DOI: https://doi.org/10.14710/j.gauss.12.2.242-253

Analisis Sentimen Pada Perusahaan Penyedia Jasa Logistik J&T Menggunakan Algoritma Multinomial Naive Bayes dan Support Vector Machine

*Helmi Aulia Rahman  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Rukun Santoso  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Tatik Widiharih  -  Departemen Statistika, Fakultas Sains dan Matematika, Undip, Indonesia
Open Access Copyright 2023 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Online shopping is a way to a faster and easier process of buying things or needs for people these days. Logistic services are essential in the process of buying things online, for they will be the one who ship the package to the buyer. PT. Global Jet Express or J&T is one of many logistics service provider company that are available in Indonesia. J&T has a Twitter account which is used for communicating with their customers. Opinions that were posted by J&T consumers on Twitter could be used as a data to do sentiment analysis which the purpose is to extract information that are told by people in Twitter about J&T. Data crawling was done for 15.000 tweets that were posted during the period of 4th to 10th of July 2022, duplicated tweets and those who has the exact same contents were removed resulting the data reduced to 2500 tweets. Tweets will be divided into two class; positive class and negative class Some classification methods are commonly used in text classification, such as Random Forest, Decision Tree, Naïve Bayes Classifier, Support Vector Machine etc. Data in this research will be classified using Multinomial Naïve Bayes and Support Vector Machine to compare their accuracy, the reason for the comparison is these methods have significant difference in their concept complexity. Multinomial Naïve Bayes classify data by finding the greatest conditional probability value, whilst Support Vector Machine classify data by finding the best hyperplane to divide into two class. Multinomial Naïve Bayes has the accuracy of 72,80% and Support Vector Machine has the accuracy of 82,40%. Based on their accuracy, Support Vector Machine has the best performance in classifying public opinions about J&T on Twitter.

Note: This article has supplementary file(s).

Fulltext View|Download |  Research Instrument
CTA
Subject
Type Research Instrument
  Download (32KB)    Indexing metadata
Keywords: J&T; Twitter; Sentiment Analysis; Multinomial Naïve Bayes; Support Vector Machine

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
K-NEAREST NEIGHBOR DENGAN ADAPTIVE BOOSTING DAN SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE UNTUK KLASIFIKASI DATA TIDAK SEIMBANG PERAMALAN INDEKS JAKARTA ISLAMIC INDEX (JII) DENGAN PENDEKATAN REGRESI PARAMETRIK LINIER SEDERHANA DAN REGRESI NONPARAMETRIK KERNEL DILENGKAPI GUI R-SHINY ANALISIS PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL INDEKS TUNGGAL DAN PENGUKURAN VALUE AT RISK DENGAN SIMULASI MONTE CARLO (Studi Kasus: Exchange Traded Fund di Bursa Efek Indonesia Periode Januari 2021 – Juni 2022) More recent articles
  1. Feldman, R., dan Sanger, J. 2007. The Text Mining Handbook: Advanced approaches in Analyzing Unstructured Data. New York: Cambridge University Press
  2. Fitriyah, N., Warsito, B. dan Di Asih, I.M. 2020. Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM). Jurnal Gaussian Vol. 9, No. 3, Hal: 376-390
  3. Han, J., Kamber, M., dan Pei, J. 2012. Data Mining: Concept and Techniques. San Fransisco: Morgan Kaufmann Publishers
  4. Lestari, D. C. 2020. Analisis Komparatif Tingkat Kualitas Layanan dan Kepercayaan Konsumen terhadap Kepuasan Konsumen antara Jasa Pengiriman Barang J&T Express dan JNE di Surabaya. Disertasi. Universitas 17 Agustus 1945 Surabaya
  5. Liu, B. 2015. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge: Cambridge University Press
  6. Manning, C., Reghavan, P., dan Schutze, H. 2009. An Introductuion to Information Retrieval. Cambridge: Cambridge University Press
  7. Mujilahwati, S. 2016. Pre-Processing Text Mining Pada Data Twitter. Seminar Nasional Teknologi Informasi Dan Komunikasi. Sentika
  8. Ningtyas, A. M. 2016. Pengaruh Penanganan Negasi Dalam Bahasa Indonesia Untuk Pelabelan Otomatis Pada Analisis Sentimen Twitter. Disertasi. Universitas Gadjah Mada Yogyakarta
  9. Nurjanah, W. E., Perdana, R. S., dan Fauzi, M. A. 2017. Analisis Sentimen Terhadap Tayangan Televisi Berdasarkan Opini Masyarakat pada Media Sosial Twitter menggunakan Metode K-Nearest Neighbor dan Pembobotan Jumlah Retweet. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X
  10. Prasetyo, E., 2012. Data Mining Konsep dan Aplikasi Menggunakan MATLAB. Yogyakarta: Andi Offset
  11. Qeis, M. I. 2015. Aplikasi Wordcloud Sebagai Alat Bantu Analisis Wacana. Jakarta: International Conference on Language, Culture, and Society - ICLCS LIPI 2015
  12. Ramasubramanian, K. dan Singh, A. 2017. Machine Learning Using R. New York: Apress Media
  13. Robertson, S. 2004. Understanding Inverse Document Frequency: on Theoretical Arguments for IDF. Journal of Documentation
  14. Wahid, D. H., dan Azhari , S. N. 2016. Peringkasan Sentimen Ekstraktif di Twitter Menggunakan Hybrid TF-IDf dan Cosine Similarity. Indonesian Journal of Coputing and Cybernetics Systems (IJCCS) Vol. 10, No. 2, Hal: 207-218
  15. Yuyun, Hidayah, N. dan Sahibu, S. 2021. Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol.5, No.4, Hal: 820-826

Last update:

No citation recorded.

Last update:

No citation recorded.