ANALISA MENENTUKAN STRATEGI PENJUALAN DI COFFE SHOP MENGGUNAKAN METODE NAÏVE BAYES

BAMBANG SYAFARUDDIN SIAGIAN, NPM 2209500179 (2026) ANALISA MENENTUKAN STRATEGI PENJUALAN DI COFFE SHOP MENGGUNAKAN METODE NAÏVE BAYES. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Persaingan bisnis coffee shop yang semakin meningkat menuntut pelaku usaha untuk mampu menentukan strategi penjualan yang tepat berdasarkan data. Namun, banyak coffee shop belum memanfaatkan data pelanggan dan data penjualan secara optimal sehingga pengambilan keputusan masih dilakukan secara subjektif. Penelitian ini bertujuan untuk menganalisis pola penjualan dan perilaku pelanggan serta menentukan strategi penjualan yang lebih efektif menggunakan metode Naive Bayes. Metode penelitian yang digunakan adalah data mining dengan algoritma Naive Bayes. Data yang digunakan berupa data pelanggan Coffee Shop Effort Coffee yang terdiri dari atribut kualitas produk, harga, dan kenyamanan. Tahapan penelitian meliputi pengumpulan data, seleksi data, preprocessing, penerapan algoritma Naive Bayes, serta evaluasi model menggunakan aplikasi Orange Data Mining. Dataset yang digunakan berjumlah 46 data pelanggan yang dibagi menjadi data training dan data testing. Hasil penelitian menunjukkan bahwa metode Naive Bayes mampu mengklasifikasikan data pelanggan untuk mendukung penentuan strategi penjualan coffee shop. Berdasarkan pengujian menggunakan Orange Data Mining, diperoleh hasil klasifikasi sebanyak 27 data pada kategori nyaman dan 19 data pada kategori kurang nyaman dari total 46 data yang dianalisis. Hasil penelitian ini menunjukkan bahwa metode Naive Bayes dapat membantu pengelola coffee shop dalam memahami pola perilaku pelanggan, menentukan strategi promosi, mengelola produk unggulan, serta mendukung pengambilan keputusan yang lebih efektif dan berbasis data. Kata Kunci: Data Mining, Naïve bayes, Coffee shop, Strategi penjualan, Klasifikasi Pelanggan ================================================================================================= The increasing competition in the coffee shop industry requires business owners to develop effective sales strategies based on data. However, many coffee shops have not utilized customer and sales data optimally, resulting in decision-making processes that are still largely subjective. This study aims to analyze sales patterns and customer behavior and to determine more effective sales strategies using the Naive Bayes method.The research method employed is data mining using the Naive Bayes algorithm. The data used in this study consist of customer data from Effort Coffee Shop, including product quality, price, and comfort attributes. The research stages include data collection, data selection, preprocessing, implementation of the Naive Bayes algorithm, and model evaluation using Orange Data Mining software. The dataset consisted of 46 customer records, which were divided into training data and testing data.The results indicate that the Naive Bayes method is capable of classifying customer data to support the determination of coffee shop sales strategies. Based on testing using Orange Data Mining, the classification results showed 27 records in the comfortable category and 19 records in the less comfortable category out of 46 analyzed records. These findings demonstrate that the Naive Bayes method can assist coffee shop managers in understanding customer behavior patterns, determining promotional strategies, managing featured products, and supporting more effective data-driven decision-making. Keywords: Data Mining, Naive Bayes, Coffee Shop, Sales Strategy, Customer Classification

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Data Mining, Naïve bayes, Coffee shop, Strategi penjualan, Klasifikasi Pelanggan==============Data Mining, Naive Bayes, Coffee Shop, Sales Strategy, Customer Classification
Subjects:	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 > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	03 Aug 2026 04:11
Last Modified:	03 Aug 2026 04:11
URI:	http://repository.ulb.ac.id/id/eprint/2623

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