SANDRA AZURA, NPM 2109100107 (2025) ANALISIS DAMPAK DISKON DAN PROMOSI TERHADAP PENINGKATAN PEMESANAN DI OREEN STUDIO DENGAN MENGGUNAKAN METODE NAIVE BAYES. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Penelitian ini dilakukan untuk menganalisis minat pelanggan terhadap layanan menggunakan metode klasifikasi berbasis data mining. Dengan perkembangan teknologi, analisis berbasis data menjadi penting agar keputusan bisnis lebih tepat sasaran. Metode Naive Bayes dipilih karena memiliki kemampuan tinggi dalam memproses data sederhana namun informatif. Teori ini berlandaskan prinsip probabilitas bersyarat yang digunakan untuk memprediksi kelas berdasarkan atribut yang dimiliki. Proses analisis dimulai dengan mengumpulkan data pelanggan, kemudian dilakukan perancangan model klasifikasi menggunakan aplikasi Orange. Model dibangun dengan menambahkan node input data, preprocessing, Naive Bayes, serta evaluasi menggunakan Test and Score dan Confusion Matrix. Hasil klasifikasi menunjukkan dari 30 data terdapat 21 pelanggan dengan minat “Ya” dan 9 pelanggan dengan minat “Tidak”. Evaluasi model memberikan akurasi sebesar 93%, presisi 100%, dan recall 81%, sehingga model dapat dianggap bekerja dengan baik dalam memprediksi minat pelanggan. Kesimpulannya, metode Naive Bayes terbukti efektif untuk mengklasifikasikan data pelanggan dengan tingkat ketepatan yang tinggi. Namun demikian, penelitian selanjutnya dapat menambahkan data yang lebih banyak dan membandingkan dengan algoritma lain agar hasil lebih maksimal. Kata Kunci: Klasifikasi; Naïve Bayes; Machine Learning; Orange; Evaluasi Model ================================================================================================== This study was conducted to analyze customer interest in services using a data mining-based classification method. With technological developments, data-driven analysis has become crucial for more targeted business decisions. The Naive Bayes method was chosen due to its high capability in processing simple yet informative data. This theory is based on the principle of conditional probability used to predict classes based on their attributes. The analysis process began with collecting customer data, followed by designing a classification model using the Orange application. The model was built by adding input data nodes, preprocessing, Naive Bayes, and evaluation using Test and Score and Confusion Matrix. The classification results showed that out of 30 data sets, there were 21 customers with "Yes" interest and 9 customers with "No" interest. The model evaluation provided an accuracy of 93%, a precision of 100%, and a recall of 81%, so the model can be considered to work well in predicting customer interest. In conclusion, the Naive Bayes method has proven effective in classifying customer data with a high level of accuracy. However, further research can add more data and compare with other algorithms for optimal results. Keywords: Classification; Naïve Bayes; Machine Learning; Orange; Model Evaluation
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Klasifikasi; Naïve Bayes; Machine Learning; Orange; Evaluasi Model==============Classification; Naïve Bayes; Machine Learning; Orange; Model Evaluation |
| 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: | 05 Jun 2026 09:41 |
| Last Modified: | 05 Jun 2026 09:41 |
| URI: | http://repository.ulb.ac.id/id/eprint/2506 |
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