PENERAPAN MACHINE LEARNING NAÏVE BAYES DAN K-NEAREST NEIGHBOR DALAM KLASIFIKASI KEPUASAN PELANGGAN WIFI INDIHOME

NIKE MARIANTI, NPM 2109100051 (2025) PENERAPAN MACHINE LEARNING NAÏVE BAYES DAN K-NEAREST NEIGHBOR DALAM KLASIFIKASI KEPUASAN PELANGGAN WIFI INDIHOME. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Kepuasan pelanggan merupakan indikator penting dalam menilai kualitas layanan sebuah perusahaan, termasuk dalam sektor penyedia jasa internet seperti IndiHome. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kepuasan pelanggan IndiHome dengan menerapkan dua algoritma machine learning, yakni Naïve Bayes dan K-Nearest Neighbor (K-NN). Penelitian dilakukan dengan mengumpulkan data primer melalui kuesioner terhadap pelanggan IndiHome di Rantauprapat, yang mencakup lima atribut utama: kekuatan sinyal, penggunaan, harga berlangganan, jumlah kapasitas pengguna, dan tingkat kepuasan. Proses analisis mencakup tahap preprocessing data, pelatihan model, serta evaluasi performa menggunakan confusion matrix untuk mengukur akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model Naïve Bayes mampu memberikan akurasi klasifikasi sebesar 89%, sedangkan model K-Nearest Neighbor hanya mencapai akurasi 86%. Berdasarkan hasil penelitian bahwa algoritma Naïve Bayes lebih efektif dalam mengklasifikasikan tingkat kepuasan pelanggan berdasarkan data yang tersedia. Penelitian ini memberikan kontribusi dalam bidang analisis sentimen pelanggan serta menjadi dasar pengambilan keputusan strategis bagi perusahaan dalam meningkatkan kualitas layanan. Kata Kunci: Kepuasan Pelanggan, IndiHome, Naïve Bayes, K-Nearest Neighbor, Machine Learning, Klasifikasi ========================================================== Customer satisfaction is a key indicator for assessing the quality of a company's services, including in the internet service provider sector such as IndiHome. This study aims to classify the level of IndiHome customer satisfaction by applying two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). The research was conducted by collecting primary data through questionnaires distributed to IndiHome customers in Rantauprapat, covering five main attributes: signal strength, usage type, subscription cost, number of users, and satisfaction level. The analysis process involved data preprocessing, model training, and performance evaluation using a confusion matrix to measure accuracy, precision, recall, and F1-score. The findings revealed that the Naïve Bayes model achieved a classification accuracy of 89%, while the K-Nearest Neighbor model reached only 86%. These results indicate that the Naïve Bayes algorithm is more effective in classifying customer satisfaction levels based on the available data. This research contributes to the field of customer sentiment analysis and serves as a foundation for strategic decision-making by companies in enhancing service quality. Keywords: Customer Satisfaction, IndiHome, Naïve Bayes, K-Nearest Neighbor, Machine Learning, Classification

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Kepuasan Pelanggan, IndiHome, Naïve Bayes, K-Nearest Neighbor, Machine Learning, Klasifikasi
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software 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:	12 Nov 2025 07:18
Last Modified:	12 Nov 2025 07:21
URI:	http://repository.ulb.ac.id/id/eprint/1959

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