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: 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: 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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