PENERAPAN METODE STACKING ENSEMBLE UNTUK ANALISIS SENTIMEN PADA ULASAN APLIKASI RUANG GURU

Detin Sofia; Pina Sekarpuji

doi:10.36080/idealis.v8i2.3559

Authors

Detin Sofia Teknik Informatika, Fakultas Teknologi Informasi dan Komunikasi, Institut Teknologi dan Bisnis Bina Sarana Global, Tangerang, Indonesia
Pina Sekarpuji Direktorat Umum - Kelembagaan, Televisi Republik Indonesia (TVRI), Jakarta, Indonesia

DOI:

https://doi.org/10.36080/idealis.v8i2.3559

Keywords:

Analisis Sentimen, CRISP-DM, Pembelajaran Mesin, Stacking Ensemble, TF-IDF

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi Ruangguru berdasarkan ulasan di Google Play Store. Studi ini berupaya untuk menggambarkan persepsi masyarakat terhadap layanan pembelajaran digital tersebut. Data berupa 99.000 ulasan pengguna dalam bahasa Indonesia dikumpulkan melalui teknik web scraping. Analisis sentiment dilakukan menggunakan pendekatan klasifikasi sentimen berbasis pembelajaran mesin. Penelitian mengikuti tahapan metodologi Cross Industry Standard Process for Data Mining (CRISP-DM), yang terdiri dari Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation dan Deployment. Sentimen dikategorikan menjadi tiga kelas: positif, negatif dan netral. Proses pra-pemrosesan data melibatkan tahapan seperti pembersihan, tokenisasi, normalisasi. Ekstraksi fitur menggunakan Term Frequency–Inverse Document Frequency (TF-IDF), serta pelabelan data yang dilakukan dengan pendekatan lexicon-based menggunakan kamus kata positif dan negatif yang diunduh dari sumber terbuka. Model klasifikasi yang digunakan dalam penelitian ini adalah Stacking Ensemble dengan Random Forest, Support Vector Machine (SVM), dan Extreme Gradient Boosting (XGBoost) sebagai base learner, serta Logistic Regression sebagai meta learner. Evaluasi performa model menggunakan confusion matrix dan Area Under the Curve (AUC). Model ini menghasilkan Akurasi 88%, Precision 87%, Recall 88% dan F1-Score 87% serta AUC 0,945. Hasil penelitian menunjukkan bahwa mayoritas ulasan memiliki sentimen positif terhadap aplikasi Ruangguru. Temuan ini dapat menjadi masukan strategis bagi pengembang dalam meningkatkan kualitas layanan dan kepuasan pengguna.

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