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JTAM (Jurnal Teori dan Aplikasi Matematika)
Published by Universitas Muhammadiyah Mataram
ISSN : 25977512     EISSN : 26141175     DOI : 10.31764/jtam
Core Subject : Education,
Mathematics
Jurnal Teori dan Aplikasi Matematika (JTAM) dikelola oleh Program Studi Pendidikan Matematika FKIP Universitas Muhammadiyah Mataram dengan ISSN (Cetak) 2597-7512 dan ISSN (Online) 2614-1175. Tim Redaksi menerima hasil penelitian, pemikiran, dan kajian tentang (1) Pengembangan metode atau model pembelajaran matematika di sekolah dasar sampai perguruan tinggi berbasis pendekatan konstruktivis (PMRI/RME, PBL, CTL, dan sebagainya), (2) Pengembangan media pembelajaran matematika berbasis ICT dan Non-ICT, dan (3) Penelitian atau pengembangan/design research di bidang pendidikan matematika, statistika, analisis matematika, komputasi matematika, dan matematika terapan.
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Articles 25 Documents
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Search results for , issue "Vol 8, No 4 (2024): October" : 25 Documents clear
A Comparison of Welch Powell Algorithm and Greedy Algorithm in Odd Semester Lecture Room Scheduling Optimization Faculty of Computer Science Fadilah, Alif Nur; Subarkah, Pungkas; Pramudya, Reyvaldo Shiva; Syabani, Amin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.23142

Abstract

Scheduling is a systematic method to optimize work time, and avoid failure when problems occur. Scheduling is widely applied in the world of education, one of which is in preparing course schedules. Scheduling itself needs to be optimized to ensure a smooth lecture process without any problems between courses. As happened at the Faculty of Computer Science, Amikom Purwokerto University, where in the preparation of the schedule there is no information about lecture rooms. Therefore, the author compiled a lecture hall scheduling optimization journal by comparing the performance between the Welch Powell Algorithm and the Greedy Algorithm as optimization and graph coloring on the lecture hall schedule.The data used in this study are 88 courses spread across 3 study programs, namely Informatics Study Program, Information Systems Study Program, and Informatics Engineering Study Program. This research uses a comparative method on graph vertex coloring, where execution time as duration, lines as algorithm complexity, and manual algorithm calculation as parameters. Based on the research that has been done, the results of 14 full spectrum colors are obtained which are then applied to 23 lecture rooms that can be used without clashes at the Faculty of Computer Science. This can minimize the possibility of overlapping room usage between courses. In addition to comparing the performance of the Welch-Powell Algorithm and the Greedy Algorithm to produce optimal scheduling of lecture rooms, this research can also optimize the schedule of lecturers when entering class to optimize students to be more organized in entering lecture classes at the Faculty of Computer Science, Amikom Purwokerto University.
Price Model with Generalized Wiener Process for Life Insurance Company Portfolio Optimization using Mean Absolute Deviation Putra, Hilman Yusupi Dwi; Silalahi, Bib Paruhum; Budiarti, Retno
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.23093

Abstract

The Financial Services Authority (OJK) has issued Regulation of the Financial Services Authority of the Republic of Indonesia Number 5 Year 2023. Article 11 paragraph 1d explains the limitations of assets allowed in the form of investment, investment in the form of shares listed on the stock exchange for each issuer is a maximum of 10% of the total investment and a maximum of 40% of the total investment. The investment manager of a life insurance company needs to adjust its investment portfolio. In 1991, Konno and Yamazaki proposed an approach to the portfolio selection problem with Mean Absolute Deviation (MAD) model. This model can be solved using linear programming, effectively solving high-dimensional portfolio optimization problems. Another problem in stock portfolio formation is that the ever-changing financial markets demand the development of models to understand and forecast stock price behavior. One method that has been widely used to model stock price movements is the generalized Wiener Process. The generalized Wiener process provides a framework that can accommodate the stochastic nature of stock price changes, thus allowing portfolio managers to be more sensitive to unanticipated market fluctuations. The stock price change model using the Generalized Wiener Process is very good at predicting stock price changes. The results of this stock price prediction can then be used to find the optimal portfolio using the MAD model. The portfolio optimization problem with the MAD model can be solved using linear programming to obtain the optimal stock portfolio for life insurance companies. 
Comparative Analysis of Decision Tree and Random Forest Algorithms for Diabetes Prediction Fadhlullah, Aufar Faiq; Widiyaningtyas, Triyanna
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24388

Abstract

Diabetes Mellitus is a long-term medical disorder marked by high blood glucose levels that raise the risk of early mortality and organ failure. It has become an increasing global health problem, so making an accurate and timely diagnosis is urgently necessary. This study aims to diagnose people with diabetes mellitus by utilizing prediction techniques in data mining using experimental research. The prediction stage for diagnosing diabetes consists of four stages: dataset collection, data pre-processing, data processing, and evaluation. Data was obtained from Electronic Health Records (EHRs), namely the public "Diabetes Prediction Dataset". The pre-processing stage involves data filtering, attribute conversion, and class selection. The data processing utilizes random forests and decision tree models for diabetes prediction. The models were evaluated using accuracy, precision, and recall metrics. The results showed that the Random Forest algorithm produced an accuracy value of 93.97%, precision of 99.88%, and recall of 66.56%, with a computational time of 16s. Meanwhile, the decision tree algorithm produces an accuracy value of 93.89%, precision of 98.73%, and recall of 66.88%, with a computation time of less than 1s. Based on these results, it can be concluded that the Decision Tree algorithm is more effective because the difference in accuracy, precision, and recall values produced by the two algorithms does not have significant differences. However, the Decision Tree algorithm has the advantage of using computational time more effectively, which is needed in detecting diabetes because it is related to someone's life. 
Mathematical Model of COVID-19 Spread with Vaccination in Mataram City Hattamurrahman, Muhammad Putra Sani; Sianturi, Paian; Sumarno, Hadi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.23113

Abstract

The COVID-19 pandemic has had a significant impact on public health worldwide.. Mathematical modeling is considered an alternative tool for understanding real-life problems, including the dynamics of COVID-19 spread. This is an applied research that purpose adds vaccination to Zeb et al. (2020) SEIQR model of COVID-19 spread and examines the dynamic of COVID-19 spread in Mataram City. First, we construct the new model by making assumptions. The fixed point and basic reproduction number (R_0 ) are then used to analyze the model using the next-generation matrix method. The next-generation matrix method is utilized to estimate the R_0 in a compartmental disease model. Two fixed points are acquired, specifically the disease-free fixed point, which is locally asymptotically stable under the condition R_0<1 determined by the Routh Hurwitz criterion via linearization using the Jacobi matrix. And the disease-endemic fixed point, which is locally asymptotically stable under the condition R_0>1 indicated by Lyapunov function. The population dynamics when R_0<1 and R_0>1 can also be observed through numerical simulation. The results of a numerical simulation indicate that giving the proportion of number vaccinated 62 per cent is effective in suppressing the number of infections. 
Forecasting Beef Production with Comparison of Linear Regression and DMA Methods Based on n-th Ordo 3 Tundo, Tundo; Yel, Mesra Betty; Nugroho, Agung Yuliyanto
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24706

Abstract

Beef is considered a high-value commodity because it is an important food source of protein. Interest in beef is increasing along with increasing people's incomes and awareness of the importance of fulfilling nutrition. Demand for beef is expected to continue to increase. According to the Central Statistics Agency (CSA), beef production in Jakarta shows an increasing trend every year. In the last 10 years, beef production has increased significantly, but in 2020 there was a decrease in production of 7,240.68 tons due to the lockdown due to the corona virus outbreak. After that, in 2021, production reached 16,381.81 tons and will continue to increase in 2022 and 2023. Based on the above phenomenon, the aim of this research is to support the success and sustainability of the beef industry by ensuring that supply matches demand, resources are used optimally, and risks can be managed well. To predict beef production, an accurate method, model or approach is needed. One way to predict beef production in Jakarta is to use the Linear Regression and Double Moving Average (DMA) methodsThe way the Linear Regression and DMA methods work is to forecast based on concepts and properties. The concepts and properties of Linear Regression are models, functions, estimates and forecasting results, while DMA performs time series analysis based on moving averages. After analysis using MAPE, it was found that the algorithm that had the smallest error value was the linear regression algorithm with a percentage for the monthly period of 15% while for the year period it was 17% compared to DMA. So in this case it would be very appropriate to use the Linear Regression method from the error values obtained.
Structural Equation Modeling Semiparametric in Modeling the Accuracy of Payment Time for Customers of Credit Bank in Indonesia Junainto, Fachira Haneinanda; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun; Hamdan, Rosita Binti
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.23668

Abstract

Credit risk assessment is crucial for financial institutions to ensure loan repayment. To enhance the prediction accuracy of creditworthiness and timely repayment, this research employs semiparametric structural equation modeling (SEM) to analyze the factors influencing credit repayment timeliness. The research was conducted to apply semiparametric SEM modeling to the timeliness of paying credit. Semiparametric SEM is structural modeling in which two combined approaches of parametric and nonparametric approaches are used. The analysis method in this research is semiparametric SEM with a nonparametric approach using a truncated spline. Truncated splines are chosen for their flexibility, ability to model complex relationships, continuity, interpretability, and strong performance in nonparametric regression tasks. The data in the study were obtained through questionnaires distributed to Bank X mortgage debtors and are confidential. The quetionnairs in the Likert scale, with five options. The study used 3 variables consisting of one exogenous variable, one intervening endogenous variable, and one endogenous variable. The results showed that: (1) the effect of capacity and willingness to pay variables on timeliness of payment is significant; (2) modeling the capacity variable on willingness to pay also produces a significant estimate; (3) the effect of the capacity variable on the timeliness of payment variable is not influenced by the willingness to pay variable as an intervening variable; and (4) the R^2 value of 0.763 or 76.33% indicates that the model has good predictive relevance. To continue to develop punctuality of paying credit, banks need to pay attention to the financial stability of consumers. Besides the financial stability, banks should pay attention to the sense of responsibility that customers have.
Forecasting Blood Availability in Pontianak City using ARIMA Models to Optimize Inventory Planning at UTD PMI Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Mauditia, Lyra
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24789

Abstract

It is of utmost importance to control the blood supply in UTD PMI because if there is a requirement for blood, PMI can fulfill the necessary blood needs and keep the ideal blood availability. PMI UTD may encounter a shortfall of blood supply if increases in blood demand are not supported by an increase in the number of donors contributing blood. A forecast of the number of blood requests is essential to estimate the quantity of blood that is necessary and the number of blood donors that are required to be prepared to fulfill the needed blood requests. This study is a quantitative investigation that use the Autoregressive Integrated Moving Average (ARIMA) method in order to provide an accurate prediction regarding the quantity of blood that is required for each blood type in Pontianak City. UTD PMI Pontianak City provided the information that was used in this study. The information that was used included information on the number of blood requests for blood types A, AB, B, and O. Following this, the data was subjected to three iterative steps of Box Jenkins analysis, which included order identification, parameter estimation, and diagnostic testing. The goal was to obtain the most accurate model, which was then utilised to forecast the quantity of blood demand that will occur in the subsequent periods. Furthermore, the findings of this investigation indicate that the ARIMA (2,0,0), ARIMA (3,0,3), ARIMA (1,0,2), and ARIMA (1,0,0) models are the most accurate models for predicting the availability of blood categories A, AB, B, and O. ..UTD Pontianak City is anticipated to be able to manufacture bloodstock consisting of 73 blood bags over the next five days. The bloodstock will include 19 bags of Group A, 6 bags of Group AB, 22 bags of Group B, and 6 bags of Group O specifics. In light of the forecast results, it is envisaged that UTD PMI will be able to maximize inventory planning for blood in Pontianak City to reduce the number of instances in which there are shortages of blood availability.
Selection Dominant Features Using Principal Component Analysis for Predictive Maintenance of Heave Engines Trihandaru, Suryasatriya; Parhusip, Hanna Arini; Heriadi, Adrianus Herry; Santosa, Petrus Priyo; Sardjono, Yohanes; Lea, Lea
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.22854

Abstract

This article aims to identify the dominant features that have a significant impact on the health of a heavy machine that relates to the digital infrastructure of a company. The importance of this research is that the authors define predictive maintenance based on Principal Component Analysis (PCA), which is the novelty of this article. The novel contribution of this research lies in the application of Principal Component Analysis (PCA) for predictive maintenance of heavy machinery, which has not been integrated into the Scheduled Oil Sampling (SOS) procedures. The recorded data are called Scheduled Oil Sampling (SOS) and historical data from an equipment called CoreDataQ, which works for recording many features from heavy machine activities. The data contain two sets data. The method is Principal Component Analysis (PCA). This method leads to obtain a maximum of 20 significant features on data based on SOS. The results have been confirmed and agreed upon by the manager who owned CoreDataQ to consider the selected dominant features for further related maintenance. 
Improvement of Real-GJR Model using Jump Variables on High Frequency Data Nugroho, Didit Budi; Wulandari, Nadya Putri; Alfagustina, Yumita Cristin; Parhusip, Hanna Arini; Tita, Faldy; Susanto, Bambang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24294

Abstract

Volatility is a key indicator in assessing risk when making investment decisions. In the world of financial markets, volatility reflects the degree to which the value of a financial asset fluctuates over a given period. The most common way to measure the future loss potential of an investment is through volatility. Focusing on the Realized GJR (RealGJR) volatility model, which consists of return, conditional volatility, and measurement equations, this study proposes the RealGJR-CJ model developed by decomposing the exogenous variable in the volatility equation of RealGJR into continuous C and discontinuous (jump) J variables. The decomposition of exogenous variables makes the RealGJR-CJ model follow realistic financial markets, where the asset volatility is a continuous process with some jump components. As an empirical illustration, the models are applied to an index in the Japanese stock market, namely Tokyo Stock Price Index, covering from January 2004 to December 2011. The observed exogenous variable in the volatility equation of RealGJR models is Realized Volatility (RV), which is calculated using intraday data with time intervals of 1 and 5 minutes. Adaptive Random Walk Metropolis method was employed in Markov Chain Monte Carlo algorithm to estimate the model parameters by updating the parameters during sampling based on previous samples from the chain. From the results of running the MCMC algorithm 20 times, the mean of the information criteria of competing models is significantly different based on standard deviation and the result suggests that the model with continuous and jump variables can improve the model without jump. The best fit model is provided by RealGJR-CJ with the adoption of 1-minute RV data. 
Modeling the Dynamics of Forest Fires: A Vector Autoregressive Approach Across Three Fire Classifications Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 4 (2024): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i4.24792

Abstract

The problem of forest fires is one that, with each passing year, gets more difficult to mitigate. A significant number of people will be affected by this case, particularly in terms of their health. The need for targeted initiatives must be balanced. Look at the forecasts for the number of forest fires expected to occur in the following period. Cases of forest fires reported to the Ministry of Environment and Forestry are categorized into three distinct categories: high, medium, and low. In addition to future estimates, it is reasonable to anticipate that classifications will also affect one another. The vector autoregressive (VAR) model is a statistical tool that may produce future projections based on three categories of forest fires in a specific period. This information can be utilized to make predictions. The aim of the study was to model 3 classifications of forest fire cases using the Vector Autoregressive (VAR) model. The data utilized is a summary of the number of forest fire cases in Pulang Pisau Regency, Central Kalimantan, categorized as low, medium, and high, from January 2013 to March 2024. During this study, the VAR modelling process was broken down into three primary stages: order identification (the findings that were achieved were VAR(4)), parameter estimation, and diagnostic testing (VAR(4) was declared to fulfil the requirements for the diagnostic test). It is possible to generate a predicted value for the subsequent three times based on these stages, which may be considered when calculating the proper amount of effort to put forward. The accuracy of forest fire case modeling utilizing the VAR(4) model is 70.23%. Moreover, the predictive outcomes for each categorization indicate a rise in medium and low-level forest fires compared to previous data, although the contrary is observed for high-level forest fire incidents.

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