Analysis of the Level of Public Satisfaction on the Tiktok Application as an E-Commerce
DOI:
10.33395/sinkron.v8i4.13040Keywords:
Keywords: TikTok App; K-Nearest Neighbor (kNN); Decision Trees; Confusion Matrix; Ecommerce; ROC AnalysisAbstract
Abstract: Online shopping has become one of the alternatives used by society today. This happens because doing online shopping saves a lot of time. There are many online shops in Indonesia and are used by many people, such as Shopee, Lazada and Tokopedia. But now there is an application that initially only acted as a social media platform, but now also doubles as an E-commerce application, namely TikTok. TikTok has now become an E-commerce application. The prices given are also very cheap and there are lots of promotions given to customers. But there are still some people who don't want to shop online on TikTok on the grounds that the goods are not good. So from these 2 things, research needs to be made to determine the level of public satisfaction with the TikTok application as an E-commerce. The aim of this research is to see how many people are satisfied shopping on TikTok. This research was carried out using a classification model in data mining using the K-Nearest Neighbor (kNN) method and the Decision Tree method. The classification results obtained were 119 community data (for representation of 96.74%) and for people who were dissatisfied with the TikTok application as an E-commerce it was 4 community data (for representation of 3.25%). These results provide the conclusion that many people are satisfied shopping on the TikTok application as E-commerce
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Alsaadi, E. M. T. A., Khlebus, S. F., & Alabaichi, A. (2022). Identification of human resource analytics using machine learning algorithms. Telkomnika (Telecommunication Computing Electronics and Control), 20(5), 1004–1015. https://doi.org/10.12928/TELKOMNIKA.v20i5.21818
Arowolo, M. O., Adebiyi, M. O., Ariyo, A. A., & Okesola, O. J. (2021). A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree. Telkomnika (Telecommunication Computing Electronics and Control), 19(1), 310–316. https://doi.org/10.12928/TELKOMNIKA.V19I1.16381
Fitri, Z. E., Sahenda, L. N., Puspitasari, P. S. D., Destarianto, P., Rukmi, D. L., & Imron, A. M. N. (2021). The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2), 91. https://doi.org/10.24843/lkjiti.2021.v12.i02.p03
Krstinić, D., Braović, M., Šerić, L., & Božić-Štulić, D. (2020). Multi-label Classifier Performance Evaluation with Confusion Matrix. 01–14. https://doi.org/10.5121/csit.2020.100801
Kurniadi, D., Mulyani, A., & Muliana, I. (2021). Prediction System for Problem Students using k-Nearest Neighbor and Strength and Difficulties Questionnaire. Jurnal Online Informatika, 6(1), 53. https://doi.org/10.15575/join.v6i1.701
Kurniawan, D., & Saputra, A. (2019). Penerapan K-Nearest Neighbour dalam Penerimaan Peserta Didik dengan Sistem Zonasi. Jurnal Sistem Informasi Bisnis, 9(2), 212. https://doi.org/10.21456/vol9iss2pp212-219
Mantik, J., Nababan, A. A., Khairi, M., & Harahap, B. S. (2022). Implementation of K-Nearest Neighbors (KNN) Algorithm in Classification of Data Water Quality. Jurnal Mantik, 6(1), 30–35. Retrieved from https://iocscience.org/ejournal/index.php/mantik/article/view/2130
Nugraha, K. A., & Herlina, H. (2021). Klasifikasi Pertanyaan Bidang Akademik Berdasarkan 5W1H menggunakan K-Nearest Neighbors. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 7(1), 44. https://doi.org/10.26418/jp.v7i1.45322
Pattnaik, G., & Parvathi, K. (2022). Machine learning-based approaches for tomato pest classification. Telkomnika (Telecommunication Computing Electronics and Control), 20(2), 321–328. https://doi.org/10.12928/TELKOMNIKA.v20i2.19740
Prasetio, R. T. (2020). Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification. Jurnal Online Informatika, 5(2), 153. https://doi.org/10.15575/join.v5i2.656
Sanjaya, R., & Fitriyani, F. (2019). Prediksi Bedah Toraks Menggunakan Seleksi Fitur Forward Selection dan K-Nearest Neighbor. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(3), 316. https://doi.org/10.26418/jp.v5i3.35324
Somantri, O., & Dairoh, D. (2019). Analisis Sentimen Penilaian Tempat Tujuan Wisata Kota Tegal Berbasis Text Mining. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(2), 191. https://doi.org/10.26418/jp.v5i2.32661
Sumiah, A., & Mirantika, N. (2020). Perbandingan Metode K-Nearest Neighbor dan Naive Bayes untuk Rekomendasi Penentuan Mahasiswa Penerima Beasiswa pada Universitas Kuningan. Buffer Informatika, 6(1), 1–10.
Supriyadi, D., Safitri, S. T., Amriza, R. N. S., & Kristiyanto, D. Y. (2022). Klasifikasi Loyalitas Pengguna Sistem E-Learning Menggunakan Net Promoter Score dan Machine Learning. JEPIN (Jurnal Edukasi Dan Penelitian Informatika), 8(April), 38–43. https://doi.org/10.26418/jp.v8i1.49300
Waliyansyah, R. R., & Fitriyah, C. (2019). Perbandingan Akurasi Klasifikasi Citra Kayu Jati Menggunakan Metode Naive Bayes dan k-Nearest Neighbor (k-NN). Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(2), 157. https://doi.org/10.26418/jp.v5i2.32473
Yulianto, L. D., Triayudi, A., & Sholihati, I. D. (2020). Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4.5. Jurnal Mantik, 4(1), 441–451. Retrieved from https://iocscience.org/ejournal/index.php/mantik/index
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