BELIUK, MUHAMMAD AKMAL (2023) Perbandingan Algoritma Support Vector Machine dan Naive Bayes untuk Klasifikasi Produk yang Diminati Berdasarkan Data Hasil Penjualan (Studi Kasus: Toko Hijab Hawfathings.id). S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The arrival of the Covid-19 pandemic caused a change in people's habits in online shopping activities. Sales through online media, be it e-commerce or other online media, are considered more efficient and have a wider market coverage. Hawfathings.id is a hijab shop that focuses on selling through online media, especially e-commerce, of course, the risk of loss cannot be avoided and one way to prevent this from happening is that the seller must know what products the buyer is interested in. Data mining with one of its methods, namely classification, which is the process of grouping data objects based on the level of similarity of data characteristics, can be a solution. This research will focus on comparing the Support Vector Machine (SVM) algorithm and the Naïve Bayes algorithm in classifying data for the product of interest based on the class label of the product's behavior level using the Python programming language and Google Colab as programming tools and validating using split validation and cross validation on Hawfathings.id hijab shop sales data. Based on the results and implementation that has been done, the Support Vector Machine algorithm gets an accuracy of 94.09%, precision 94.35%, recall 94.03% while the Naïve Bayes algorithm gets an accuracy of 88.25%, precision 88.70%, recall 88.40%. Key words: Classification, Support Vector Machine, Naïve Bayes, Split Validation, Cross Validation. Datangnya pandemi Covid-19 menyebabkan perubahan kebiasaan masyarakat dalam aktivitas belanja online. Penjualan melalui media online baik itu ecommerce ataupun media online lainnya dinilai lebih efisien dan memiliki cangkupan pasar yang lebih luas. Hawfathings.id sebuah toko hijab yang memfokuskan penjualannya melalui media online khususnya e-commerce yang tentunya resiko kerugian tidak dapat dihindari dan salah satu cara untuk mencegah hal itu terjadi yaitu penjual harus mengetahui produk apa saja yang diminati oleh para pembeli. Data mining dengan salah satu metodenya yaitu klasifikasi yang merupakan proses pengelompokan objek data berdasarkan tingkat kesamaan karakteristik data bisa mejadi sebuah solusi. Penelitian ini akan difokuskan pada perbandingan algoritma Support Vector Machine (SVM) dan algoritma Naïve Bayes dalam melakukan klasifikasi data untuk produk yang diminati berdasarkan label kelas tingkat lakunya sebuah produk dengan menggunakan bahasa pemrograman python dan google colab sebagai tools pemrograman serta melakukan validasi menggunaan split validation dan cross validation pada data penjualan toko hijab Hawfathings.id. Berdasarkan hasil dan implementasi yang sudah dilakukan algoritma Support Vector Machine mendapatkan nilai accuracy 94,09%, precision 94,35%, recall 94,03% sedangkan algoritma Naïve Bayes mendapatkan accuracy sebesar 88,25%, precision 88,70%, recall 88,40%. Kata kunci: Klasifikasi, Support Vector Machine, Naïve Bayes, Split Validation, Cross Validation.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 063 |
| NIM/NIDN Creators: | 41519010148 |
| Uncontrolled Keywords: | Klasifikasi, Support Vector Machine, Naïve Bayes, Split Validation, Cross Validation. |
| 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 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | CALVIN PRASETYO |
| Date Deposited: | 08 Sep 2023 02:12 |
| Last Modified: | 08 Sep 2023 02:12 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/80521 |
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