Performa Support Vector Machine Pada Klasifikasi  Lahan dan Air Tanah

Angellina Angellina; Dyah Erny Herwindiati; Janson Hendryli

doi:10.30865/mib.v7i1.5279

Authors

Angellina Angellina Universitas Tarumanagara, Jakarta
Dyah Erny Herwindiati Universitas Tarumanagara, Jakarta
Janson Hendryli Universitas Tarumanagara, Jakarta

DOI:

https://doi.org/10.30865/mib.v7i1.5279

Keywords:

Groundwater, Landsat 8 Imageries, EVI, NDVI, Remote Sensing, SAVI, SVM

Abstract

Groundwater is one of the sources of water in the world. The availability of groundwater is one of the factors that plays an important role in carrying out daily life activities, including for drinking, cooking, washing, irrigating rice fields, and many others. One source of groundwater in Jakarta is obtained from the Ciliwung River which is passed by the Bogor and Depok areas. However, the existence of springs and groundwater continues to decrease until now. The purpose of this paper is to discuss the first stage of the classification of groundwater availability in several sub-districts in the Bogor and Depok areas. The results of phase one will present a mapping of green areas along with their classification. Data taken from Landsat 8 Satellite Imagery - United States Geological Survey (USGS). The Support vector Machine (SVM) method is used to classify the availability of groundwater. The input data for the training process are the Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Enhanced Vegetation Index constants. The results of the evaluation using linear kernel produced a green F1 score of 89.58%, half green 65.62%, and dry 83.44%. While the results of the evaluation using the polynomial kernel produced a green F1 score of 83.58%, half green 25.68%, and dry 66.59%.

References

F. R. Harahap, "Dampak Urbanisasi Bagi Perkembangan Kota di Indonesia", Journal Society, Vol. 11, No. 1, p. 36, 2018.

H. Nazar, A. Kasry, Z. Saam, "Kebijakan Pengendalian Pencemeran sumber Air Bersih Perumahan Sederhana di Kota Pekanbaru (Kasus di Kecamatan Tampan), Journal of Environmental Science, Vol. 1, p. 67, 2010.

R. A. P. Rini, "Ketersediaan Mata Air dan Air Tanah Terus Berkurang", Tersedia di https://www.tribunnews.com/kesehatan/2022/03/22/ketersediaan-mata-air-dan-air-tanah-terus-berkurang.

M. R. Sopany, D. E. Herwindiati, J. Hendryli, "Prediksi Kelembapan Tanah Pada Tingkat Kecamatan di Bogor, Depok, dan Tangerang Selatan dengan Data Remote Sensing", Computatio : Journal of Computer Science and Information Systems, Vol. 6, No. 1, pp. 1-9, 2022.

C. Christian, J. Hendryli, D. E. Herwindiati, "Program Pendeteksi Perubahan Fungsi Lahan Menggunakan Metode Ridge Regression Dan Support Vector Machine (Studi Kasus: 95 Kecamatan Di Wilayah Bekasi, Depok Dan Tangerang)", Computatio : Journal of Computer Science and Information Systems, Vol. 4, No. 1, pp. 9-20, 2020.

M. B. Wiyono, T. N. Adji, L. W. Santosa, "Analisis Ketersediaan Airtanah dengan Metode Statis di Pulau Pasaran", Media Komunikasi Geografi, Vol. 21, No. 2, pp. 223-233, 2020.

D. Budi, D. E. Herwindiati, J. Hendryli, "Land Use Change Using Least Absolute Shrinkage and Selection Operator Regression in Jakarta's Buffer Cities", Symposium on Computer Applications & Industrial Electronics (ISCAIE), Vol. 11, No. 1, pp. 30-35, 2021.

T. Widodo, "Kajian Ketersediaan Air Tanah Terkait Pemanfaatan Lahan di Kabupaten Blitar", Jurnal Pembangunan Wilayah dan Kota, Vol. 9, No. 2, pp. 122-133, 2013.

D. E. Herwindiati, J. Hendryli, L. Haryanto, "Impervious Surface Mapping Using Robust Depth Minimum Vector Variance Regression", European Journal of Sustainable Development, Vol. 6, No. 3, pp. 29-39, 2017.

Anonymous, "Letak Geografis", Tersedia di https://www.kotabogor.go.id/index.php/page/detail/9/letak-geografis.

Anonymous, "Geografi", Tersedia di https://www.depok.go.id/geografi.

N. I. Fawzi, V. N. Husna, Landsat 8 - Sebuah Teori dan Teknik Pemrosesan Tingkat Dasar. Elmarkazi, 2021, p. 5.

R. Yudistira, A. I. Meha, S. Y. J. Prasetyo, "Perubahan Konversi Lahan Menggunakan NDVI, EVI, SAVI dan PCA pada Citra Landsat 8 (Studi Kasus : Kota Salatiga)", Indonesia Journal of Computing and Modeling, Vol. 1, pp. 25-30, 2019.

A. R. Huete, "A soil-adjusted vegetation index (SAVI)", Remote Sensing of Environment, Vol. 25, No. 3, pp. 295-309, 1988.

N. D. Mutmainna, M. Achmad, S. Suhardi, "Pendugaan Lengas Tanah Inceptisol Pada Tanaman Hortikultura Menggunakan Citra Landsat 8", Jurnal Agritechno, Vol. 10, No. 2, pp. 135-151, 2017.

K. Supribadi, N. Khakhim, T. H. Purwanto, "Analisis Metode Support Vector Machine (Svm) untuk Klasifikasi Penggunaan Lahan Berbasis Penutup Lahan pada Citra Alos Avnir-2", Majalah Geografi Indonesia, Vol. 28, No. 1, pp. 71-80, 2014.

P. A. Octaviani, Y. WIlandari, D. Ispriyanti, "PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG", Jurnal Gaussian Vol. 3, No. 4, pp. 811-820, 2014.

R. Diani, "Analisis Pengaruh Kernel Support Vector Machine (SVM) pada Klasifikasi Data Microarray untuk Deteksi Kanker", Indonesian Journal on Computing, Vol. 2, No. 1, pp. 109-118, 2017.

M. A. Alkromi, "Komparasi Algoritma Klasifikasi untuk dataset iris dengan rapid miner", Jurnal STMIK Widya Pratama, Vol. 1, No. 1, pp. 23-30, 2015.

Trivusi, "Apa itu Kernel Trick? Pengertian dan Jenis-jenis Fungsi Kernel SVM", Tersedia di https://www.trivusi.web.id/2022/04/fungsi-kernel-svm.html.