Judul: Crude Palm Oil (CPO) Prices Modeling Using ARIMA-GARCH and ARIMA-GARCHX Model Approach
Abstrak: Indonesia merupakan negara agraris yang perkembangannya didukung oleh sektor pertanian. Salah satu komoditas ekspor utama Indonesia adalah minyak sawit mentah atau Crude Palm Oil (CPO). Tingginya volatilitas harga CPO membuat pemodelan harga sangat dibutuhkan untuk mengurangi risiko kerugian bagi para pelaku perdagangan. Akibat volatilitas harga yang tinggi membuat pemodelan dengan ARIMA saja tidak cukup, dibutuhkan model GARCH untuk menyelesaikan masalah heteroskedastisitas ragam residual. Selain itu adanya variabel eksogen yang berpengaruh cukup penting dalam pembentukan harga CPO, mengakibatkan pemodelan dengan memasukkan variabel eksogen tersebut ke dalam model ARIMA-GARCH perlu dibandingkan untuk mendapatkan model terbaik. Adapun langkah pemodelan harga yaitu pengecekan asumsi, identifikasi model, pendugaan parameter, peramalan, dan evalusi model. Model ARIMA(0,1,1)-GARCH(1,1) adalah model terbaik karena telah memenuhi semua asumsi dengan nilai MAPE terkecil sebesar 2.89%., Indonesia is an agricultural country whose development is supported by the agricultural sector. One of Indonesia’s main export commodities is Crude Palm Oil (CPO). The high volatility of CPO prices makes price modeling necessary to reduce the risk of loss for traders. Due to high price volatility, ARIMA modeling is not enough, a GARH model is needed to solve residual variety heteroskedasticity. In addition, there are exogenous variables that have an important influence on CPO prices, causing modeling by including the exogenous variable in the ARIMA-GARCH model, needs to be compared to get the best model. The price modeling steps are checking assumptions, model identification, parameter estimation, forecasting, and model evaluation. ARIMA(0,1,1)-GARCH(1,1) is the best model because it meets all assumptions with a MAPE value of 2.89%.
Keyword: ARIMA-GARCH, ARIMA-GARCHX, exogenous variable, price modelling

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: Morfologi Organ Reproduksi Betina Walet Linchi (Collocalia linchi)
Abstrak: This research is aimed to describe the morphology of the female reproductive organs of the cave swiftlets (C. linchi ) macroscopic and microscopically. Four female reproduction organs were used in this study. Microscopic observation was done using Hematoxilin – eosin (HE), alcian blue (AB) pH 2.5 and periodic acid Schiff (PAS) staining methods. The reproductive organs were consisted of ovarium and oviduct. The oviduct has five parts: infundibulum, magnum, isthmus, uterus and vagina. The mucosa of oviduct has mucosal folds and lined with columnar ciliated epithelial cells. Glandular glands were found in the lamina propria of oviduct, except in the vagina. The thick muscle of sphincter uterovagina was found in the border between uterus and vagina. Using AB-PAS staining methods, it was detected that oviduct contained neutral and acid carbohydrate, especially in the secrete glands of oviduct.
Keyword: oviduct., ovarium, Collocalia linchi