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All Journal Infotech Journal JITEK (Jurnal Ilmiah Teknosains) Format : Jurnal Imiah Teknik Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) JIEET (Journal of Information Engineering and Educational Technology) InComTech: Jurnal Telekomunikasi dan Komputer Jurnal ULTIMA Computing JURIKOM (Jurnal Riset Komputer) KOMPUTEK Majalah Ilmiah UNIKOM Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Infortech Djtechno: Jurnal Teknologi Informasi Computer Science (CO-SCIENCE) IMTechno: Journal of Industrial Management and Technology Simpatik: Jurnal sistem Informasi dan Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Artificial Intelligence and Digital Business International Journal of Advanced Science Computing and Engineering Journal Medical Informatics Technology Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Waeisul Bismi
Universitas Bina Sarana Informatika
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ANALISIS SENTIMEN ULASAN APLIKASI X PADA GOOGLE PLAY STORE MENGGUNAKAN KOMPARASI SVM, KNN, DAN NAIVE BAYES Manampiring, Jim Maxwell; Halawa, Jenianus; Zai, Jefiri; Kurniawati, Ika; Fahlapi, Riza; Bismi, Waeisul
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8095

Abstract

Transformasi rebranding media sosial Twitter menjadi X menimbulkan respons yang beragam dari pengguna global, termasuk di Indonesia. Ulasan pengguna pada platform Google Play Store memuat informasi berharga mengenai kepuasan dan keluhan pengguna, namun volume data yang besar dan tidak terstruktur menyulitkan analisis secara manual. Studi ini difokuskan pada penerapan analisis sentimen guna mengklasifikasikan opini pengguna menjadi kategori positif dan negatif, serta membandingkan kinerja tiga algoritma Machine Learning, yaitu Support Vector Machine (SVM), K-Nearest Neighbor (KNN), dan Naive Bayes. Dataset yang digunakan berjumlah 10.000 data berbahasa Indonesia yang dikumpulkan melalui scraping. Melalui tahapan preprocessing yang meliputi cleaning, tokenizing, dan stemming, data dilatih dengan pembagian rasio 80:20. Hasil pengujian menunjukkan algoritma SVM menggunakan kernel linear menghasilkan kinerja terbaik dengan akurasi sebesar 84,8%, diikuti oleh Naive Bayes sebesar 83,1%, dan KNN sebesar 79,7%. Kesimpulan dari studi ini menegaskan bahwa SVM merupakan metode yang paling efisien guna menangani klasifikasi teks pada data ulasan aplikasi X yang memiliki dimensi tinggi, meskipun terdapat ketidakseimbangan kelas pada dataset.
Comparative Performance Analysis of ML, DL, and Transformer Models for Sentiment Classification of Indonesian Mobile Banking User Reviews Bismi, Waeisul; Qomaruddin, Muhammmad; Marlina, Siti
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid development of digital technology has encouraged the adoption of mobile banking applications in Indonesia, but it has also led to an increase in user complaints and reviews regarding performance and ease of use. This study aims to conduct a comparative analysis of the performance of Machine Learning, Deep Learning, and Transformer (IndoBERT) models in classifying the sentiment of user reviews of Indonesian-language mobile banking applications. Data was collected through web scraping from the Google Play Store on ten leading banking applications in Indonesia with a total of 200,000 reviews. After going through the preprocessing stages of cleaning, normalisation, tokenisation, and stemming, automatic labelling was carried out based on ratings into three sentiment classes: positive, neutral, and negative. Machine learning models (Naïve Bayes, Logistic Regression, Random Forest, and SVM) were built using TF-IDF feature representation, while deep learning models (LSTM, Bi-LSTM, GRU, and CNN) utilised 128-dimensional word embeddings. The Transformer-based IndoBERT model was fine-tuned with a sequence classification configuration. The evaluation used accuracy, precision, recall, and weighted F1-score metrics, accompanied by an analysis of training and testing time efficiency. The results show that the Bi-LSTM model performs best with an accuracy of 83.47% and an F1-score of 80.78%, followed by CNN (83.11%) and SVM (82.85%), while IndoBERT records an accuracy of 81.73% with a precision of 76.96%. In terms of efficiency, Logistic Regression showed an optimal balance between accuracy and training time (27.7 seconds), while deep learning and transformer models required higher computational resources. This study emphasises the importance of model selection based on requirements, between maximum accuracy and computational efficiency, and enriches the literature on Indonesian sentiment analysis in the domain of digital financial services.
Co-Authors Abdullah, Fikrian Nur Ade Setiawan Ade Setiawan Anisa Febriyani Anton . Arina Selawati Bela, Sintia Dava Al Riziq Deny Novianti Dhiaulhaq Ramadhan Dicky Hariyanto Dwiza Riana Dwiza Riana Fadhil Marzuqi Fahlapi, Riza Fajar Shidiq Farid Ramadhan Febrianto, Ade Ilham Febriyani, Anisa Firmansyah Firmansyah Gata, Windu Gibran, Afrizal Achmad Halawa, Jenianus Hani Harafani Harianja, Putri Alletheia Hewiz , Alya Shafira Hildan Zafa Riyadi Ika Kurniawati Ika Kurniawati Jordy Lasmana Putra Jufriadif Na`am, Jufriadif Kurniawan, Ery Liya, Amel Manampiring, Jim Maxwell Maysaroh, Maysaroh Mugi Raharjo Muhamad Abdul Salam Muhammad Qomaruddin muhammad qomaruddin Musriatun Napiah Noor, Mohamad Nurardian, Ridwana Septian Nurbaety Nurbaety Putra Muamar Kadafi Putri, Destiana Putri, Halimmatussa’diyah Putri, Halimmatussa’diyah Qomaruddin, Muhammmad Rachmat Adi Purnama Rachmawati Darma Astuti Rachmawati Darma Astuti Raharjo, Mugi Rahmandhani, Rifqi Raka Satria Gumilang Raya Ramadhan, Farid Rangga Wardhana RIZA FAHLAPI Rizal Fahlapi Rizkiansyah, Dimas Rosandi, Rivana Sakata, Yama Sulkhan Sandra Dewi Saraswati Saputra, Ari Setia Saputra, Atio Wahyudi Siregar, Aris Parnius Siti Marlina, Siti Taufik Asra Thalut Syaputra Tommi Alfian Armawan Sandi Wijaya, Aryanata Yerico Purba Zai, Jefiri