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All Journal Communications in Science and Technology Sewagati Jurnal Penginderaan Jauh Indonesia Geoid - Journal of Geodesy and Geomatics
Hepi Hapsari Handayani, Hepi Hapsari
Institut Teknologi Sepuluh Nopember
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Studi Pembuatan DTM Menggunakan Metode Slope Based Filtering dan Grid Based Filtering (Studi Kasus: Kelurahan Wonokromo Dan Lontar, Kota Surabaya) Wirantiko, Mahardi; Handayani, Hepi Hapsari; Cahyono, Agung Budi
GEOID Vol. 16 No. 1 (2020)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v16i1.1668

Abstract

Digital Terrain Model (DTM) merupakan model medan digital yang memuat informasi ketinggian permukaan tanah (bare earth surface) tanpa terpengaruh oleh vegetasi atau fitur buatan manusia lainnya, sedangkan  Digital Surface Model (DSM) merupakan representasi permukaan bumi yang memuat lebih banyak informasi ketinggian termasuk semua objek yang berada di atas permukaan bumi seperti vegetasi, gedung, dan fitur lainnya. Perlu dilakukan percepatan dalam penyediaan informasi geospasial, dalam hal ini DTM sebagai unsur pembentuk peta topografi skala besar. Untuk itu diperlukan metode pembentukan DTM yang lebih efektif. Sehingga tujuan pada penelitian ini adalah mengkaji metode yang dapat menghasilkan DTM secara otomatis dan menghasilkan DTM turunan yang mendekati akurat. Pada penelitian ini metode yang digunakan untuk dikaji yaitu Slope Based Filtering (SBF) atau metode penyaringan berbasis lereng dan Grid Based Filtering (GBF) atau Metode Penyaringan Berbasis Grid. Terdapat dua daerah yang diteliti. Pada area pertama yaitu lokasi yang memiliki karakteristik daerah padat penduduk sehingga terdapat banyak bangunan yang saling berhimpit, area tersebut berlokasi di Kelurahan Wonokromo, Surabaya Selatan. Pada area kedua yaitu lokasi yang memiliki karakteristik terbuka, sedikit pemukiman dan banyak medan datar dan kosong, area tersebut berlokasi di Kelurahan Lontar, Surabaya Barat. Hasil data dari kedua metode tersebut kemudian dibandingkan terhadap DTM Stereoplotting yang digunakan sebagai referensi. Perbandingan tersebut berupa geomorfologi atau visualisasi, dan ketelitian geometri vertikal. Hasil dari penelitian ini menunjukkan metode Slope Based Filtering memiliki keakuratan yang lebih tinggi dibandingkan dengan Grid Based Filtering. Hal tersebut dibuktikan oleh hasil klasifikasi pengolahan data menggunakan delapan parameter pada masing-masing metode. Rata-rata RMS Error yang diperoleh di Wonokromo lebih kecil yaitu 0,605 meter dibandingkan dengan Kelurahan Lontar sebesar 1,605 m. Kelurahan Wonokromo memiliki rata-rata skala ketelitian peta 1: 2.500 sedangkan Kelurahan Lontar memiliki rata-rata kelas ketelitian peta 1: 5.000. Secara visual geomorfologi yang dihasilkan dari metode SBF lebih halus dibandingkan dengan GBF yang masih kasar. Digital Terrain Model (DTM) is a digital terrain model that only contains ground level information (bare earth surface) without being affected by vegetation or other man-made features. While Digital Surface Model (DSM) is a representation of the earth's surface that contains more height information including all objects that are located on the surface of the earth such as vegetation, buildings, and other features. It is necessary to accelerate the provision of geospatial information, in this case DTM as an element of forming large-scale topographic maps. For this reason, a more effective DTM formation method is needed. The study was conducted to examine methods that can produce DTM automatically, in order to obtain a fast and efficient mapping method. In this study the method used are Slope Based Filtering (SBF) and Grid Based Filtering (GBF) method. Those approaches are applied in two different characteristics of study area. In the first area, which is a location that has characteristics of densely populated areas so that there are many buildings that coincide with each other, the area is located in Wonokromo Sub-District, South Surabaya. The second area has characteristics of open space with few settlements and a lot of barelands. The area is located in Lontar Village, West Surabaya. The results of the data processing based on two methods are then compared to the Stereoplotting DTM used as a reference. The comparison is performed as geomorphology analysis or visualization, and vertical geometry accuracy. The results of this study indicate that SBF method has a higher accuracy compared to the one of GBF. This is revealed by the results of the classification of data processing using eight parameters in each method. The average of RMS Error obtained in Wonokromo is smaller that is 0.605 meters compared to Lontar Village of 1.605 m. Wonokromo has an average map accuracy scale of 1: 2,500 while Lontar which has an average map accuracy class of 1: 5,000. Visually, the geomorphology produced from the SBF method is finer than the GBF which is still rough.
Analysis of Urban Built-up Expansion Based on Combination of Spectral Indices in Surabaya City Ramadhan, Fendra Dwi; Hariyanto, Teguh; Handayani, Hepi Hapsari
GEOID Vol. 17 No. 1 (2021)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v17i1.1702

Abstract

The urban built-up land indicates the physical changes of the city that represent the development indicator in the city of Surabaya. The expansion of urban built-up land needs to be monitored to control and direct urban areas development. In this study, the identification of urban built-up land was conducted by applying a combination of algorithm-based spectral indices consisting of combination A (UI – NDVI – MNDWI) and combination B (NDBI – NDVI – MNDWI) with the spatial expansion analysis that focused on the speed of the urban built-up land expansion and the direction of change of the urban built-up object. Based on the results, the proposed spectral indices combination able to identify the urban built-up land pixels including bare land that free of vegetation and water body object. The pattern of the urban built-up land direction shows a tilt toward the west and east side of the city of Surabaya, while the speed of change shows that combination A and combination B have the same trend, there are a decreasing number of built-up land from 2015 to 2017 and an increasing number of built-up land from 2017 to 2019.
Analisis Peta Rawan Banjir Metode Pembobotan dan Peta Genangan Banjir Metode NDWI terhadap Kejadian Banjir (Studi Kasus: Kabupaten Sidoarjo) Ramadhan, Alkindi Gifty; Handayani, Hepi Hapsari; Darminto, Mohammad Rohmaneo
GEOID Vol. 17 No. 2 (2022)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v17i2.1743

Abstract

Perkembangan Sistem Informasi Geografis dan penginderaan jauh memberikan manfaat di berbagai bidang, salah satunya adalah kebencanaan. Banjir merupakan bencana yang rawan terjadi di negara-negara tropis yang memiliki curah hujan tinggi. Kabupaten Sidoarjo mengalami genangan banjir hampir setiap tahun. Pemanfaatan SIG dan penginderaan jauh dapat meminimalkan dampak bencana banjir. Peta rawan banjir bertujuan untuk memetakan wilayah yang berpotensi terjadi banjir dengan menerapkan metode skoring dan pembobotan parameter-parameter penyebab terjadinya genangan banjir. Penginderaan jauh bisa dimanfaatkan untuk mendeteksi genangan banjir yang terjadi dengan menerapkan analisis Normalized Difference Water Index (NDWI). Pemanfaatan kedua analisis ini bertujuan untuk mengetahui bagaimana sebaran kelas tingkat kerawanan banjir dari genangan banjir yang terjadi di Kabupaten Sidoarjo. Daerah rawan banjir diidentifikasi menggunakan metode skoring dan pembobotan parameter untuk mengetahui sebaran tingkat kerawanan banjir di wilayah Kabupaten Sidoarjo. Parameter tingkat kerawanan banjir yang digunakan meliputi tutupan lahan, kelerengan, curah hujan, densitas drainase, dan jenis tanah. Genangan banjir didapatkan dari analisis NDWI pada platform Google Earth Engine (GEE). Data citra pada musim kemarau dan hujan yang di-threshold sebesar <0,3 untuk mengeliminasi data yang dianggap non-air sehingga menghasilkan genangan banjirnya. Hasil pengolahan menunjukkan tingkat rawan banjir di Kabupaten Sidoarjo yang terbagi menjadi 4 kelas yaitu kelas rawan dengan 17.06%, kelas sedang dengan 65.10%, kelas kurang rawan dengan 17.84%, dan kelas aman dengan 0,00% dari luas wilayah Kabupaten Sidoarjo dengan mayoritas daerahnya dalam kelas rawan di Kecamatan Jabon dan Sedati. Peta genangan banjir dan peta rawan banjir sesuai terhadap kejadian banjir dari BPBD Kabupaten Sidoarjo karena hasil analisis menunjukkan bahwa kejadian genangan banjir dengan kelas rawan sebesar 86,56% dari total luas kejadian genangan banjir.
Analisis Pemetaan Skala 1:1000 Menggunakan Data Unmanned Aerial Vehicle (UAV) (Studi Kasus: Waduk Selorejo - Kabupaten Malang) Cahyono, Agung Budi; Handayani, Hepi Hapsari; Nurwatik , Nurwatik
GEOID Vol. 18 No. 2 (2023)
Publisher : Departemen Teknik Geomatika ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/geoid.v18i2.1771

Abstract

UAV Fotogrametri telah dipergunakan di berbagai sektor, salah satunya adalah untuk pemetaan dasar di Waduk Selorejo di desa Pandansari, Kecamatan Ngantang, Kabupaten Malang. Penelitian ini adalah kerjasama Departemen Teknik Geomatika ITS dengan Perum Jasa Tirta I dalam rangka inventarisasi data dan kegiatan Mata Kuliah Kemah Kerja tahun 2022. Akuisisi data foto ini dilakukan dengan menggunakan wahana UAV jenis tipe rotor QuadCopter Dji Phantom 4 Pro sebanyak 3 unit. Berdasarkan pemotretan udara yang dilakukan di area wisata Waduk Selorejo, didapatkan hasil sebanyak 678 buah foto udara. Wilayah pemetaan mencakup bagian Selatan wilayah Waduk Selorejo. Perencanaan kontrol koordinat dilakukan terhadap 4 GCP (Ground Control Point) dan misi terbang pada 4 buah misi terbang. Misi terbang 1 dan 2 digunakan untuk pengolahan Blok I, misi terbang 2 dan 3 digunakan untuk pengolahan Blok II, serta misi terbang 3 dan 4 digunakan untuk pengolahan Blok III. Didapatkan hasil tinggi terbang rata-rata adalah 150 meter hingga didapatkan nilai Ground Sampling Distance (GSD) 4.23 cm/piksel. Dari hasil pengolahan data, didapatkan Circular Error (CE) 90% senilai 0.0602 meter dan memenuhi standar ketelitian peta RBI yang berada di rentang skala 1:1000 dan 1:2500 berdasarkan perka BIG No. 6 Tahun 2018.
Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting Cahyono, Bambang Kun; Ummah, Muhammad Hidayatul; Andaru, Ruli; Andika, Neil; Pamungkas, Adjie; Handayani, Hepi Hapsari; Atmodiwirjo, Paramita; Nathan, Rory
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1638

Abstract

Rainfall forecasts are essential for human activities enabling communities to anticipate any impacts. Rainfall events correlate with other natural and hydro-meteorological phenomena, which can be used in modeling and prediction. This study used daily CHIRPS for the Gajahwong watershed in Yogyakarta, Indonesia as the precipitation data. It also used Sea Surface Temperature, Land Surface Temperature (Day and Night), Minimum and Maximum Temperatures, Solar Radiation, Wind Speed (U and V components), Cloud Pressure (Top and Base), and Cloud Height (Top and Base) as the parameters. Further, data processing was performed by means of the Google Earth Engine (GEE) platform. Machine learning methods, including Support Vector Regression, Gradient Boosting Regression, Random Forest, and Deep Neural Networks, were applied. The correlation analysis revealed that only the Wind Speed V-component showed significant correlation with rainfall, other seven parameters showed moderate and four showed weak ones. Meanwhile, accuracy assessments indicated that Support Vector Regression had the most accurate predictions accompanied by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, and Coefficient Correlation (CC) at 1.366, 0.947, 1.866, 0.948 and 0.982 respectively. This study demonstrated that utilizing openly accessible atmospheric datasets processed through the GEE could yield reliable rainfall predictions, facilitating informed decisions on a wide scale. The methodology is adaptable and can be reproduced for any comparable research or operational purposes.
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.
Co-Authors Adjie Pamungkas Agung Budi Cahyono Alifah Noraini Andika, Neil Bangun Muljo Sukojo, Bangun Muljo Bimanjaya, Alfian Darminto, Mohammad Rohmaneo Deradjat, Andy Kusuma Firdiansyah Eka Rachmawan, Firdiansyah Eka Gutama, Andika Yudha Hadjoe, Muhammad Nabil Hermawan, Raihan Daffa Hidayat, Vlado Yusyanda Husnul Hidayat, Husnul Indra Jaya Kusuma, Indra Jaya Jaelani, Lalu Muhamad Jaelani, Lalu Muhammad Luberta, Sendi Khoirul Irham Maryam , Rizkia Amaliyah Maulida, Putra Miftahurrahman, Miftahurrahman Nathan, Rory Nugroho, Dimas Aprian Nurjati , Chatarina Nurwatik , Nurwatik Nurwatik, Nurwatik Nusantara, Candida Aulia De Silva Paramita Atmodiwirjo Pratomo, Danar Guruh Pribadi , Cherie Bhekti Putri, Klarissa Ardilia Rachmadi, Reza Fuad Raihan, Muhammad Anis Ramadhan, Alkindi Gifty Ramadhan, Fendra Dwi Ramadhani, Alita Tri Utami Ridha Rahmawan, Ridha Rizky Andianto, Rizky Rozaqi, Muhammad Abduh Ruli Andaru, Ruli Srimulyarini, Yenny Taslyanto, Chelsea Alfarelia Putri Teguh Hariyanto, Teguh Udiana Wahyu Deviantari, Udiana Wahyu Ummah, Muhammad Hidayatul Wirantiko, Mahardi Yanto Budisusanto, Yanto