Judul: Modeling World Crude Oil Prices for 2022 - 2023 using ARIMA, LSTM, and GRU Methods
Abstrak: Minyak mentah adalah sumber daya alam yang berharga dengan dampak ekonomi yang signifikan. Mengingat sifat fluktuasi harga minyak mentah dunia dan jaminan ekonomi yang signifikan, peramalan harga yang akurat sangat penting untuk pengambilan keputusan bisnis yang efektif. Karakteristik harga minyak mentah dunia fluktuatif dan tidak linier. Penelitian ini membandingkan tiga metode peramalan, yaitu Autoregressive Integrated Moving Average (ARIMA), Long Short-term Memory (LSTM), dan Gated Recurrent Unit (GRU). Meskipun ARIMA dapat menangkap hubungan linier dalam waktu singkat, namun metode ini memiliki kelemahan dalam meramalkan hubungan nonlinier dan jika datanya fluktuatif. Untuk mengatasi keterbatasan ini digunakan metode peramalan LSTM dan GRU. Metode ini dikenal dengan kemampuannya untuk mempelajari pola nonlinier. Penelitian ini bertujuan untuk menentukan metode peramalan yang paling efektif dalam meramalkan harga minyak mentah dunia (WTI) dan melakukan peramalan harga minyak mentah dunia (WTI) sebanyak 23 amatan pada bulan Januari 2024. Evaluasi metrik pada keseluruhan data dengan menggunakan model GRU memiliki nilai MAPE sebesar 2,0883% sedangkan pada model LSTM memiliki nilai MAPE sebesar 2,6335%. Hasil penelitian menunjukkan bahwa dalam mengevaluasi metrik pada data validasi untuk rentang waktu 5 hari per minggu, 1 bulan, dan 3 bulan, metode GRU menunjukkan keunggulan dalam meramalkan harga minyak mentah dunia (WTI) tahun 2022 sampai 2023., Crude oil is a valuable natural resource with a significant economic impact. Given the volatile nature of global crude oil prices and their substantial economic implications, accurate price forecasting is crucial for effective business decision making. The characteristics of global crude oil prices are volatile and nonlinear. This study compares three forecasting methods: Autoregressive Integrated Moving Average (ARIMA), Long Short-term Memory (LSTM), and Gated Recurrent Unit (GRU). While ARIMA can capture short-term linear relationships, it has limitations in forecasting nonlinear relationships, especially with volatile data. To address these limitations, LSTM and GRU forecasting methods are employed. These methods are known for their ability to learn nonlinear patterns. This study aims to determine the most effective forecasting method for predicting global crude oil prices (WTI) and to forecast crude oil prices (WTI) for 23 observations in January 2024. The evaluation metrics on the entire dataset indicate that the GRU model achieved a MAPE of 2.0883%, while the LSTM model achieved a MAPE of 2.6335%. The results of the study show that in evaluating the metrics on the validation data for the time ranges of 5 days per week, 1 month, and 3 months, the GRU method demonstrated superiority in forecasting global crude oil prices (WTI) from 2022 to 2023.
Keyword: ARIMA, gru, LSTM, minyak mentah, peramalan

Judul: Penerapan Metode Generalized AutoRegressive Conditional Heteroskedasticity untuk Peramalan Harga Minyak Mentah Dunia.
Abstrak: Minyak mentah merupakan salah satu komoditas yang sangat dibutuhkan di berbagai bidang. Harga minyak mentah dunia yang terus mengalami fluktuasi tentunya mengambil pengaruh yang besar terhadap perekonomian negara. Data harga minyak mentah yang dikumpulkan bersifat time series atau proses pengumpulannya dilakukan dari waktu ke waktu dengan periode bulanan. Oleh karena itu, diperlukan suatu sistem yang dapat membuat peramalan harga minyak mentah dunia kedepannya yang diharapkan dapat menjadi bahan pertimbangan pemerintah untuk pengambilan keputusan. Salah satu metode yang dapat digunakan untuk meramalkan harga minyak mentah dunia adalah ARIMA (Auto-Regressive Integrated Moving Average) dan model GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity). Pemodelan yang dilakukan terbukti bahwa data harga minyak mentah dunia periode Januari tahun 2002 hingga Juni 2022 memiliki efek heteroskedastisitas yang tidak dapat diatasi jika hanya menggunakan model ARIMA. Hasil dari pengolahan data diperoleh bahwa model ARIMA (0,1,2) yang dilanjutkan model ARCH (2) merupakan model terbaik dengan nilai MAPE sebesar 5,32%. Nilai akurasi yang diperoleh tergolong sangat baik untuk peramalan harga minyak mentah dunia., Crude oil is one of the commodities that are needed in various fields. World crude oil prices that continue to fluctuate, of course, have a big influence on the country's economy. Crude oil price data collected is time series or the collection process is carried out from time to time with monthly periods. Therefore, we need a system that can forecast future world crude oil prices which are expected to be taken into consideration by the government for decision making. One method that can be used to predict world crude oil prices is ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) model. After modeling, it is proven that the world crude oil price data for the period January 2002 to June 2022 has a heteroscedasticity effect that cannot be overcome if only using the ARIMA model. The results of data processing show that the ARIMA (0,1,2) followed by the ARCH model(2) is the best model with a MAPE value of 5,32%. The accuracy values obtained are classifield as very good for forecasting world crude oil prices.
Keyword: ARIMA, ARCH, GARCH, harga minyak mentah, peramalan

Judul: Photovoltaic Effect In A CdS/P3HT-Chitosan Junction
Abstrak: A CdS/Polymer junction has been fabricated and it’s photovoltaic effect has been studied. The polymer is a blend of poly-3hexyltiophene (P3HT), chitosan and polyethileneglicol (PEG). Boron in situ doping of CdS using Chemical Bath Deposition (CBD) was investigated. The effect of B-doping and annealing on optical properties as well as on electrical and morphology of CdS film were studied. The structure and morphology of the film were detected by XRay Diffraction (XRD) and Scanning Electron Microscopy (SEM). The polymer film was sandwiched between the CdS semiconductor and an ITO glass to form an ITO/CdS/Polymer/ITO photovoltaic cell. Time constant (τ) and open circuit voltage (Voc) were investigated by using voltage censor. The bandgap of films were determined using absorbance edge method. The results of annealing optical bandgap of undoped CdS shifted from 2,49 eV down to 2,39 eV. Phase transition of CdS due to annealing temperature and induced lattice damage due to doping. The room temperature conductivity of polymer is 3,8 – 6,6 x 10-7 S/cm as function of frequency between 100 – 5000 Hz. The Voc of the fabricated cells ranges between 107 – 286 mV and time constant ranges between 2,5 to 43,75 s.
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