Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Journal of the Indonesian Mathematical Society BAREKENG: Jurnal Ilmu Matematika dan Terapan Variance : Journal of Statistics and Its Applications
Zainuddin, Ahmad Fuad
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Joint-Life Insurance Premium Model Using Archimedean Copula: The Study of Mortality in Indonesia Ramadhan, Muhammad Akhirul; Zainuddin, Ahmad Fuad; Pasaribu, Udjianna Sekteria; Sari, RR Kurnia Novita
Journal of the Indonesian Mathematical Society Vol. 31 No. 1 (2025): MARCH
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.v31i1.1783

Abstract

Joint-life insurance pays a sum insured when the first death occurs. This insurance has a case based on the order of exit from the cohort, namely joint life and last survivor. The former means that one of the insured leaves the cohort, while the latter means the last member of the insured has left his or her cohort. For some reasons of simplicity, the insurance premium is usually calculated with the assumption that the husband and wife are mutually independent. However, this assumption is considered unrealistic. Couples are open to the same risks, hence explaining joint survival model should involve dependence structures between the distribution of spouse mortality. In line with this, to understand the dependence structure of multiple random variables, the approach used is Copula. In this context, Copula relates the marginal distribution function of these variables to the joint life distribution. One of the advantages from Copula is that the random variables do not have to come from the same distribution, hence Copula is considered good enough to explain the dependence of the mortality rate between husband and wife. This study aimed to develop a joint survival model for calculating joint life insurance premiums using the concept of Archimedean Copula to discover the minimum premium value by conducting the following steps: first, identifying the marginal distributions of mortality for genders using Indonesian Mortality Table IV (TMI/Tabel Mortalitas Indonesia IV); second, Archimedean copula function-based constructing survival models that captures the relationship between these variables; third, setting dependency parameter θ; fourth, calculating the joint life premium using Archimedean copula based survival modeled for each correlation dependency level; and carrying out optimization to find the minimum premium value. This can be achieved by formulating the problem as an optimization problem, considering an objective function that yields the lowest premium till satisfying the financial requirements of the insurance company.
LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX Anggasari, I Gusti Ayu Wulan; Zainuddin, Ahmad Fuad; Indratno, Sapto Wahyu; Yunus, Muhammad Haekal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0893-0902

Abstract

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.
PREDIKSI NILAI KURS MATA UANG DOLLAR AMERIKA (USD) DAN YUAN CHINA (CNY) DENGAN RUPIAH (IDR) MENGGUNAKAN METODE ARIMA Teja, Satya Dhira Alfa; Derick, Luigi; Tauryawati, Mey Lista; Zainuddin, Ahmad Fuad
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 1 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss1page99-112

Abstract

Bonus demografi yang sedang terjadi di Indonesia tidak sebanding dengan lapangan pekerjaan yang tersedia. Hal ini menyebabkan banyak dari mereka untuk mencari sumber pendapatan dari berbagai tempat, salah satunya dengan berinvestasi atau trading. Trading forex juga merupakan sumber pendapatan yang potensial jika dilakukan dengan benar. Analisa dengan menggunakan metode yang benar dapat membantu untuk sukses dalam dunia trading forex. Dalam dunia forex, mata uang Dollar Amerika (USD) dan Yuan China (CNY) merupakan mata uang yang sering dipilih karena paling berpotensi menghasilkan keuntungan. Penelitian ini bertujuan untuk melakukan prediksi harga kurs Dollar Amerika (USD) dan Yuan China (CNY) terhadap Rupiah (IDR) menggunakan metode Autoregressive Integrated Moving Average (ARIMA). Data kurs mata uang USD dan CNY akan dibagi menjadi data train dan test untuk memprediksi secara long term (10 hari), dan short term (5 hari). Dari hasil analisa tersebut, diperoleh bahwa model ARIMA (2,0,2) adalah model terbaik untuk memprediksi kurs USD terhadap IDR, sedangkan model ARIMA (3,0,2) adalah model terbaik untuk prediksi kurs CNY terhadap IDR. Model terbaik diperoleh berdasarkan nilai AIC terendah dan signifikansi parameter. Setelah mendapatkan model terbaik untuk nilai tukar kurs mata uang USD dan CNY terhadap IDR, selanjutnya dilakukan prediksi untuk jangka waktu short term dan long term. Hasil menunjukkan bahwa untuk meramalkan secara short term, model ARIMA yang telah diperoleh, cocok untuk digunakan. Namun, untuk meramalkan secara long term, model ARIMA tersebut masih kurang akurat untuk digunakan, karena keterbatasan data train.
MAPPING DISASTER-PRONE AREAS ON JAVA ISLAND USING THE K-PROTOTYPES ALGORITHM Tauryawati, Mey Lista; Zainuddin, Ahmad Fuad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0179-0196

Abstract

Clustering in disaster areas is often implemented as a disaster mitigation effort with the aim of minimizing risk. Determining the appropriate clustering method based on the data set will influence the clustering results. K-Prototypes is a clustering method that is capable of handling mixed data, numerical and categorical data, so this method is suitable to clustering disaster prone area with mixed data of disaster factors such as incident intensity, type of disaster, population density, and level of infrastructure vulnerability. This research focuses on disaster prone areas on Java Island and clustering using K-Prototypes to group and map areas that have the highest to lowest levels of disaster vulnerability based on the number of incidents, number of victims, and the amount of damage to facilities and the type of disaster. The clustering results obtained mapping of cities in the province into cluster groups based on the level of vulnerability and calculated potential losses based on disasters in each province. Afterward, the clustering results are used to determine priority areas for disaster mitigation to minimize losses.
Co-Authors Anggasari, I Gusti Ayu Wulan Derick, Luigi Indratno, Sapto Wahyu Mey Lista Tauryawati Muhammad Haekal Yunus Pasaribu, Udjianna Sekteria Ramadhan, Muhammad Akhirul Sari, RR Kurnia Novita Teja, Satya Dhira Alfa