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Like Indrawati
Program Studi Penginderaan Jauh Dan SIG, Departemen Teknologi Kebumian, Sekolah Vokasi Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science

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Klasifikasi Pohon Keputusan untuk Kajian Perubahan Penggunaan Lahan Kota Semarang Menggunakan Citra Landsat TM/ETM+ Like Indrawati; Hartono Hartono; Sunarto Sunarto
Majalah Geografi Indonesia Vol 23, No 2 (2009): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1274.449 KB) | DOI: 10.22146/mgi.13330

Abstract

ABSTRAK Kota Semarang masih berkembang pesat. Dengan jumlah penduduk sekitar 1.434.025 jiwa (BPS, 2006) yang tinggal di kota, kota ini bisa disebut kota metropolitan. Pertumbuhan penduduk Kota Semarang sejak tahun 1994 ketika ekspansi ke 16 daerah kabupaten menunjukkan perbaikan. Kondisi ini menyebabkan kebutuhan lahan yang lebih tinggi, sehingga konversi lahan pertanian menjadi nonpertanian akan meningkat. Untuk yang terakhir, data dari jarak jauh-merasakan memainkan peran penting yang memberikan informasi terbaru untuk penggunaan lahan. Hal ini harus didukung oleh canggih metodologi pengolahan gambar seperti otomatis klasifikasi spektral. Penelitian ini mencoba untuk membandingkan dua algoritma klasifikasi Landsat TM digital / ETM + adalah classifier kemungkinan dan keputusan pohon maksimum, akurasi tertinggi berikutnya digunakan untuk studi perubahan penggunaan lahan di Kota Semarang. Penggunaan lahan klasifikasi yang diterapkan memiliki berbeda dua-tahap detail untuk skala 1: 250.000 (tingkat I) dan 1: 100.000 (level II). Hasil ini pada penelitian ini menunjukkan bahwa penggunaan lahan peta klasifikasi pohon keputusan pada akurasi keseluruhan dan Kappa Indeks lebih tinggi dari penggunaan lahan peta hasil maximun klasifikasi kemungkinan dan penggunaan lahan klasifikasi tingkat I memiliki akurasi yang lebih baik daripada penggunaan lahan klasifikasi tingkat II. Akurasi tingkat I klasifikasi di peta tahun 1994, untuk klasifikasi kemungkinan maksimum yang diperoleh adalah 54,14% yang memiliki indeks Kappa adalah 0,4822, dan akurasi untuk klasifikasi pohon keputusan adalah 66,34% dengan indeks Kappa 0,6256. Akurasi peta tahun 2002 untuk klasifikasi kemungkinan maksimum yang diperoleh adalah 75,12% yang memiliki indeks Kappa 0713, dan keputusan klasifikasi pohon akurasi 81,46% yang memiliki indeks Kappa 0787. Pada peta tahun 2006 untuk klasifikasi kemungkinan maksimum yang diperoleh adalah akurasi keseluruhan 78,05% yang memiliki indeks Kappa 0,7641 dan keputusan klasifikasi pohon akurasi 82,45% yang memiliki indeks Kappa 0805. Perubahan penggunaan lahan di Kota Semarang menginstruksikan turunnya perkebunan dan lahan pertanian dan meningkatnya penyelesaian dan industri. ABSTRACT The  Semarang  City  is  still  growing  rapidly.   With  total  population  of approximately 1,434,025 people (BPS, 2006) who lived in the city, this city can be called a metropolitan city. Growth of Semarang City population since 1994  when expansion into 16 district areas showed improvement. This condition caused the need of  land higher, so that the conversion of agricultural into nonagricultural land will increased. For the latter, remotely-sensed data plays an important role which provide updated information for land use. This is must be supported by the advanced of image processing methodology such as automated   spectral  classification. This study attempted to compare two classification algorithm of digital Landsat TM/ETM+ is the maximum likelihood and decision tree classifier, the next highest accuracy used for the study of land use change in the Semarang City. Land use classification which was applied has different two-stage of the detail for scale of 1 : 250.000 (level I)  and 1 : 100.000 (level II). This  result  on  this  study indicate  that  the  landuse  map  of  decision  tree classification  on overall accuracy and Kappa Index was higher than landuse map of result maximun likelihood classification and land use classification of level I   have accuration which better than land use classification of level II. The accuracy of level  I classification at map year 1994, for maximum likelihood classification obtained is 54,14%  that  have  Kappa  index  is  0,4822,  and  the  accuracy  for  decision  tree classification is 66,34% with Kappa index 0,6256. The accuracy of map year 2002 for maximum likelihood classification obtained is 75,12% that have Kappa index 0,713, and for decision tree classification accuration of 81,46% that have Kappa index 0,787. At map year 2006 for maximum likelihood classification obtained  is overall accuration of 78,05% that have Kappa index 0,7641 and for decision tree classification accuration of 82,45% that have Kappa index 0,805. Change of land use in Semarang City instruct the  descent  of  plantation  and  agricultural  land and increasing  of  settlement  and industrial.
Pemanfaatan Data Landsat Multitemporal Untuk Pemetaan Pola Ekspansi Perkotaan Secara Spasiotemporal (Studi Kasus Pada Tiga Perkotaan Metropolitan Di Pulau Jawa) Like Indrawati; Ari Cahyono
Jurnal Nasional Teknologi Terapan (JNTT) Vol 2, No 1 (2018): MEI
Publisher : Penelitian dan Pengabdian Kepada Masyarakat Sekolah Vokasi Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2014.105 KB) | DOI: 10.22146/jntt.39091

Abstract

Utilization of multitemporal remote sensing data among others can be used todetermine thepattern of changes in urban expansion. One of the most important types of cities in urban systems isthe metropolitan urban area that covers several districts and cities. This is because the regiongenerally acts as the capital of the country, the provincial capital, and the center of economicactivities that are national or strategic. Understanding urban expansion at different metropolitanurban levels is important for expanding knowledge in times of urban growth and its impact on theenvironment. Aims in this study are: (1) utilization of multitemporal Landsat data for mapping urbanexpansion patterns, (2) knowing the effectiveness of object-based classification for mapping of urbansettlements and (3) spatiotemporal urban expansion pattern analysis in three metropolitan cities onJava Island.. In this study focused on three metropolitan urban in Java, namely DKI. Jakarta,Surabaya and Semarang. This study utilizing Landsat TM, ETM + and OLI image data to map urbansettlement land cover using object-based classification with Random Forest algorithm. Next,quantifying the typology of urban expansion and compare the spatiotemporal pattern of urbanexpansion during 2005-2015 on the results of land cover mapping. This research has found that (1)object-based classification with Random Forest algorithm is quite effective in terms of time of work tomap urban settlement cover on Landsat digital data having medium spatial resolution; (2) the threeurban metropolia is experiencing rapid and massive development and has a very variedspatiotemporal pattern; (3) Size of the city affect the pattern of urban expansion, followed by rapidexpansion of the region. Larger city size with relatively rapid expansion is more likely to experiencethe edge extension model, while smaller cities tend to develop with outlying models.
Pola spektral tanaman tebu (saccharum officinarum l.) Menggunakan spektrofotometer dan citra penginderaan jauh di kabupaten bantul Karen Slamet Hardjo; Like Indrawati
Jurnal Nasional Teknologi Terapan (JNTT) Vol 2, No 1 (2018): MEI
Publisher : Penelitian dan Pengabdian Kepada Masyarakat Sekolah Vokasi Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2545.877 KB) | DOI: 10.22146/jntt.39196

Abstract

Sugar becomes one of the commodities targeted to achieve Indonesia national food security. Sugar is produced from sugarcane (Saccharum officinarum L.), extensive plantations require effective and efficient handling and low cost. Remote sensing is a technology that is considered appropriate to answer those needs, through remote sensing image can be analyzed to the physical condition of sugar cane plant based on the spectral response recorded on the image. The spectral response captured by the sensor is expected to help analyze this plant in relation to plant growth, plant health, as well as the production of sugar yields in sugarcane. The lack of spectral pattern research, especially the in situ spectral pattern that is used as a reference for remote sensing data analysis, makes this research important to do. Bantul Regency is a region that has a large agricultural area, including sugar cane plantation. The method used in this research is digital data processing on remote sensing image to be analyzed spectral pattern especially in sugarcane which then compared with data of spectral pattern of sugar cane which is measured directly in the field using spectrophotometer. Spectral patterns obtained from two sources are then analyzed to determine the agility of sugarcane. The result of this research is give description about spectral pattern characteristic or reflectance pattern in sugar cane plant so it can be used for mapping of sugar cane plantation. The Sugar cane Plant reflected curve from the field measurements by spectrophotometer is noticeably smoother and has more wavelengths than the reflected curves of the Landsat 8 OLI Image. The distribution of sugar cane plant based on the spectral reflectance pattern of the object of field measurement is more relevant than the distribution of sugarcane based on the spectral reflection pattern of the object from Landsat 8 OLI Image. The classification of the field measurement spectral library and the Landsat 8 OLI Image shows that the spectral library of field measurements is better to serve as the basis for spectral-based mapping than the sampled spectral libraries in Landsat 8 OLI Image.
Aplikasi ALOS PALSAR Full Polarimetric Untuk Pemetaan Penutup Lahan Di Sebagian Kabupaten Sleman Like Indrawati
PROMINE Vol 6 No 1 (2018): PROMINE
Publisher : Jurusan Teknik Pertambangan, Fakultas Teknik, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.725 KB) | DOI: 10.33019/promine.v6i1.934

Abstract

The simplest way to interpret polarimetric imagery for land cover classification is to use visualinterpretation methods. The existence of interpretations key as a tool for visual interpretation becomesimportant when different interpreters can produce different results. The quality of the results of theinterpretation of land cover is then determined by the quality of the interpretation tool, in this case, thekey to the interpretation of land cover. The purpose of this study was to make the key to land coverclass interpretation in the Full Polarimetric ALOS PALSAR image, then the interpretation key wasused for reference in making land cover maps and measuring the accuracy of the results of the visualinterpretation. The image used in this study consisted of HH, VV, HV and VH bands. The location ofthe study was in parts of Sleman District. The analysis is done visually by on-screen digitizing onALOS Palsar composite HH + VV HV + VH HH-HV image, which is then interpreted key. The truetest is done by means of the overall accuracy test and Kappa. Visually, ALOS PALSAR imagery isable to distinguish 12 land cover classes in the research area, namely built land, rice fields, mixedgardens, moorlands, salak garden, grass, forest, shrubs, open land, airports, water bodies and lavawith 83% Overall accuracy, and 78% Kappa accuracy.
Co-Authors Ari Cahyono Hartono Hartono Karen Slamet Hardjo Sunarto Sunarto