Judul: Perbandingan Pengklasifikasian Metode Support Vector Machine dan Random Forest (Kasus Perusahaan Kebun Kelapa Sawit)
Abstrak: 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.
Keyword: units of oil palm, classification, Support Vector Machine, random forest

Judul: Aboveground Biomass Classification in Oil Palm Plantation using Random Forest Method
Abstrak: Kelapa sawit merupakan salah satu tumbuhan penyerap karbon terbesar di Indonesia. Karbon disimpan dalam bentuk biomassa dari kelapa sawit dapat dihitung menggunakan metode non-destruktif dengan persamaan alometrik. Pemantauan biomassa terutama pada area perkebunan sawit yang luas dapat digunakan metode penginderaan jauh yang dikombinasikan dengan machine learning seperti klasifikasi random forest (RF). Tujuan dari penelitian ini adalah untuk menduga nilai biomassa atas permukaan di perkebunan kelapa sawit menurut kelas umur menggunakan metode non destruktif, menganalisis hubungan antara reflektansi kelapa sawit yang berasal dari citra Sentinel-2 dan biomassa atas permukaan kelapa sawit, dan memetakan sebaran biomassa atas permukaan kelapa sawit menggunakan metode RF. Hasil penelitian menunjukkan adanya korelasi positif antara biomassa atas permukaan dengan umur kelapa sawit. Nilai reflektan citra Sentinel-2 pada semua band yaitu blue, green, red, dan NIR memiliki korelasi positif dengan biomassa sawit. Klasifikasi biomassa menggunakan RF menghasilkan nilai akurasi keseluruhan sebesar 76,4% pada kombinasi training dan testing data 60% : 40%., Oil palm is one of the largest carbon absorbing plants in Indonesia. Carbon stored in the form of biomass from oil palm can be quantified using non-destructive methods with allometric equations. To monitor biomass, especially in large areas of oil palm plantations, remote sensing methods can be used combined with machine learning such as random forest (RF) classification. The purpose of this study was to estimate the value of aboveground biomass in oil palm plantations according to age class using non-destructive methods, to analyze the relationship between the reflectance of oil palm derived from Sentinel-2 imagery and the aboveground biomass of oil palm, and to map the distribution of aboveground biomass of oil palm using the RF method. The results showed a positive correlation between the aboveground biomass and the age of the oil palm. The reflectance value of Sentinel-2 imagery in all bands namely Blue, Green, Red, and NIR has a positive correlation with oil palm biomass. Classification of biomass using RF produces an overall accuracy value of 76.4% on a combination of training and testing data 60%: 40%.
Keyword: DOS1, kelapa sawit, machine learning, DOS1, oil palm, machine learning

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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