Judul: Stock Price Simulation Using Geometric Brownian Motion (GBM) and Variance Gamma (VG) processes
Abstrak: Investasi merupakan penempatan sejumlah dana saat ini untuk memperoleh manfaat di masa yang akan datang. Investasi yang banyak menarik investor khususnya di bidang finansial adalah investasi di pasar modal. Salah satu surat berharga yang banyak diperdagangkan di pasar modal adalah saham. Keuntungan investasi saham dapat dilihat dari nilai return saham. Pergerakan harga saham yang mengikuti proses stokastik bergerak acak pada selang waktu tertentu yang menyebabkan harga saham sulit untuk diprediksi, sehingga digunakan metode Monte Carlo untuk memprediksi harga saham yang terjadi. Penelitian ini bertujuan melakukan simulasi Monte Carlo dalam menentukan harga saham menggunakan proses Geometric Brownian Motion (GBM) dan Variance Gamma (VG), serta membandingkan statistik deskriptifmya. Adapun sumber data yang yang digunakan adalah harga saham mingguan Microsoft periode 1 Januari 2015 − 31 Desember 2019. Berdasarkan hasil yang didapatkan, simulasi menggunakan proses GBM nilai statistik deskriptifnya lebih mengikuti pola data sebenarnya dengan nilai Mean Absolute Scaled Error yang lebih kecil dibandingkan dengan simulasi proses VG., Investment is the placement of a number of funds today to obtain benefits in the future. Investments that attract many investors, especially in the financial sector, are investments in the capital market. One of the securities that are widely traded in the capital market is shares. The advantages of stock investment can be seen from the value of stock returns. The movement of stock prices following a stochastic process moves randomly at certain intervals which makes stock prices difficult to predict, so the Monte Carlo method is used to predict stock prices that occur. This study aims to conduct a Monte Carlo simulation in determining stock prices using Geometric Brownian Motion (GBM) and Variance Gamma (VG) processes, as well as comparing descriptive statistics. The data source used is Microsoft's weekly stock price for the period 1 January 2015 – 31 December 2019. Based on the results obtained, the simulation using the GBM process has a descriptive statistical value that follows the actual data pattern with a smaller Mean Absolute Scaled Error value compared to the simulation of the VG process.
Keyword: Geometric Brownian Motion (GBM), Monte Carlo simulation, return, stock, Variance Gamma (VG)

Judul: Prediksi Harga Saham Menggunakan Simulasi Monte Carlo dengan Model Geometric Brownian Motion dan Estimasi Value at Risk
Abstrak: Investasi adalah aktivitas penanaman uang atau modal (aset berharga) untuk tujuan memperoleh keuntungan. Salah satu bentuk investasi adalah penanaman modal ke dalam bentuk saham suatu perusahaan. Risiko investasi pada saham lebih tinggi dibandingkan pada bentuk investasi lainnya karena perubahan harga saham yang tidak dapat diprediksi sehingga keuntungan juga tidak pasti. Pergerakan harga saham mengikuti proses stokastik bergerak acak pada selang waktu tertentu yang menyebabkan harga saham tidak menentu, untuk memprediksi harga saham di masa yang akan datang salah satunya menggunakan metode Geometric Brownian Motion berdasarkan harga saham di masa lampau jika return saham berdistribusi normal. Pemodelan saham dengan metode Geometric Brownian Motion yang dilakukan dengan simulasi Monte Carlo pada saham BBCA.JK menghasilkan prediksi saham yang sangat baik berdasarkan nilai MAPE, yaitu sebesar 4.46%. Estimasi kerugian prediksi harga saham dapat diketahui melalui value at risk. Estimasi value at risk pada prediksi harga saham dilakukan pada tingkat kepercayaan 99% , 95%, dan 90% yang menghasilkan -0.003456279, -0.002339218, dan -0.001360275 secara berturut-turut., Investment involves investing money or capital (valuable assets) for profit. Investing capital in the form of company shares is one way to invest. Investing in shares involves a higher risk than other investments because price changes are unpredictable and have an uncertain impact on profits. Stock price movements follow a stochastic process that moves randomly at certain time intervals, which causes stock prices to be uncertain. If stock returns are normally distributed, the Geometric Brownian Motion method can be utilized to predict future stock prices by analyzing past stock prices. Stock price modeling using the Geometric Brownian Motion method on BBCA.JK shares resulted in very good stock predictions based on MAPE value, which is 4.46%. Value at risk can be used to find estimated losses from share price predictions. Value at risk estimates for share price predictions were carried out at 99%, 95%, and 90% confidence levels, which resulted in -0.003456279, -0.002339218, and -0.001360275, respectively.
Keyword: Monte Carlo, Geometric Brownian Motion, Value at Risk, Investasi, Return

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
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