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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.
Arjuna Subject : -
Articles 540 Documents
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Implementation of Data Mining and Spatial Mapping in Determining National Food Security Clusterization Sifriyani, Sifriyani; Budiantara, I Nyoman; Mardianto, M. Fariz Fadillah; Febriyani, Eka Riche; Chairunnisa, Nurul Rizky; Putri, Asyifa Charmadya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.19912

Abstract

This study proposes a cluster analysis of provinces based on national food security data. The research objective is to determine provincial clusters based on food indicators which include rice harvest area, distribution of rice stocks, percentage of trade margin and transportation of rice distribution, percentage of average per capita expenditure, and total per capita consumption of rice. The source of observation data for the Rice Harvested Area by Province variable is the Ministry of Agriculture, Central Bureau of Statistics and Agriculture Services throughout Indonesia. This study uses data mining techniques in data processing with the K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break down data sets into several groups. The advantage of this method is that it can overcome the weakness of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the order in which the dataset is entered. The k-medoids clustering method can be applied to food security data by province. From grouping the data obtained three clusters, with silhouette coefficient values for cluster 1, cluster 2, and cluster 3 respectively 0.33; 0.32; and 0.44. With the largest silhouette coefficient value obtained in cluster 3 and the cluster has entered into a strong cluster structure. The research results can provide information to the government about food security grouping data in Indonesia which has an impact on the distribution and availability of food in Indonesia.
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. 
Forecasting Roof Tiles Production with Comparison of SMA and DMA Methods Based on n-th Ordo 2 and 4 Yel, Mesra Betty; Tundo, Tundo; Arinal, Veri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22225

Abstract

This research aims to predict roof tile production trends at one of the roof tile companies in Kebumen to assist company management in determining and providing management recommendations for the tile production that occurs. A comparison of Single Moving Average (SMA) and Double Moving Average (DMA) Forecasting methods was used to better accommodate trends in roof tile production data optimally. Where the forecast is presented for several steps ahead, and is equipped with a value measuring the accuracy of the forecast using Mean Absolute Percentage Error (MAPE), on roof tile production transaction data over 60 months, namely January-December 2019 to January-December 2023 to produce a monthly forecast for predicting roof tile production with n-th ordo 2 and 4. The total sample of training data processed was 1,415,987 records which were roof tile production transaction data, as well as data in January 2024 as test data (to test the accuracy of the forecast). The results of testing the forecast results produced a MAPE calculation of 6.6% for SMA with n-th ordo 2, while for n-th ordo 4 it was 7.2%. The MAPE value for DMA is 6.3% for n-th ordo 2, while for n-th ordo 4 it is 8.2%, which means the accuracy level is very good, namely above 90%. Based on the MAPE results obtained, the DMA method with n-th ordo 2 is a suitable method for carrying out periodic forecasting for roof tile companies in carrying out the production process to maintain stability and avoid unexpected events.
Identifying Factors Affecting Waste Generation in West Java in 2021 Using Spatial Regression Djuraidah, Anik; Rizki, Akbar; Alfan, Tony
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19664

Abstract

Responsible consumption and production is the 12th of the seventeen SDGs which is difficult for developing countries to achieve due to high waste production. Indonesia is the second largest producer of food waste in the world. Garbage is solid waste generated from community activities. Population density is an indicator to estimate the amount of waste generated in an area. The choice of West Java Province as the research area is based on the fact that this Province has the second highest population density in Indonesia. This study aimed to determine the predictors/factors that influence waste production in the districts/cities of West Java Province. The data used in this study are total waste as a response variable and GRDP (gross domestic product), total spending per capita, average length of schooling, literacy rate, number of MSMEs (micro, small, and medium enterprises), and several recreational and tourism places, the number of people's markets, and the number of restaurants as predictors. The methods used in this research are spatial autoregressive regression/SAR, spatial Lag-X/SLX, and spatial Durbin/SDM. The results of this study show that the SAR is the best model with the lowest BIC (74.442) and pseudo-R-squared (0.7934). Factors that significantly affect total waste production are literacy levels, the number of MSMEs, the number of traditional markets, and the number of recreational and tourist places. 
ARIMA Time Series Modeling with the Addition of Intervention and Outlier Factors on Inflation Rate in Indonesia Utami, Dewi Setyo; Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.17487

Abstract

Extreme events in a time series model can be detected when the precise timing of the event, known as the intervention, is known. When the exact timing of an event is unknown, it is referred to as an outlier.  If these factors are neglected, the model's accuracy will be affected. To overcome this situation, it is possible to add the intervention or outlier factor into the time series model. This study proposes the combination of intervention and outlier analysis in time series models, especially ARIMA. It is intended to minimize the residuals and increase the accuracy of the model so that it is suitable for forecasting. Using the data of inflation rate in Indonesia, the conflict between Russia and Ukraine was used as an intervention factor in this case. Pre-intervention data (before February 2022) is used to construct the ARIMA model (1st  model). After that, the modeling process continued by adding the intervention factor to the ARIMA model. The effect caused by the intervention allows an outlier to appear, so the process is continued by adding the outlier factor, called an additive outlier, into the model before (2nd model). The MAPE for the first and second models is 7.96% and 7.57%, respectively. The finding of this research shows that the ARIMA model with intervention and outlier factors, named as the 2nd model, is the best model. This study shows that combining the intervention and outlier factors into ARIMA model can improve the accuracy. The forecasting of the inflation rate in Indonesia for one period ahead in 2023 is in the range of 2.06%.
Improving the Accuracy of Discrepancies in Farmers' Purchasing and Selling Index Prediction by Incorporating Weather Factors Yulianti, Silvina Rosita; Effendie, Adhitya Ronnie; Susyanto, Nanang
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22584

Abstract

One measure that can be used to see the level of farmer welfare is the farmer exchange rate (NTP), which is a comparative calculation between the price index received by farmers (IJ) and the price index paid by farmers (IB), expressed as a percentage. In reality, NTP cannot explain the actual welfare situation of farmers because the ratio value has the potential to produce biased values. Another alternative that can be used to look at farmer welfare with less potential bias is to look at the difference between the sales index and the farmer purchasing index (ID). ID data forecasting can be a reference for developing and optimizing things that need to be improved in the agricultural sector. Despite the fact that a number of external factors, such as variations in the weather throughout the year, had a significant impact on the ID value, previous research used the ARIMA model to forecast without taking exogenous factors into account. Therefore, the goal of this research is to identify the optimal ARIMAX regression model for achieving accurate forecasting results with minimal error values. This research was carried out with limitations using data from the Central Statistics Agency and the Meteorological, Climatological, and Geophysical Agency in Central Java from 2008 to 2023. The first method in this research is to prepare the data, which involved collecting secondary data such as IJ and IB along with climate data such as rainfall, duration of sunlight, air pressure, wind speed, and rice prices. Next, calculate the difference between IJ and IB to determine the ID value. Then, verify the ID data's stationarity and perform AR and MA calculations. After determining the AR and MA values, construct an ARIMAX model that incorporates external factors, search for the optimal model, and utilize the optimal model to make future predictions. The results show that the accuracy of the ARIMAX model (1,1,0) has a better value than the accuracy of the ARIMA model (1,1,0), and the results obtained in this study are better than previous studies. The authors hope that the findings of this research will serve as a benchmark for the forecasting analysis of time series data in the agricultural sector, providing the local government with a foundation for policy decisions.
Exploring the Characteristics of Digital Pedagogy Model for Developing Computational Thinking in Mathematical Problem Solving Anwar, Vita Nova; Darhim, Darhim; Suhendra, Suhendra; Nurlaelah, Elah
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.17419

Abstract

Challenges in the 21st century are increasingly complex, technology is developing rapidly and competition is getting tougher. Therefore we need quality human resources that can keep up with and anticipate the times. The use of technology involves computational thinking (CT) skills which are closely related to the problem-solving process. The stages in computational thinking are part of mathematical thinking, meaning that learning mathematics can support students' CT skills. Through the development of digital pedagogical models in CT integrated mathematics learning, it can improve problem-solving skills. This research uses  design based implementation research with 4 phases including; preliminary research, prototyping, results, and design principle. The participants were 28 grade 8 junior high school students who took part in two rounds of experiment in direct CT activities and digital CT activities. In this paper, we present an iterative mathematical problem-solving process in the digital pedagogy model. The computational task, environment, tool and practices were iteratively improved over two rounds to incorporate CT effectively in mathematics. The results from CT environment demonstrated that direct CT activities are more effective than digital CT activities in mathematical problem-solving.  Based on empirical research, we summarize the characteristic of the digital pedagogy model from computational tasks, computational environment and tools, and computational practices in mathematical problem solving.
Analysis of Multi-Input ARIMA Interventions with Additive Outlier for Forecasting Price of Crude Oil West Texas Intermediate Nabil, Ilhan Nail; Satyahadewi, Neva; Huda, Nur'ainul Miftahul
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22147

Abstract

Crude oil is a liquid characterized by a thick texture and blackish color. It is composed of complex hydrocarbon compounds with various benefits that are spread around the world. Crude oil derived from fossil fuels can be used as primary fuels, such as gasoline, and is the most important of the energy resources. Based on that, crude oil play a crucial role in the global economy movement because can be used as the main sources of energy all over the world. However, one of the benchmarks for crude oil from the USA is West Texas Intermediate (WTI). Known to produce high-quality oil, the price of crude oil of WTI fluctuates. In addition, fluctuations occur because of several factors, such as the availability of oil supplies, the embargo on oil imports, and the COVID-19 pandemic. The research aims to analyze price forecasting that occurs over the next five months and the accuracy level of the method used. The data that exists outliers is usually removed from forecasting that contains outliers, but that can affect the estimation result in the model. So, in this research intervention and outlier factors are added to the research to overcome the constraints In this study, the Multi-Input ARIMA Intervention and Additive Outlier (AO) method are used by modelling ARIMA pre-intervention and then. After that, the procedure is adding intervention factorsand additive outlier with iterative procedures. Multi-Input ARIMA Intervention and Additive Outlier (AO) are used to determine the magnitude of fluctuations that occur. Data shocks causing outlier data can be used by adding AO factors. WTI oil price data was retrieved from investing.com with monthly data from January 2011 to June 2023. Based on the results of Mmulti-Iinput ARIMA intervention with Additive Outlier method, it has been determined that the movement of WTI oil prices in the next five months will increase compared to the last five periods of actual data. Because of incrased price of crude oil, it will impact of the economic growth all over the world. So, the government better controlled the price of crude oil at lower price. . withMulti-Input ARIMA interventions resulting in AIC, MAPE, and RMSE model each 941.490, 6.979%, and 5.913 . So, Multi-Input and AO proven can be used to forecast data with fluctuate that data occur. 
Structural Equation Modeling: The Influence of School Environment on Students' Interest in Selecting State University Simarmata, Justin Eduardo; Mone, Ferdinandus; Chrisinta, Debora
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19921

Abstract

Despite prior research on student interest in colleges, this study focuses specifically on how the school environment, including individual factors, friends, and teachers, influences students' interest in attending a state university. Understanding these influences can help improve educational systems to better guide students towards higher education. This study aims to determine the influence of the school environment on students' interest in selecting a state university. This research employs a quantitative approach, utilizing structural equation modeling to analyze the relationships between variables. This study examines how a school environment, captured by twelve indicators across individual, friend, and teacher influences, impacts students' interest in state universities. Therefore, data retrieval based on questionnaires is designed accordingly based on latent variables. The sample in this study were 474 high school and vocational school students on the Indonesia-Timor Leste border, which is precisely located in Timor Tengah Utara Regency. The results showed that the school environment that came from individuals, partners, and teachers had a major influence on students' interest in choosing State University. Based on the analysis of structural equations, it was found that individual environments had a direct influence of 98%, partner environments had an indirect influence of 90%, and teachers also indirectly affected 71%. This study contributes to the field by quantifying the distinct influences of individual, peer, and teacher aspects of the school environment on students' interest in attending state universities. This knowledge can inform the development of targeted interventions to improve educational guidance and support student decision-making.
Partial Fourier Transform Method for Solution Formula of Stokes Equation with Robin Boundary Condition in Half-space Maryani, Sri; Suhada, Dede Bagus; Guswanto, Bambang Hendriya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.16917

Abstract

The area of applied science known as fluid dynamics studied how gases and liquids moved. The motion of the fluid in the liquid and vapour phases is described by a special system of partial differential equations. The research purpose of this article investigated the solution formula of incompressible Stokes equation with the Robin boundary condition in half-space case. The solution formula for Stokes equation was calculated using the partial Fourier transform. This calculation was carried out over the Weis’s multipliers theorem. Our calculation showed that the solution formula of Stokes equation with Robin boundary condition in half-space for velocity and pressure were contained multipliers as due to work Shibata & Shimada. Due to our consideration of the half-space situation, the partial Fourier transform approach is the most appropriate one to use to get the velocity and pressure for the Stokes equation with Robin boundary condition. Furthermore, research methods in this article, in the first stage, we use the resolvent problem of the model. Secondly, we apply the partial Fourier transform to the model problem and finally, we use inverse partial Fourier transform to get the solution formula of the incompressible type of Stokes equation for velocity and pressure. This result indicates that Weis' multiplier theorem also allows us to find the local well-posedness of the model problem in addition to the maximal Lp-Lq regularity class (Gerard-Varet et al., 2020).

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