ANALISIS SENTIMEN PT TIKI JALUR NUGRAHA EKAKURIR (PT TIKI JNE) PADA MEDIA SOSIAL TWITTER MENGGUNAKAN MODEL FEED FORWARD NEURAL NETWORK

Salma Farah Aliyah(1), Hasbi Yasin(2*), Suparti Suparti(3), Budi Warsito(4), Tatik Widiharih(5)


(1) Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
(2) Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
(3) Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
(4) Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
(5) Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
(*) Corresponding Author

Abstract


In the 2000s until now, e-commerce systems have continued to develop throughout the world and even in Indonesia. PT Tiki Jalur Nugraha Ekakurir (PT Tiki JNE) is a freight forwarding company that provides convenience for the public in carrying out online shopping activities, and shipping other goods. The large volume of shipments makes PT Tiki JNE have several problems in service that have led to several kinds of responses from users. Sentiment analysis on Twitter social media can be an option to see how PT Tiki JNE’s users respond to services that have been provided. These responses are classified into positive sentiments and negative sentiments. In this research data processing is performed using text mining as the initial source of numerical data from document data which will later be classified using the Artificial Neural Network model with the Resilient Backpropagation algorithm. Data labeling is done manually and sentiment scoring. The test results show that the best model obtained is FFNN 867-7-1 by using the evaluation model 10-Fold Cross Validation to get an overall accuracy performance of 80.27%, kappa accuracy of 39.13%, precision of 69.04%, recall of 70.56%, and f-measure of 69.8% which can be interpreted that the model used is quite good. Analysis of the results using wordcloud shows the tendency of opinion sentiment categories depending on the words used in the tweet.


Keywords


PT Tiki Jalur Nugraha Ekakurir, Twitter, Text Mining, Artificial Neural Network, Resilient Backpropagation

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DOI: https://doi.org/10.26714/jsunimus.8.2.2020.103-113

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