AZIZ, SYADAM THORIKH AL (2025) PREDIKSI TREN PENJUALAN PRODUK UNTUK TRANSAKSI RITEL MENGGUNAKAN ALGORITMA NAIVE BAYES. S1 thesis, Universitas Mercu Buana.
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Abstract
Product sales in the retail industry are influenced by various factors, such as seasonal changes, consumer preferences, and evolving market trends. Therefore, retail companies need effective methods to forecast future sales trends in order to formulate relevant and efficient business strategies. This study applies the Naïve Bayes algorithm, a probability-based machine learning technique, to predict changes in product sales trends using retail transaction data. The dataset used includes information on product types, transaction volume, purchase time, and. After undergoing preprocessing and feature extraction stages, the model is trained to recognize consumer behavior patterns and predict the likelihood of an increase or decrease in product sales in the upcoming period. The predictions generated by this model are expected to support retail businesses in making informed decisions, such as planning promotions, managing inventory, and developing products. Thus, implementing the Naïve Bayes algorithm on retail transaction data not only enhances operational efficiency but also improves business strategy accuracy and customer satisfaction. . Kata kunci: Naïve Bayes, Retail Transaction Dataset, Sales Trend Prediction, Machine Learning, Data Analysis. Penjualan produk dalam industri ritel dipengaruhi oleh berbagai faktor, seperti musim, preferensi konsumen, dan dinamika tren pasar. Oleh karena itu, perusahaan ritel memerlukan metode yang tepat untuk memprediksi arah penjualan di masa depan guna menyusun strategi bisnis yang relevan dan efektif. Penelitian ini menerapkan algoritma Naïve Bayes, salah satu teknik machine learning berbasis probabilitas, untuk memprediksi perubahan tren penjualan produk berdasarkan data transaksi ritel. Dataset yang digunakan mencakup informasi mengenai jenis produk, jumlah transaksi, waktu pembelian, serta. Setelah melalui tahap pra-pemrosesan dan ekstraksi fitur, model dilatih untuk mengenali pola perilaku konsumen dan memprediksi kemungkinan kenaikan atau penurunan penjualan suatu produk di periode berikutnya. Hasil prediksi dari model ini diharapkan dapat membantu pelaku usaha ritel dalam pengambilan keputusan, seperti perencanaan promosi, pengelolaan stok, dan pengembangan produk. Dengan demikian, penerapan algoritma Naïve Bayes pada data transaksi ritel tidak hanya mendukung efisiensi operasional, tetapi juga meningkatkan ketepatan strategi bisnis dan kepuasan pelanggan. Kata kunci: Naïve Bayes, Dataset Transaksi Ritel, Prediksi Tren Penjualan, Machine Learning, Analisis Data.
| Item Type: | Thesis (S1) |
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| Call Number CD: | FIK/INFO. 25 180 |
| NIM/NIDN Creators: | 41521010204 |
| Uncontrolled Keywords: | Naïve Bayes, Retail Transaction Dataset, Sales Trend Prediction, Machine Learning, Data Analysis |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 300 Social Science/Ilmu-ilmu Sosial > 380 Commerce, Communications, Transportation (Perdagangan, Komunikasi, Transportasi) > 381 Commerce, Trade/Perdagangan > 381.1 Retail Trade/Perdagangan Ritail, Pasar |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Pandu Risdiyanto |
| Date Deposited: | 26 Sep 2025 01:16 |
| Last Modified: | 26 Sep 2025 01:16 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/98313 |
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