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All Journal Building of Informatics, Technology and Science IndraTech
Wulandari, Heptyana Sri
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Religion Computer Science & IT Control & Systems Engineering Library & Information Science Other

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Analisis Kuantitatif Sistem Komunikasi Pengiriman Data Pengamatan Cuaca Otomatis di Provinsi Lampung Wulandari, Heptyana Sri; Sriyanto, Sriyanto; Aziz, Abdul
IndraTech Vol 5, No 2 (2024): Oktober 2024
Publisher : STMIK Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56005/jit.v5i2.167

Abstract

Jaringan Komunikasi saat ini telah banyak perkembangan guna menunjang kecepatan informasi, dalam hal ini BMKG menggunakan jaringan data AWS dan AWL  memiliki sistem pengiriman data yang menggunakan Modem GSM 4G, Internet Servis Provider ( ISP ) dan Satelit Bakti guna pengiriman data dari beberapa jaringan Stasiun Cuaca Otomatis ( AWS )  dan stasiun dan otomatisasi tinggi muka air ( AWL ) yang tersebar di seluruh wilayah Indonesia, berdasarkan ketiga jaringan komunikasi yang di gunakan oleh BMKG dalam mengirimkan data realtime, penulis menggunkan metode Analisis data kuantitafif. Hasil analisis kuantitatif menunjukan bahwa penggunaan modem GSM 4G sangat baik apabila digunakan di kota / daerah yang mempunyai coverage sinyal yang baik.Kata Kunci: Jaringan Komunikasi, Badan Meteorologi Klimatologi dan Geofisika (BMKG), Analisis KuantitatifABSTRACTCommunication networks currently have many developments to support the speed of information, in this case BMKG uses the AWS data network and AWL has a data delivery system that uses a 4G GSM Modem, Internet Service Provider (ISP) and Bakti Satellite to send data from several Automatic Weather Station networks (AWS) and stations and automatic water level (AWL) spread throughout Indonesia, based on the three communication networks used by BMKG in sending real-time data, the author uses the quantitative data analysis method. The results of the quantitative analysis show that the use of the GSM 4G modem is very good when used in cities/regions that have good signal coverage.Keyword: Communication Network, Meteorology, Climatology and Geophysics Agency (BMKG), Quantitative Analysis
Implementasi Model LSTM, CNN+LSTM Hybrid, dan Transformer untuk Prediksi Cuaca Harian Berbasis Data Multivariat Wulandari, Heptyana Sri; Aziz, RZ Abdul
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7655

Abstract

Global climate change and the increasing frequency of extreme weather events demand more accurate and adaptive weather prediction systems. This study aims to implement and compare three deep learning models, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)+LSTM Hybrid, and Transformer for predicting next-day weather events using daily multivariate meteorological data. The dataset was obtained from the Climatology Station Class IV Lampung and includes air temperature, rainfall, humidity, solar radiation, air pressure, wind direction, and wind speed, collected in CSV format from February 2000 to March 2025. The analysis results indicate that the CNN+LSTM Hybrid model achieved the best performance, with an RMSE of 1.158, MAE of 0.521, R² Score of 0.323, accuracy of 75%, and Macro F1 score of 0.75. The LSTM model demonstrated moderate performance, while the Transformer model yielded the lowest results among the three. These findings suggest that combining CNN's spatial feature extraction with LSTM's sequential processing enhances the prediction quality of short-term weather forecasts based on multivariate data. This study is expected to contribute to the development of AI-based weather forecasting systems in Indonesia, particularly for hydrometeorological disaster mitigation.
Co-Authors Abdul Aziz Aziz, RZ. Abdul Sriyanto Sriyanto