PREDIKSI TEMPORAL MEDAN LISTRIK ATMOSFER MENGGUNAKAN LSTM BERBASIS DATA ELECTRIC FIELD MILL

Rafanda, M. Alfin (2026) PREDIKSI TEMPORAL MEDAN LISTRIK ATMOSFER MENGGUNAKAN LSTM BERBASIS DATA ELECTRIC FIELD MILL. Diploma thesis, Politeknik Negeri Sriwijaya.

	Text (COVER) COVER.pdf - Published Version Download (422kB)
	Text (ABSTRAK) ABSTRAK.pdf - Published Version Download (216kB)
	Text (BAB I Pendahuluan) BAB I.pdf - Published Version Restricted to Repository staff only Download (251kB) | Request a copy
	Text (BAB II Tinjauan Pustaka) BAB II.pdf - Published Version Restricted to Repository staff only Download (519kB) | Request a copy
	Text (BAB III Metode Penelitian) BAB III.pdf - Published Version Restricted to Repository staff only Download (568kB) | Request a copy
	Text (BAB IV Hasil dan Pembahasan) BAB IV.pdf - Published Version Restricted to Repository staff only Download (754kB) | Request a copy
	Text (BAB V Penutup) BAB V.pdf - Published Version Restricted to Repository staff only Download (180kB) | Request a copy
	Text (DAFTAR PUSTAKA) DAFTAR PUSTAKA.pdf - Published Version Restricted to Repository staff only Download (199kB) | Request a copy
	Text (LAMPIRAN) LAMPIRAN.pdf - Published Version Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

Penelitian ini bertujuan untuk memprediksi perubahan medan listrik atmosfer secara temporal menggunakan metode Long Short-Term Memory (LSTM) berbasis data hasil pengukuran Electric Field Mill (EFM). Data diperoleh secara real-time dan melalui tahapan preprocessing yang meliputi penyaringan sinyal, penanganan missing value, normalisasi data, serta pembentukan sequence time series. Model LSTM digunakan untuk mempelajari pola perubahan medan listrik atmosfer dan dievaluasi menggunakan parameter Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) dan Coefficient of Determination (R2). Hasil penelitian menunjukkan bahwa model LSTM mampu memprediksi perubahan medan listrik atmosfer dengan baik sehingga hasil prediksi mendekati data aktual. Penelitian ini diharapkan dapat mendukung pengembangan sistem pemantauan kondisi atmosfer dan peringatan dini potensi petir berbasis Internet of Things (IoT).

Item Type:	Thesis (Diploma)
Uncontrolled Keywords:	Medan Listrik Atmosfer, Electric Field Mill, Long Short-Term Memory (LSTM), Prediksi Temporal, Time Series, Internet of Things (IoT).
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Electronic Engineering > Undergraduate Theses
Depositing User:	Pustaka Teknik Elektro
Date Deposited:	09 Aug 2026 15:21
Last Modified:	09 Aug 2026 15:21
URI:	http://eprints.polsri.ac.id/id/eprint/24400

Actions (login required)

View Item