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Journal : Journal of Information Technology and Computer Science (JOINTECS)

Sentimen Analisis Aplikasi Belajar Online Menggunakan Klasifikasi SVM Adi Ariyo Munandar; Farikhin Farikhin; Catur Edi Widodo
JOINTECS (Journal of Information Technology and Computer Science) Vol 8, No 2 (2023)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v8i2.4747

Abstract

Google Play Store adalah tempat berbagai macam aplikasi tersedia, baik berbayar ataupun tidak. Halaman Google Play Store menjadi tempat pengguna aplikasi untuk menyampaikan pendapat, ulasan dan penilaian. Ruang Guru, Zenius dan Quipper tersedia di platform tersebut. Data pada ulasan, menjadi sangat bermanfaat untuk dianalisa. Analisa dilakukan dengan menggunakan sentimen analisis dan algoritma SVM. Data diperoleh dengan menggunakan teknik scraping data, dengan menggunakan bantuan library google-play-scraper. Proses web Scraping, dibagi menjadi 3 tahap yaitu Fetching, Extraction, dan Transformation. Data dikumpulkan sebanyak 30.000 data, yang dibagi menjadi 10.000 data Ruang Guru, Zenius dan Quipper. Peneltian diawali dengan Tahap preprocesing data meliputi normalisasi, case folding, cleaning, tokenizing, danĀ  Stopword. kemudian data dibagi menjadi 90% data latih dan 10% data uji. Data latih diberi label dengan nilai 1, 0, dan -1. Nilai 1 berarti positif, nilai 0 berarti netral dan -1 berarti negatif. Hasil sentimen klasifikasi menggunakan SVM, menunjukkan bahwa Ruang Guru memiliki nilai positif tertinggi dibandingkan Zenius dan Quipper. Akan tetapi, respon pengguna sama-sama memberikan nilai positif untuk aplikasi tersebut. Nilai akurasi dari penelitian menunjukkan bahwa, data Klasifikasi sentimen dengan SVM, mempunyai akurasi rata-rata untuk Ruang Guru sebesar 99%, Zenius sebesar 96%, dan Quipper sebesar 82%.
Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Restoran Menggunakan LSTM Dengan Adam Optimizer Wardianto Wardianto; Farikhin Farikhin; Dinar Mutiara Kusumo Nugraheni
JOINTECS (Journal of Information Technology and Computer Science) Vol 8, No 2 (2023)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v8i2.4737

Abstract

Consumers believe that restaurant reviews are very important when choosing a restaurant. Due to the fact that reviews have become one of the most effective ways to influence customer decisions, research that has been done on restaurant customer reviews is about sentiment analysis. Previous studies have only used sentiment analysis at the sentence or document level, while a better level uses Aspect-Based Sentiment Analysis (ABSA), or a type of aspect-based sentiment analysis. LSTM is a variant of RNN that stores long-term information in memory cells. Use of global max pooling to reduce output resolution features and prevent overfitting. In addition, the optimization method used by Adam Optimizer is an adaptive learning rate optimization algorithm specifically designed to train deep neural networks. This study aims to classify restaurant customer opinions based on aspects (food, place, service, and price) based on restaurant customer reviews on Indonesian-language TripAdvisor with LSTM and global max pooling for sentiment classification (negative, half negative, neutral, half positive, positive). The results of this study indicate that the ABSA in restaurant customer reviews for sentiment classification accuracy is 78.7% and the aspect category accuracy is 78%, both are interconnected and can help understand restaurant customer opinions on TripAdvisor.
Co-Authors A. Haris A. Rusgiyono Acep Irham Gufroni Adi Ariyo Munandar Adi Suliantoro Ahmad Abdul Chamid Ahmad Lubis Ghozali Aprilia, Maita Aris Sugiharto Arnelli Arnelli B. Raharjo Bambang Irawanto Bambang Irawanto Bambang Subeno Bayu Surarso Bayu Surarso Beta Noranita Bibit Waluyo Aji Budi Warsito Carolin Carolin Catur Edi Widodo D. Ispriyanti Didik Setiyo Widodo Dinar Mutiara Kusumo Nugraheni Djuwandi Djuwandi DONNY IRAWAN MUSTABA Dwinta Rahmallah Pulukadang, Dwinta Rahmallah E. Setiawati Erikha Feriyanto Erlin Dwi Endarwati, Erlin Dwi Esti Wijayanti, Esti F. Ariyanto Faozi, Safik Fauzi, Irza Nur Feriyanto, Erikha Ferry Jie, Ferry Fitika Andraini H. Sutanto Heny Maslahah, Heny I. Marhaendrajaya Iswahyudi Joko Suprayitno J. E. Suseno Kartono . Keszya Wabang Kusworo Kusworo Laily Rahmania, Laily LM Fajar Israwan, LM Fajar M. Izzati M. Nur Madani, Faiq Mansur Mansur Meryta Febrilian Fatimah, Meryta Febrilian Mustafid Mustafid Neza Zhevira Septiani Nikken Prima Puspita Nikken Prima Puspita Nur Khasanah Oky Dwi Nurhayati Pangestika, Vidya Dwi Pradana, Fadli Dony Prantiastio Prastio, Wahyu Tedi Priyono Priyono Purwanto Purwanto R. Hariyati R. Hastuti Rachmat Gernowo Ratri Wulandari Retno Kusumaningrum Rezki Kurniati, Rezki Rinta Kridalukmana Robertus Heri Sulistyo Utomo S. Tana Safik Faozi, Safik Satriani, Rineka Brylian Akbar Siti Khabibah Siti Khabibah Sri Wahyuni Sugito Sugito Suhartono Suhartono Sunarsih . Suparti Suparti T. Windarti Titi Udjiani SRRM Toni Prahasto Udjiani , Titi Udjiani S.R.R.M, Titi Usman, Carissa Devina Uswatun Khasanah W. H. Rahmanto Wardani, Novita Koes Wardianto, Wardianto Warsito , Budi Wicaksono, Mahad Wyne Mumtaazah Putri Yosza Dasril Yully Estiningsih Z. Muhlisin