Judul: Kualitas papan partikel dari serutan kayu kamper dengan penambahan bulu domba garut pada beberapa jenis perekat
Abstrak: The hair of Indonesian local sheep, are courses and cannot be utilised to be good, cannot be explaiterd to textile material so that now is still considered a waste. Hair consist of hair fibre which is from hard fobrous protein called keratin. Hairs have some good properted such as good insulations, strong and light, very elastic and are unflanmable (Ensminger, 1962). By considering its good potency, it is consedered to study its posibility to be one of the component of partcleboard, which is a composite product made from lignocellulosesd materials with aglutinant through a forging process (Maloney, 1977).
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Judul: Pengaruh jenis dan kadar ekstender kulit akasia (Acacia mangium Willd) terhadap kualitas papan partikel yang dihasilkannya
Abstrak: INTRODUCTION : The usage of synthetic adhesive has already been increased along with the development of the composite’s wood industry which turns into the scarcity of raw materials synthetical adhesives resources. Therefore, we need an alternative to save the usage of synthetic resin adhesives by replacing with some of the resin which also has a function as an extender. The potency of industrial timber estate’s (HTI) wood bark waste, especially from acacia wood species (Acacia mangium Willd) is so bright in the future along with acacia plantation development. The availability of the wood bark waste in Indonesia is quite plentiful so that the chances of its utilization as a mixed material adhesive composite boards is really possible. One of the utilization of wood bark waste is to produce a product which has high added value by using the tannin substances which was contained as an adhesive or as an extender for wood composite production.
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Judul: Support Vector Regression (SVR) with Optimal Hyperparameters and its Application in Rupiah Exchange Rate Prediction
Abstrak: Nilai tukar Rupiah terhadap United States Dollar (USD) merupakan harga satu unit USD dalam Rupiah. Support Vector Regression (SVR) digunakan untuk memprediksi nilai tukar Rupiah beberapa minggu ke depan. Data yang digunakan pada penelitian ini berada pada periode 6 Januari 2019 hingga 31 Desember 2023. Tujuan dari penelitian ini adalah untuk melihat pengaruh dari harga-harga pasar internasional seperti gas alam, nikel, dan minyak bumi, tingkat inflasi di Indonesia, dan tingkat suku bunga Bank Indonesia terhadap nilai tukar Rupiah, membandingkan model Support Vector Regression dengan berbagai kernel dan skenario, dan memprediksi nilai tukar Rupiah menggunakan model paling baik dalam penelitian ini pada periode 7 Januari 2024 hingga 24 Maret 2024. Model SVR paling baik pada penelitian ini adalah model SVR dengan kernel RBF yang dilatih pada data training dengan hyperparameter C = 10, e = 0.2, dan ? = 0.01 menggunakan skenario ke-3. Model tersebut memiliki MAPE sebesar 0.984% dan trend accuracy sebesar 0.635. Nilai tukar Rupiah diprediksi mengalami tren negatif selama 12 minggu ke depan., The Rupiah exchange rate against the United States Dollar (USD) is the price of one USD unit in Rupiah. Support Vector Regression (SVR) model used to predict the Rupiah exchange rate for over few weeks. The data used in this research covers the period from January, 6-th 2019 to December, 31-st 2023. The aim of this research is to see the influence of international market prices such as natural gas, nickel, and petroleum, the inflation rate in Indonesia, and Bank Indonesia interest rate on the Rupiah exchange rate, to compare Support Vector Regression models with various kernels and scenarios, and to predict the Rupiah exchange rate using the best model in this study for the period from January, 7-th 2024 to March, 24-th 2024.The best SVR model in this research is the SVR model with RBF kernel which is trained on training data with hyperparameters C = 10, e = 0.2, and ? = 0.01 using the 3rd scenario. This model has a MAPE of 0.984% and a trend accuracy of 0.635. The Rupiah exchange rate is predicted to experience a negative trend over the next 12 Weeks.
Keyword: Support Vector Regression, Rupiah Exchange Rate, Trend Accuracy, Prediction