Judul: Nonparametric Regression  Spline Truncated Modelling on External Factors Affecting Stock Prices
Abstrak: Saham adalah salah satu instrumen investasi yang diminati investor. Akan tetapi, harga saham bergerak cepat dan fluktuatif. Pergerakan harga saham dipengaruhi oleh banyak faktor di antaranya adalah inflasi, suku bunga, dan kurs USD, yang merupakan faktor eksternal di luar perusahaan. Penelitian ini bertujuan membuat model pengaruh inflasi, suku bunga, dan kurs USD terhadap harga saham menggunakan data longitudinal. Model dibuat menggunakan regresi nonparametrik spline truncated. Data yang digunakan dalam penelitian ini terdiri dari dua emiten, yaitu PT. Astra Agro Lestari (AALI) dan PT. Perusahaan Perkebunan London Sumatra (LSIP). Data dipisahkan menjadi dua sampel, yaitu data in sample dimulai dari Januari 2017 – Desember 2020 yang digunakan untuk membuat model dan data out sample dimulai dari Januari 2021 – Desember 2021 untuk diprediksi. Metode MAPE digunakan sebagai alat pengukur kinerja prediksi. Hasil penelitian menunjukkan model terbaik untuk saham AALI memiliki tiga titik knot dan memiliki nilai MAPE sebesar 17,27%. Model terbaik untuk saham LSIP memiliki tiga titik knot dan memiliki nilai MAPE sebesar 13,42%., Stock is one of investment instruments that an investor has an interest in. However, stocks move fast and fluctuate. The movement of the stock price is influenced by many factors including inflation, interest rate and USD exchange rate which are external factors outside the company. This manuscript aims to create a model for influence of inflation, interest rate, and USD exchange rate on stock prices uses longitudinal data. The models were obtained by using nonparametric regression spline truncated. The data consisted of stocks from two companies, namely PT. Astra Agro Lestari (AALI) and PT. Perusahaan Perkebunan London Sumatra (LSIP). The data were split into two sub-samples which were “in sample” data from January 2017 – December 2020 that were used to generate the models and “out sample” data from January 2021 – December 2021 that were used in predictions. MAPE was used as measurement of predictive performance. The results show that the best model for AALI has three knot points and has a MAPE value of 17,27%. The best model for LSIP has three knots and has a MAPE value of 13,42%.
Keyword: data longitudinal, regresi nonparametrik spline truncated, saham, longitudinal data, nonparametric regression spline truncated, stock

Judul: Comparison of Handling Violations of Assumptions of Linear Regression Models Using Linear Time Series Regression and the Cochrane-Orcutt Method
Abstrak: Seorang investor memiliki keinginan untuk mendapatkan keuntungan dari hasil berinvestasi dengan cara membeli saham dari suatu perusahaan. Selain memberikan imbal hasil yang tinggi, berinvestasi pada saham juga memiliki risiko yang tinggi. Oleh karena itu, untuk mengurangi risiko dilakukan peramalan harga saham di masa depan. Adapun metode yang digunakan yaitu melalui model regresi linear. Model tersebut memiliki asumsi-asumsi yang harus dipenuhi. Dalam penelitian ini dilakukan studi kasus pada harga saham BMRI yang diregresikan terhadap IHSG. Terdapat pelanggaran asumsi kebebasan galat yang ditangani dengan metode Cochrane-Orcutt dan regresi linear time series. Dari dua metode tersebut dibandingkan dan didapatkan bahwa metode Cochrane-Orcutt lebih baik dengan MAPE sebesar 0,886% daripada metode regresi time series dengan MAPE sebesar 5,178%., An investor has a desire to benefit from investment results by buying shares of a company. Apart from providing high returns, investing in stocks carries a high risk. Therefore, to reduce risk, stock price forecasting is carried out in the future. The method used is a linear regression model. The model has assumptions that must be met. This research, conducted a case study on BMRI's stock price which was regressed against the IDX Composite. There is a violation of the assumption of freedom of error which is handled by the Cochrane-Orcutt method and linear time series regression. The two methods were compared and it was found that the Cochrane-Orcutt method was better, with a MAPE of 0.886% than the time series regression method, with a MAPE of 5.178%.
Keyword: ARIMA, Cochrane-Orcutt, Harga Saham, Regresi Time Series

Judul: Detoxification of Cassava’s Acid Hydrolyzate by Activated Carbon Method for Bioethanol Production
Abstrak: Cassava is one of natural resources for carbohydrate that also available for bioethanol production. Starch and fibers hydrolysis maybe conducted on acid condition. The restriction in acid hydrolysis is 5-hydroxymethyl furfural (HMF) and furfural formation, which inhibit yeast fermentation. This study applied two stages of detoxification, which are overliming and activated carbon adsorption methods for lowering HMF and furfural concentrations. In order to obtain tolerable amount in sugar solution, detoxification by activated carbon adsorption was done by concentration of 1%, 2.5%, 5% and 10% and duration of contact are 30 45 and 60 minutes. The results showed that overliming method decreased HMF and furfural concentrations 34.38% and 60.81%, respectively. Overliming followed by 5% activated carbon adsorption for 30 minutes produced the best sugar solution which has a lower HMF and furfural concentrations by 72.03% and 89.11%, respectively. Adsorption constants of HMF and furfural by 5% activated carbon were 0.044 g/min and 3,7 x 10-5 g/min, respectively. The concentration of ethanol produced was 5.00% (b/v) or 63.22% higher than obtained from detoxification by neutralization (NH4OH) which produced ethanol only 3.06%.
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