Judul: Peramalan Harga Saham PT Garuda Indonesia Saat Covid-19   dengan Menggunakan Model ARIMA
Abstrak: Avlation is one of the industries that has an impact on the restriction system to prevent the spread of the Covid-19 virus. Therefore, investors find it difficult to assume whether making an investment at the time of restriction will bring future profits or losses. To overcome this, forecasting is needed. One method that is often used is the ARIMA model. The data used is the daily stock price of PT Garuda Indonesia from January 1, 2020 to April 26, 2021. The most suitable model is ARIMA(2,1,2) with a MAPE around 4.3%. After forecasting for the next 10 periods, the MAPE is obtained at around 0.78%, this means that the error rate is quite low., Penerbangan merupakan salah satu industri yang berdampak pada saat sistem pembatasan untuk mencegah penyebaran virus Covid-19. Oleh karena itu, investor merasa kesulitan untuk mengasumsikan apakah melakukan investasi pada saat pembatasan akan mendatangkan keuntungan atau kerugian di masa yang akan datang. Untuk mengatasi hal tersebut perlu dilakukan peramalan. Salah satu metode yang sering digunakan adalah Model ARIMA. Data yang digunakan adalah data saham harian PT Garuda Indonesia dari 1 Januari 2020 sampai dengan 26 April 2021. Model yang paling sesuai adalah ARIMA(2,1,2) dengan MAPE sebesar 4.3%. Setelah dilakukan peralaman untuk 10 periode yang akan datang diperoleh MAPE sebesar 0.78%, artinya tingkat kesalahan yang dihasilkan cukup rendah.
Keyword: ARIMA, Covid-19, forecasting, avlation.

Judul: Comparison of Symmetric and Asymmetric GARCH Model in Forecasting Jakarta Composite Index 2018-2023
Abstrak: Metode peramalan deret waktu yang sering digunakan adalah metode ARIMA. Metode ARIMA yang menyertakan deret waktu lain sebagai peubah eksogen disebut metode ARIMAX. Metode ARIMAX mengasumsikan ragam sisaan homogen tetapi data dengan volatilitas yang tinggi dapat menyebabkan pelanggaran asumsi tersebut. Metode yang dapat digunakan adalah metode ARCH/GARCH tetapi metode ARCH/GARCH kurang baik dalam memodelkan volatilitas asimetris pada data. Metode lain yang dapat digunakan adalah metode GARCH asimetris seperti TGARCH. Tujuan dari penelitian ini adalah untuk membandingkan akurasi peramalan model simetris ARCH/GARCH dan asimetris TGARCH dalam peramalan data IHSG mingguan periode 1 Januari 2018 sampai 24 April 2023 dengan melibatkan pengaruh COVID-19 sebagai peubah kovariat serta menerapkan beberapa skenario validasi model pada data latih dan data uji. Berdasarkan model terbaik yang terpilih, dilakukan peramalan pada periode 1 Mei 2023 sampai 3 Juli 2023. Data yang digunakan dalam penelitian ini adalah data pembukaan IHSG mingguan periode 1 Januari 2018 sampai 24 April 2023 dengan peubah kovariat berupa periode COVID-19. Model terbaik yang dihasilkan adalah model ARIMAX(3,1,3)-TGARCH(1,2) dengan pengaruh peubah COVID-19 yang signifikan dan rata rata nilai MAPE dari empat skenario data sebesar 3,158%. Peramalan dilakukan dengan hasil peramalan yang cenderung stabil dan selang kepercayaan yang cenderung melebar di setiap periode waktunya., The time series forecasting method that often used is the ARIMA method. The ARIMA method which includes other time series as exogenous variables is called the ARIMAX method. The ARIMAX method assumes a homogeneous residual variance but data with high volatility can cause violations of this assumption. The method that can be used is the ARCH/GARCH method but the ARCH/GARCH method is not good at modeling asymmetric volatility in data. Another method that can be used is an asymmetric GARCH method such as TGARCH. The purpose of this study is to compare the accuracy of the symmetric ARCH/GARCH and asymmetric TGARCH models in forecasting weekly JCI data for the period January 1 2018 to April 24 2023 by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best selected model, forecasting was carried out for the period May 1 2023 to July 3 2023. The data used in this study is weekly JCI opening data for the period January 1 2018 to April 24 2023 with the covariate variable being the COVID-19 period. The best model produced is the ARIMAX(3,1,3)-TGARCH(1,2) model with a significant influence of COVID-19 variables and an average MAPE value of the four data scenarios of 3,158%. Forecasting is done with forecasting results that tend to be stable and confidence intervals that tend to widen in each time period.
Keyword: ARCH, ARIMAX, GARCH, stock prices, TGARCH

Judul: Karakteristik Carboxymethyl Chitosan Dengan Variasi Konsentrasi NaOH
Abstrak: Carboxymethyl chitosan is chitosan derivative product obtained from chemical modification. CMCh were made by alkalization process and carboxymethylation process. Factors that affect CMCh characteristics were excess sodium hydroxide in alkalization process, ratio between chitosan and monochloroacetic acid, and carboxymethylation temperature. The objectives of this research were to study the NaOH effect to CMCh characteristics, to learn process of making CMCh, to set the proper concentration of NaOH, and to analyze CMCh characteristics. CMCh yields were 110.72%-114.22%. CMCh pH value were 3.74-4.31. The optimum NaOH concentration to obtain the best CHCh was 10 M. Percentage of CMCh moisture content was 10.70%-11.42%. Ash content and nitrogen content of CMCh were 0.49%-0.50% and 4.52%-4.81%. CMCh viscousity was 8.25 cps. Percentage of CMCh solubility was 61.61%-91.50%. The different functional groups from chitosan which observed in CMCh were C=O group, CH group from alkene functional group, C−O group, and C−O−C group.
Keyword: solubility., functional group, CMCh characteristics, chitosan, carboxymethyl chitosan