DEWI ANTIKA, NPM 2109100021 (2025) PENERAPAN DATA MINING KLASIFIKASI TINGKAT KEPUASAN MAHASISWA TERHADAP PELAYANAN AKADEMIK MENGGUNAKAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (STUDI KASUS PROGRAM STUDI SISTEM INFORMASI UNIVERSITAS LABUHANBATU). Tugas_Akhir(Artikel) Journal of Computer Science and Information Systems (JCoInS), 6 (3). pp. 221-232. ISSN 2747-2221(e-ISSN)
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Abstract
This study was conducted to classify public satisfaction levels using the Support Vector Machine (SVM) algorithm as the primary data analysis method. The objective of this study was to obtain an accurate and reliable prediction model for determining the Satisfaction and Dissatisfaction categories based on the available data. The theoretical basis used refers to the concept of machine learning, specifically SVM, which works by forming an optimal hyperplane to separate data classes. In addition, model evaluation theories such as the Confusion Matrix were used to objectively measure prediction performance. The research methodology included data collection, pre-processing, dividing the dataset into training and test data, and training the SVM model. Evaluation was conducted using accuracy, sensitivity, and specificity metrics to assess the model's ability to predict data accurately. The results and discussion indicate that the SVM successfully classified the majority of data correctly, with the Satisfaction class having a perfect prediction rate while the Dissatisfaction class still had a small error. Further analysis indicated the need for SVM parameter optimization to improve accuracy in the minority class. The conclusion of this study states that the SVM has good performance in classifying public satisfaction data, although it still requires refinement in recognizing certain class patterns. This finding opens up opportunities for developing more adaptive methods to improve predictive performance. Keywords : Support Vector Machine, Classification, Public Satisfaction, Model Evaluation, Confusion Matrix
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Support Vector Machine, Classification, Public Satisfaction, Model Evaluation, Confusion Matrix |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 10 Nov 2025 07:36 |
| Last Modified: | 10 Nov 2025 07:36 |
| URI: | http://repository.ulb.ac.id/id/eprint/1949 |
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