DELLA ATIKA SURI, NPM 2109100020 (2025) ANALISIS KEPUASAN PELANGGAN PADA PENJUALAN MOCHI TERLARIS DENGAN MENGGUNAKAN METODE NAÏVE BAYES DAN K-MEANS CLUSTERING 12 SEPTEMBER 2025 SI. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Penelitian ini untuk menganalisis tingkat kepuasan pelanggan terhadap penjualan mochi terlaris dengan menggunakan metode Naïve Bayes dan K-Means Clustering. Mochi sebagai salah satu produk kuliner yang sedang populer mengalami peningkatan permintaan, sehingga penting bagi pelaku usaha untuk memahami faktor-faktor yang memengaruhi kepuasan pelanggan. Penelitian ini dilakukan dengan mengumpulkan data selama tiga bulan dari konsumen mochi pada usaha rumahan SweetDessert yang berlokasi di Labuhanbatu Selatan. Metode Naïve Bayes digunakan untuk mengklasifikasikan tingkat kepuasan pelanggan berdasarkan atribut seperti rasa, kemasan, dan pelayanan. Sementara itu, metode K-Means Clustering digunakan untuk mengelompokkan pelanggan berdasarkan karakteristik dan preferensi mereka. Hasil pengujian menggunakan Naïve Bayes menunjukkan tingkat akurasi sebesar 94%, sedangkan metode K Means berhasil mengelompokkan pelanggan ke dalam klaster yang berbeda secara jelas. Hasil penelitian ini memberikan kontribusi dalam meningkatkan strategi pelayanan dan kualitas produk guna mencapai tingkat kepuasan pelanggan yang lebih tinggi. Kata Kunci: Kepuasan Pelanggan, Mochi, Naïve Bayes, K-Means Clustering, RapidMiner, Data Mining ====================================================================== This study aims to analyze customer satisfaction in the sales of the best-selling mochi using the Naïve Bayes and K-Means Clustering methods. As a trending culinary product, mochi has gained increasing popularity, making it important for business owners to understand the factors influencing customer satisfaction. This research collected three months of customer data from SweetDessert, a home based business located in Labuhanbatu Selatan. The Naïve Bayes method was used to classify customer satisfaction levels based on attributes such as flavor, packaging, and service. Meanwhile, the K-Means Clustering method was applied to group customers based on their characteristics and preferences. The results of the Naïve Bayes model testing showed an accuracy of 94%, and K-Means successfully segmented customers into clearly defined clusters. This study provides valuable insights for improving service strategies and product quality to achieve higher customer satisfaction. Keywords: Customer Satisfaction, Mochi, Naïve Bayes, K-Means Clustering, RapidMiner, Data Mining
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Kepuasan Pelanggan, Mochi, Naïve Bayes, K-Means Clustering, RapidMiner, Data Mining================Customer Satisfaction, Mochi, Naïve Bayes, K-Means Clustering, RapidMiner, Data Mining |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software 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: | 09 Oct 2025 04:59 |
| Last Modified: | 09 Oct 2025 04:59 |
| URI: | http://repository.ulb.ac.id/id/eprint/1784 |
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