Judul: Isolasi dan Karakterisasi Senyawa Antibakteri Ekstra Daun Miana (Coleus scuatellariodes [L] Benth)
Abstrak: Miana is Lamianceae. It is commonly used as traditional medicine for infection and inflamation, cause by bacterial. The aim of this study to isolate and characterize ofantibacterial compound from miana leave extract. It is expected that the result will give information on characteristics and minimum inhibition concentration (MIC) of miana leave extract which have the highest antibacterial activity. The method of this research were extraction by Harborne method, antibacterial activity test by agar well diffution to staphylococcus aureus, staphylococcus epidermidis, Escherichia coli and Salmonella entereditidis and determination of MIC by dilution methode. The result showed that miana extract has the highest antibacterial activity on S. aureus and S. epidermidis. Miana extract was better than fresh and "infusan" miana leave on inhibition activity of S. aureus and S. epidermidis. MIC value of S. aureus and S. epidermidis were 3 mg/ml. The active fraction of maina leave extract contain phthalic acid derivates by GG-MS.
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Judul: Aktivitas Antifungi Ekstrak Daun Miana (Coleus scutellariodes [L] benth) pada Pertumbuhan Candida albicans Secara In Vitro
Abstrak: Miana (Coleus scuttellariodes [L] Benth) is one of the traditional medicine from family Lamiceae having flavonoids, tannins, saponins, steroids, and triterpenoids compound those can be used as antifungal. The objectives of this research were to examine the antifungal activity of Coleus scuttellariodes [L] Benth leaves extract against C. albicans and to analyze the active compounds in Coleus scuttellariodes [L] Benth leaves extract that has potential as an antifungal against C. albicans. The methods of this research were extraction with aquades, etanol 70%, and acetone, antifungal activity test by agar well diffusion and identification of active compound extract with the best inhibition in antifungal activity with Gas Chromatography-Mass Spectrometry (GC-MS). The result showed that acetone extract 400 mg/mL had the highest inhibition activity with the zone of 4.66 mm included in the low category, meanwhile aquades and etanol 70% extract did not show antifungal activity. Positive control nystatin concentration of 1.028 mg/mL is more effective in inhibiting of C. albicans. Minimum inhibition concentration value of acetone extract was 1.56 mg/mL. Identification of acetone extract with GC-MS result phytol in amount of 41.29%.
Keyword: Coleus scutellariodes, Candida albicans, antifungal

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