PENERAPAN METODE MACHINE LEARNING NAIVE BAYES UNTUK ANALISIS KINERJA KEUANGAN PADA JUARA COFFEE

NURMALA PANJAITAN, NPM 2209100097 (2026) PENERAPAN METODE MACHINE LEARNING NAIVE BAYES UNTUK ANALISIS KINERJA KEUANGAN PADA JUARA COFFEE. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Penelitian ini bertujuan untuk menerapkan metode Machine Learning menggunakan algoritma Naive Bayes dalam menganalisis dan mengklasifikasikan kinerja keuangan pada Juara Coffee. Permasalahan dalam penelitian ini adalah proses analisis kinerja keuangan yang masih membutuhkan pengolahan data secara sistematis untuk menghasilkan informasi yang objektif sebagai dasar pengambilan keputusan. Data yang digunakan meliputi pendapatan, biaya bahan baku, gaji karyawan, biaya sewa dan utilitas, total biaya, serta laba bersih. Kinerja keuangan diklasifikasikan menjadi tiga kategori, yaitu rendah, sedang, dan baik berdasarkan nilai laba bersih. Proses pengolahan data dilakukan melalui tahapan pengumpulan data, pembersihan dan transformasi data, pembentukan data training dan data testing, penerapan algoritma Naive Bayes, serta evaluasi menggunakan Orange Data Mining. Hasil pengujian menunjukkan bahwa pada data training sebesar 80%, algoritma Naive Bayes memperoleh nilai Accuracy sebesar 93,2%, Precision 93,6%, Recall 93,2%, F1-Score 93,1%, AUC 0,996, dan MCC 0,891. Sementara itu, pada data testing sebesar 20%, model memperoleh Accuracy, Precision, Recall, F1-Score, AUC, dan MCC masing-masing sebesar 100%. Seluruh 23 data testing berhasil diklasifikasikan dengan benar tanpa terjadi kesalahan klasifikasi. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes dapat diterapkan dengan baik untuk mengklasifikasikan kinerja keuangan Juara Coffee dan dapat menjadi salah satu alternatif dalam mendukung pengambilan keputusan keuangan yang lebih cepat, objektif, dan berbasis data. Kata Kunci: Machine Learning, Naive Bayes, Kinerja Keuangan, Juara Coffee, Klasifikasi ================================================================================================= This study aims to apply a Machine Learning method using the Naive Bayes algorithm to analyze and classify the financial performance of Juara Coffee. The problem addressed in this study is the need for systematic financial data processing to produce objective information that can support decision-making. The data used in this study consist of revenue, raw material costs, employee salaries, rent and utilities, total costs, and net profit. Financial performance is classified into three categories: low, medium, and good, based on the net profit obtained. The data processing stages include data collection, data cleaning and transformation, preparation of training and testing datasets, implementation of the Naive Bayes algorithm, and model evaluation using Orange Data Mining. The testing results show that the Naive Bayes algorithm achieved an Accuracy of 93.2%, Precision of 93.6%, Recall of 93.2%, F1-Score of 93.1%, AUC of 0.996, and MCC of 0.891 on the 80% training data. Meanwhile, on the 20% testing data, the model achieved 100% Accuracy, Precision, Recall, F1-Score, AUC, and MCC. All 23 testing data were correctly classified without any misclassification. The results indicate that the Naive Bayes algorithm can be effectively applied to classify the financial performance of Juara Coffee and can serve as an alternative approach to support faster, more objective, and data-driven financial decision making. Keywords: Machine Learning, Naive Bayes, Financial Performance, Juara Coffee, Classification

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Machine Learning, Naive Bayes, Kinerja Keuangan, Juara Coffee, Klasifikasi===============Machine Learning, Naive Bayes, Financial Performance, Juara Coffee, Classification
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 > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	03 Sep 2026 02:59
Last Modified:	03 Sep 2026 02:59
URI:	http://repository.ulb.ac.id/id/eprint/2715

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