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All Journal INFORMATIKA Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Community: Jurnal Pengabdian Pada Masyarakat
Febriyanti Darnis
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering

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Journal : INFORMATIKA

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SMART MAPPING BERBASIS QGIS: PEMETAAN DIGITAL DAERAH RAWAN BENCANA MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS Teguh Setiadi; Febriyanti Darnis
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.954

Abstract

Batang Regency features diverse geographical characteristics, including coastal areas, lowlands, and mountains, which make it vulnerable to various natural disasters such as floods, landslides, and volcanic eruptions. The increasing activity of sand and stone mining along rivers has further exacerbated environmental degradation, heightening the risk of such disasters. To address this issue, a system is needed to help the local government accurately classify disaster-prone areas. A Geographic Information System (GIS) for disaster risk mapping serves as an effective solution to identify and visualise vulnerable locations across Batang Regency. This system supports disaster prevention, response, and targeted aid distribution. With public access to the system, it also promotes data transparency, strengthens community trust in the local government, and raises public awareness of potential disaster threats in their surroundings.
PERAMALAN SUHU UDARA MENGGUNAKAN ALGORITMA LONG SHORT TERM MEMORY Yulian Ansori; Arief Rahman; Febriyanti Darnis; Miftahus Sholihin
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 5 No. 1 (2025): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v5i1.1033

Abstract

This study presents an air temperature forecasting model employing the Long Short-Term Memory (LSTM) algorithm to address the challenges posed by climate variability and extreme weather conditions. Historical daily temperature data from NASA POWER—collected between January 1, 2014, and December 31, 2024, in Serang City (totaling 4,018 records)—were used. The data were normalized using a min–max scaling technique and divided into training (70%) and testing (30%) sets. Multiple experimental scenarios were run by varying the number of training epochs and the hidden layer unit counts. The optimal configuration was achieved in Scenario 7, which incorporated two hidden layers, each with 50 units, and employed 30 epochs; this setup yielded a prediction accuracy of 98.4% with a Root Mean Squared Error (RMSE) of 27.11. The results indicate that the LSTM model effectively captures the seasonal variations and long-term trends in air temperature, making it a reliable tool for forecasting and supporting decision-making in climate adaptation strategies. Keywords: Air Temperature Forecasting, Long Short-Term Memory, Deep Learning, Climate Change, Data Normalization.
Co-Authors Arief Rahman Miftahus Sholihin Sendy Suprianto Sigit Wijayanto Siswanto, Edy Teguh Setiadi Wiwid Wahyudi Yulian Ansori