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: Support Vector Regression (SVR) Analysis with Grid Search Algorithm in Predicting Stock Prices
Abstrak: Dalam dunia investasi, saham merupakan salah satu instrumen pasar keuangan yang paling populer karena dapat memberikan keuntungan yang besar bagi para investor. Akan tetapi data harga saham bersifat tidak stasioner dan non linear, serta perubahan dari harga saham yang relatif cepat dari waktu ke waktu. Oleh karena itu, diperlukan metode yang mampu memprediksi pergerakan harga saham untuk membantu para investor dalam mengambil keputusan kapan sebaiknya saham dijual atau tetap dipertahankan. Tujuan dari penelitian ini adalah membuat model prediksi harga saham INDF dan MYOR serta melakukan prediksi satu periode selanjutnya pada kedua perusahaan tersebut dengan menggunakan metode Support Vector Regression dan Algoritma Grid Search. Hasil penelitian menunjukkan model prediksi terbaik untuk data saham INDF diperoleh nilai MAPE dan R^2 pada data testing berturut-turut sebesar 5.570% dan 79.9%, sedangkan untuk data saham MYOR diperoleh nilai MAPE dan R^2 pada data testing berturut-turut sebesar 2.954% dan 96%. Hasil penelitian juga menunjukkan prediksi harga saham INDF dan MYOR untuk satu periode selanjutnya (31 Desember 2021) berturut-turut sebesar Rp 6326.88/lembar dan Rp 2039.31/lembar., In the investment world, stocks are one of the most popular financial market instruments because they can provide large returns for investors. However, stock price data is non-stationary and non-linear, and changes in stock prices are relatively fast from time to time. Therefore, it is needed a method that is able to predict stock price movements to assist investors in making decisions about when stock prices should be sold or maintained. The purpose of this study is to introduce a stock price prediction model for INDF and MYOR and to predict one next period stock prices for the two companies using the Support Vector Regression method and the Grid Search Algorithm. The results showed that, the best prediction model for INDF stock data having MAPE and R^2 for the testing data are 5.570% and 79.9% respectively, while for the MYOR stock data, the MAPE and R^2 values obtained for the testing data were 2,954% and 96% respectively. The results also show that the stock price predictions for INDF and MYOR for one next period (December 31, 2021) are Rp. 6326.88/share and Rp. 2039.31/share, respectively.
Keyword: Grid Search, stock prices, predictions, Support Vector Regression

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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