KLASIFIKASI KERAMAIAN VIDEO KULINER BERBASIS FITUR ENGAGEMENT, SENTIMEN, DAN KONTEN MENGGUNAKAN METODOLOGI CRISP-DM

Authors

  • Adinda Rahimah Azzahra
  • Sukamto Sukamto

Keywords:

CRISP-DM, Tiktok, Cheese Content, Random Forest, Decision Tree

Abstract

 

Abstrak

Penelitian ini bertujuan menganalisis faktor yang memengaruhi keramaian video kuliner TikTok dengan membandingkan konten bertema keju dan non-keju. Metode yang digunakan adalah Cross-Industry Standard Process for Data Mining (CRISP-DM) sebagai kerangka kerja utama. Dataset mencakup fitur engagement serta sentimen komentar, kemudian diproses menggunakan dua algoritma klasifikasi, yaitu Decision Tree (DT) dan Random Forest (RF). Evaluasi model dilakukan menggunakan cross-validation untuk memperoleh performa yang stabil. Hasil menunjukkan bahwa konten kuliner berbahan keju cenderung memperoleh interaksi lebih tinggi, khususnya melalui likes dan shares. Model RF memberikan akurasi terbaik dibandingkan DT. Penelitian ini membuktikan bahwa pemilihan bahan makanan, terutama keju, dapat menjadi indikator penting dalam strategi penyusunan konten kuliner di TikTok.

Kata kunci : CRISP-DM, TikTok, Keju, Random Forest, Decision Tree.

Abstract

This study aims to analyze the factors that influence the popularity of culinary TikTok videos by comparing cheese-based content with non-cheese content. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was applied as the main methodological framework. The dataset includes engagement metrics and comment sentiment features, which were processed using two classification algorithms: Decision Tree (DT) and Random Forest (RF). Model evaluation was performed using cross-validation to obtain stable performance results. The findings indicate that cheese-related content tends to generate higher engagement, particularly through likes and shares. RF achieved better accuracy than DT in predicting whether a video becomes “viral” or not. This research demonstrates that food ingredients, especially cheese, can serve as important indicators in developing effective culinary content strategies on TikTok.

Keywords : CRISP-DM, TikTok, cheese content, Random Forest, Decision Tree.

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Published

2026-03-31

Issue

Vol. 22 No. 1 (2026): Maret 2026

Section

Engineering Articles

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