Abstract
IndiHome is an internet service provider from PT. Telekomunikasi Indonesia, Tbk with the widest internet coverage in Indonesia. Customer satisfaction is one of the things that must be considered in a company, including the IndiHome company. IndiHome's customer service satisfaction level can be seen from customer reviews via Twitter social media. This study discusses the classification of IndiHome customer reviews by applying the CRISP-DM research stages and the application of the Naïve Bayes Classifier algorithm and the Linear Support Vector Machine Kernel. Customer review data were obtained from Twitter, totaling 1000 tweets using the Rapid Miner and R library tools. The preprocessing stages applied were cleansing, case folding, tokenizing, word conversion, stopword, and stemming. The results of data visualization are presented in the form of a word cloud which is categorized based on positive and negative opinions of words that often appear. The results showed that the application of the Support Vector Machine Kernel Linear algorithm is better than the Naïve Bayes Classifier algorithm with an accuracy value of 82.11%, 76.44% precision, 88.01% recall, and an AUC value of 0.909.
Highlights
Teknologi yang semakin berkembang menjadikan internet sebagai salah satu media yang umum digunakan
The results showed that the application of the Support Vector Machine Kernel Linear algorithm is better than the Naïve Bayes Classifier algorithm with an accuracy value of 82.11%, 76.44% precision, 88.01% recall, and an AUC value of 0.909
P. Shakina Rizkia, Erwin Budi Setiawan S.Si., M.T, Diyas Puspandari S.S., “Analisis Sentimen Kepuasan Pelanggan Terhadap Internet Provider Indihome di Twitter Menggunakan Metode Decision Tree dan Pembobotan TF-
Summary
Teknologi yang semakin berkembang menjadikan internet sebagai salah satu media yang umum digunakan. Data yang dikumpulkan biasanya diungkapkan dalam kritikan atau keluhan pelanggan yang dapat dilihat dari sentimen opini pengguna pada media sosial. Saat ini analisis sentimen telah banyak digunakan untuk penelitian dengan tujuan memberikan informasi mengenai penilaian atau penyaringan opini. Seperti pada penelitian yang telah dilakukan oleh Shania Rizkia (2019) mengenai analisis kepuasan pelanggan terhadap Internet Provider IndiHome di Twitter dengan menggunakan metode Decision Tree yang diklasifikasikan berdasarkan opini positif, negatif, maupun netral. Pada penelitian Tineges (2020) dengan analisis sentimen terhadap layanan IndiHome menggunakan metode klasifikasi Support Vector Machine (SVM) mampu memberikan hasil akurasi sebesar 87% dengan evaluasi yang digunakan yaitu Confusion Matrix [6]. Adapun manfaat dari hasil analisis sentimen pelanggan IndiHome ini dapat digunakan sebagai bahan evaluasi atau feedback untuk IndiHome dalam melakukan peningkatan pelayanan agar dapat memberikan kepuasan terhadap pelanggan yang lebih baik lagi.
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