SETIAWAN, FADLI AZIZ (2022) ANALISIS PERMASALAHAN PERANGKAT PENCETAK MENGGUNAKAN METODE ALGORITMA K-MEANS DAN K-MEDOIDS. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
PT Amido Makmor Tulus Sejati is a distributor of Kyocera brand multifunction printers in Indonesia. Performance evaluation is needed to maintain customer satisfaction with the use of Kyocera multifunction printers. The evaluation process is carried out manually which results in the evaluation results given being less than optimal, so it is necessary to process data quickly and precisely with data mining techniques using grouping. The clustering method is used to group problems that often occur based on the type of Kyocera multifunction printer machine. In this study, the K-Means and K-Medoids clustering algorithms were applied, which was then tested for optimal clustering using the Elbow and Silhouette Score methods. The data used in this study were 1620 instants which were quantitative data. The process to find the optimal clustering value is done by finding the average Silhouette Score and Purity Value with the outer side of the K-Means and K-Medoids algorithms. The results of this study indicate that the optimal number of clusters is 2 (two) for the K-Means algorithm with a Silhouette Score of 0.606 and the optimal number of clusters is 4 (four) for the K-Means algorithm with a Silhouette Score of 0.240. Key Words: Clustering, TF-IDF Vectorizer, Silhouette Score, K-Means, K-Medoids PT Amido Makmor Tulus Sejati merupakan perusahaan distributor multifunction printer merk Kyocera di Indonesia. Evaluasi kinerja teknisi diperlukan untuk mempertahankan kepuasan customer terhadap penggunaan multifunction printer Kyocera. Proses evaluasi kinerja teknisi masih diproses secara manual yang mengakibatkan hasil evaluasi kinerja teknisi yang diberikan kurang akurat atau kurang maksimal, sehingga perlu dilakukan suatu teknik pengolahan data secara cepat dan lebih akurat salah satunya dengan mempergunakan teknik data mining dengan menggunakan metode algoritma clustering. Metode algoritma clustering dipergunakan untuk mengelompokkan problem yang sering terjadi berdasarkan tipe mesin multifunction printer Kyocera. Pada penelitian ini diterapkan algoritma clustering K-Means dan K-Medoids, yang kemudian dilakukan uji clustering yang optimal dengan mempergunakan Metode Elbow dan Silhouette Score. Data yang dipergunakan dalam penelitian ini sebanyak 1620 instan yang merupakan Data Kuantitatif. Proses untuk mencari nilai clustering yang optimal dilakukan dengan mencari rata-rata Silhouette Score dan Nilai Kemurnian dengan sisi luar dari algoritma K-Means dan K-Medoids. Hasil penelitian ini menunjukkan bahwa jumlah cluster optimal adalah 2 (dua) untuk algoritma K-Means dengan nilai Silhouette Score 0,606 dan jumlah cluster optimal 4 (empat) untuk algoritma K-Medoids dengan nilai Silhouette Score 0,240. Kata Kunci: Clustering, TF-IDF Vectorizer, Silhouette Score, K-Means, K-Medoids
| Item Type: | Thesis (S1) |
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| Call Number CD: | FIK/INFO. 22 007 |
| NIM/NIDN Creators: | 41517120027 |
| Uncontrolled Keywords: | Clustering, TF-IDF Vectorizer, Silhouette Score, K-Means, K-Medoids |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 600 Technology/Teknologi > 600. Technology/Teknologi |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Dede Muksin Lubis |
| Date Deposited: | 24 Jun 2022 07:12 |
| Last Modified: | 24 Jun 2022 07:12 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/63974 |
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