Judul: Aplikasi Metode ARIMA, LSTM, dan Hybrid ARIMA-LSTM pada Peramalan Harga Crude Palm Oil (CPO) Dunia
Abstrak: Minyak sawit mentah atau Crude Palm Oil (CPO) adalah salah satu komoditas unggulan ekspor Indonesia di sektor pertanian. Indonesia juga merupakan produsen CPO terbesar di dunia. Harga CPO dunia berdampak signifikan terhadap volume ekspor CPO serta pertumbuhan ekonomi Indonesia. Peramalan harga CPO secara akurat sangat penting untuk membantu pengusaha, investor, produsen dan juga pemerintah dalam perencanaan ekonomi dengan mempertimbangkan perubahan dan fluktuasi harga. Penelitian ini menggunakan data harga bulanan CPO dunia dari Januari 2003 hingga April 2023 yang diperoleh dari rata-rata harga harian CPO yang bersumber dari kompilasi data ISTA Mielke GmbH, Oil World, US Department of Agriculture dan World Bank. Metode yang digunakan dalam penelitian ini adalah Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) dan Hybrid ARIMA-LSTM. Penggunaan ketiga metode ini didasarkan pada kondisi data yang berbentuk linear dan nonlinear. Metode LSTM terbukti lebih efektif dalam memprediksi harga CPO dunia dibanding metode ARIMA dan Hybrid ARIMA-LSTM. Model LSTM terbaik adalah model dengan hyperparameter epoch 100, batch size 1, dan learning rate 0,001. Model ini menghasilkan nilai Mean Absolute Percentage Error 2,33% dan Root Mean Squared Error 34,708. Hal ini karena model LSTM lebih stabil dalam mempelajari pola data deret waktu., Crude Palm Oil (CPO) is one of Indonesia's main export commodities in the agricultural sector. Indonesia is also the largest producer of CPO in the world. The world CPO prices have a significant impact on Indonesia's export volume of CPO as well as its economic growth. It is essential to forecast CPO prices accurately to assist entrepreneurs, investors, producers, and the government in economic planning for the future while considering changes and price fluctuations. This study uses world CPO monthly price data from January 2003 to April 2023 which is compilation of data from ISTA Mielke GmbH, Oil World, US Department of Agriculture and World Bank. The methods used in this research are Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Hybrid ARIMA-LSTM. The use of these methods is based on linear and nonlinear data conditions. Due to its ability to learn the data pattern well, the LSTM was found to be more effective in forecasting world CPO prices. The best LSTM model in this study is a model with hyperparameter epoch 100, batch size 1, and learning rate 0,001. This model produces a MAPE and an RMSE value of 2,33% and 34,708.
Keyword: ARIMA, crude palm oil prices, forecasting, hybrid ARIMA-LSTM, LSTM

Judul: Application of Univariate and Multivariate Long Short Term Memory for World Crude Palm Oil Price Prediction
Abstrak: Analisis deret waktu sangat penting untuk memprediksi faktor ekonomi dan faktor-faktor penting lainnya, hal itu dapat dilakukan secara peubah tunggal dan peubah ganda. Perkembangan teknologi menciptakan long short term memory (LSTM) yang dapat menangani gradien yang menghilang dan ketergantungan jangka panjang. Penelitian ini akan memprediksi harga minyak kelapa sawit dunia karena Indonesia sebagai produsen minyak kelapa sawit terbesar di dunia sangat dipengaruhi oleh harga minyak kelapa sawit dunia. Penelitian ini menggunakan data bulanan harga minyak kelapa sawit, minyak kedelai, dan minyak bumi pada bulan Januari 2002 hingga Mei 2024 yang didapat dari World Bank Commodity Price Data. Penelitian ini menerapkan LSTM seara peubah tunggal dan peubah ganda dalam memprediksi harga minyak kelapa sawit. Penggunaan LSTM dikarenakan data menunjukkan unsur non-linear dan dan volatilitas yang tinggi. Input yang digunakan untuk LSTM peubah tunggal adalah harga minyak kelapa sawit sedangkan LSTM peubah ganda menggunakan harga minyak kelapa sawit, minyak kedelai dan minyak bumi. LSTM peubah tunggal terbukti lebih efektif pada kasus prediksi harga minyak kelapa sawit dunia. Hal ini terbukti dengan MAPE yang lebih rendah 6,574% dibandingkan LSTM peubah ganda 6,689%. LSTM peubah tunggal ini menggunakan kombinasi hyperparameter neuron 32, epoch 100, time steps 1, batch size 64, dan learning rate 0,01., Time series analysis is essential for predicting economic and other important factors; it can be done univariately or multivariately. Technological developments created long short term memory (LSTM) that can handle vanishing gradients and long-term dependencies. This research will predict the world price of crude palm oil (CPO) because Indonesia, as the world's largest CPO producer, is strongly influenced by the world CPO price. This study uses monthly data on CPO, soybean, and canola oil prices from January 2002 to May 2024 obtained from the World Bank Commodity Price Data. This research applies univariate and multivariate LSTM to predicting CPO prices. The use of LSTM is because the data shows nonlinear elements and high volatility. The input used for univariate LSTM is the CPO price, while multivariate LSTM uses CPO, soybean oil, and crude oil prices. The univariate LSTM proved to be more effective in the case of world CPO price prediction. This is proven by the lower MAPE of 6,574% compared to the multivariate LSTM of 6,689%. This univariate LSTM uses a combination of hyperparameters: neuron 32, epoch 100, time steps 1, batch size 64, and learning rate 0,01.
Keyword: LSTM, CPO price, LSTM, multivariate, time series, univariate

Judul: Aktivitas Antimikrob dan Karakterisasi Nanosilver Ekstraseluler Veronaea sp. KT19
Abstrak: Synthesis of nanosilver biologically may use fungi. The purpose of this study was to characterize and to determine antimicrobial activity of extracellular nanosilver of Veronaea sp. KT19. The highest activity was shown by extract of day 6 with diameter of inhibition zones on the Escherichia coli was 15.5±0.7 mm and Bacillus subtiliswas 13±2.8 mm. Spectrum of the nanoparticles of A6, A12, B6 (x), B6 (y), B12 (x), and B12 (y) was measured at a wavelength of 450 nm, 400 nm, 263.5 nm, 262 nm, 262.5 nm and 256.5 nm. The smallest particle size average obtained by A6 was 32.25 nm. Antimicrobial activity against 5 microorganisms showed weak activity againts Bacillus subtilis and Staphylococcus aureus, showed moderate activity againts Escherichia coli, Pseudomonas aeruginosa and Candida maltosa.
Keyword: Veronaea sp., nanosilver, biosynthesis, antibacteria