Comparative Sentiment Analysis of Delivery Service PT.POS Indonesia and J&T Express on Twitter Social Media Using The Support Verctor Machine Algorithm

Euis Nur Fitriani Dewi(1), Aldy Putra Aldya(2), Andi Nur Rachman(3*), Ara Ramdani(4),

(1) Universitas Siliwangi, Tasikmalaya, Indonesia
(2) Universitas Siliwangi, Tasikmalaya, Indonesia
(3) Universitas Siliwangi, Tasikmalaya, Indonesia
(4) Universitas Siliwangi, Tasikmalaya, Indonesia
(*) Corresponding Author


Based on a survey conducted by the Top Brand Award in the courier service category, the J&T Express company is in the highest position from 2018 to 2021 beating Pos Indonesia. Social media Twitter is a place often used by customers to submit complaints and opinions regarding the services of a company. The method used to determine the tendency of the views to contain positive or negative sentiments is sentiment analysis. Sentiment analysis will classify the polarity of the text in sentences or documents to determine whether the opinions expressed are positive or negative. This study uses the Support Vector Machine (SVM) algorithm. The results of the user tweet data used are as many as 1000 data with details of data 206 (20.6%) have positive sentiments and 794 (79.4%) have negative sentiments. In the Pos Indonesia tweet data, 110 positive sentiment data were obtained, while the positive sentiment data in the J&T Express tweet data was 96 data. This shows that the Pos Indonesia delivery service has better customer service than J&T Express. The highest level of accuracy using the SVM algorithm in classifying sentiment is 80.14% with a comparison of 70% training data and 30% test data with an average precision of 90%, an average recall of 51.74%, and an average f-measure of 47.80%.

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FM Matulatuwa, E Sediyono, A Iriani, " Text mining dengan metode lexicon based untuk sentiment analysis pelayanan PT. Pos Indonesia melalui media sosial Twitter", 2017.

MT Nitami, F Februariyanti, "Analisis Sentimen Ulasan Ekspedisi J&T Express Menggunakan Algoritma Naïve Bayes", 2022.

RI Pratiwi, F Adams, E Ermatita, N Chamidah, " Sentimen Analisis Media Sosial Twitter Terhadap Layanan First Media Menggunakan Metode Naïve Bayes", 2021.

N Ruhyana, D Rosiyadi, " Klasifikasi Komentar Instagram Untuk Identifikasi Keluhan Pelanggan Jasa Pengiriman Barang Dengan Teknik Smote", 2019.

Azzahra, S Aliyah, " Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor", 2020.

Nasution, Hayatni, "Perbandingan Akurasi dan Waktu Proses

Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter", 2019.

AP Giovani, Ardiansyah, T Haryanti, "Analisis Sentimen Aplikasi Ruang Guru Di Twitter Menggunakan Algoritma Klasifikasi", 2020.

Ananda, Pristyanto, "Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine", 2021.

MI Fikri, TS Sabrila, Y Azhar, " Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter", 2020.

Agustina, DA, Subanti, Zukhronah E, "Implementasi Text Mining Pada Analisis Sentimen Pengguna Twitter Terhadap Marketplace di Indonesia Menggunakan Algoritma Support Vector Machine. Indonesian", 2020.

A Salam, J Zeniarja, RSU Khasanah, "Analisis Sentimen Data Komentar Sosial Media Facebook Dengan K-Nearest Neighbor (Studi Kasus Pada Akun Jasa Ekspedisi Barang J&T Ekspress Indonesia)", 2018.

Nasution, Hayatni, "Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter", 2019.



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