Analisis Volume Timbulan Sampah Menggunakan Estimasi Populasi dari Data LiDAR dan Ortofoto (Studi Kasus: RW 5 Perumdos Blok U, Sukolilo, Surabaya)

Galih, Prima (2021) Analisis Volume Timbulan Sampah Menggunakan Estimasi Populasi dari Data LiDAR dan Ortofoto (Studi Kasus: RW 5 Perumdos Blok U, Sukolilo, Surabaya). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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03311740000025-Undergraduate_Thesis.pdf
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Abstract

Laju produksi sampah terus meningkat, tidak saja sejajar dengan laju pertumbuhan penduduk tetapi juga sejalan dengan meningkatnya pola konsumsi masyarakat. Data Kementerian Lingkungan Hidup dan Kehutanan (KLHK) menunjukkan, pada 2017-2018 sebanyak 2,9 juta penduduk Surabaya menghasilkan 2.800 ton sampah per hari. Dari 2.800 ton sampah di Surabaya, rata-rata hanya 1.600 ton yang sampai ke Tempat Pembuangan Akhir (TPA) Benowo. Proporsi paling besar (43,5 persen) timbulan sampah Surabaya itu berasal dari rumah tangga. Sampah bisa dikurangi manakala pengelolaan dilakukan sejak dari asalnya, salah satu langkahnya ialah melakukan pemilahan sampah. Dengan memanfaatkan teknologi penginderaan jauh dan SIG untuk melakukan estimasi volume timbulan sampah. Ditambah lagi, dengan tersedianya data citra resolusi tinggi, ortofoto, maupun LiDAR yang dapat meninjau tempat tinggal penduduk dengan lebih detail. Dalam penelitian ini dilakukan estimasi volume timbulan sampah dengan memanfaatkan hasil estimasi penduduk yang diperoleh dengan memanfaatkan metode klasifikasi berbasis objek (OBIA) rule-based untuk mendapatkan informasi spasial. Klasifikasi OBIA menggunakan data foto udara dan DSM & DTM LiDAR. Klasifikasi dilakukan dengan 2 tahap yaitu tutupan lahan, dan penggunaan lahan bangunan. Estimasi populasi penduduk memanfaatkan data sampel penduduk dengan survei langsung dilapangan melalui wawancara kepada penduduk. Kemudian dilakukan estimasi populasi penduduk dengan metode perhitungan matematis demografi serta regresi linear. Hasil dari estimasi populasi penduduk divalidasi menggunakan data referensi penduduk yang berasal dari sekretaris RW 5 Perumdos Blok U. Berdasarkan hasil OBIA, didapatkan ketelitian yang tinggi melalui uji akurasi dengan menggunakan matriks konfusi. Pada klasifikasi tutupan lahan didapatkan koefisien kappa sebesar 0,9 dan 93% pada overall accuracy. Sedangkan pada klasifikasi bangunan hunian mendapatkan 0,903 koefisien kappa dan 95% overall accuracy. Kedua uji akurasi memanfaatkan total 130 titik ground truth. Pada hasil estimasi populasi penduduk, metode regresi linear memiliki kesalahan yang lebih kecil dengan nilai RMSE sebesar 0,175 jika dibandingkan metode matematis demografi dengan RMSE sebesar 3,811. Hasil dari estimasi populasi metode regresi linear selanjutnya digunakan untuk memperoleh estimas volume timbulan sampah dan diperoleh volume sampah harian tiap orang di RW 5 Perumdos Blok U ialah 0,004 m3. ========================================================== The rate of waste production continues to increase, not only in line with the rate of population growth but also in line with the increasing consumption patterns of the community. Data from the Ministry of Environment and Forestry (KLHK) shows that in 2017-2018 2.9 million residents of Surabaya produced 2,800 tons of waste per day. Of the 2,800 tons of waste in Surabaya, on average only 1,600 tons reach the Benowo Final Disposal Site (TPA). The largest proportion (43.5 percent) of Surabaya's waste generation comes from households. Waste can be reduced when management is carried out from the beginning, one of the steps is to sort waste. By utilizing remote sensing technology and GIS to estimate the volume of waste generation. In addition, with the availability of high-resolution image data, orthophoto, and LiDAR, which can review residents' residences in more detail. In this study, an estimation of the volume of waste generation was carried out by utilizing the population estimation results obtained by using the rule-based object-based classification (OBIA) method to obtain spatial information. The OBIA classification uses aerial photographic data and DSM & DTM LiDAR. Classification is carried out in 2 stages, namely land cover and building land use. Population estimation utilizes population sample data with direct field surveys through interviews with residents. Then the population estimation is carried out using the mathematical calculation method of demography and linear regression. The results of the population estimate were validated using population reference data from the secretary of RW 5 Perumdos Blok U. Based on the results of the OBIA, high accuracy was obtained through an accuracy test using a confusion matrix. In the land cover classification, the kappa coefficient was 0.9 and 93% for the overall accuracy. While the classification of residential buildings get 0.903 kappa coefficient and 95% overall accuracy. Both accuracy tests utilize a total of 130 ground truth points. In the population estimation results, the linear regression method has a smaller error with an RMSE value of 0.175 when compared to the demographic mathematical method with an RMSE of 3.811. The results of the population estimation using the linear regression method were then used to obtain an estimated volume of waste generation and the daily volume of waste per person in RW 5 Perumdos Blok U was 0.004 m3.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: DSM, DTM, Estimasi Penduduk, Timbulan Sampah, OBIA DSM, DTM, Population Estimation, Waste Generation, OBIA
Subjects: G Geography. Anthropology. Recreation > G Geography (General) > G70.212 ArcGIS. Geographic information systems.
G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography > GA102.4.R44 Cartography--Remote sensing
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography > GA105.3 Cartography.
G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography > GA139 Digital Elevation Model (computer program)
G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
Divisions: Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geomatics Engineering > 29202-(S1) Undergraduate Thesis
Depositing User: Prima Galih
Date Deposited: 20 Aug 2021 03:12
Last Modified: 20 Aug 2021 03:12
URI: https://repository.its.ac.id/id/eprint/88430

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