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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Manajemen Informatika Jurnal Teknologi dan Informasi Big Data Analytics and Data Science
Bella Hardiyana
Fakultas Teknik Dan Ilmu Komputer Universitas Komputer Indonesia
Computer Science & IT

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PENERAPAN NAIVE BAYES CLASSIFIER UNTUK PEMILIHAN KONSENTRASI MATA KULIAH Annisa Paramitha Fadillah; Bella Hardiyana
Jurnal Teknologi dan Informasi (JATI) Vol 8 No 2 (2018): Jurnal Teknologi dan Informasi (JATI)
Publisher : Program Studi Sistem Informasi, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (266.645 KB) | DOI: 10.34010/jati.v8i2.1039

Abstract

UNIKOM merupakan perguruan tinggi yang berkembang pesat di Indonesia, yang memanfaatkan perkembangan teknologi berupa layanan sistem informasi. UNIKOM menggunakan sistem informasi untuk membantu berjalannya proses akademik, terutama dalam pengolahan data yang berkaitan dengan kegiatan perkuliahan. Sistem informasi merupakan salah satu program studi yang cukup besar yang ada di UNIKOM. Sebagai program studi yang cukup besar, program studi sistem informasi memiliki mahasiswa  cukup banyak untuk setiap angkatan. Pada prodi sistem informasi mahasiswa semester 6 wajib melakukan pemilihan konsentrasi matakuliah, akan tetapi mahasiswa merasa cukup kesulitan dalam melakukan pemilihan konsentrasi mata kuliah, bahkan dosen wali kesulitan dalam memberikan rekomendasi pilihan konsentrasi mata kuliah kepada mahasiswanya. Oleh karena itu, akan dilakukan penelitian mengenai pemilihan konsentrasi matakuliah, dengan menggunakan naïve bayes. Dengan menggunakan naïve bayes diharapkan  dapat memberikan rekomnedasi pemilihan konsentrasi mata kuliah baik untuk mahasiswa maupun dosen wali.  Penggunaan naïve bayes classifier, dikarenakan naïve bayes classifier merupakan metode yang mudah dipahami dan cukup sederhana.
Artificial Intelligence-Based Early Warning System for Disaster Management: A Literature Review Systematic and Bibliometric Analysis Ridwan Zulkifli; Zainal Arifin Hasibuan; Irawan Afrianto; Bella Hardiyana; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.392

Abstract

The increasing frequency and intensity of natural disasters globally demands the development of more accurate and responsive Early Warning Systems (EWS). In recent years, Artificial Intelligence (AI) has been increasingly applied in natural disaster mitigation, but the approaches used are still diverse and spread across various domains. This study aims to present a systematic literature review on the application of AI and deep learning in natural disaster early warning systems. This review was conducted following the PRISMA 2020 guidelines by analyzing literature published during the 2020–2025 period. The selection process resulted in 102 studies meeting the inclusion criteria, with 30 full-text articles being analyzed in depth to map disaster types, AI methods, data sources, and characteristics of early warning systems developed in various regions, including Asia and Africa. The review results show the dominance of deep learning approaches, particularly time series-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), particularly in flood forecasting and land deformation prediction. More advanced architectures, such as Transformer, are beginning to be adopted to capture long-term temporal patterns, while the combination of convolutional neural networks (CNN) with remote sensing data is widely used for spatial mapping of disaster events. Furthermore, the integration of sensor data and the Internet of Things (IoT) shows potential in supporting more responsive early warning systems. However, most research remains limited to the modeling or simulation stage, with little discussion of the real-time and operational implementation of EWS. This review highlights the gap between AI model development and the implementation of reliable early warning systems and provides a conceptual foundation for the future development of more integrated AI-based disaster mitigation systems.
Co-Authors Angga Gantira Annisa Paramitha Fadillah Diana Effendi Eko Gusdiono Irawan Afrianto Irfan Suendi Ridwan Zulkifli Sri Supatmi Taufiq Hidayat Yayang Nopandi Zainal Arifin Hasibuan