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https://repositori.uma.ac.id/handle/123456789/22777
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DC Field | Value | Language |
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dc.contributor.advisor | Lubis, Andre Hasudungan | - |
dc.contributor.advisor | Muliono, Rizki | - |
dc.contributor.author | Silaban, Monica Angelina S | - |
dc.date.accessioned | 2024-01-23T05:18:42Z | - |
dc.date.available | 2024-01-23T05:18:42Z | - |
dc.date.issued | 2023-07-11 | - |
dc.identifier.uri | https://repositori.uma.ac.id/handle/123456789/22777 | - |
dc.description | 103 Halaman | en_US |
dc.description.abstract | Minat membaca seorang siswa memiliki dampak yang signifikan terhadap kualitas pendidikan. Minat baca dapat ditingkatkan dengan adanya pengelompokan tingkat minat baca siswa. Pihak sekolah membutuhkan adanya sistem yang dapat mengelompokkan minat baca siswa berdasarkan daya tarik siswa dalam membaca, data buku yang dipinjam siswa di perpustakaan dan frekuensi membaca diperpustakaan sehingga mendapatkan hasil maksimal yang dapat meningkatkan minat baca siswa. Untuk membangun sistem tersebut dapat digunakan teknik data mining yaitu dengan algoritma K-Medoids. Data yang akan diuji sebanyak 320 siswa. Tingkat minat baca dibagi menjadi tiga bagian yaitu minat baca tinggi, sedang dan rendah. Hasil akhir dari pengelompokan yang dilakukan pada data yang di uji adalah iterasi sebanyak tujuh dan hasil Davies Bouldin Index sebesar 0,098523157 yaitu menghasilkan cluster yang optimal. Dengan siswa yang memiliki minat baca tinggi sebanyak 91 siswa, siswa yang memiliki minat baca sedang sebanyak 164 siswa dan siswa yang memiliki minat baca rendah sebanyak 51 siswa. A student's interest in reading has a significant impact on the quality of education. Reading interest can be increased by grouping students' reading interest levels. The school needs a system that can classify students' reading interest based on students' interest in reading, data on books borrowed by students in the library and the frequency of reading in the library so as to get maximum results that can increase students' reading interest. To build the system, data mining techniques can be used, namely the K-Medoids algorithm. The data to be tested is 320 students. The reading interest level is divided into three parts, namely high, medium and low reading interest. The final result of the clustering performed on the data tested is seven iterations and Davies Bouldin Index result of 0.098523157, which produces an optimal cluster. With 91 students who have a high reading interest, 164 students who have a moderate reading interest and 51 students who have a low reading interest. | en_US |
dc.language.iso | id | en_US |
dc.publisher | Universitas Medan Area | en_US |
dc.relation.ispartofseries | NPM;188160089 | - |
dc.subject | Cluster Analysis | en_US |
dc.subject | K-Medoids | en_US |
dc.subject | Interest in Reading | en_US |
dc.subject | Students | en_US |
dc.subject | Analisis Cluster | en_US |
dc.subject | Minat Baca | en_US |
dc.subject | Siswa | en_US |
dc.title | Analisis Pengelompokan Tingkat Minar Baca Siswa Menggunakan Algoritma K- Medoids (Studi Kasus : SMPN 27 Medan) | en_US |
dc.title.alternative | Analysis of Student Reading Minar Level Grouping Using the K-Medoids Algorithm (Case Study: SMPN 27 Medan) | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | SP - Informatic Engineering |
Files in This Item:
File | Description | Size | Format | |
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188160089 - Monica Angelina S Silaban - Chapter IV.pdf Restricted Access | Chapter IV | 533.44 kB | Adobe PDF | View/Open Request a copy |
188160089 - Monica Angelina S Silaban - Fulltext.pdf | Cover, Abstract, Chapter I, II, III, V, Bibliography | 2.05 MB | Adobe PDF | View/Open |
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