Sentiment Analysis Classification on PLN Mobile Application Reviews using Random Forest Method and TF-IDF Feature Extraction - Dalam bentuk buku karya ilmiah

MUHAMAD DAFA RIZKIYANTO

Informasi Umum

Kode

24.04.5377

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Natural Language Processing

Dilihat

276 kali

Informasi Lainnya

Abstraksi

Abstract PT PLN (Persero) has developed the PLN Mobile application to provide electricity services. The large number of users has resulted in various reviews regarding the strengths, weaknesses, and issues of the application. To evaluate the application's quality, sentiment analysis is conducted on user reviews. Review data is obtained through the Google-Play-Scraper API and cleaned through text preprocessing. This study utilizes the TF-IDF feature extraction method and Random Forest for classification. TF-IDF involves weighting each word in a text. This method transforms words into numerical representations, indicating both their frequency and relevance within the document's context. Random Forest is a supervised machine learning algorithm that utilizes ensemble learning which categorizes reviews into positive and negative, This study produced the best model using stemming data and TF-IDF unigram, along with a combination of hyperparameters. The n- estimator was set to 100, maxfeature to log2, maxdepth to unlimited (none), and entropy criterion, resulting in the highest F1-Score of up to 93.14%. Keywords: sentiment analysis, user reviews, pln mobile, random forest, tf-idf.