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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika POSITIF JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Repositor Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Algoritma
S, Vinna Rahmayanti
Universitas Muhammadiyah Malang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Public Health

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DANA App Sentiment Analysis: Comparison of XGBoost, SVM, and Extra Trees Setiawan, Muhamad Jodi; Nastiti, Vinna Rahmayanti Setyaning
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2239

Abstract

This research aims to analyze sentiment on DANA application reviews to find out user perceptions by comparing Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Extra Trees Classifier classification methods. DANA application review data is obtained from the Kaggle site which consists of 50,000 Indonesian-language reviews labeled with positive and negative sentiments. The research stages include data preprocessing to clean and prepare the review text, applying word weighting using Word2Vec to give weight to words based on their context, balancing sentiment classes using SMOTE to address the imbalance of positive and negative review classes. It should be noted that the initial proportion of data before applying SMOTE may affect the results. The data is then divided into training and testing sets, then the models are trained and evaluated using Confusion Matrix and K-Fold Cross-Validation. The results of the three classification methods are measured by the accuracy matrix and F1-Score to assess model performance, the SVM and XGBoost methods obtained an accuracy of 93% and the ETC method achieved an F1-Score value of 96% at K=6, the three models proved to be very accurate in predicting the sentiment of DANA application reviews both positive and negative. The practical implications of this research can identify areas for application improvement, develop popular features, personalize services based on user preferences, and manage application reputation.
Co-Authors Abdy Yoga Syantara Achmad Rizal Yogaswara Adnan Burhan Hidayat Kiat Ahmad Junjung Sudrajad AINA, LISTYA NUR Akbi, Denar Regata Amelia Deastu Amelia Dwi Deastu Andriani Eka Pramudita Anhar Aria Maulana Asty Try Yuliandari Bagas Aji Aprian Bagus Wicaksono Basuki, Setio Bella Chintia Merita Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Dede Nor Alfiansyah Dewi Aprilitasari Diah Risqiwati Dignifo Nauval Muhammady Dini Tri Purwaningsih Dirfas, Naufal Angling Evi Dwi Wahyuni Gian Bagus Sadewa Giffari Zakawaly Gita Indah Marthasari Haidar Aldy Eka Nugraha Hilman Hilman Imsa Nur Dwijayanti Kenanga, Larynt Sawfa Lailiyah, Asmaul Lidya Fankky Oktavia Putri Maryaeni Maryaeni Muhammad Gofur Muhammad Rivaldi Asyhari Muhammad Robby Fathuda Naila, Tia Cahyani Naufal Adiftya Falah Novita Daian Marlissa Nur Hayatin Nuryasin, Ilyas Oktaviani, Chintya Tria Diana Pratama, Ramadan Qori Raditya Damayanti Ramadan Pratama Reikisyifa, Clarissa Sanindita Rifky Ahmad Saputra Riska Septiana Putri Saputri, Indah Setiawan, Muhamad Jodi Sirril Asrory Titin Eka Puspitawati Wahyu Andhyka Kusuma Wildan Amru Hidayat Wildan Suharso Wiyono, Briansyah Setio Yufis Azhar Zamah Sari