Klasifikasi Sentimen Terhadap Maxim Menggunakan Algoritma SVM Pada Media Sosial Twitter

Abstract

Abstrak - Twitter merupakan media sosial yang penggunanya paling pesat. Seiring perkembangan waktu, setiap ojek online memiliki popularitas di masing-masing user. Contoh saja Maxim, pendatang baru yang menyediakan layanan yang berbeda dengan ojek lain. Salah satu aktivitas yang biasa dilakukan para fans atau haters kepada akun twitter ojek online yaitu memberikan komentar pada tweet, untuk mengetahui komentar para fans dan haters diperlukan klasifikasi dengan menggunakan machine learning. Pada penelitian ini, langkah pertama adalah dilakukan proses sortir dan pemberian label pada data tersebut. Hasilnya akan tercipta 3 label yaitu label data positif, netral dan label data negatif dengan jumlah 1200 data. Selanjutnya melakukan analisa preprocessing data yang meliputi case folding, cleansing data, tokenizing, filtering dan stemming. Lalu dilakukan pembobotan dengan metode TF-IDF dan diklasifikasikan dengan metode Support Vector Machine. Hasil pengujian dilakukan dengan metode Confussion Matrix, berdasarkan hasil pengujian diperoleh akurasi terbaik pada perbandingan data 90:10 sebesar 85% dengan menggunakan Kernel RBF dan Polynomial, dilanjutkan dengan kernel Sigmoid sebesar 82,5% dimana hasil klasifikasi didominasi kalimat positif.Kata kunci: analisis sentiment, klasifikasi, ojek online, support vector machine, twitter Abstract - Twitter is one of the Social Medias which has a rapid user. Over the time, every Ojek Online, has its own popularity among their users. Maxim, for instance, a newcomer which provides a different service from other online motorcycle taxies. One of the activities which is always do by the fans or haters toward twitter account of online motorcycle taxies is giving comments on tweet. To identify the comments from fans or haters is required classification by using Machine Learning. In this research, the first step was sorting process and labelling the data. As the result 3 labels would have created, which were positive data label, neutral data label, and negative data label with total of 1200 data. The next step was conducting the analysis of preprocessing data which included case folding, data cleansing, tokenizing, filtering and stemming. Then, the weighting was carried out using the TF-IDF method and classified by the Support Vector Machine method. The test results were carried out using the Confusion Matrix method, based on the test results, the best accuracy was obtained at a data comparison of 90:10 by 85% using the RBF Kernel and Polynomial, followed by the Sigmoid kernel of 82.5% where the classification results were dominated by positive sentences.Key word: classification, Ojek Online, sentiment analysis, support vector machine, twitter

Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm

“…Confusion matrix is an evaluation method used to obtain accuracy, recall, precision, and F1 score results from a classification process [19]. This method measures the performance of a classification model by displaying the number of data points that were classified correctly and incorrectly, along with their true labels.…”