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: 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: Etno-ornitologi Burung Kancilan Flores (Pachycephala nudigula nudigula Hartert 1897) pada Masyarakat Lio di Taman Nasional Kelimutu
Abstrak: Bare-throathed whistler is one of several wild birds in Kelimutu National Park (TNKL) that is believed to have correlation with Lio Community‟s belief. The estimation of Bare-throated whistler‟s population in Kelimutu National Park was about 186.41-1668.07 individual by the density about 0.0389-0.3481 ind/ha with the sex ratio 6.2:1 that spreaded on area with height of 1100 – 1600 masl. The characteristic of Bare-throated whistler‟s habitat is a natural forest with various canopy level, high plant species diversity and high density. The habitat characteristics related to the availability of food in the form of insects, cover, safety and a nesting site. Generally the most of Lio community knew the existence of Bare-throated whistler directly or from their parents‟ stories. The perceptions of Lio Community to the Bare-throated whistler‟s existence is generally support the conservation efforts. The perceptions construct the Lio Community‟s atitude that tend to be conservative to the existence of Bare-throated whistler. The attitude shown by their behavior that not cathing the Bare-throated whistler from the TNKL‟s area or the outside. The attitude have an important role to the existence of Bare-throated whistler in Kelimutu National Park‟s area.
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