Judul: Peformance  Comparison of XGBoost, CatBoost, and LightGBM Algorithms in Classifying Smallholder Palm Oil Productivity in Riau Province
Abstrak: Perkebunan sawit di Provinsi Riau memegang peran sentral dalam industri perkebunan nasional. Faktor utama yang memengaruhi tingkat produktivitas sawit yaitu perbedaan karakteristik iklim tiap wilayah dan variabilitas iklim. Studi ini bertujuan membandingkan algoritma machine learning yaitu XGBoost, CatBoost, dan LightGBM yang digunakan dalam pemodelan iklim untuk mengklasifikasikan tingkat produktivitas sawit rakyat di Provinsi Riau. Data yang digunakan merupakan data Statistik Perkebunan Indonesia 1990-2022 dan data iklim yang didapatkan dari Climate Hazards Group Precipitation with Station (CHIRPS). Pemodelan dievaluasi melalui tiga skenario partisi model terbaik hasil hyperparameter tuning. Hasil penelitian model klasifikasi produktivitas sawit rakyat di Provinsi Riau menunjukkan bahwa CatBoost memiliki AUC uji terbaik yaitu sebesar 0,857 pada skenario partisi 75% data latih dan 25% data uji. Kombinasi hyperparameter yang digunakan yaitu ntree = 100, learning rate = 0,03, dan max-depth = 9. CatBoost juga menunjukkan konsistensi terbaik dalam menghindari overfitting untuk tiap skenario yang diuji. LightGBM dan XGBoost memiliki performa yang lebih rendah karena cukup sensitif terhadap data berdimensi kecil dalam pelatihan. Hal ini menyebabkan inkonsistensi kinerja algoritma ketika diterapkan pada proporsi data latih dan data uji yang berbeda., Oil palm plantations in Riau Province play a central role in the national plantation industry. The main factors affecting oil palm productivity are the differences in climate characteristics across regions and climate variability. This study aims to compare the machine learning algorithms XGBoost, CatBoost, and LightGBM, used in climate modeling to classify the productivity levels of smallholder oil palm plantations in Riau Province. The data used includes Indonesian Plantation Statistics from 1990 to 2022 and climate data from the Climate Hazards Group Precipitation with Station (CHIRPS). The modeling was evaluated through three partitioning scenarios of the best models resulting from hyperparameter tuning. The results showed that CatBoost had the best test AUC of 0.857 in the scenario with 75% training data and 25% test data. The hyperparameters used were ntree = 100, learning rate = 0.03, and max-depth = 9. CatBoost also demonstrated the best consistency in avoiding overfitting across all tested scenarios. LightGBM and XGBoost performed lower due to their sensitivity to small-dimensional data during training, leading to inconsistent performance when applied to different proportions of training and test data.
Keyword: klasifikasi, algoritma boosting, pemodelan iklim, perbandingan performa, sawit rakyat

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: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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