Judul: Implementation of Long Short-Term Memory for Gold Prices Forecasting
Abstrak: Emas merupakan salah satu bentuk investasi dan disebut sebagai aset safe haven karena kestabilannya dalam kondisi pasar yang tidak stabil. Peramalan harga emas menjadi penting bagi investor sebagai alat bantu pengambilan keputusan. Penelitian ini bertujuan untuk melihat pengaruh validasi silang dalam memperoleh hyperparameter LSTM (optimizer, learning rate, dan epoch) terbaik yang akan digunakan dalam peramalan. LSTM sebagai bagian metode deep learning dapat digunakan dalam peramalan selain menggunakan metode klasik. Algoritma ini merupakan pengembangan dari recurrent neural network yang banyak digunakan dalam peramalan deret waktu. LSTM dianggap unggul dan mampu meminimalkan kesalahan, serta mampu melakukan peramalan untuk periode jangka panjang dibanding metode lain. Walk forward validation dengan skenario pergeseran jendela (tahunan, semester, dan kuartal) sebagai bentuk validasi silang digunakan untuk melihat keakuratan metode. Data yang digunakan berasal dari World Gold Council dengan periode data harian untuk tanggal 1 Januari 2003 sampai dengan 20 Januari 2023. Optimizer yang digunakan adalah adam dan RMSProp dengan nilai learning rate 0,01; 0,001; dan 0,0001, serta nilai epoch 100, 500, dan 1000. Model terbaik adalah model dengan nilai MAPE terkecil sebesar 12,41% pada proses validasi menggunakan optimizer RMSProp, nilai learning rate 0,01, dan epoch 100. Hasil peramalan menunjukkan kecenderungan harga emas untuk meningkat dalam delapan tahun mendatang., Gold is a form of investment and is referred to as a safe haven asset because of its stability in unstable market conditions. Gold price forecasting is important for investors as decisions making tool. This study aims to study the effect of cross-validation in obtaining the best LSTM hyperparameters (optimizer, learning rate, and epoch) to be used in forecasting. LSTM as part of deep learning methods can be used in forecasting beside the classical method. Algorithm in the LSTM is developed based on a recurrent neural network that is widely used in time series forecasting. It was found that the LSTM is superior compared to other methods for its ability to minimize errors and to forecast for long-term periods. Walk forward validation with window shift scenarios (annual, semester, and quarter) as a form of cross validation is used to see the accuracy of the method. The data used came from the World Gold Council with daily data periods for January 1, 2003, to January 20, 2023. The optimizer used is adam and RMSProp each with learning rate values of 0.01; 0.001; and 0.0001, and with epoch values of 100, 500, and 1000. The best model was the model with the smallest MAPE value of 12.41% in the validation process using an optimizer RMSProp, a learning rate of 0.01, and epoch value of 100. Forecasting results show a tendency for gold prices to increase in the next eight years.
Keyword: deep learning, forecasting, gold, hyperparameter, LSTM

Judul: COMPARATIVE STUDY OF GOLD PRICE TIME  SERIES DATA USING XGBOOST, STL, AND FACEBOOK PROPHET  METHODS
Abstrak: Harga emas merupakan aspek penting bagi para pelaku pasar karena nilainya yang stabil dan cenderung naik. Penelitian ini membandingkan tiga model analisis data time series harga emas yakni Extreme Gradient Boosting (XGBoost), Seasonal-Trend Decomposition Procedure Based on LOESS (STL), dan Facebook Prophet. Dalam penelitian ini, ketiga model tersebut dievaluasi dengan mempertimbangkan faktor eksternal seperti inflasi dan nilai tukar mata uang. Data harga emas dan faktor eksternal dikumpulkan dari Januari 2010 hingga April 2023. Hasil penelitian menunjukkan bahwa pada pendekatan univariate, model Facebook Prophet memiliki Symmetrical Mean Absolute Percentage Error (SMAPE) sebesar 6,28% dan Mean Absolute Scaled Error (MASE) sebesar 6,0458. Pada pendekatan multivariate, XGBoost memiliki SMAPE sebesar 12,63% dan MASE sebesar 11,815. Dari hasil penelitian, dapat disimpulkan bahwa Facebook Prophet dengan pendekatan univariate lebih akurat dibandingkan pendekatan XGBoost dan STL. Sementara itu, XGBoost dengan pendekatan multivariate lebih akurat daripada Facebook Prophet dalam kasus yang diujikan
Keyword: Emas, Facebook Prophet, Keuangan, peramalan, STL, XGBoost

Judul: Aktivitas Antioksidan Ekstrak Buah Takokak (Solanum torvum Swartz.)
Abstrak: Turkey berry fruit (Solanum torvum Swartz.) is one of indigenous fruit vegetables that widely used in traditional medicine. Because, it has some functional properties such as antioxidant, antibacterial, antifungal, and antiviral. The functional properties come from the activity of some phytochemicals compound that Turkey berry fruit contents, such as phenolics and non phenolics. This research was designed to identify the chemical content of Turkey berry fruit, change content, and antioxidant activity of crushed form. The research method consisted of treatment phase of fresh Turkey berry fruit through the destruction process and extraction phase of Turkey berry fruit powder with organic solvents (methanol, etil acetat, and hexane). The results showed that methanol extract of both whole and crushed Turkey berry fruit, contains alkaloids, flavons, tannins, and saponins. The etil acetat and hexan extracts of both whole and crushed form, contains alkaloids, flavons, terpenoids, and saponins. Thus, the presence of phenolic compounds in Turkey berry fruit can be affected by Phenylalanin Ammonia Lyase (PAL) activity, but not always influenced by the total anthocyanin. In addition, the antioxidant activity (DPPH assay) between whole and crushed Turkey berry fruit extracts is not significantly different (p>0.05) by t-test. It was demonstrated that the destruction process has a little affecting to the presence of phenolic compounds that act as antioxidants, such as anthocyanin. However, the antioxidant activity of extracts is not always determined from total phenolic, but depending on the ability of -OH group to donate H atoms and to bind with free radicals rapidly. The antioxidant activity of Turkey berry fruit extract was slightly be affected by ascorbic acid (vitamin C) compound.
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