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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro
Bulkis Kanata
Mataram University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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PENERAPAN METODE SUPERVISED CLASSIFICATION MAXIMUM LIKELIHOOD PADA CITRA SATELIT LANDSAT UNTUK MEMETAKAN PERUBAHAN TUTUPAN LAHAN DI TAMAN NASIONAL BUKIT BARISAN SELATAN (TNBBS) Bulkis Kanata; Muhamad Syamsu Iqbal; Ramdayanti Ramdayanti
DIELEKTRIKA Vol 8 No 1 (2021): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v8i1.265

Abstract

Taman nasional merupakan kawasan pelestarian alam yang mempunyai fungsi sebagai perlindungan sistem penyangga kehidupan, pengawetan keanekaragaman jenis tumbuhan dan satwa serta ekosistemnya yang dikelola dengan sistem zonasi, yang terdiri dari zona inti, zona pemanfaatan, dan zona lain sesuai keperluan (Undang-undang No. 5 Tahun 1990). Namun sejauh ini masih sering muncul permasalahan yang terjadi hampir disetiap taman nasional, seperti perburuan satwa liar, illegal loging, perambahan, pencurian kayu/tumbuhan langka dan tata batas kawasan. Salah satu taman nasional yang ada di Indonesia yang tidak luput dari permasalahan adalah Taman Nasional Bukit Barisan Selatan. Penelitian ini dilakukan untuk mengetahui perubahan tutupan lahan yang terjadi di TNBBS. Setelah dilakukan penelitian dengan menggunakan software ArcGIS dengan metode supervised maximum likelihood classification diketahui bahwa dalam kurun tahun 2015 sampai 2018 terjadi peningkatan luas pemukiman sebesar 0,32% dan penurunan luas vegetasi tinggi (kawasan hutan) hingga 7,99%. Lahan terbuka mengalami penurunan luas 0,79%, lahan pertanian mengalami penurunan hingga 1,12%, tutupan lahan berupa rumput/semak meningkat hingga 4,58% dan tutupan lahan vegetasi sedang meningkat hingga 5,03%.
Pengolahan Sinyal Geomagnetik di Regional Lombok dengan Metode Fraktal Bulkis Kanata; Teti Zubaidah; I Gusti Ayu Kusdiah
DIELEKTRIKA Vol 7 No 1 (2020): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v7i1.215

Abstract

Lombok Island is one of the islands in Indonesia with a high level of seismic activity, because Indonesia itself is in the ring of fire. Mount Rinjani is an active volcano that can be associated with seismicity on the island which can affect changes in geomagnetic fields in this case can cause geomagnetic anomalies. Based on this, a study of the fractal dimensions of geomagnetic signals in the Lombok region used data of Kupang Observatory (KPG), Kakadu (KDU) and Guam (GUA) to determine anomalies that could be suspected as earthquake precursors. The fractal dimension of a destructive earthquake is seen in the range 12-43 days before the earthquake event and 5-46 days for small earthquakes. Fractal dimension values are linear with earthquake distance to the observatory.
Rainfall Prediction Using Gate Recurrent Unit (Gru) for The Mataram City Area Galih Dimas Aryoso; Bulkis Kanata; Made Sutha Yadnya
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.9874

Abstract

Rainfall prediction is crucial for urban planning, agriculture, and disaster mitigation. This study predicts rainfall intensity in Mataram City using the Gated Recurrent Unit (GRU), a variant of Recurrent Neural Networks (RNN) optimized for sequential data. The dataset consists of hourly rainfall data from NASA's MERRA Power (2010–2021). Data preprocessing includes normalization, feature engineering, and dataset splitting. The GRU model architecture comprises input, GRU, and dense layers. Model performance is evaluated using Root Mean Squared Error (RMSE), yielding 67, 112, 69, and 109 for Ampenan, Cakranegara, Majeluk, and Selaparang, respectively. Results show that the GRU model captures rainfall trends but has limitations in predicting extreme values. This study demonstrates GRU’s potential for improving rainfall forecasting while highlighting the need for further optimization to enhance accuracy.
Co-Authors Galih Dimas Aryoso I Gusti Ayu Kusdiah Made Sutha Yadnya Muhamad Syamsu Iqbal Ramdayanti Ramdayanti Teti Zubaidah