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All Journal EKSAKTA: Journal of Sciences and Data Analysis Media Statistika Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Science and Technology Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) International Journal of Supply Chain Management Jurnal Matematika UNAND Journal of Fundamental Mathematics and Applications (JFMA) Journal of Research Applications in Community Services Eduvest - Journal of Universal Studies Indonesian Journal of Mathematics and Applications Indonesian Actuarial Journal

Adhitya Ronnie Effendie, Adhitya Ronnie

Departemen Matematika, FMIPA, Universitas Gadjah Mada

Author-ID : 522854

Religion Aerospace Engineering Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

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COPULA MODELING TO IDENTIFY THE DEPENDENCY STRUCTURE OF AGRICULTURAL PRODUCTION AND ITS ENVIRONMENT INDICATORS IN INDONESIA Ahdika, Atina; Rosadi, Dedi; Effendie, Adhitya Ronnie; Gunardi, Gunardi
International Journal of Supply Chain Management Vol 7, No 4 (2018): International Journal of Supply Chain Management (IJSCM)
Publisher : International Journal of Supply Chain Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.803 KB)

Agriculture is a very potential field developed in agrarian countries such as Indonesia. The country has abundant natural wealth as a food source for plants. In addition, the natural condition also has an important role on the quality and quantity of agricultural products. This study aims to model dependency structure of rice production and its environment indicators, in this case, includes temperature change, CO2 emission, and rainfall precipitation, in Indonesia using copula model. We identify the linearity of correlation between variables by comparing Pearson correlation with normality assumption and dependency structure modeled by copula function with any marginal distribution. We analyze and discuss how copula model shows the dependency between variables which cannot be identified by linear correlation.

K-means and bayesian networks to determine building damage levels Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.11756

Many troubles in life require decision-making with convoluted processes because they are caused by uncertainty about the process of relationships that appear in the system. This problem leads to the creation of a model called the Bayesian Network. Bayesian Network is a Bayesian supported development supported by computing advancements. The Bayesian network has also been developed in various fields. At this time, information can implement Bayesian Networks in determining the extent of damage to buildings using individual building data. In practice, there is mixed data which is a combination of continuous and discrete variables. Therefore, to simplify the study it is assumed that all variables are discrete in order to solve practical problems in the implementation of theory. Discretization method used is the K-Means clustering because the percentage of validity obtained by this method is greater than the binning method.

Discretization methods for Bayesian networks in the case of the earthquake Devni Prima Sari; Dedi Rosadi; Adhitya Ronnie Effendie; Danardono Danardono
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2007

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study.

Collective Modified Value at Risk in Life Insurance Muhammad Iqbal Al-Banna Ismail; Abdul Talib Bon; Sukono Sukono; Adhitya Ronnie Effendie
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 2, ISSUE 1, February 2021
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol2.iss1.art4

Insurance is seen as a tool which individuals can transfer risks to others, where insurance collect funds from individuals to meet financial needs related to damage. Therefore analysis of risk in life insurance claims is really be needed bt the insurance company actuary. In an insurance system, the risk is the event when an insured party puts forward a claim. Claim is the compensation for a risk loss. Individual claim in one period insurance is called aggregation claim while aggregation claim is collective risk

ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE DETERMINISTIK DAN STOKASTIK Yuciana Wilandari; Gunardi Gunardi; Adhitya Ronnie Effendie
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 9, No 1 (2021): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.9.1.2021.56-63

The estimated of claims reserve has a very important in insurance companies, because it is the company's liability to policyholders in the future and can also result in the bankruptcy of the insurance company. In general, there are two methods for calculating claims reserves are the deterministic method (Chain Ladder and Bornhuetter Ferguson) and the stochastic method (Benktander Hovinen and Cape Cod). This article compares the two methods and determines the best method. Using the claim payments data that have been paid by an insurance company in Indonesia, the best method is the Benktander Hovinen method.

ESTIMASI CADANGAN KLAIM MENGGUNAKAN GENERALIZED LINEAR MODEL (GLM) DAN COPULA Yuciana Wilandari; Sri Haryatmi Kartiko; Adhitya Ronnie Effendie
Jurnal Gaussian Vol 9, No 4 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i4.29260

In the articles of this will be discussed regarding the estimated reserves of the claim using the Generalized Linear Model (GLM) and Copula. Copula is a pair function distribution marginal becomes a function of distribution of multivariate. The use of copula regression in this article is to produce estimated reserves of claims. Generalized Linear Model (GLM) used as a marginal model for several lines of business. In research it is used three kinds of line of business that is individual, corporate and professional. The copula used is the Archimedean type of copula, namely Clayton and Gumbel copula. The best copula selection method is done using Akaike Information Criteria (AIC). Maximum Likelihood Estimation (MLE) is used to estimate copula parameters. The copula model used is the Clayton copula as the best copula. The parameter estimation results are used to obtain the estimated reserve value of the claim.

PEMODELAN COPULA CLAYTON UNTUK PREDIKSI KLAIM PADA DATA LONGITUDINAL DENGAN EXCESS ZEROS Anaviroh Anaviroh; Adhitya Ronnie Effendie
Unisda Journal of Mathematics and Computer Science (UJMC) Vol 1 No 01 (2015): Unisda Journal of Mathematics and Computer Science
Publisher : Mathematics Department of Mathematics and Natural Sciences Unisda Lamongan

This papper discuss about longitudinal data models of claim counts withexcess-zeros, in which time-dependence of the claim counts is modeled by using acopula function. The copula approach extensively to model the serial dependence ofthe claim counts in car insurance, to model this serial dependence of the claimcounts (between the history and future claims). The maximum likelihood is appliedto estimate the parameters of the discrete copula model. A two-step procedure isproposed to estimate the parameters and predict the claim counts of the next periodusing the estimated parameters.

ESTIMATION OF IBNR AND RBNS RESERVES USING RDC METHOD AND GAMMA GENERALIZED LINEAR MODEL Tiara Yulita; Adhitya Ronnie Effendie
MEDIA STATISTIKA Vol 15, No 1 (2022): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.15.1.24-35

Estimation of claims reserves is a very important role for insurance companies because the information will be used to assess the insurance company’s ability to meet future claim payment obligations. In practice, claims reserves are divided into two Incurred but Not Reported (IBNR) and Reported but Not Settled (RBNS). Reserving by Detailed Conditioning (RDC) is one of the individual methods that can estimate claims reserves of both the IBNR and RBNS, which involves detailed condition so-called claim characteristics, and some information else so-called background variable. The result of estimating claims reserves using RDC with background variable is not stable because many combinate of calculation from each background variable. The purpose of this study is to overcome these problems, which we can combine RDC and Gamma Generalized Linear Model (GLM) as an effective method for estimating claims reserves. By using Bootstrapping Individual Claims Histories (BICH) method, the results show that estimation of claims reserves using RDC and Gamma GLM gives the fewest value of Mean Square Error of Prediction (MSEP) rather than RDC with Poisson GLM, RDC, and Chain Ladder. Where the smaller the value of the resulting MSEP estimate, the closer to the actual claim reserve value.

The The Use of Distance Blocks Representative to Avoid Empty Groups Due to Non-unique Medoids: - Kariyam Kariyam; Abdurakhman Abdurakhman; Adhitya Ronnie Effendie
Eduvest - Journal of Universal Studies Vol. 2 No. 10 (2022): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

The existence of identical objects in a data set is a necessity. This paper proposes a new indicator and procedure to obtain the initial medoids. The new algorithm guarantees no empty groups and identical objects in the same group, either in the initial or final groups. We use six real data sets to evaluate the proposed method and compare the results of other methods in terms of adjusted Rand index and clustering accuracy. The experiment results show that the performance of the proposed method is comparable with other methods

Application of Block-Based K-Medoids and Ward's Method to Classify Provinces in Indonesia Based on Environmental Quality Index Kariyam Kariyam; Abdurakhman Abdurakhman; Adhitya Ronnie Effendie
Eduvest - Journal of Universal Studies Vol. 3 No. 2 (2023): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Environmental quality is an essential aspect of the world's sustainability. This paper presents a combination of several methods to obtain a profile of provinces in Indonesia according to the environmental quality indices. We apply the block-based k-medoids algorithm, Ward's method, and the Hartigan index to determine the number of clusters. Based on Hartigan index suggested classifying 34 provinces in Indonesia into four groups. A comparison of the mean vector of four groups using multivariate analysis of variance concluded that three areas have the best environment quality index and eight regions require serious attention to improve their environmental quality methods.

Co-Authors A'yun, Qonita Qurrota Abdul Talib Bon Abdurakhman Abdurakhman Abdurakhman Abdurakhman Alsitaningtyas, Yunike Jemis Fifnelavindy Anaviroh Anaviroh Danardono Danardono Danardono, Danardono Dani, Andrea Tri Rian Dedi Rosadi Devni Prima Sari Faihatuz Zuhairoh Fidia Deny Tisna Amijaya Gunardi Gunardi Hardina Sandariria HARTATI indarsih, Indarsih Indradewa, Rhian Kariyam - Kariyam Kariyam Muhammad Iqbal Al-Banna Ismail Muhammad, Hubbi Mulyadi, Taqriri Kamal Putra, Fachrian Bimantoro Rohmatul Fajriyah Sandelvia, Krismona Saruan , Sandy Salomo Selvi Faristasari Sifriyani, Sifriyani Solikhatun, Solikhatun Sri Haryatmi Kartiko Sri Wahyuni Sukono Sukono Suryanto, Wiwit Susyanto, Nanang Syaripuddin Syaripuddin Uha Isnaini Utami, Rianti Siswi Wahyujati, Mohamad Fahruli Yotenka, Rahmadi Yuciana Wilandari Yuciana Wilandari Yulianti, Silvina Rosita Yulita, Tiara

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