Tugas Akhir Risqi Dwi Prasetio NIM 19215265

• RISQI DWI PRASETIO
• 2026

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ABSTRAK

Risqi Dwi Prasetio (19215265), Analisis Pengaruh Algoritma TikTok terhadap Minat Pembeli Pengguna TikTok Shop Menggunakan Naïve Bayes

Perkembangan media sosial telah mendorong munculnya konsep social commerce, salah satunya melalui fitur TikTok Shop yang mengintegrasikan konten hiburan dengan aktivitas belanja daring. TikTok memanfaatkan algoritma rekomendasi berbasis machine learning untuk menampilkan konten dan produk yang dipersonalisasi sesuai dengan perilaku pengguna. Algoritma ini diduga memiliki pengaruh signifikan terhadap minat beli pengguna, namun penelitian yang mengkaji pengaruh tersebut secara komputasional masih terbatas, khususnya di Indonesia.

Penelitian ini bertujuan untuk menganalisis pengaruh algoritma TikTok terhadap minat beli pengguna TikTok Shop menggunakan metode data mining Naïve Bayes. Data penelitian diperoleh dari kuesioner pengguna TikTok Shop yang mencakup variabel intensitas kemunculan konten pada For You Page (FYP), relevansi rekomendasi, durasi interaksi, tingkat engagement, serta indikator minat beli. Dataset yang diperoleh kemudian melalui tahap preprocessing, pembagian data training dan testing, serta proses klasifikasi menggunakan algoritma Naïve Bayes dengan bantuan aplikasi RapidMiner.

Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes mampu mengklasifikasikan minat beli pengguna dengan tingkat akurasi 88%, precision 89,47%, dan recall 94,44%, serta nilai AUC sebesar 0,825 yang termasuk dalam kategori baik. Temuan ini mengindikasikan bahwa algoritma TikTok berpengaruh terhadap minat beli pengguna TikTok Shop, terutama melalui faktor relevansi rekomendasi dan engagement pengguna. Penelitian ini diharapkan dapat menjadi referensi bagi pengembangan sistem rekomendasi dan strategi pemasaran pada platform e-commerce berbasis media sosial.

Kata Kunci: Algoritma TikTok, Minat Beli, TikTok Shop, Data Mining, Naïve Bayes.

 

 

 

 

ABSTRACT

Risqi Dwi Prasetio (19215265), Analysis of the Influence of the TikTok Algorithm on the Purchase Intention of TikTok Shop Users Using Naïve Bayes

The development of social media has encouraged the emergence of the social commerce concept, one of which is represented by TikTok Shop, a feature that integrates entertainment content with online shopping activities. TikTok utilizes a machine learning–based recommendation algorithm to present personalized content and product suggestions according to user behavior. This algorithm is believed to have a significant influence on users’ purchase intention; however, studies that examine this influence using a computational approach are still limited, particularly in Indonesia.

This study aims to analyze the effect of the TikTok algorithm on the purchase intention of TikTok Shop users using the Naïve Bayes data mining method. The research data were collected through questionnaires distributed to TikTok Shop users, covering variables such as the intensity of content exposure on the For You Page (FYP), recommendation relevance, interaction duration, user engagement, and purchase intention indicators. The collected dataset was then processed through preprocessing stages, data splitting into training and testing sets, and classification using the Naïve Bayes algorithm with the assistance of RapidMiner software.

The results show that the Naïve Bayes algorithm is able to classify users’ purchase intention with high accuracy 88%, precision 89,47%, and recall 94,44%, as well as an AUC value of 0.825, which falls into the very good category. These findings indicate that the TikTok algorithm has a significant influence on the purchase intention of TikTok Shop users, particularly through recommendation relevance and user engagement factors. This study is expected to serve as a reference for the development of recommendation systems and marketing strategies on social media–based e-commerce platforms.

Keywords: TikTok Algorithm, Purchase Intention, TikTok Shop, Data Mining, Naïve Bayes.

 

• Algoritma TikTok
• Minat Beli
• TikTok Shop
• Data Mining
• Naïve Bayes.

https://repository.bsi.ac.id/repo/81748/Tugas-Akhir-Risqi-Dwi-Prasetio-NIM-19215265
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