IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK PREDIKSI POTENSI LAHAN BERPRODUKSI DI PERKEBUNAN TEBU (STUDI KASUS: DESA DJENGKOL, KAB. KEDIRI, JAWA TIMUR)

LINDISARI, KARINA EKA NADYA (2021) IMPLEMENTASI ALGORITMA K-NEAREST NEIGHBOR UNTUK PREDIKSI POTENSI LAHAN BERPRODUKSI DI PERKEBUNAN TEBU (STUDI KASUS: DESA DJENGKOL, KAB. KEDIRI, JAWA TIMUR). S1 thesis, Universitas Mercu Buana Jakarta.

[img]
Preview
 Text (HAL COVER)
01 Cover.pdf
Download (253kB) | Preview
[img] Text (BAB I)
02 Bab 1.pdf
Restricted to Registered users only
Download (83kB)
[img] Text (BAB II)
03 Bab 2.pdf
Restricted to Registered users only
Download (260kB)
[img] Text (BAB III)
04 Bab 3.pdf
Restricted to Registered users only
Download (95kB)
[img] Text (BAB IV)
05 Bab 4.pdf
Restricted to Registered users only
Download (732kB)
[img] Text (BAB V)
06 Bab 5.pdf
Restricted to Registered users only
Download (26kB)
[img] Text (DAFTAR PUSTAKA)
07 Daftar Pustaka.pdf
Restricted to Registered users only
Download (86kB)
[img] Text (LAMPIRAN)
08 Lampiran.pdf
Restricted to Registered users only
Download (288kB)

Abstract

As an agricultural country, Indonesia has great potential in agriculture. Indonesia is ranked 5th in the world as an agricultural exporter. One of the agricultural subsectors, namely the plantation sub-sector. Sugarcane plays an important role in supporting the Indonesian economy because it has a high investment value. One of the factors determining the level of sugarcane production is the land used. Therefore it is necessary to do a land mapping to find out whether the land is producing or not. The purpose of this research is to predict which land has high sugarcane production potential. The method used is the Normalized Difference Vegetation Index (NDVI) and the k-Nearest Neighbor algorithm. The data used are Landsat 8 satellite imagery with the acquisition period of 10 February 2019, 14 March 2019 and 30 March 2019. The results show that the k-Nearest Neighbor Regression algorithm can predict the potential for productive land with prediction results that have a high level of greenness on the index. vegetation. The results also showed the results of the assessment using Mean Squared Error (MSE) of 3.397%, Root Mean Squred Error (RMSE) of 0.1154%, Mean Absolute Error (MAE) of 0.1154%, R Squared of 87.157% and Explained Variance. Score (EVS) of 87,129%. Key words: k-nearest neighbor regression, ndvi, prediction, satellite imagery Sebagai negara agraris, Indonesia memiliki potensi besar dalam bidang pertanian. Indonesia menduduki peringkat ke-5 dunia sebagai eksportir pertanian. Salah satu sub sektor pertanian yaitu sub sektor perkebunan. Tebu memegang peran penting sebagai penyokong perekonomian Indonesia karena memiliki nilai investasi yang tinggi. Salah satu faktor penentu tingkat produksi Tebu adalah lahan yang digunakan. Oleh karena itu perlu dilakukan suatu pemetaan lahan untuk mengetahui apakah lahan tersebut berproduksi atau tidak. Tujuan dilakukannya penelitian ini yaitu untuk memprediksi lahan mana yang memiliki potensi produksi Tebu yang tinggi. Metode yang digunakan yaitu Normalized Difference Vegetation Index (NDVI) dan algoritma k-Nearest Neighbor. Data yang digunakan yaitu citra satelit Landsat 8 dengan periode akuisisi 10 Februari 2019, 14 Maret 2019 dan 30 Maret 2019. Hasil penelitian menunjukkan bahwa, algoritma k-Nearest Neighbor Regression dapat melakukan prediksi potensi lahan berproduksi dengan hasil prediksi yang memiliki tingkat kehijauan tinggi pada indeks vegetasi. Hasil penelitian juga menunjukkan hasil penilaian menggunakan Mean Squared Error (MSE) sebesar 3,397%, Root Mean Squred Error (RMSE) sebesar 0,1154%, Mean Absolute Error (MAE) sebesar 0,1154%, R Squared sebesar 87,157% dan Explained Variance Score (EVS) sebesar 87,129%. Kata kunci: citra satelit, k-nearest neighbor regression, ndvi, prediksi

Item Type: Thesis (S1)
NIM/NIDN Creators: 41816120065
Uncontrolled Keywords: citra satelit, k-nearest neighbor regression, ndvi, prediksi
Subjects: 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 005 Computer Programmming, Programs, Data/Pemprograman Komputer, Program, Data > 005.5 General Purpose Application Programs/Program Aplikasi dengan Kegunaan Khusus
500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
600 Technology/Teknologi > 650 Management, Public Relations, Business and Auxiliary Service/Manajemen, Hubungan Masyarakat, Bisnis dan Ilmu yang Berkaitan > 658 General Management/Manajemen Umum > 658.01-658.09 [Management of Enterprises of Specific Sizes, Scopes, Forms; Data Processing]/[Pengelolaan Usaha dengan Ukuran, Lingkup, Bentuk Tertentu; Pengolahan Data] > 658.05 Data Processing Computer Applications/Pengolahan Data Aplikasi Komputer
Divisions: Fakultas Ilmu Komputer > Sistem Informasi
Depositing User: Dede Muksin Lubis
Date Deposited: 28 Mar 2023 03:05
Last Modified: 28 Mar 2023 03:05
URI: http://repository.mercubuana.ac.id/id/eprint/75548

Actions (login required)

View Item View Item
UMB Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.