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: Prediksi Harga Minyak Goreng Curah dan Kemasan  Menggunakan Algoritme Long Short Term Memory(LSTM)
Abstrak: Kenaikan harga bahan kebutuhan pokok yang signifikan akan menimbulkan dampak negatif bagi perekonomian masyarakat Indonesia, seperti penurunan daya beli. Berdasarkan monitoring yang dilakukan oleh Pusat Informasi Harga Pangan Strategis sejak November 2021 hingga Agustus 2022 minyak goreng merupakan kebutuhan pokok yang yang mengalami kenaikan harga secara signifikan dan terjadi merata di seluruh indonesia, termasuk Jawa Barat. Dampak negatif dapat diatasi jika, fluktuasi harga dapat diprediksi sebelumnya. Salah satu metode yang umum digunakan untuk prediksi fluktuasi harga adalah LSTM. LSTM merupakan bagian dari deep Learning yang khusus menangani data time series. Pada penelitian ini, penulis membangun model prediksi harga minyak goreng curah dan kemasan pasa pasar tradisional di provinsi Jawa Barat menggunakan metode Long Short-Term Memory (LSTM). Pemodelan prediksi harga minyak goreng curah dan kemasan telah berhasil dibentuk dan memperoleh nilai yang cukup baik untuk memprediksi harga di pasar tradisional. Berdasarkan beberapa bercobaan yang dilakukan, hasil model prediksi terbaik untuk harga minyak goreng curah dan kemasan diperoleh nilai NRMSE terkecil sebesar 0,01751 dan 0,03278. Berdasarkan nilai NRMSE yang diperoleh, pembentukan model menggunakan LSTM menunjukan hasil pada model mendekati variasi nilai aktualnya., A significant increase of price of basic necessities will have a negative impact on the economy of the Indonesian people, such as a decrease in purchasing power. Based on monitoring conducted by the Strategic Food Price Information Center from November 2021 to August 2022, cooking oil is a basic need that has experienced significant price increases and is occurring evenly throughout Indonesia, including West Java. Negative impacts can be overcome if price fluctuations can be predicted beforehand. One of the commonly used methods for predicting price fluctuations is LSTM. LSTM is part of deep learning that specifically handles time series data. In this study, the authors built a price prediction model for bulk cooking oil and packaging in traditional markets in West Java province using the Long Short-Term Memory (LSTM) method. Price prediction modeling for bulk and packaged cooking oil has been successfully established and obtained a good enough value to predict prices in traditional markets. Based on several experiments conducted, the best prediction model results for bulk and packaged cooking oil prices obtained the smallest NRMSE values of 0.01751 and 0.03278. Based on the NRMSE values obtained, the model formation using LSTM shows that the results on the model are close to the actual value variations.
Keyword: Cooking Oil, Deep Learning, Long Short-Term Memory (LSTM), Time Series

Judul: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
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