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All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Journal : Jurnal Teknologi Sistem Informasi dan Aplikasi

 1 
Perbandingan Evaluasi Kernel Support Vector Machine dalam Analisis Sentimen Chatbot AI pada Ulasan Google Play Store Putri, Celine Mutiara; Afdal, M.; Novita, Rice; Mustakim, Mustakim
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41354

Abstract

AI (Artificial Intelligence) is becoming very important these days due to its ability as a personal assistant to increase efficiency, automate routine tasks, and speed up manual processes. AI chatbot are one of the practical applications of AI in language understanding, have various benefits and drawbacks that cause various comments from users in the review column on the Google Play Store. This research discusses sentiment analysis of AI chatbot application reviews using four SVM kernels. Labeling uses InSet Lexicon and hyperparameters to produce the best parameters. The purpose of the research is to find out how users respond to interactions with ChatGPT, Perplexity AI, and Bing Chat and prove whether the kernel in SVM can increase the accuracy value. The percentage division between test data and training data is 70:30, 80:20, and 90:10, data labeling using 2 sentiment classes and 3 sentiment classes, and using and not using the SMOTE Oversampling technique. The experimental results obtained the highest accuracy using SVM kernel Linear scenario 90:10 with an accuracy value of 92.68%.
Analisis Sentimen Ulasan Pengguna Aplikasi Penjualan Pulsa Menggunakan Algoritma Naïve Bayes Classifier Syafi'i, Azis; Afdal, M.; Saputra, Eki; Novita, Rice
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41364

Abstract

Many credit sales applications are commonly used by outlets or counters, such as DigiPOS, Tetra Pulsa, and Orderkuota. However, there are common problems with these applications such as prices that are starting to be less competitive, difficult to use, transactions that often fail, security, service and others. Therefore, this study analyzes the sentiment of user reviews to identify the strengths and weaknesses of these apps, to help developers improve their services, and to guide agents in choosing the right app. NBC algorithm is proposed to be used for sentiment classification. The analysis results show the dominance of positive sentiments on all apps, with Tetra Pulsa having the highest positive sentiment (97.10%), followed by Orderkuota (84.40%) and DigiPOS (64.00%). Then the results of the implementation of the NBC algorithm can perform sentiment classification well. Tetra Pulsa application has an accuracy of 97.10%, Orderkuota 92.39%, and DigiPOS 91.10%. The results of this study can be considered to evaluate and improve the application so that it can provide better service to users of the credit sales application.
Analisis Sentimen Ulasan Pengguna Aplikasi Mobile Banking Menggunakan Algoritma K-Nearest Neighbor Munandar, Darwin; Afdal, M.; Zarnelly, Zarnelly; Novita, Rice
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41409

Abstract

Mobile banking is evident in the improvement of business processes in the banking industry. Even so, the m-banking application cannot be separated from the problems experienced by its users. Therefore, further analysis is required. This research proposes a sentiment analysis technique using K-Nearest Neigbor (KNN) algorithm to identify user opinions and reviews of m-banking applications. Three popular m-banking apps were selected for further analysis namely BRImo, BSI Mobile, and Livin' by Mandiri. The analysis shows that BRImo is the most popular m-banking application, with a positive sentiment percentage of 58.25%, Livin' by Mandiri with 22.50%, and BSI Mobile with the lowest percentage of 12.70%. Modeling results using the KNN algorithm with K = 3, 5 and 7 test values show K = 3 has better capabilities. Based on the application, the best modeling is produced on BRImo with 82.9% accuracy, then Livin' by Mandiri with 70.3% accuracy, and BSI Mobile with 71.35% accuracy. Analysis and visualization were also conducted using word clouds to see keywords that are often discussed in reviews. As a result, m-banking apps have problems with difficult login, complicated registration or verification, and balance deduction despite failed transfer status.
Co-Authors Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Alfakhri, Rezky Alfian, Zhevin Andriawan, Ahmad Rizky Angraini Angraini Anisa Halifa Anisa Putri Arif Marsal Arifin, Abdullah Arrazak, Fadlan Auliani, Sephia Nazwa Azzahra, Aura Butar Butar, Febiola Siska Dwinnie, Zairy Cindy Eki Saputra F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Inggih Permana Irwanda, Mahyuda Jazman, Muhammad Khairil Ahsyar, Tengku Kurniawan, Viki Loka, Septi Kenia Pita M Afdal M. Afdal M. Afdal Megawati - Mochammad Imron Awalludin Mubarak, Haykal Alya Mulya, Anggi Munandar, Darwin Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Nabiilah, Nabiilah Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Celine Mutiara Putri, Suci Maharani Rahayu, Dwi Sri Rahmawita M, Medyantiwi Ramadani, Faradila Rozanda, Nesdi Evrilayan Safiq Tama, Naufal Sasmita, Indah Siti Rohimah Syafi'i, Azis Syafrizal Syafrizal Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly Zarqani, Zarqani Zulkarnain Zulkarnain