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: Peramalan Harga Emas Menggunakan Metode Bagging Pemulusan Eksponensial
Abstrak: Gold is one of the precious metals that remain popular among investors nowadays. It provides investors a safe haven when the economy is questionable. It is crucial to provide better information about the prospect of gold prices before investing. One way to do it in the statistical realm is through forecasting using the exponential smoothing method. In order to tackle challenges that may be encountered, an additional approach called bootstrap aggregating (bagging) was proposed. This study aims to compare the performance of exponential smoothing in a condition with and without bagging. Data that were used are gold prices from January 4th, 2010 until February 27th, 2022. As there is only sign of a trend component, it can be modelled using the Holt’s linear and the additive damped trend method. Models were evaluated based on MAPE and RMSE. Based on Wilcoxon signed-rank test, adding bagging is proven to bring statistically proven difference. For the daily and weekly series, the best model is Holt’s linear smoothing with bagging. While for the monthly series, the best one is Holt’s Linear smoothing without bagging. Lastly, it is predicted that gold prices will keep increasing until reaching more than Rp1000000/g in February 2023., Emas merupakan salah satu logam mulia yang popular di kalangan investor. Emas dapat menjadi alternatif investasi yang aman ketika kondisi perekonomian sedang lesu. Tentu peramalan harga emas ke depan menjadi poin pertimbangan bagi investor sebelum menanamkan modalnya. Dalam ranah statististik, salah satu metode yang dapat digunakan ialah pemulusan eksponensial. Untuk mengatasi tantangan yang timbul saat proses pemodelan, bootstrap aggregating diperkenalkan sebagai pendekatan tambahan. Penelitian ini bertujuan untuk membandingkan performa pemulusan eksponensial pada kondisi dengan dan tanpa bagging serta meramalkan harga emas satu tahun ke depan. Data yang digunakan ialah data harian harga emas dari 4 Januari 2010 s.d 27 Februari 2022. Tipe pemulusan eksponensial yang digunakan ialah Holt’s linear dan the additive damped trend method. Model kemudian dievaluasi berdasarkan MAPE dan RMSE. Hasil pengujian dengan Wilcoxon signed-rank test menunjukkan bahwa penggunaan bagging sebagai pendekaan tambahan terbukti memberikan hasil peramalan yang berbeda nyata. Untuk data harian dan mingguan, model terbaik ialah pemulusan Holt’s linear dengan bagging sedangkan untuk data bulanan, model terbaik ialah pemulusan Holt’s linear tanpa bagging. Pada bulan Februari 2023, diprediksi harga emas akan melampaui Rp1000000/g.
Keyword: bagging, exponential smoothing, gold price, mape, rmse

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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