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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sistemasi: Jurnal Sistem Informasi
Agsaria, Fabelina
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Computer Science & IT Electrical & Electronics Engineering

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Rainfall Prediction based on Historical Weather Data using Naive Bayes Classification Model in Southeast Sulawesi Samudin, Ayustina; Saputra, Rizal Adi; Agsaria, Fabelina; Judanto, Nurendro Hardjo; Badarudin, Ade Syifa
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.3882

Abstract

Southeast Sulawesi is one of the provinces in Indonesia characterized by diverse topography and climate, making it challenging to accurately identify and predict rainfall patterns. The aim of this study is to enhance our understanding of weather behavior in Southeast Sulawesi and provide a foundation for developing more advanced and region-specific weather prediction methods. The data used in this research consists of historical weather records obtained from the official BMKG (Meteorology, Climatology, and Geophysics Agency) website, containing features that significantly contribute to rainfall prediction. The method employed in this study is the Naive Bayes classification model, which involves several stages including data collection, pre-processing, and preparation for the modeling phase, ultimately generating rainfall prediction outputs. The results of the study yielded a rainfall prediction accuracy of 74.66%. For the rainfall class (0.0), the model achieved a precision of 82%, recall of 66%, and F1-score of 73%. Meanwhile, for the rainfall class (1.0), the model achieved a precision of 69%, recall of 84%, and F1-score of 76%. Despite some prediction errors, these findings indicate that the Naive Bayes method can serve as a solid foundation for the development of more sophisticated and tailored weather prediction models for the Southeast Sulawesi region.
Analisis Kualitas dan Klasifikasi Jenis Tanah Berbasis Pengolahan Citra: Teknik Image Sharpening dan CNN ResNet untuk pemetaan pemanfaatan Daerah Pesisir Harnelia, Harnelia; Saudi, Septiyani Bayu; Agsaria, Fabelina; Saputra, Rizal Adi
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14516

Abstract

Tujuan: Penelitian ini bertujuan untuk menganalisis kualitas dan klasifikasi jenis tanah di wilayah pesisir Teluk Kendari dengan menggunakan teknik penajaman gambar dan Convolutional Neural Network (CNN) ResNet152V2, guna mendukung pengelolaan sumber daya wilayah pesisir yang berkelanjutan.Perancangan/metode/pendekatan: Penelitian menggunakan pendekatan pengolahan citra digital dengan tahapan: pengumpulan dataset dari Kaggle dan lapangan, image preprocessing, image sharping, dan klasifikasi menggunakan model CNN ResNet152V2. Dataset terdiri dari 880 gambar dari Kaggle dan 110 gambar dari wilayah Teluk Kendari, dibagi menjadi data latih (80%), uji (10%), dan validasi (10%).Hasil: Model CNN ResNet152V2 berhasil mencapai akurasi klasifikasi sebesar 90.91% dalam mengidentifikasi delapan jenis tanah (Aluvial, Andosol, Entisol, Humus, Inceptisol, Laterit, Kapur, dan Pasir). Teknik penajaman gambar terbukti efektif meningkatkan kualitas citra visual, memperjernih detail tekstur tanah, dan memudahkan proses klasifikasi.Keaslian/ state of the art : Penelitian ini mengintegrasikan teknik penajaman gambar dan CNN ResNet untuk menganalisis tanah pesisir, yang sebelumnya belum banyak dilakukan di Indonesia. Pendekatan ini memberikan kontribusi dalam memahami kondisi tanah di wilayah pesisir dan mendukung strategi pengelolaan berkelanjutan
Co-Authors Badarudin, Ade Syifa harnelia, harnelia Judanto, Nurendro Hardjo Rizal Adi Saputra Samudin, Ayustina Saudi, Septiyani Bayu