HADRON SOFYAN, NPM 2309103199 (2026) ANALISIS PROYEK DATA GAJI PEGAWAI BIAYA OPERASIONAL DI SWAN CAFFEE & RESTO RANTAUPRAPAT MENGGUNAKAN METODE KNN. Tugas_Akhir(Artikel) Informatika : Fakultas Sains dan Teknologi, 14 (2). pp. 1-8. ISSN 2615-1855 (e-ISSN) 2303-2863 (p-ISSN)
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Abstract
Pemanfaatan data operasional sebagai dasar pengambilan keputusan penting dalam pengelolaan usaha kuliner. Data gaji pegawai dan biaya operasional yang tercatat secara rutin memiliki potensi untuk dianalisis guna mengidentifikasi pola pengeluaran dan mendukung efisiensi operasional. Namun, data tersebut umumnya masih digunakan sebatas laporan administratif. Penelitian ini bertujuan untuk menganalisis dan mengklasifikasikan data gaji pegawai serta biaya operasional pada Swan Caffee & Resto Rantauprapat menggunakan pendekatan Knowledge Discovery in Databases (KDD) dengan algoritma K-Nearest Neighbor (KNN). Penelitian menggunakan pendekatan kuantitatif dengan desain studi kasus. Data dianalisis melalui tahapan pembersihan, transformasi, dan klasifikasi menggunakan KNN. Evaluasi model dilakukan dengan perangkat lunak Orange menggunakan skema random sampling dengan data pelatihan 70%. Hasil penelitian menunjukkan nilai akurasi sebesar 53,3% dan AUC sebesar 0,407, yang mengindikasikan keterbatasan model akibat ketidakseimbangan kelas. Meskipun demikian, KNN dapat dimanfaatkan sebagai alat eksploratif dalam mendukung pengambilan keputusan berbasis data pada usaha kuliner. Kata kunci: Data Mining, KNN, KDD, Gaji Pegawai, Biaya Operasional, Usaha Kuliner ============================================================= The use of operational data as a basis for decision making is essential in culinary business management. Employee salary and operational cost data, which are routinely recorded, have the potential to be analyzed to identify expenditure patterns and improve operational efficiency. However, such data are often limited to administrative reporting. This study aims to analyze and classify employee salary and operational cost data at Swan Caffee & Resto Rantauprapat using the Knowledge Discovery in Databases (KDD) approach with the K-Nearest Neighbor (KNN) algorithm. This research employs a quantitative approach with a case study design. Data were processed through data cleaning, transformation, and classification stages using the KNN algorithm. Model evaluation was conducted using the Orange software with a random sampling scheme and a 70% training data proportion. The results show an accuracy of 53.3% and an AUC value of 0.407, indicating limitations due to class imbalance. Nevertheless, KNN can be utilized as an exploratory tool to support data-driven decision making in culinary businesses. Keywords: Data Mining, K-Nearest Neighbor, KDD, Employee Salary, Operational Cost
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Data Mining, KNN, KDD, Gaji Pegawai, Biaya Operasional, Usaha Kuliner==============Data Mining, K-Nearest Neighbor, KDD, Employee Salary, Operational Cost |
| 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 > 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 Nov 2026 03:20 |
| Last Modified: | 03 Nov 2026 03:20 |
| URI: | http://repository.ulb.ac.id/id/eprint/2958 |
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