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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Informatika Information Management For Educators And Professionals (IMBI) Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Infotek : Jurnal Informatika dan Teknologi INTELEKTIVA JIKI (Jurnal Ilmu Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks)
Andreyestha Andreyestha
Universitas Bina Sarana Informatika
Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Journal : Infotek : Jurnal Informatika dan Teknologi

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Analisa Sentimen Kicauan Twitter Tokopedia Dengan Optimalisasi Data Tidak Seimbang Menggunakan Algoritma SMOTE Andreyestha Andreyestha; Qudsiah Nur Azizah
Infotek: Jurnal Informatika dan Teknologi Vol 5, No 1 (2022): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (98.549 KB) | DOI: 10.29408/jit.v5i1.4581

Abstract

Along with the development of technology, social media is now often used to provide an assessment, one of which is Twitter is one of the most popular microblogging. On Twitter, users don't just review a product, but they often complain or share their experiences about their level of satisfaction while using Tokopedia. Sentiment analysis on tokopeida provides useful indicators for various purposes which can be found in comments, feedback or criticism. This study uses the Naïve Bayes and Random Forest algorithms to provide the expected classification results, the analysis will be carried out by comparing several combinations of algorithms that will be tested on twitter tweets about Tokopedia, including the combination with Synthetic Minority Oversampling Technique (SMOTE) to optimize unbalanced data. In the Tokopedia Twitter tweet test, SMOTE was able to increase the accuracy of the Naive Bayes algorithm to 86.93%, an increase of 3.4% from the previous 83.53%. Meanwhile, Random Forest with SMOTE has an accuracy value of 88.44%, an increase of 1.55% from the previous Random Forest test of 86.89%.
Co-Authors ADHI DHARMA SURIYANTO Agus Subekti Andi Taufik Billyam Eko Pramono Eko Pramono Eko Pramono Eko Pramono, Eko Fajar Akbar Fajar Akbar, Fajar Imam Nawawi Masruri Sentiko , Ahmad Oky Kurniawan Qudsiah Nur Azizah Rahdian Kusuma Atmaja Richky Faizal Amir Richky Faizal Amir Rino Ramadan Wisti Dwi Septiani WITRIANA ENDAH PANGESTI