PENERAPAN ALGORITMA K-MEANS DAN NAÏVE BAYES DALAM MENGANALISA TINGKAT KEPUASAN MAHASISWA TERHADAP PELAYANAN KAMPUS
| dc.contributor.author | NAIBAHO, JOHAN LEONARDO | |
| dc.date.accessioned | 2025-07-16 | |
| dc.date.available | 2025-07-16 | |
| dc.date.issued | 2024 | |
| dc.identifier.uri | https://repositori.unhar.ac.id/handle/1504/penerapan-algoritma-k-means-dan-naive-bayes-dalam-menganalisa-tingkat-kepuasan-mahasiswa-terhadap-pelayanan-kampus | |
| dc.description.abstract | This study aims to analyze the level of student satisfaction with campus services using two data mining algorithms, namely K-Means and Naive Bayes. K-Means algorithm is used to cluster students based on their satisfaction level, while Naive Bayes algorithm is used to predict student satisfaction level based on certain attributes such as facilities, academic services, and administrative support. The results of clustering with K-Means show that there are several main groups that describe different levels of satisfaction among students. Meanwhile, the prediction results with Naive Bayes showed a fairly high level of accuracy in classifying student satisfaction levels. Thus, the combination of these two algorithms can provide deep insights for campus management in improving service quality and student satisfaction. This research also provides recommendations for service improvement based on the findings from data analysis. Keyword : Data Mining,Algoritma K-means,Naïve Bayes,Classtering,Classification | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Universitas Harapan Medan | en_US |
| dc.subject | ALGORITMA K-MEANS DAN NAÏVE BAYES | en_US |
| dc.title | PENERAPAN ALGORITMA K-MEANS DAN NAÏVE BAYES DALAM MENGANALISA TINGKAT KEPUASAN MAHASISWA TERHADAP PELAYANAN KAMPUS | en_US |
| dc.type | Skripsi | en_US |
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