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Title: | Perbandingan Pengklasifikasian Metode Support Vector Machine dan Random Forest (Kasus Perusahaan Kebun Kelapa Sawit) |
Authors: | Rizki, Akbar Soleh, Agus M Achmad, Nabila Destyana |
Issue Date: | 2022 |
Publisher: | IPB University |
Abstract: | Kelapa sawit adalah salah satu komoditi unggulan yang menopang
perekonomian di Indonesia. Salah satu perusahaan yang bergerak di sektor
perkebunan kelapa sawit memiliki 146 unit kebun kelapa sawit. Pengoptimalan
produksi kelapa sawit sangat penting dilakukan sehingga diperlukan
pengklasifikasian status unit kebun. Pengklasifikasian bertujuan untuk
memprediksi unit kebun baru dan melihat peubah yang paling penting dalam proses
pemodelan. Peubah yang digunakan adalah status unit kebun sebagai peubah respon
dan sembilan peubah penjelas yaitu luas panen, curah hujan, buah normal, produksi
tandan buah segar, brondolan, produksi, prestasi panen, luas kelompok pusingan
panen, dan tenaga kerja. Proses pengklasifikasian dilakukan menggunakan metode
Support Vector Machine (SVM) dan random forest untuk melihat metode mana
yang paling baik. Data dibagi menjadi 80% data latih dan 20% data uji dengan
sepuluh kali iterasi sehingga dihasilkan sepuluh model pada setiap metode. Evaluasi
hasil model dilakukan dengan membandingkan nilai akurasi, skor F1, dan nilai Area
Under Curve (AUC). Hasil pemodelan menunjukkan bahwa metode random forest
memiliki performa yang lebih baik dibandingkan dengan metode SVM. Nilai rataan
performa metode random forest pada akurasi, skor F1, dan AUC berturut-turut yaitu
90%, 86%, dan 89%. Peubah prestasi panen, brondolan, luas panen, curah hujan,
dan luas kelompok pusingan panen adalah peubah penting yang berkontribusi lebih
dari 10% dalam model. Hasil penelitian digunakan untuk proses evaluasi dan
pengembangan perusahaan sawit dengan memerhatikan hasil peubah penting yang
memengaruhi produktivitas dan hasil prediksi unit kebun baru. Palm oil is one of the leading commodities that support the economy in Indonesia. One of the companies engaged in the oil palm plantation sector has 146 units of oil palm plantations. It is very important to optimize oil palm production, so it is necessary to classify the status of plantation units. Classification aims to predict new plantation units and find the most important variables in the modeling process. The variables used were the status of the garden as a response variable and nine explanatory variables, namely harvested area, rainfall, percentage of normal fruit, fresh fruit bunches production, oil palm loose fruits, production, harvest job performance, harvesting rotation, and farmers. The classification process is carried out using the Support Vector Machine and Random Forest methods to find which method is the best. The data is divided into 80% training data and 20% test data with ten iterations so that ten models are produced for each method. Comparing accuracy value, F1 score, and Area Under Curve (AUC) to evaluate the model. The modeling results show that the random forest method has better performance than the SVM method. The random forest has an average of accuracy, F1 score, and AUC, respectively, is 90%, 86%, and 89%. Variables of harvest job performance, oil palm loose fruits, harvested area, rainfall, and harvesting rotation are important variables that contribute more than 10% of the model. The results of the research are used for the evaluation and development process of oil palm companies by taking into account the result of important variables that affect productivity and predictive results of new plantation units. |
URI: | http://repository.ipb.ac.id/handle/123456789/110742 |
Appears in Collections: | UT - Statistics and Data Sciences |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Cover, Lembar Pengesahan,Prakata, Daftar Isi.pdf Restricted Access | Cover | 967.23 kB | Adobe PDF | View/Open |
G14170026_Nabila Destyana Achmad.pdf Restricted Access | Full text | 1.73 MB | Adobe PDF | View/Open |
Lampiran.pdf Restricted Access | Lampiran | 806.35 kB | Adobe PDF | View/Open |
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