cover
Contact Name
Elvina Herawati
Contact Email
elvina@usu.ac.id
Phone
-
Journal Mail Official
jormtt@usu.ac.id
Editorial Address
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Sumatera Utara Building 1st, Floor 2nd, Jalan Bioteknologi No. 1, Kampus USU Padang Bulan Medan 20155, Indonesia
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Research in Mathematics Trends and Technology
ISSN : -     EISSN : 26561514     DOI : https://doi.org/10.32734
Core Subject : Science, Education,
JoRMTT is an international blind peer-review journal dedicated to interchange for the results of research in mathematical sciences and related fields. The journal publishes papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the mathematics trends and technology, the JoRMTT follows the open access policy that allows the published articles freely available online without any subscription. The journal scopes include (but not limited to) the followings: - Numerical Analysis - Mathematical Physics - Probabiliy Theory and Stochastic Processes - Quantitative Finance - Algebra - Mathematics Education - Analysis - Dynamical System and Differential Equations - Geometry and Topology - Operator Theory - Combinatorics and Graph Theory - Mathematical Computer Sciences - Optimization and Approximation Theory - Game Theory
Articles 64 Documents
Efficiency of Environmental Performance Measurement Using Data Envelopment Analysis (DEA) With Fuzzy Approach: English Nadhila, Nurul; Tulus; Mardiningsih
Journal of Research in Mathematics Trends and Technology Vol. 7 No. 2 (2025): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v7i2.21717

Abstract

This study aims to analyze and measure environmental performance efficiency in residential areas in Medan City using the Data Envelopment Analysis (DEA) method combined with a fuzzy approach. Environmental performance is measured based on relevant inputs and outputs, where the data used are secondary data obtained from previous research reports. Fuzzification is applied to address uncertainty in the data by converting input and output values into Triangular Fuzzy Numbers (TFN). The results of the study show that out of the 4 Decision Making Units (DMUs) studied without using the fuzzy approach, only the Helvetia housing complex achieved 100% efficiency. Meanwhile, the efficiency values for Tuntungan, Martubung, and Johor housing estates were 85.92%, 88.69%, and 94.87%, respectively. When the fuzzy approach was applied, the efficiency values of the Johor and Helvetia housing estates reached 100% efficiency, while the Tuntungan and Martubung housing estates had an efficiency of 78.22%, indicating inefficiency. This inefficiency is caused by excessive use of drainage inputs, indicating that these housing complexes are unable to produce environmental outputs commensurate with the inputs used. This study recommends improving the quality and quantity of environmental management and cleanliness, as well as the availability and quality of green spaces, to enhance environmental efficiency in housing. The findings of this study provide important insights into the efficiency of environmental performance measurement and highlight opportunities for improvement in environmental management within housing.
Adjusting Anomalies in International Tourist Arrivals to North Sumatra During the Peak COVID-19 Period (April 2020 to June 2022) to Enhance the Validity of Time Series Modeling Eddy, Thaswin; Open Darnius
Journal of Research in Mathematics Trends and Technology Vol. 7 No. 2 (2025): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v7i2.21718

Abstract

The feasibility of time series modeling is significantly influenced by both the availability and the structural patterns of the data. Regular and continuous data collection over time is essential for constructing reliable time series models, particularly for forecasting purposes. Generally, a minimum of 50 time series data points is considered ideal to ensure the robustness and predictive power of such models. However, the presence of extreme fluctuations—such as sharp spikes or drops—can severely affect the integrity of the model. In the context of international tourist arrivals to North Sumatra during the peak period of the COVID-19 pandemic (April 2020 to June 2022), substantial data anomalies were observed. The results of modifying these anomalies indicate that increasing the number of adjusted data points during this period leads to a greater number of feasible time series models suitable for predictive analysis.
Analysis of Rainfall Transition Probability Using Markov Chain Method Pasaribu, Suhendri; Suwilo, Saib; Mawengkang, Herman
Journal of Research in Mathematics Trends and Technology Vol. 7 No. 2 (2025): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v7i2.21719

Abstract

This research applies the Markov Chain model to examine daily rainfall data in Medan City. Markov chain is one of the methods used for forecasting in various fields, such as economics, industry, and climate. This research uses secondary data of daily rainfall intensity from the BMKG Station of the Center for Meteorology, Climatology and Geophysics Region I. The purpose of this research is to determine the transition probability (probability of transition). This study aims to determine the chance of transition (displacement) of daily rainfall intensity, There are four conditions of rainfall intensity that are categorized, namely no rain, light rain, moderate rain, and heavy rain. The Markov Chain method used is the Champman- Kolmogorov Equation and the steady state equation. The fixed probability of not raining is 59.16%, the fixed probability of light rain is 17.67%, the fixed probability of moderate rain is 16.28%, and the fixed probability of heavy rain is 6.86%.
Fuzzy Logic in Education: Profile of Students’ Readiness to Prepare for Test-Based National Selection in Study Centers Dwi Aldi Hidayatulloh; Sikky El Walida; Sandha Soemantri; Abdur Rohim
Journal of Research in Mathematics Trends and Technology Vol. 7 No. 2 (2025): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v7i2.22107

Abstract

Test-Based National Selection demands students' readiness not only in material mastery, but also in critical thinking skills and high-level problem solving. Tutoring institutions have become a popular choice to improve students' readiness to face the selection, but evaluating students' readiness objectively and adaptively is still a challenge. This research develops a decision support system model based on Mamdani type fuzzy inference system to evaluate students' readiness for Test-Based National Selection. Two main indicators are used as linguistic input variables, namely study frequency and try out results. The modeling process is carried out qualitatively with the stages of fuzzification, IF-THEN rule base formulation, Mamdani inference, and defuzzification using the centroid method. Data is processed with the help of Microsoft Excel as a fuzzy logic processing tool. The results of the implementation on 30 students showed that the system was able to classify the level of readiness into three categories: not ready, moderately ready, and ready, with high precision and flexibility to data uncertainty. The findings suggest that fuzzy models can be used as adaptive and contextualized evaluation tools in tutoring environments, and support data-driven instructional decision-making.