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CAUCHY: Jurnal Matematika Murni dan Aplikasi
Published by Universitas Islam Negeri Maulana Malik Ibrahim Malang
ISSN : 20860382     EISSN : 24773344     DOI : 10.18860
Core Subject : Education,
Mathematics
Jurnal CAUCHY secara berkala terbit dua (2) kali dalam setahun. Redaksi menerima tulisan ilmiah hasil penelitian, kajian kepustakaan, analisis dan pemecahan permasalahan di bidang Matematika (Aljabar, Analisis, Statistika, Komputasi, dan Terapan). Naskah yang diterima akan dikilas (review) oleh Mitra Bestari (reviewer) untuk dinilai substansi kelayakan naskah. Redaksi berhak mengedit naskah sejauh tidak mengubah substansi inti, hal ini dimaksudkan untuk keseragaman format dan gaya penulisan.
Arjuna Subject : -
Articles 438 Documents
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Application of K-Means Cluster Analysis for Grouping State Islamic University in Indonesia based on the Readiness Indicators for World Class University (WCU) Marhayati, Marhayati; Fa'ani, Arini Mayan; Ruhmanasari, Sulistya Umie; Faridah, Siti
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 8, No 2 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v8i2.18046

Abstract

Based on Moscow Ranking 2021, State Islamic University in Indonesia is still lower than other non-Islamic State University. This shows that the mapping of higher education readiness is an important aspect in preparation for WCU. Thus, there is a need for more in-depth research related to the level of readiness of universities. This study aims to determine the data description of the readiness of State Islamic Religious Colleges (PTKIN) to World Class University (WCU) and classify them based on that readiness. Quantitative methods are used in this study. The data were analyzed by K-Means Clustering. The data used in this research obtained from the Ministry of Religion's e-SMS Diktis system. The e-SMS system is a collection of data in each unit at a university based on WCU indicators, namely Good Governance University (GUG), University's Performance Improvement (UPI), Competitive Advantages University (CAU), and Global Recognition University (GRU). The results of the analysis show that from the four indicators it has not been able to achieve 100%. In addition, there are three clusters produced, namely PTKIN is very ready, ready, and not ready to go to World Class University (WCU). PTKIN need hard work in each indicator to be an international standard
Comparisons between Resampling Techniques in Linear Regression: A Simulation Study Fitrianto, Anwar; Linganathan, Punitha
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i3.14550

Abstract

The classic methods used in estimating the parameters in linear regression need to fulfill some assumptions. If the assumptions are not fulfilled, the conclusion is questionable. Resampling is one of the ways to avoid such problems. The study aims to compare resampling techniques in linear regression. The original data used in the study is clean, without any influential observations, outliers and leverage points.  The ordinary least square method was used as the primary method to estimate the parameters and then compared with resampling techniques. The variance, p-value, bias, and standard error are used as a scale to estimate the best method among random bootstrap, residual bootstrap and delete-one Jackknife. After all the analysis took place, it was found that random bootstrap did not perform well while residual and delete-one Jackknife works quite well. Random bootstrap, residual bootstrap, and Jackknife estimate better than ordinary least square. Is was found that residual bootstrap works well in estimating the parameter in the small sample. At the same time, it is suggested to use Jackknife when the sample size is big because Jackknife is more accessible to apply than residual bootstrap and Jackknife works well when the sample size is big.
Long Short Term Memory Using Stochastic Gradient Descent and Adam for Stock Prediction Fawazdhia, Muhammad Athanabil Andi; Rafsanjani HSM, Zani Anjani
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 8, No 2 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v8i2.17789

Abstract

The stock market is a place to carry out stock buying and selling transactions, the expected return of course has a profitable difference. Predicting stock prices can be done in various ways, one of which is by using deep learning models. Long Short Term Memory (LSTM) is a method that can be used to predict time series data. This method is a development of the Recurrent Neural Network (RNN), so this method is more complicated and powerful. To conduct training on the LSTM model, optimization is needed to minimize errors. There are lots of optimizations that can be used, but in this research, we use SGD and Adam. Several parameters such as learning rate 0.01, 0.001, 0.0001 and several variations of epochs such as epoch 25, epoch 50, epoch 100, epoch 200, epoch 400, and epoch 1000 were used in this study. The research data used are stock data of BBRI, BBNI, BMRI, and BBTN. This study also tries to predict stock prices on the next day using 5 historical stock price data, the result is that LSTM SGD and LSTM Adam succeeded in predicting the next day
Systematic Literature Review on Adjustable Robust Shortest Path Problem Fauziyah, Wida Nurul; Chaerani, Diah; Napitupulu, Herlina
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.17648

Abstract

In real-world optimization problems, effective path planning is important. The Shortest Path Problem (SPP) model is a classical operations research that can be applied to determine an efficient path from the starting point to the end point in a plan. However, in the real world, uncertainty is often encountered and must be faced. Significant uncertainty factors in the problem of determining the shortest path are problems that are difficult to predict, therefore new criteria and appropriate models are needed to deal with uncertainty along with the required efficient solution. The uncertainty factor can be formulated using an uncertain SPP optimization model, assuming parameters that are not known with certainty but are in an uncertain set. Problems with uncertainty in mathematical optimization can be solved using Robust Optimization (RO). RO is a methodology in dealing with the problem of data uncertainty caused by errors in data measurement. The uncertainty in the linear optimization problem model can be formed by loading the uncertainty that only exists in the constraint function by assuming its uncertainty using the Robust Counterpart (RC) methodology. In this paper, we will review the literature on the two-stage optimization model for the SPP problem using an Adjustable Robust Counterpart (ARC).
LQR and Fuzzy-PID Control Design on Double Inverted Pendulum Damayanti, Erlyana Trie; Mardlijah, Mardlijah; Rohman Wijaya, Ridho Nur
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i1.22070

Abstract

Double inverted pendulum is a non-linear and unstable system. Double inverted pendulum can be stabilized in the upright position by providing control to the system. In this research we compare two types of controllers namely Linear Quadratic Regulator (LQR) and Fuzzy-PID. The objective is to determine the control strategy that provides better performance on the position of the cart and pendulum angle. We modelled the system which is then linearized and given control. From the simulation results, it is proven that LQR and Fuzzy-PID controllers have been successfully designed to stabilize the double inverted pendulum. However, when given a disturbance in the form of noise step, the LQR controller has not been able to achieve the desired reference for up to 20 seconds. In another hand, the Fuzzy-PID controller is able to achieve the desired reference after 8 seconds. Therefore, it can be concluded that the Fuzzy-PID controller when applied to the Double Inverted pendulum system has better performance than the LQR controller.
Forecasting the Number of Tuberculosis Patients Using Automatic Clustering And Fuzzy Logical Relationship Method Rahmawati, Rahmawati; Sarbaini, Sarbaini; Rahma, Ade Novia; Lestari, Tri Uci; Aryani, Fitri
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 8, No 2 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v8i2.18073

Abstract

Tuberculosis is an infectious disease caused by the bacterium Mycobacterium tuberculosis bacillus, which infects the lungs and can potentially cause death.  This study aims to predict the number of tuberculosis sufferers in Kampar Regency in 2022. The method used is the automatic clustering and fuzzy logical relationship method. The data analyzed is secondary data obtained from the Kampar District Health Office from 2017 to 2021. From the results of the analysis carried out using the automatic clustering and fuzzy logical relationship method, it was obtained to forecast the number of tuberculosis patients in 2022, as many as  944 people with MAPE of 0.0882%, the accuracy of forecasting results of 99.9118%, and an increase in the number of tuberculosis sufferers from 2021 to 2022 as many as 4 people.
Bayesian Hurdle Poisson Regression for Assumption Violation Sa'diyah, Nur Kamilah; Astuti, Ani Budi; Mitakda, Maria Bernadetha T.
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i3.15549

Abstract

Violation of the Poisson regression assumption can cause the model formed will produce an unbiased estimator. There is a good method for estimating parameters on small sample sizes and on all distributions, namely the Bayesian method. The number of death from chronic Filariasis data violates the Poisson regression assumption, so it is modeled with the Bayesian Hurdle Poisson Regression. With the Bayesian method, convergence is fullfilled when 300000 iterations and 7 thin are performed. The results showed that in the logit model only one predictor variable had a significant effect on the number of cases of death due to chronic Filiariasis in 34 Provinces in Indonesia . The Truncated Poisson model resulted in all predictor variables having a significant effect on the number of cases of death due to chronic Filariasis.
Systematic Literature Review (SLR) on Annuity Modeling of Plantation Replanting Cost Reserves Based on the Cobb-Douglas Model Fasa, Rayyan Al Muddatstsir; Napitupulu, Herlina; Sukono, Sukono
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i1.25831

Abstract

Annuity is a financial concept that involves a series of periodic payments or receipts. In oil palm plantation management, the annuity concept is adapted to model and estimate the reserves required for replanting costs over time. The Cobb-Douglas model is a model that considers the contribution of various factors in the production process. This model can be used to estimate the income of plantations. This study discusses the Systematic Literature Review on Annuity Modeling of Plantation Replanting Cost Reserves through the application of the Cobb-Douglas Model using the Reporting Method of Choice for Systematic Review and Meta-Analysis (PRISMA) method. The study systematically collected and analyzed relevant literature from Scopus, Science Direct, Dimensions, and SAGE databases. The review followed a structured methodology that included four main stages: Identification, Screening, Eligibility, and Inclusion. Analysis was conducted on the datasets obtained at the Eligibility and Inclusion stages. Statistical techniques facilitated by the "bibliometrix" package in RStudio software were used to process the findings. In addition, the results can be accessed through the "biblioshiny ()" command, allowing easy access through a web interface for in-depth exploration. Based on the inclusion and exclusion criteria carried out in this study, it can be concluded that there is no research that discusses the topic of annuity modeling of plantation replanting cost reserves using the Cobb-Douglas model specifically. This can be further research on this topic. 
Determination of Term Life Insurance Premiums with Varying Interest Rates Following The CIR Model and Varying Benefits Value Puspita, Dian; Purnaba, I Gusti Putu; Lesmana, Donny Citra
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.20542

Abstract

Term life insurance is an insurance that provides protection for a certain period that has been agreed upon in the policy. The policy is an agreement that contains the participant's obligation to pay premiums contributions to the insurance company and the insurance company's obligation to pay benefits in the event of a risk to the insurance participant as agreed in the policy. Interest rates will influence the calculation of premium value and benefits in the long term. So we need a model of interest rates that will change by time. One of the models that can be used is the CIR model. This research purposes to simulate the CIR model that will be carried out to determine interest rates for calculating term life insurance premiums for five years, with premiums paid at the beginning of the 1/m interval or monthly premium payments and benefits paid at the end of the 1/m interval when the participant dies. The case that will be discussed is when the benefit various. The results of this study are the CIR model can be applied to calculate the term life insurance premiums for five years and the premium calculation results show that the amount of the premium increase every year with varying benefits.
Comparison between Statistical Approaches and Data Mining Algorithms for Outlier Detection Utami, Annisa Putri; Fitrianto, Anwar; Notodiputro, Khairil Anwar
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i1.25450

Abstract

Outliers are observation values that are very different from most observations. The presence of outliers in data can have a negative impact on research but can contain important information for other research. So, identifying outliers before conducting data analysis is a crucial thing to do. Outlier detection methods/techniques were first pioneered by researchers in statistics. However, due to rapid technological advances which have an impact on the ease of collecting extensive data, the development of outlier detection techniques is now handled mainly by researchers in the field of computer science (data mining) using computing facilities. This research aims to examine the results of simulation studies by comparing methods for identifying several outliers using statistical approaches and data mining algorithm approaches in various predetermined data scenarios. Based on the scenario carried out, the outlier detection method using a statistical approach is generally better than the outlier detection method using a data mining-based approach. Suggestions for further research are to improve the data mining method by focusing more on statistical analysis apart from focusing on data processing computing time so that the expected results of outlier detection are faster and more precise.

Page 27 of 44 | Total Record : 438

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