluanunclassified

Pengelompokkan Mahasiswa Akademik Keperwatan Berdasarkan Asal Sekolah dan Nilai Akademik Menggunakan Metode Clustering K-Means

Fajri

Jones

et al. 2021

bits

Self Cite

Nursing Academic of Karya Bakti Husada (AKPER KBH) Bantul is one of the academics that opened the 2000 department. Based on an interview with the Director of AKPER KBH, the registration requirements to become a student of the Academy are currently graduates from all majors and all high schools. AKPER KBH has not analyzed student data whether there is a relationship between high school history and passing grades of GPA as an evaluation material in the learning process, although at this time with the variation of new students causing difficulty in learning difficult compared to before, while GPA achievement is very important in finding job after graduation. The purpose of this study is to classify academic data of AKPER students based on data on school origin, GPA scores, and Medical Surgical Nursing II (KMB II), Mental Nursing II (Kep Jiwa II), Child Nursing II (Kep Anak II), Maternity Nursing II (Kep Maternitas II), and Medical Surgical Nursing ( KMB V). The stages in this study include data collection, data search, data selection, data transformation, data grouping using the K-Means method and knowledge representation, the test used in this study is the purity test. From the experiments conducted, the accuracy value is 0.924 with the number of clusters 3

“…Data in the form of text or numeric data can be grouped using clustering. Clustering has been employed in texts before [12], [13]. The Analytical Hierarchy Clustering (AHC) approach and the K-Medoids method are two examples of the numerous techniques that can be applied.…”

Section: Introductionmentioning

confidence: 99%

Comparison of K-Medoids Method and Analytical Hierarchy Clustering on Students' Data Grouping

JOIV : Int. J. Inform. Visualization

Linarti

et al. 2023

One sign of how successfully the educational process is carried out on campus in a university is the timely graduation of students. This study compares the Analytic Hierarchy Clustering (AHC) approach with the K-Medoids method, a data mining technique for categorizing student data based on school origin, region of origin, average math score, TOEFL, GPA, and length study. This study was carried out at University X, which contains a variety of architectural styles. The R department, the S department, the T department, and the U department make up one of them. K-Medoids and AHC techniques Utilize the number of clusters 2, 3, and 4 and the silhouette coefficient approach. The evaluation's findings indicate a value. Although there is a linear silhouette between the AHC and K-Medoids methods, the AHC approach (departments R: 0.88, S: 0.87, T: 0.88, and U: 0.88) has a more excellent Silhouette value than K-Medoids (department R: 0.35, department S: 0.65 number of cluster 2, department T: 0.67 number of cluster 2 and program Study U: 0,52). The results of the second approach, which includes the K-Medoids and AHC procedures, are determined by the data distribution to be clustered rather than by the quantity of data or clusters. Based on this methodology, University X can refer to the grouping outcomes for the four departments with two achievements to receive results on schedule.