Judul: Comparing the Accuracy of Geometric Brownian  Motion and ARIMA Models in Predicting the BIRD Stock Prices
Abstrak: Saham merupakan salah satu instrumen investasi yang dapat dipilih untuk memperoleh keuntungan di masa yang akan datang. Harga saham bergerak fluktuatif mengikuti proses stokastik. Model matematika diperlukan dalam membaca pergerakan saham, misalnya model Geometric Brownian Motion (GBM) dan Autoregressive Integrated Moving Average (ARIMA). Penelitian ini bertujuan untuk membandingkan tingkat keakuratan model GBM dan ARIMA dalam memprediksi harga saham. Data yang digunakan adalah harga penutupan saham PT Blue Bird Tbk periode 11 Februari 2023 – 11 Februari 2024. Data tersebut dibagi menjadi data training sebanyak 191 data dan data testing sebanyak 46 data. Di antara model ARIMA, model ARIMA(4,1,4) adalah model terbaik dengan nilai AIC sebesar 2008.16 dan semua parameternya signifikan pada taraf nyata 5%. MAPE dipilih menjadi alat dalam mengukur tingkat keakuratan model dalam kinerjanya memprediksi harga saham karena data yang digunakan tidak memiliki pencilan. Berdasarkan hasil penelitian diperoleh nilai MAPE model GBM sebesar 10.3634%, sedangkan model ARIMA(4,1,4) sebesar 7.7838%., Stocks are one of the investment instrumens that can be chosen to obtain profits in the future. Stock prices fluctuate following a stochastic process. Mathematical models, such as Geometric Brownian Motion (GBM) and Autoregressive Integrated Moving Average (ARIMA) models, can be used in reading stock movements. This study aims to compare the accuracy of GBM and ARIMA models in predicting stock prices. The data used are the closing price of PT Blue Bird’s Tbk shares for the period February 11, 2023 – Februari 11, 2024. The data are divided into 191 training data and 46 testing data. Among the ARIMA models, the ARIMA(4,1,4) was the best model with an AIC value of 2008.16 and all its parameters were significant at level 5%. MAPE was chosen as a tool in measuring accuracy of the model in predicting stock prices because the data used do not have outliers. Based on the results of the study, the MAPE value of GBM model was 10.36345%, while the MAPE value of ARIMA(4,1,4) model was 7.783817%.
Keyword: ARIMA, MAPE, Blue Bird, gbm, stock

Judul: Prediksi Harga Saham BBCA dan BMRI dengan Model Seasonal ARIMA
Abstrak: Perusahaan saat ini tidak dimiliki oleh satu orang atau kelompok yang bekerja di perusahaan tersebut. Mereka dimiliki oleh beberapa orang yang memiliki sebagian kepemilikan perusahaan. Bukti kepemilikan ini biasanya disebut saham. Saham umumnya diperdagangkan di era modern karena memiliki kemungkinan untuk menghasilkan keuntungan dalam jumlah yang besar. Informasi mengenai harga saham di masa depan adalah sesuatu yang diinginkan oleh banyak orang. Model Seasonal ARIMA merupakan model konvensional berbasis statistik yang sering digunakan untuk memprediksi harga saham. Hal ini dikarenakan harga pasar saham tidak statis, sering berubah sepanjang waktu dan terkadang memiliki pola musiman. Hal tersebut dapat diprediksi oleh model Seasonal ARIMA. Dalam penelitian ini model Seasonal ARIMA digunakan untuk memprediksi harga saham BBCA dan BMRI tahun 2022 dengan data historis harga saham BBCA dan BMRI tahun 2012- 2021. Didapatkan model ARIMA(0,1,0)(1,0,1)12 merupakan model terbaik untuk prediksi saham BBCA karena memenuhi semua asumsi dengan nilai MAPE sebesar 9,33%. dan ditemukan bahwa model ARIMA musiman tidak cocok digunakan untuk memprediksi harga saham BMRI karena tidak memenuhi beberapa asumsi., Companies nowadays are not owned by a single person or group who works in the company. They are owned by multiple people who have a portion of the share belonging to the company. These shares are usually called stocks. Stocks are commonly traded in the modern age as it has the possibility to yield high amounts of profit. The information of future stocks prices is desired by many people. Seasonal ARIMA model is a conventional model based on statistics that is often used to predict the stock market. This is because stock market prices are not static, they often vary over time and might have seasonal pattern, which Seasonal ARIMA is able to predict. In this study, Seasonal ARIMA model is used to predict 2022 stock prices of BBCA and BMRI using historical stock price data of BBCA and BMRI in 2012-2021. The seasonal ARIMA Model (0,1,0)(1,0,1)12 was decided to be the best model for BBCA stock prediction because it meets all assumptions with a MAPE score of 9.33%. It is concluded that seasonal ARIMA model isn’t suitable to predict BMRI stock price because it doesn’t meet some of the assumptions.
Keyword: forecasting, seasonal ARIMA, stock, stock price prediction

Judul: Sintesis Flavonol melalui 2’-Hidroksikalkon
Abstrak: Flavonol is a class of flavonoids commonly found in fruits and vegetables. Flavonol is very useful, especially in food sector and health. In this study, flavonol unsubstituted was successfully synthesized from an intermediate material 2’-hydroxychalcone. The 2’-hydroxychalcone was synthesized by Claisen-Schmidt condensation method between o-hydroxyacetophenone and benzaldehyde using 4 different catalysts, namely KOH 60%, solid pellet of KOH-grinding technique, BF3-Et2O, and NaH in DMF. The KOH 60% was the best catalyst, giving the highest yield (57%). Afterward, the flavonol was synthesized in 1-stage oxidation cyclization of o-hydroxyacetophenone using Algar-Flynn-Oyamada (AFO) reaction, giving 76%. The flavonol total yield from 2’-hydroxychalcone was 43%. All synthesized products were characterized by their melting points and their spectra. The success of the synthesis flavonol opens the way to synthesis of useful flavonol derivatives such as quercetin and kaempferol
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