scholarly journals ANALISIS PERPIDAHAN PENGGUNAAN MEREK SIMCARD DENGAN PENDEKATAN RANTAI MARKOV

2018 ◽  
Vol 7 (1) ◽  
pp. 56
Author(s):  
NURMA ALIYUWANINGSIH ◽  
I WAYAN SUMARJAYA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Markov Chain ◽  
Market Share ◽  
Data Transfer ◽  
Transition Probabilities ◽  
Internet User ◽  
Equilibrium Conditions ◽  
Probabilistic Information ◽  
Brand Switching ◽  
Markov Chain Method ◽  
Usage Data

The aim of this research is to know the displacement made by consumers of GSM cards and predictions of market share when reaching equilibrium conditions for each of the displacements made by consumers of GSM cards. This research uses Markov chain method. Markov chain method produces probabilistic information that can be used to assist for making decision. In Markov’s analysis, the equilibrium conditions are conditions in which the variables in the system ultimately bring the transition probabilities in stable or unchanged conditions. Data from this research is divided into two categories namely data about brand switching of GSM cards and data about the transfer of GSM cards brand usage. Data brand switching of GSM cards obtained from users who use one GSM cards, while the data transfer use of GSM cards obtained from user more than one GSM cards. The results of this research indicate that GSM card displacement equilibrium conditions was achieved in the 9th period, whereas the results of switching the use of GSM card shows that equilibrium conditions for phone and SMS users is in the 15th period, and equilibrium conditions for internet user is reached in 5th period.

scholarly journals Analisis Pangsa Pasar Beberapa Merek Produk Minuman Susu Fermentasi dalam Kemasan dengan Metode Rantai Markov di Lingkungan Kampus Universitas Udayana

2019 ◽  
Vol 7 (4) ◽  
pp. 561
Author(s):  
Keisa Az-zahra ◽  
A. A. P. A. Suryawan Wiranatha ◽  
Luh Putu Wrasiati
Keyword(s):  
Markov Chain ◽  
Market Share ◽  
Fermented Milk ◽  
Primary Data ◽  
Markov Chain Method ◽  
Stable Conditions ◽  
Products Analysis ◽  
Milk Market ◽  
Average Consumption

The objectives of this research were to determine the consumer characteristics of fermented milk products, analysis market share of several packaged fermented milk brands at this time until next five years ahead and determine the long-term market balance at Udayana University. The data used in the study was primary data collected through a questionnaire, for analysis this research was using the markov chain method by software QM 5.3. The result of the characteristics consumers of fermented milk were dominated by women with an average age 21-25 years old and mostly profession are students, with an average spending money for snacks/drinks was Rp 100,000-300,000/month with an average consumption of fermented milk 2-3 times/week. The reason of respondents consuming fermented milk was because of the taste was so special and respondents also get the information from advertisements on television, respondents usually buy the products from supermarkets on their own initiative by planning them first. The result of the research market share analysis was Yakult brand always leads market share, in the 2019 the Yakult brand was 55%, Cimory 30%, Calpico 8%, Vitacharm 4% and other brands 3%. in 2020 there was an increase for Yakult to 56%, a decrease for Cimory 29.5%, Calpico 7.5%, Vitacharm 3.9% and other brands rising to 3.1%. In 2021 only for Yakult change to 56.1% and Vitacharm 3.8%. In 2022 all brands were still in the same percentage but in 2023 Yakult change to 56.2% and Vitacharm 3.7%. The market share will reach stable conditions where the market share of each brand was Calpico 7.0%, Cimory 25.7%, Vitacharm 3.4% and Yakult 60.6% and other brands 3.3%. Keywords: fermented milk, market share, markov chain method

scholarly journals Markov Chain and Techno-Economic Analysis to Identify the Commercial Potential of New Technology: A Case Study of Motorcycle in Surakarta, Indonesia

2018 ◽  
Author(s):  
Wahyudi Sutopo ◽  
Indah Kurniyati ◽  
Roni Zakaria
Keyword(s):  
Markov Chain ◽  
Economic Analysis ◽  
Market Share ◽  
New Technology ◽  
Lithium Ion ◽  
Economic Feasibility ◽  
Commercial Potential ◽  
Markov Chain Method ◽  
Technology Market

LiFePO4 (LFP) or Lithium-ion battery with its advantages compared to common current motorcycle battery is an appropriate alternative in substituting wet and dry cell battery. Huge amount of demand of motorcycle along with the battery in Indonesia also make it an interesting product for business. In order to assess the commercial potential for such a new technology, market share needs to be estimated as well as the techno-economic feasibility. Hence, market share prediction using the residents of Surakarta Region and techno-economic analysis using NPV, IRR and PBP indicators have been conducted in this study. Calculation using markov chain method shows that LFP battery tends to dominate the market after certain period. Techno-economic analysis also figures out that the commercialization is feasible in three conditions - first mover, even with market leader and equilibrium point. Therefore, there is a great commercial potential for LFP battery especially in Indonesia.

UGLEO: A WEB BASED INTELLIGENCE CHATBOT FOR STUDENT ADMISSION PORTAL USING MEGAHAL STYLE

2018 ◽  
Vol 23 (3) ◽  
pp. 175-191
Author(s):  
Anneke Annassia Putri Siswadi ◽  
Avinanta Tarigan
Keyword(s):  
Markov Chain ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Need ◽  
Web Based ◽  
Markov Chain Method ◽  
Information Center

To fulfill the prospective student's information need about student admission, Gunadarma University has already many kinds of services which are time limited, such as website, book, registration place, Media Information Center, and Question Answering’s website (UG-Pedia). It needs a service that can serve them anytime and anywhere. Therefore, this research is developing the UGLeo as a web based QA intelligence chatbot application for Gunadarma University's student admission portal. UGLeo is developed by MegaHal style which implements the Markov Chain method. In this research, there are some modifications in MegaHal style, those modifications are the structure of natural language processing and the structure of database. The accuracy of UGLeo reply is 65%. However, to increase the accuracy there are some improvements to be applied in UGLeo system, both improvement in natural language processing and improvement in MegaHal style.

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

scholarly journals A Bayesian model for binary Markov chains

2004 ◽  
Vol 2004 (8) ◽  
pp. 421-429 ◽  
Author(s):  
Souad Assoudou ◽  
Belkheir Essebbar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Bayesian Estimation ◽  
Bayesian Model ◽  
Transition Probabilities ◽  
Simulated Data ◽  
Bayesian Estimator ◽  
Jeffreys Prior ◽  
Data Set

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.

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