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All Journal Jurnal Informatika Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Responsif : Riset Sains dan Informatika Bulletin of Computer Science Research Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Computer Science (CO-SCIENCE) Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Abdimas Komunikasi dan Bahasa Profitabilitas Indonesian Journal of Networking and Security - IJNS JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi J-Intech (Journal of Information and Technology) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY JEECS (Journal of Electrical Engineering and Computer Sciences) JURSIMA Sinergi: Jurnal Pengabdian Kepada Masyarakat Journal of Accounting Information System Bulletin of Informatics and Data Science Jurnal Sistem Informasi dan Manajemen Journal of Artificial Intelligence and Technology Information Media Teknologi dan Informatika Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Komtika (Komputasi dan Informatika) Journal of Information Technology Jurnal Teknoinfo
Riska Aryanti
Universitas Bina Sarana Informatika, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : journal of artificial intelligence and technology information

 1 
Optimasi Model Machine Learning Menggunakan Teknik SMOTE pada Analisis Sentimen Pengguna RedBus Arman Ramadhani; Riska Aryanti; Sarifah Agustiani
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 1 (2026): Volume 4 Number 1 March 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i1.182

Abstract

Perkembangan teknologi digital semakin memudahkan masyarakat dalam memenuhi kebutuhan transportasi, salah satunya melalui aplikasi pemesanan tiket bus seperti RedBus. Aplikasi ini menghadirkan layanan pemesanan secara praktis, namun ulasan pengguna yang semakin banyak di Google Play Store bersifat tidak terstruktur sehingga memerlukan analisis lebih lanjut untuk menilai kualitas layanan secara objektif. Penelitian ini bertujuan untuk mengklasifikasikan sentimen kepuasan pengguna aplikasi RedBus dengan memanfaatkan algoritma Naïve Bayes dan Random Forest. Untuk mengatasi masalah ketidakseimbangan data, digunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Data yang digunakan berjumlah 2.000 ulasan yang dikumpulkan melalui metode web scraping, kemudian diproses melalui tahapan preprocessing yang meliputi data cleaning, cleansing, case folding, tokenization, stopword, dan stemming. Selanjutnya, data diberi label kepuasan berdasarkan rating, lalu dikonversi menjadi fitur numerik dengan metode TF-IDF. Data dibagi menjadi 90% data latih dan 10% data uji agar dapat dievaluasi secara menyeluruh. Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes menghasilkan akurasi 91%, precision 97%, recall 89%, dan F1-score 92%. Sementara itu, algoritma Random Forest memperoleh akurasi 90%, precision 94%, recall 90%, dan F1-score 92%. Keunggulan Naïve Bayes terlihat pada nilai precision yang tinggi, menunjukkan kemampuannya dalam meminimalkan kesalahan klasifikasi positif palsu. Kesimpulannya, penerapan Naïve Bayes dengan dukungan SMOTE dinilai lebih optimal dalam mengklasifikasikan sentimen ulasan, sehingga dapat menjadi masukan bagi pengembang RedBus dalam meningkatkan kualitas layanan dan kepuasan pengguna.
Enhancing Sentiment Classification Performance on Tentang Anak Application Reviews Using Optimized Support Vector Machine Riska Aryanti; Eka Fitriani; Royadi Royadi; Dian Ardiansyah
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.271

Abstract

The increasing use of parenting and child development applications has generated a large volume of user reviews containing valuable insights regarding application quality, usability, and user satisfaction. One of the widely used applications in Indonesia is Tentang Anak: Kehamilan & Anak. However, manually analyzing these reviews is inefficient due to the large amount of unstructured textual data. Therefore, this study aims to enhance sentiment classification performance on user reviews of the Tentang Anak: Kehamilan & Anak application using an optimized Support Vector Machine (SVM) model. The dataset consisted of user reviews collected from application platforms, which were processed through several text preprocessing stages, including cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was conducted using polarity scores to classify reviews into positive and negative sentiments. The proposed model was evaluated using different test size scenarios (0.1, 0.2, 0.3, and 0.4) and random state configurations to identify the optimal parameter setting. Experimental results demonstrate that the best performance was achieved at a test size of 0.1 with random state 0, obtaining an accuracy of 89.8%, precision of 91.7%, recall of 55.0%, and F1-score of 68.8%. The findings indicate that the optimized SVM model is effective in classifying sentiment in reviews of the Tentang Anak: Kehamilan & Anak application, particularly in achieving high precision and classification stability across multiple testing scenarios. Furthermore, the study highlights the importance of parameter optimization in improving sentiment analysis performance for user-generated textual data.
Co-Authors Agus Junaidi Agustiani, Sarifah Aldian Mauluda Alif Rizqi Mulyawan Andi Saryoko Andika Bayu Hasta Yanto Andreas Roy Prasetya Ari Sulistiyawati Ari Sulistiyawati Arifin, Yosep Tajul Arman Ramadhani Asriyani Sagiyanto ASRIYANI SAGIYANTO, ASRIYANI Atang Saepudin Atang Saepudin Atang Saepudin Azis, Munawar Abdul Bayu Kusuma Ilyasa Universitas Bina Sarana Informatika Dahlia Dahlia Darma Setiawan Putra Dede Firmansyah Dede Firmansyah Saefudin Dedi Darwis Deni Gunawan Diah Puspitasari Dian Ardiansyah Dian Ardiansyah Dyah Ayu Megawaty Eka Dyah Setyaningsih Eka Fitriani Eka Fitriani Eka Fitriani Eka Fitriyani Fachri, Muhamad Faruk Ulum Haliza Ramadhanti, Pristya Harefa, Kristine Haryani Hasan, Fuad Nur Henny Leidiyana Herdian Pratama I Gede Iwan Sudipa Irfan Ridwan Jananto Watori Junhai Wang Kamil, Anton Abdul Basah KOMALASARI, YULI Martenia, Rina Masngud Megawaty, Dyah Ayu Mesran, Mesran Mochamad Wahyudi Oktaviyani Oktaviyani Oprasto, Raditya Rimbawan Pasaribu, A. Ferico Octaviansyah Perani Rosyani Pristya Haliza Ramadhanti Rachilsyah Ramdhani Efendi Rahmat Hidayat Rahmat Hidayat Ramadhani, Arya Richardus Eko Indrajit Rifky Permana Rifqi Rizaldi Rina Martenia Rizqi Nur Esmeralda Rosiun Universitas Bina Sarana Informatika Roy Prasetya, Andreas Royadi Royadi - Royadi Royadi Royadi, Royadi Salman Alfarizi SALMAN ALFARIZI Samudi Sari Dewi Universitas Bina Sarana Informatika PSDKU Pontianak Setiawansyah Setiawansyah Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Sumanto Sumanto Titik Misriati tri wahyuni Tri Wahyuni Ulum, Faruk Wahyudi, Agung Deni Walim Walim Wang, Junhai Yarimani Laia