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: Comparison of ARIMA and Fuzzy Time Series Methods in Forecasting West Texas Intermediate Crude Oil Prices
Abstrak: Minyak dunia merupakan komoditas penting dan bernilai tinggi di pasar internasional. Salah satu jenis minyak mentah dunia adalah West Texas Intermediate (WTI) yang memiliki kandungan sulfur 0,24% dan gravity 39,6 derajat. Patokan harga minyak WTI digunakan oleh Amerika Serikat dan sangat mempengaruhi pasar global. Amerika Serikat menyumbang 36,3% pasar modal global sehingga menjadikan harga WTI sebagai standar internasional. Di pasar global, minyak mentah sangat aktif diperdagangkan dengan volatilitas tahunan 25% sehingga menyebabkan fluktuasi harga yang sulit diprediksi dan berdampak pada perekonomian dunia, terutama negara berkembang seperti Indonesia. Kenaikan harga minyak yang signifikan dan ketidakpastian ekonomi global mempengaruhi semua sektor masyarakat. Peramalan harga minyak yang akurat penting untuk pengambilan keputusan para pemangku kepentingan. Peramalan menggunakan data historis melibatkan metode seperti Autoregressive Integrated Moving Average (ARIMA) dan fuzzy time series (FTS). Penelitian ini membandingkan metode ARIMA dan fuzzy time series Stevenson Porter untuk peramalan harga minyak WTI dan dievaluasi berdasarkan nilai MAPE terkecil. Kedua metode berfungsi sangat baik dalam peramalan, dengan ARIMA menunjukkan kestabilan dalam membaca pola data yang lebih baik dan MAPE senilai 1,45% sedangkan nilai MAPE untuk FTS Stevenson Porter senilai 2,13%. Hal ini menunjukkan akurasi peramalan yang sangat baik dari kedua metode., World oil is an important and highly valuable commodity in the international market. One type of crude oil is West Texas Intermediate (WTI), which has a sulfur content of 0,24% and a gravity of 39,6 degrees. The price benchmark for WTI oil is used by the United States and significantly influences the global market. The United States accounts for 36,3% of the global capital market, making WTI prices an international standard. In the global market, crude oil is actively traded with an annual volatility of 25%, causing price fluctuations that are difficult to predict and impacting the global economy, especially developing countries like Indonesia. Significant increases in oil prices and global economic uncertainty affect all sectors of society. Accurate oil price forecasting is important for stakeholders' decision-making. Forecasting using historical data involves methods such as Autoregressive Integrated Moving Average (ARIMA) and fuzzy time series (FTS). This study compares the ARIMA and fuzzy Time Series Stevenson Porter methods for forecasting WTI oil prices and evaluates them based on the smallest MAPE value. Both methods perform very well in forecasting, with ARIMA showing better stability in reading data patterns and a MAPE of 1,45%, while the MAPE for FTS Stevenson Porter is 2,13%. This indicates a very good forecasting accuracy for both methods.
Keyword: ARIMA, peramalan, fuzzy time series, MAPE, minyak mentah

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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