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All Journal Jurnal Computech & Bisnis (e-Journal) Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Empowerment Community Tematik : Jurnal Teknologi Informasi Komunikasi International Journal of Quantitative Research and Modeling SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism)
Titan Parama Yoga
Program Studi Sistem Informasi, Fakultas Teknologi Dan Informasi, Universitas Informatika Dan Bisnis Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Physics Social Sciences Other

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Analisis Sentimen Aplikasi BYOND by BSI di Google Play Store Menggunakan Metode SVM Akbar, Imannudin; Sinaga, Arnold Ropen Sinaga; Yoga, Titan Parama; Hendra, Acep; Setiana, Elia Setiana
Jurnal Accounting Information System (AIMS) Vol. 8 No. 2 (2025)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v8i2.1583

Abstract

The BYOND by BSI application has received various user reviews on the Google Play Store, reflecting user perceptions and satisfaction. Sentiment analysis is needed to understand these opinion patterns and support service quality improvement. This study aims to analyze the sentiment of BYOND by BSI user reviews by applying the Support Vector Machine (SVM) method. Review data were collected from the Google Play Store and processed through text preprocessing stages followed by SVM classification modeling. The results show a classification accuracy of 87%, with strong performance in the Positive class (F1-score 0.91) and Negative class (F1-score 0.88), but SVM failed to detect the Neutral class due to data imbalance, where the Neutral class accounted for only 5.85% of the total samples. In conclusion, these findings highlight the importance of handling class imbalance through approaches such as resampling, ensemble algorithms, or class-weight optimization in SVM to improve the accuracy of Neutral sentiment detection.
PENINGKATAN LITERASI KEUANGAN PADA TINGKAT TK, SD, SMP, SMA DAN MASYARAKAT UMUM DI DESA TUNGGILIS, KECAMATAN KALIPUCANG – KABUPATEN PANGANDARAN Dikdik Purwadisastra; Titan Parama Yoga; Aninditha Putri Kusumawardhani
Journal of Empowerment Community Vol. 5 No. 2 (2023): Oktober 2023
Publisher : Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/jec.v5i2.1472

Abstract

Literasi keuangan sangat berkaitan dengan kesejahteraan individu. Pengetahuan keuangan dan ketrampilan dalam mengelola keuangan pribadi sangat penting dalam kehidupan sehari-hari. Kesulitan keuangan bukan hanya dipengaruhi dari rendahnya pendapatan. Kesulitan keuangan juga dapat muncul jika terjadi kesalahan dalam pengelolaan keuangan seperti kesalahan penggunaan hutang dan tidak adanya perencanaan keuangan. Keterbatasan finansial dapat menyebabkan stress, dan rendahnya kepercayaan diri. Literasi keuangan sejak dini bertujuan untuk memberikan edukasi literasi keuangan sejak dini karena pengetahuan dan pengalaman keuangan yang ditanamkan akan melekat dalam diri anak sehingga membentuk karakter dan kebiasaan mengelola keuangan mereka di masa depan sebagai suatu budaya baik, seperti mengenal makna uang, kebiasaan menabung, hingga mendahulukan kebutuhan dari pada keinginan. Metode yang digunakan dalam pengabdian kepada masyarakat ini adalah Participatory Action Research (PAR) Metode ini digunakan dalam mengatasi permasalahan mitra melalui tahapan atau langkah-langkah untuk melaksanakan solusi yang ditawarkan. Melihat hasil kegiatan literasi keuangan yang telah dilaksanakan mulai dari tingkat TK, SD, SMP, SMA dan Masyarakat umum, dapat disimpulkan bahwa kegiatan ini sangat bermanfaat dan memberikan edukasi serta pemahaman mengenai pengelolaan keuangan dengan baik dan bijak, mengingat masih kurangnya literasi keuangan yang dimiliki oleh masyarakat desa.
A Metaheuristic wrapper approach to feature selection with genetic algorithm for enhancing XGBoost classification in diabetes prediction Alamsyah, Nur; Budiman; Danestiara, Venia Restreva; Yoga, Titan Parama; Nursyanti, Reni; Kaunang, Valencia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2366

Abstract

This study addressed the problem of selecting the most relevant features for improving the accuracy of diabetes classification using health indicator data. The research focused on a binary classification task based on the Behavioral Risk Factor Surveillance System dataset, which comprised over seventy thousand records and twenty-one predictive features related to individual health behaviors and conditions. A metaheuristic wrapper approach was developed by integrating a Genetic Algorithm for feature selection with an XGBoost classifier to evaluate the predictive quality of each feature subset. The fitness function was defined as the average classification accuracy obtained through cross-validation. In addition to feature selection, hyperparameter optimization of the XGBoost model was carried out using a Bayesian-based search strategy to further enhance performance. The proposed method successfully identified a subset of fourteen optimal features that contributed most significantly to the prediction of diabetes. The final model, combining the selected features and optimized parameters, achieved an accuracy of 0.753, outperforming baseline models trained with all features and models using features selected by deterministic methods. These results confirmed the effectiveness of combining evolutionary feature selection with model tuning to build efficient and interpretable predictive models for medical data classification. This approach demonstrated a practical solution for managing high-dimensional data in the context of chronic disease prediction.
Co-Authors Acep Hendra Achmad, Fariz Agus Nurfaturrohman Agustillah Akbar, Imannudin Alamsyah, R Yadi Rakhman Aliman Fauzi Arnold Ropen Sinaga Aziz, Nizar Hizbi Abdul Bambang Saras Yulistiawan Budiman Chairul Habibi Chairul Habibi Deni Kadarisman Dikdik Purwadisastra Habibi, Chairul Iis Ismail Januantara Prima, Alif Kaunang, Valencia Kharisma Wiati Gusti Kusumawardhani, Aninditha Putri Ludi Priyadi M Nurdin Afgani Marwondo Naufal Dwi Maulana Niqotaini, Zatin Nizar Hizbi Abdul Aziz Nur Alamsyah Nur Alamsyah, Nur Purwadisastra, Dikdik Putra Pamungkas Sukmana R. Yadi Rakhman Alamsyah Reni Nursyanti Reni Nursyanti Samsudin Samsudin Santi Dwi Putri Sardjono Sardjono Seny Nurhasanah Setiana, Elia Setiana Silca Silkillah Adwa Sinaga, Arnold Ropen Sinaga Suci Fitriani Setiawan Tutik Ultsa Rahmatika Vani Maharani Venia Restreva Danestiara Wahyu Kurnianto Wasnadi . Yusup Mulya Zaidiah, Ati