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All Journal dCartesian: Jurnal Matematika dan Aplikasi Gadjah Mada International Journal of Business BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal of Active Learning Indonesian Journal of Data and Science Jurnal Statistika dan Aplikasinya Journal of Fundamental Mathematics and Applications (JFMA) d'Cartesian: Jurnal Matematika dan Aplikasi
Tundjung Mahatma
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Simulation-Based Pricing and Settlement Price Distributions of Indonesian Structured Warrants Sasongko, Leopoldus Ricky; Mahatma, Tundjung; Robiyanto, Robiyanto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.29282

Abstract

The Indonesian capital market has experienced significant growth, marked by the introduction of Structured Warrants (SWs) as innovative financial instruments. This study aims to develop a robust simulation-based pricing model for Indonesian Call SWs utilizing the Geometric Brownian Motion (GBM) framework and to determine their settlement price distributions. Monte Carlo simulations were employed to accurately capture the specific characteristics of Indonesian Call SWs, notably their average-price settlement mechanism and conversion rates. The results indicate that the settlement prices conform to a lognormal distribution, validating the GBM assumption and aligning with key trading metrics such as implied volatility, which is widely utilized in the Indonesian SW market. Additionally, the Symmetrical Auto Rejection rule, which imposes realistic constraints on underlying asset price movements, significantly enhances model realism and better reflects actual market conditions. The findings reveal that simulated Indonesian Call SW prices are slightly lower compared to values derived from the Black-Scholes model adjusted for conversion rates, highlighting opportunities for further refinement of pricing methodologies. Investors can leverage these insights to better assess risks and returns by anticipating volatility and price trends, with paying close attention to conversion rates and settlement mechanisms. Issuers may benefit from improved pricing accuracy, thus minimizing mispricing risks, while regulators can utilize this research to assess current market rules and design policies aimed at increasing market efficiency and transparency. 
CALCULATION OF CENTRAL JAVA PROVINCE REGION AREA USING SHOELACE FORMULA BASED ON THE GADM DATABASE Setiawan, Adi; Sediyono, Eko; Mahatma, Tundjung
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (511.692 KB) | DOI: 10.30598/barekengvol16iss2pp597-606

Abstract

This study proposes the use of the shoelace formula to determine the area of the regencies and towns/cities in the province of Central Java, which is based on the boundaries of the cities, regencies, (sub-) districts, and villages using the database from the GADM (Global Administrative Area). The results obtained are then compared with the Karney polygon method. With the shoelace formula, the area of Central Java province is 34365.40 km2 (4.77% wider than the reference area), while the Karney polygon method yields 34379.48 km2 (4.81% wider than the reference area). The area calculated using the boundaries of sub-districts is closer to the reference area if compared to using the boundaries of the regencies/cities and of villages. MdAPE values of 6.46 % and 6.54 % are obtained using the shoelace formula and the Karney polygon method respectively.
PERFORMANCE COMPARISON OF DECISION TREE AND LOGISTIC REGRESSION METHODS FOR CLASSIFICATION OF SNP GENETIC DATA Setiawan, Adi; Setivani, Febi; Mahatma, Tundjung
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0403-0412

Abstract

This research was conducted to compare the accuracy when decision tree and logistic regression methods are used on some data. Decision tree is one method of classification techniques in data mining. In the decision tree method, very large data samples will be represented as smaller rules, and logistic regression is a method that aims to determine the effect of an independent variable on other variables, namely dichotomous dependent variables. Both algorithms were written and analyzed using R software to see which method is better between the decision tree method and the logistic regression method applied to SNP (Single Nucleotide Polymorphism) genetic data, namely Asthma data. SNP Genetic Data was obtained from R software with the package name "SNPassoc" and the data name "asthma". Asthma data has 57 features, namely Country, Gender, Age, BMI, Smoke, Case control, and SNP (Single Nucleotide Polymorphism) genetic code. Comparative analysis was carried out based on the results of the accuracy values obtained in the two methods. Variations in the proportion of the test data used were 40%, 30%, 20% and 10% and were simulated 1000 times on the grounds of obtaining a better accuracy value. The results obtained show that the decision tree method obtains an accuracy value of 0.5793, 0.5777, 0.5745, 0.5526, respectively, while the logistic regression method is 0.7696, 0.7729, 0.7763, 0.7788, respectively and they are achieved at the proportion of test data of 40%, 30%, 20%, 10%. Thus it can be concluded that in this case the logistic regression method is better than the decision tree method in classifying Asthma data.
Co-Authors Adi Setiawan Arianti Ina Restiani Hunga Bambang Susanto David Elsa Kharisma Didit Budi Nugroho Eko Sediyono EMMA NOVITA SARI Indah Kristin Utami Leopoldus Ricky Sasongko Leopoldus Ricky Sasongko Leopoldus Sasongko Lilik Linawati Nugroho, Jeffrey Nur rohman Nur Rohman Robiyanto Robiyanto, Robiyanto Setivani, Febi wahyu ngestisari Yulius Pratomo