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Lalu Muhamad Jaelani
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INDONESIA
Jurnal Penginderaan Jauh Indonesia
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 26570378     DOI : https://doi.org/10.12962/jpji
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Astronomy Computer Science & IT Earth & Planetary Sciences Engineering
Jurnal Penginderaan Jauh Indonesia disingkat JPJI (e-ISSN: 2657-0378) pertama kali terbit sejak 1 Februari 2019. JPJI adalah media komunikasi dan diseminasi hasil penelitian, kajian dan pemikiran terkait teori, sains, dan teknologi penginderaan jauh serta pemanfaatannya. Fokus jurnal mencakup penginderaan jauh untuk objek dipermukaan bumi, baik di darat, laut maupun atmosfer. JPJI diterbitkan oleh Institut Teknologi Sepuluh Nopember (ITS) bersama Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN/ISRS).
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 4 Documents
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Search results for , issue "Vol 4 No 1 (2025)" : 4 Documents clear
ANALISIS BANJIR DAN TANAH LONGSOR TERKAIT PERUBAHAN TUTUPAN LAHAN DAN INDEKS VEGETASI DI KOTA BATU MENGGUNAKAN CITRA SATELIT MULTI-TEMPORAL Mahmud, Fahrin Ajie; Kurniawan, Akbar
Jurnal Penginderaan Jauh Indonesia Vol 4 No 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jpji.v4i1.3209

Abstract

Natural disasters pose significant threats to communities, often resulting from natural factors and human activities, such as landslides and floods. In 2023, Indonesia experienced 5,400 disasters, with 99.35% being hydrometeorological events. Batu City, East Java, has seen an increase in disasters, particularly landslides and floods, indicating ecosystem disturbances due to land-use changes. This study employs multi-temporal satellite imagery data (Landsat-8 and Sentinel-2) from 2013 to 2023 to analyze land cover changes and vegetation indices. The maximum likelihood supervised classification method and the Normalized Difference Vegetation Index (NDVI) were utilized to map land cover and vegetation distribution. Results reveal significant land cover changes, with non-vegetated areas increasing by 189.291 hectares and vegetated areas decreasing by 177.477 hectares. These changes contribute to the rising incidence of landslides and floods, particularly in residential and agricultural areas. Spatio-temporal analysis demonstrates a correlation between land cover changes, vegetation indices, and disaster frequency, underscoring the importance of sustainable land management in mitigating disaster risks.
KLASIFIKASI TUTUPAN LAHAN TAHUN 2021 DENGAN METODE RANDOM FOREST (RF) DAN SUPPORT VECTOR MACHINE (SVM) (STUDI KASUS: KOTA MATARAM) Raihan, Muhammad Anis; Hidayat, Husnul; Handayani, Hepi Hapsari
Jurnal Penginderaan Jauh Indonesia Vol 4 No 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jpji.v4i1.3369

Abstract

Land cover is all types of features that exist on the earth's surface on certain land, either artificial or natural. Information related to monitoring and processing satellite image data to obtain land cover classification can be done in various ways, one of which is machine learning methods. This study aims to apply machine learning methods in monitoring land cover using Landsat-8 imagery, to obtain a technique that has high accuracy and is suitable for monitoring land cover. This study uses machine learning methods, namely Support Vector Machine (SVM) and Random Forest (RF). The classification of land cover in this study consists of five classes, namely, built-up areas, water bodies, vacant land, agriculture, and vegetation, where the determination of this land cover class is based on the type of land cover that exists on the RTRW Map of Mataram City in 2011-2031. match the image used. This study shows that the method with the best accuracy is the Support Vector Machine (SVM) method with overall accuracy and kappa accuracy values of 0.9101 and 0.8748. However, there is a misclassification caused by several factors such as the reflectance value of each pixel which is almost the same, the cropping period, and other factors. These factors need to be considered because they affect the land cover classification results.
ANALISIS KETELITIAN KLASIFIKASI PENUTUPAN LAHAN MENGGUNAKAN METODE DIGITIZE ON SCREEN DAN DEEP LEARNING SERIES CONVOLUTIONAL NEURAL NETWORK (CNN) BERDASARKAN CITRA LANDSAT-8 OLI (STUDI KASUS: PROVINSI KALIMANTAN TIMUR) Sukojo, Bangun Muljo; Ramadaningtyas, Niken
Jurnal Penginderaan Jauh Indonesia Vol 4 No 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jpji.v4i1.3370

Abstract

Land cover is dynamic due to human needs or natural events that can occur in a planned or unplanned manner. Dit. IPSDH-KLHK uses remote sensing satellite imagery data to generate land cover data using a visual interpretation method (manual interpretation). Object identification is done by digitizing on screen. Along with the development of the times and current technology, several studies have emerged regarding the classification of land cover and its accuracy test using the latest technology, one of which uses deep learning. In this study, the accuracy of the digitize on screen classification results and land cover classification using deep learning was carried out along with the accuracy test. The test for the accuracy of land cover classification as a result of digitizing on screen was carried out using the centroid method. Validation was carried out using high resolution satellite imagery, namely google earth pro according to the temporal acquisition of Landsat 8 imagery used, namely July 2019-June 2020 by spreading 360 samples randomly. The results show that East Kalimantan Province has 21 land cover classes with an overall accuracy value of 87.22% in the very good category and in the tolerance category. Land cover classification using deep learning is carried out using segmented Landsat-8 OLI images. Sampling was carried out with a segment picker for 20 land cover classes without the Mixed Dry Land Agriculture class. The classification results show overlapping because one land cover class is also classified into other classes and not all image areas are classified. The accuracy test was carried out with the same location of the sample point as the test sample for the digitize on screen method. The accuracy value of the deep learning method using 188 samples in classified areas resulted in an accuracy of 70.21% for 21 land cover classes. This is due to the many land cover classes with almost similar interpretation keys. The interpretation key of 21 land cover classes is more suitable for the digitize on screen method.
IDENTIFIKASI SEBARAN TINGKAT BAHAYA EROSI DI DAS BRANTAS (WILAYAH ADMINISTRASI KOTA SURABAYA) TAHUN 2022 Sianturi, Ignatius Bennito; Pribadi, Cherie Bhekti
Jurnal Penginderaan Jauh Indonesia Vol 4 No 1 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jpji.v4i1.3449

Abstract

Erosion is a critical environmental degradation event that has profound implications for agricultural productivity, ecosystem stability, and sustainable development. This study aims to quantify the rate of soil erosion in various agro-ecological zones and to evaluate the effectiveness of soil conservation practices. Given that soil conservation processes require predicting the rate of erosion that occurs, erosion rate modeling was conducted. The commonly used modeling of erosion rate values is often limited to modeling the rate of erosion caused by water, such as sheet erosion, gully erosion, and several other erosions. High and uncontrolled erosion rates can lead to the loss of soil fertility and the accumulation of thick sediment in river flows, which can cause disasters such as floods and others. In this study, the determination of erosion hazards was conducted using the RUSLE (Revised Universal Soil Loss Equation) method in the Brantas River Basin (Administrative Boundary of Surabaya City). From the obtained erosion rate values, it was found that the Brantas River Basin area (Surabaya City area) on average has a “light” hazard level.

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