Firdaus, Gilang Slamet (2025) KLASIFIKASI KEPUASAN PELANGGAN TERHADAP LAYANAN APLIKASI SHOPEE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) dan DECISION TREE. S1 thesis, Universitas Mercu Buana-Menteng.
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Abstract
Perkembangan pesat industri e-commerce di Indonesia menghadirkan tantangan bagi platform seperti Shopee dalam menjaga keloyalitasan pelanggan. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor paling berpengaruh dan mengklasifikasikan tingkat kepuasan pelanggan terhadap layanan aplikasi Shopee dengan membandingkan dua algoritma machine learning, yaitu Support Vector Machine (SVM) dan Decision Tree. Data dikumpulkan dari 353 responden yang disebarkan melalui kuesioner, mencakup variabel seperti kemudahan mencari produk, harga produk, ketepatan pengiriman, kualitas produk, kelengkapan informasi produk, ketersediaan promo, kecepatan proses pembelian, konfirmasi pesanan, serta keamanan transaksi. Data kemudian diproses melalui tahapan preprocessing dan menggunakan metode SMOTE untuk mengatasi ketidakseimbangan. Hasil menunjukkan bahwa akurasi data testing Support Vector Machine 93% sedikit lebih tinggi dibandingkan Decision Tree 87%. Faktor paling berpengaruh terhadap kepuasan pelanggan adalah kelengkapan informasi produk, keamanan transaksi, ketepatan pengiriman, dan harga produk. Penelitian ini diharapkan menjadi referensi bagi platform e-commerce untuk memperbaiki mutu kualitas layanan. The rapid growth of the e-commerce industry in Indonesia presents challenges for platforms like Shopee in maintaining customer loyalty. This study aims to identify the most influential factors and classify customer satisfaction levels with the Shopee application service by comparing two machine learning algorithms: Support Vector Machine (SVM) and Decision Tree. Data were collected from 353 respondents through questionnaires, covering variables such as ease of finding products, product price, delivery accuracy, product quality, completeness of product information, availability of promotions, speed of the purchase process, order confirmation, and transaction security. The data then underwent preprocessing stages and used the SMOTE method to address class imbalance. Results show that the testing accuracy of Support Vector Machine (93%) is slightly higher than that of Decision Tree (87%). The most influential factors affecting customer satisfaction are completeness of product information, transaction security, delivery accuracy, and product price. This research is expected to serve as a reference for e-commerce platforms in improving service quality.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41821010089 |
| Uncontrolled Keywords: | Kepuasan pelanggan, Klasifikasi, SVM, Decision Tree, E-Commerce. Customer satisfaction, Classification, SVM, Decision Tree, E-Commerce. |
| 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | ZAIRA ELVISIA |
| Date Deposited: | 11 Sep 2025 06:26 |
| Last Modified: | 11 Sep 2025 06:26 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/97694 |
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