Gold is a one of high selling value items in the market, and it can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 -2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using Maximum Likelihood Estimation (MLE). To evaluate the model, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are used. In this research, the estimated model of AR (1) and ARCH (1) given as X t = − 0.012X t-1 + ε t and X t = ε t . 0.000053 + 0.011958X t-1 2 respectively. Where, ε t is the error which is generated by mean and variance of each models. Moreover, the result of MAE and RMSE using AR (1) model are 0.0261 and 0.0342 respectively, meanwhile for ARCH (1) model are 0.0170 and 0.0251 respectively. AbstrakEmas merupakan barang yang memiliki nilai jual tinggi di pasaran, tidak hanya itu emas sering digunakan sebagai barang investasi. Harga emas yang cenderung stabil dan tidak mengalami perubahan yang terlalu signifikan membuat emas menjadi barang yang sangat berharga. Penelitian ini bertujuan untuk memprediksi harga emas menggunakan model AR (1) dan ARCH (1) yang merupakan salah satu bagian dari metode time series. Data yang digunakan adalah data harga emas yang diperoleh dari website historis harian ANTAM dari tahun 2007 -2017. Dalam penelitian ini, informasi dasar mengenai data menggunakan statistika deskriptif dan estimasi parameter pada masing-masing model menggunakan Maksimum Likelihood Estimation (MLE). Nilai error untuk mengevaluasi model AR (1) dan ARCH (1) didapatkan menggunakan Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Pada penelitian ini, model estimasi dari AR (1) dan ARCH (1) menggunakan data harga emas harian ANTAM adalah X t = − 0.012X t-1 + ε t dan X t = ε t . 0.000053 + 0.011958X t-1 2 secara berurutan. Dengan ε t adalah error yang dibangkitkan menggunakan mean dan variansi dari masing-masing model. Performansi MAE dari model AR (1) dan ARCH (1) masing-masing adalah 0.0261 dan 0.0170. Selain itu nilai RMSE dari model AR (1) dan ARCH (1) adalah 0.0342 dan 0.0251.

Current technological advances have caused rapid dissemination of information, especially on social media, one of which is Twitter. Retweeting or reposting messages is considered an easily available information diffusion mechanism provided by Twitter. By finding out why a user retweets a tweet from another person and by making this prediction we can understand how information diffuses on Twitter. In this study, Artificial Neural Network – Genetic Algorithm is used in the classification process and uses user-based and Content-Based features. Evaluation result obtained in this study are 90% accuracy, 72% precision, 83% recall, and 65% F1-Score value on the model by Oversampling.

Technical analysis plays an important role in a stock market. Traders using technical analysis to find the trading strategy on the market stock. There are some technical indicators tools that can support the technical analysis, such as Moving Average, Stochastic, and others. Candlestick pattern also parts of the tools that used in technical analysis to develop the trading strategy since Candlestick represents the stock behavior. Therefore, understanding the Candlestick pattern and technical indicator tools will be valuable for the traders to predict the trading strategy. This study performs the prediction of trading strategy by analyzing the Candlestick pattern using an Artificial Neural Network (ANN). The technical indicator tools and Candlestick pattern will be generated as the features and label data in the modeling process. The method is applied to four stocks from IDX through their technical indicators for a certain period of time. We find that in the period of 28 days, the model generates the highest accuracy that reached 85.96%. We also used K-Fold Cross-Validation to evaluate the result of model performance that generates

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Public figures are often scrutinized by social media users, either because of what they say or even because of their role in a television series. Generally, public figures upload something on their social media accounts to help shape their image. But not everyone who sees it is happy. Some even dislike the upload. This study aims to determine public sentiment towards public figure Anya Geraldine conveyed on Twitter in Indonesian. The classification process in this study uses the Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) classification methods with text preprocessing using cleaning, case folding, tokenization, and filtering. The data used are tweets in Indonesian with the keyword ”@anyaselalubenar”, with a total dataset of 7,475 tweets divided into 6,887 positive and 588 negative tweets. From the label results using oversampling to avoid excessive overfitting problems. The feature used is TF-IDF weighting. Four experimental scenarios were carried out to validate the effectiveness of the model used: first model performance without oversampling, second model performance with oversampling, third model performance with undersampling, and fourth model performance with Hyperparameter tune. The experimental results show that XGBoost+SMOTE+Hyperparameter achieved 95% compared to AdaBoost+SMOTE+Hyperparameter of 87%. The application of SMOTE and Hyperparameter tune is proven to overcome the problem of data imbalance and get better classification results.