ASHARI, IVAN SETO (2023) Perbandingan Algoritma K-Means Dengan Algoritma Fuzzy C-Means Untuk Analisis Pengelompokan Nilai Akademik Siswa SMPN 35 Jakarta Berdasarkan Nilai Ujian Akhir Semester (UAS). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Every institution must be diverse have data, including 35 Junior High School of Jakarta certainly have various data related to its students. Every day, the more likely the data is piled up in the school database, the data stack is a problem because it hasn't been used at its maximum. These stacks of data can be used as much as possible to be a very useful and informative information. Therefore, this research aims to clustering the academic scores of SMPN 35 Jakarta students based on Semester Final Examination scores using the K-Means and Fuzzy C-Means algorithms into 3 clusters smart, sufficient and insufficient. As well as performing a comparison of the performance of the two algorithms using the silhouette coefficient method. The clustering results show that the number of students in smart clusters always increases every semester even though in semester 6 there is a slight decrease, sufficient clusters have an increase and decrease in the number of students each semester, less clusters the number of students always decreases in each semester. The silhouette coefficient score of the K-Means algorithm is better than the Fuzzy C-Means algorithm in clustering data on UAS scores, because the silhouette coefficient score of K-Means is always higher than Fuzzy C-Means every time you do clustering in every semester. Keywords: Clustering, K-Means, Fuzzy C-Means, Silhouette Coefficient, Semester Final Examination scores. Setiap institusi pastinya memiliki data yang beragam termasuk Sekolah Menengah Pertama Negeri (SMPN) 35 Jakarta tentunya memiliki berbagai data yang berkaitan dengan siswanya. Semakin hari pastinya data-data tersebut semakin bertumpuk pada database sekolah, tumpukan data tersebut menjadi sebuah masalah karena belum digunakan secara maksimal. Tumpukan data tersebut dapat dimanfaatkan dengan sebaik mungkin untuk dijadikan suatu informasi yang sangat berguna dan informatif. Oleh karena itu, penelitian ini bertujuan untuk mengelompokan nilai akademik siswa SMPN 35 Jakarta berdasarkan nilai Ujian Akhir Semester (UAS) menggunakan algoritma K-Means dan Fuzzy C-Means menjadi 3 cluster pintar, cukup dan kurang. Serta melakukan perbandingan kinerja kedua algoritma tersebut menggunakan metode silhouette coefficient. Hasil pengelompokkan menunjukkan jumlah siswa pada cluster pintar setiap semesternya selalu meningkat walaupun di semester 6 terjadi sedikit penurunan, cluster cukup jumlah siswanya setiap semester terjadi kenaikan dan penurunan, cluster kurang jumlah siswanya selalu menurun pada setiap semester. Score silhouette coefficient algoritma K-Means lebih baik dibandingkan dengan algoritma Fuzzy C-Means dalam melakukan clustering data nilai UAS, karena score silhouette coefficient K-Means selalu lebih tinggi dari Fuzzy C-Means setiap melakukan clustering di setiap semesternya. Kata Kunci : Pengelompokkan, K-Means, Fuzzy C-Means , Silhouette Coefficient, Nilai akademik Ujian Akhir Semester (UAS).
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