Judul: Random Forest and XGBoost method: A Case Study of Predicting the Direction of Closing Stock Prices
Abstrak: Kemampuan dalam memprediksi tren pergerakan harga pasar saham dengan baik dan akurat sangat penting bagi investor karena akan mempengaruhi keputusan untuk membeli atau menjual asset yang dapat menghasilkan keuntungan yang signifikan. Namun, karena harga saham dinilai tidak tetap dan bersifat non-parametrik, maka ada ketidakpastian yang harus diperhitungkan. Dalam beberapa tahun terakhir, algoritma Machine Learning khususnya algoritma pembelajaran gabungan telah digunakan untuk memprediksi arah harga saham. Tujuan dari penelitian ini adalah menerapkan metode Random Forest dan XGBoost dengan algoritma grid search untuk membangun model prediksi arah harga saham BBRI. Hasil penelitian menunjukkan model prediksi metode Random Forest menghasilkan nilai akurasi sebesar 90,01% dengan AUC sebesar 0,95 dan model prediksi metode XGBoost menghasilkan nilai akurasi sebesar 89,45% dengan AUC sebesar 0,94., The ability to predict trends in stock market price movements well and accurately is very important for investors because it will affect the decision to buy or sell assets that can generate significant profits. However, since stock prices are considered non-fixed and non-parametric, there is uncertainty that must be accounted for. In recent years, Machine Learning algorithms, especially ensemble learning algorithms, have been successful in producing good accuracy in predicting the direction of stock prices. The purpose of this research is to apply Random Forest and XGBoost methods with a grid search algorithm to build a prediction model for BBRI stock price direction. The result shows that the Random Forest method prediction model produces an accuracy value of 90,01% with an AUC of 0,95 and the XGBoost method prediction model produces an accuracy value of 89,45% with an AUC of 0,94.
Keyword: Machine Learning, Random Forest, stock prices, XGBoost

Judul: Implementation of the Random Forest and XGBoost Methods to predict the direction of closing prices on bitcoin cryptocurrencies
Abstrak: Kemampuan dalam memprediksi tren pergerakan harga cryptocurrency bitcoin dengan baik dan akurat sangat penting bagi investor karena akan mempengaruhi keputusan untuk membeli atau menjual aset yang dapat menghasilkan keuntungan yang signifikan. Namun, karena harga cryptocurrency bitcoin cukup fluktuatif maka ada ketidakpastian yang harus diperhitungkan. Dalam beberapa tahun terakhir, algoritma machine learning khususnya algoritma pembelajaran gabungan telah digunakan untuk memprediksi arah harga penutupan cryptocurrency bitcoin. Hasil penelitian menunjukkan model random forest memberikan akurasi sebesar 90.75% dengan AUC sebesar 90% dan model XGBoost memberikan akurasi sebesar 90.12% dengan AUC sebesar 89%. Variabel fitur yang paling berpengaruh dari masing-masing model yaitu Moving Average Convergence Divergence (MACD) dan William Percentage Range (William%R)., The ability to predict bitcoin cryptocurrency price movement trends well and accurately is very important for investors because it will influence decisions to buy or sell assets that can generate significant profits. However, because the price of the bitcoin cryptocurrency is quite volatile, there are exposures that must be taken into account. In recent years, machine learning algorithms especially federated learning algorithms have been used to predict the closing direction of the price of the cryptocurrency bitcoin. The research results show that the random forest model provides an accuracy of 90.75% with an AUC of 90% and the XGBoost model provides an accuracy of 90.12% with an AUC of 89% with the most influential feature variable from each model, namely Moving Average Convergence Divergence ( MACD) and William Percentage Range (William%R).
Keyword: machine learning, random forest, XGBoost, cryptocurrency, bitcoin

Judul: Isolation and Characterization of Protease Keratinolytic Enzyme in Water Monitor Lizard Intestine.
Abstrak: Protease enzyme isolated from mucosa layer in intestine water monitor lizard (Varanus salvator) showed proteolytic activity. Therefore, the purpose of this research was to isolate and characterize of protease produced by water monitor lizard intestine. The intestine mucous layer of water monitor lizard (2%) was dissolved in 50mM buffer phosphate pH 7,0, then centrifuged at 6000 G in 4 ºC temperature for 10 minute. The centrifuged extract was used to analyze the maximum enzyme activity by heating the extract at 45–65 ºC range temperature and 5–20 minute range time. Maximum enzyme activity analysis obtained at 55 ºC for 5 minute for the sample duodenum, jejunum, ileum, colon respectively were 4, 3482 U/ml, 3,9247 U/ml, 3,9176 U/ml, and 3,0353 U/ml. Protease from extract enzyme has seven bands with molecular weight 66,54 kDa, 57,61 kDa, 48,94 kDa, 48,47 kDa, 36,80 kDa, 24,28 kDa, and 12,80 kDa. The enzyme has optimum temperature at 55 ºC and optimum at pH 9, and also inhibited by ion Ca2+ and Mg2+ . The in vitro analysis used chicken powder showed no keratinase activity.
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