relation: http://repository.upi.edu/141057/
title: PREDIKSI LOYALITAS PELANGGAN TEMPAT CUCI KENDARAAN MENGGUNAKAN METODE NAÏVE BAYES
creator: M Ramdhan Qurrota A'yun, -
creator: Agus Heri Setya Budi, -
subject: Q Science (General)
subject: TK Electrical engineering. Electronics Nuclear engineering
subject: TX Home economics
description: Perkembangan Usaha Mikro, Kecil, dan Menengah (UMKM) di sektor jasa cuci kendaraan menghadapi persaingan tinggi, sehingga memerlukan strategi efektif untuk mempertahankan pelanggan. Loyalitas pelanggan menjadi kunci keberlangsungan usaha, namun faktor-faktor yang memengaruhinya belum teranalisis secara mendalam berbasis data. Penelitian ini bertujuan merancang sistem prediksi loyalitas pelanggan tempat cuci kendaraan menggunakan metode Naïve Bayes. Metode penelitian melibatkan pengumpulan data kuesioner dari 92 responden pelanggan Cristal Carwash dan pengembangan model berbasis machine learning dengan algoritma Naïve Bayes. Sistem diimplementasikan melalui antarmuka pengguna grafis atau Graphical User Interface (GUI) dan diuji menggunakan metode black box testing. Hasil penelitian menunjukkan model mencapai akurasi 94,74% dengan presisi 100% serta recall 93,3% untuk kelas loyalitas tinggi dan recall 100% untuk kelas loyalitas rendah. Analisis korelasi menunjukan kepuasan pelanggan sebagai faktor dominan (r ≈ 0,902), diikuti harga (r ≈ 0,869) dan kualitas layanan (r ≈ 0,844). Sistem GUI berhasil diintegrasikan dengan fitur prediksi, update, hapus, reset, dan ekspor data yang berfungsi dengan baik. Penelitian ini membuktikan Naïve Bayes efektif memprediksi loyalitas pelanggan dengan akurasi tinggi. Sistem ini diharapkan dapat membantu pemilik UMKM mengambil keputusan strategi bisnis berbasis data untuk meningkatkan retensi pelanggan, sekaligus menjadi referensi pengembangan serupa di sektor jasa lainnya.    The growth of Micro, Small, and Medium Enterprises (MSMEs) in the car wash service sector faces intense competition, necessitating effective strategies to retain customers. Customer loyalty is crucial for business sustainability, yet the influencing factors have not been thoroughly analyzed using data-driven approaches. This study aims to design a customer loyalty prediction system for car wash services using the Naïve Bayes method. The research methodology involves collecting questionnaire data from 92 respondents at Cristal Carwash and developing a machine learning model based on the Naïve Bayes algorithm. The system was implemented through a Graphical User Interface (GUI) and tested using black box testing. Results show that the model achieved 94.74% accuracy, with 100% precision and 93.3% recall for the high-loyalty class and 100% recall for the low-loyalty class. Correlation analysis revealed customer satisfaction as the dominant factor (r ≈ 0.902), followed by price (r ≈ 0.869) and service quality (r ≈ 0.844). The GUI system was successfully integrated with functional features, including prediction, update, delete, reset, and data export. The study proves that Naïve Bayes is highly effective in predicting customer loyalty. This system is expected to assist MSME owners in making data-driven business decisions to improve customer retention while serving as a reference for similar developments in the service sector.
date: 2025-08-25
type: Thesis
type: NonPeerReviewed
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identifier: http://repository.upi.edu/141057/1/S_TE_1800680_Title.pdf
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identifier: http://repository.upi.edu/141057/2/S_TE_1800680_Chapter1.pdf
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identifier: http://repository.upi.edu/141057/3/S_TE_1800680_Chapter2.pdf
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identifier: http://repository.upi.edu/141057/4/S_TE_1800680_Chapter3.pdf
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identifier: http://repository.upi.edu/141057/5/S_TE_1800680_Chapter4.pdf
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identifier: http://repository.upi.edu/141057/6/S_TE_1800680_Chapter5.pdf
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identifier: http://repository.upi.edu/141057/7/S_TE_1800680_Appendix.pdf
identifier:   M Ramdhan Qurrota A'yun, - and Agus Heri Setya Budi, -  (2025) PREDIKSI LOYALITAS PELANGGAN TEMPAT CUCI KENDARAAN MENGGUNAKAN METODE NAÏVE BAYES.  S1 thesis, Universitas Pendidikan Indonesia.   
relation: https://repository.upi.edu/