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INDONESIA
Journal of Mathematics and Applied Statistics
Published by Yayasan Insan Literasi Cendekia Indonesia
ISSN : -     EISSN : 29874548     DOI : -
Core Subject : Education,
Mathematics
Journal of Mathematics and Applied Statistics (ISSN: 2987-4548) is scientific, peer-reviewed, and open access journal managed by Yayasan Insan Literasi Cendekia (INLIC) Indonesia. Published twice a year on June and December. Mathstat publishes original research and/or library analysis on Mathematics and Statistics. This journal is useful to researchers, engineers, scientists, teachers, managers and students who are interested in keeping a track of original research and development work being carried out in the broad area of Mathematics and Statistics.
Arjuna Subject : Matematika - Matematika (Lain-Lain)
Articles 25 Documents
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Mathematical Model of Love Dynamics in the Story of the Romantic Legend of the Mandar Tribe Rifandi, Muhammad; Apriyanto, Apriyanto; Alnisari, Alnisari
Journal of Mathematics and Applied Statistics Vol. 2 No. 1 (2024): June 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i1.153

Abstract

In this study, the mathematical model of love is discussed based on the legend of the Mandar tribe area in West Sulawesi Province. Love is an active action / activity carried out by humans towards other objects, in the form of self-sacrifice, empathy, attention, affection, help, obey words, follow, obey, and want to do whatever the object wants. This study aims to determine the mathematical model for the dynamics of love in the story of two lovebirds contained in the legend of Mandar and determine the equilibrium point and analyze the stability of the mathematical model of their love dynamics so that simulations and interpretations of mathematical models of dynamics can be carried out. Based on the model obtained, it can be known how the dynamics of love that occur between kaco and cicci based on several possible cases that can occur between the two of them. By using maple software, an equilibrium point is obtained consisting of two types of equilibrium points, namely the equilibrium point of the mathematical model of love if there is no struggle from kaco  ' and the equilibrium point of the mathematical model of love if there is a struggle from kaco'.
The Effect of Problem-Based Learning on Students’ Mathematics Problem Solving Ability Yulyana, Yulyana; Hamid, Hariaty; Anwar, Azwar
Journal of Mathematics and Applied Statistics Vol. 2 No. 1 (2024): June 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i1.167

Abstract

The purpose of this study was to determine the effect of problem-based learning on students' mathematics problem solving ability at SMA Negeri 1 Tanjung Palas Tengah. This research is an experimental research with pretest- posttest control group design. The population in this study were all grade XI students at SMA Negeri 1 Tanjung Palas Tengah. The sample was drawn using cluster random sampling technique and the research sample was obtained, namely class XI IPA 1 with a total of 15 students as an experimental class and class XI IPA 2 with a total of 15 students as a control class. The data collection technique in this study was a written test. Data analysis in this study used descriptive analysis, namely the average and standard deviation and prerequisite tests, namely normality test and homogeneity test for initial data and hypothesis testing using independent sample t-test statistical test. The results of the descriptive analysis showed that the average pretest score in the experimental class was greater than the average pretest score in the control class, which was 59.87 in the experimental class while in the control class it was 52.87. While the posttest value in the experimental class was 79.93, while in the control class it was 65.33. Based on the results of the independent sample t-test analysis with a significant level of 5% (α = 0.05). Thus H0 is rejected and H1 is accepted, so it can be concluded that the problem-based learning model affects the problem solving ability of class XI students of SMA Negeri 1 Tanjung Palas Tengah.
Multivariate Analysis of the Attributes of a Central Attacking Midfielder Nwokike, Innocent Chukwudozie; Ngozie, Erumaka Ephraim; Chrysogonus, Nwaigwe; Chuks, Obi Martin
Journal of Mathematics and Applied Statistics Vol. 2 No. 1 (2024): June 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i1.179

Abstract

Central attacking midfielders are one of the most vital squad members because of their decisive contributions to the team in the field of play. With teams now acquiring some talented players for as much as 103 million euros, it has become more important than ever to critically investigate the attributes of a central attacking midfielder. This study is designed to investigate the correlation between attributes (variables) such as crossing, finishing, short passing, volleys, dribbling, curve, long passing, ball control, acceleration, sprint speed, agility, stamina, vision, penalties, and composure, using multivariate analysis techniques. The study used canonical correlation analysis (CCA) to analyze these correlations. The study produced three statistically significant canonical functions with canonical correlations of , , and , respectively. The study also showed that canonical function 1 showed more significance (Wilks's and ). A principal component analysis (PCA) was conducted to determine the most important variables for a central attacking midfielder. The PCA generated four components. The first component showed itself to be optimal, with variables such as short passing, long passing, vision, composure, and ball control highlighted as the most relevant attributes of a central attacking midfielder. The study also developed a dynamical system that can be analyzed to understand the efficiency of the central attacking midfielder with respect to the given variables and parameters.
A Stochastic Modeling on Mixture Distribution with Application to Using Cancer Survival Data Sakthivel, M.; Pandiyan, P.
Journal of Mathematics and Applied Statistics Vol. 2 No. 1 (2024): June 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i1.180

Abstract

In this paper, specific statistical considerations are typically required, in order to select the best model for fitting survival data. The proposed the new mixture of Gamma and Shanker Distribution (MGSD), so named because it specifically mixes of two distributions: Shanker and gamma. There is also Reliability Analysis, statistical features such as stochastic ordering, moments, order statistics, entropy, and the Maximum Likelihood Estimation of the model parameters estimating. Lastly, a two real cancer data set is used to demonstrate the use of the AIC, BIC, and AICC model selection methods. It is compared with the fit and shows that the (MGS) distribution is more flexible than the other distributions.
Opportunities and Strategies for Accelerating Digital Transformation: Spatial Distribution Analysis and Clustering Using K-Medoids Method In South Sulawesi, Indonesia Idris, Aditya; Susilawati, Sumarni
Journal of Mathematics and Applied Statistics Vol. 2 No. 1 (2024): June 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i1.188

Abstract

The increase in internet usage among the population in South Sulawesi prsesents a golden opportunity for accelerating digital transformation efforts. This can be leveraged to boost economic growth, particularly by maximizing the key sectors in South Sulawesi. The aim of this study is to analyze the spatial distrubution and perform clustering of the regencies/cities in South Sulawesi based on digital transformation indivators, as well as to determine the multiplier effect on other regions in Sulampua. The research method employed is quantitative, utilizing descriptive analysis and clustering analysis of regencies/cities in South Sulawesi with the K-Medoids method. The results show that spatially, the distribution of internet users, e-commerce users and SMEs using the internet have quite varied patterns and reveal a high disparity in service users among regencies/cities in South Sulawesi. Meanwhile, the clustering results indicate that the regencies/cities in South Sulawesi are balanced between cluster 1 and cluster 2, each consisting of 12 regencies/cities. It was found that cluster 2, which includes regencies and cities in South Sulawesi, has better variable values in almost all variables comparated to the regencies/cities in cluster 1. Generally, cluster 2 dominates all the variables used, while cluster 1 is only superior in the variable of the number of SMEs. The suggestions and policy recommendations for the government based on this study on the acceleration of digital transformation with spatial distribution and clustering analysis can help create on environment that supports sustainable digital economic growth. With coordinated and collaborative policy strategies, South Sulawesi can achieve significant acceration in digital transformation. 
The Importance of Integrating Observation Data as Input to Weather Models in Europe and Indonesia During-Early the Covid-19 Pandemic Giarno, Giarno; Muflihah, Muflihah; Manuhung, Suparman
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.195

Abstract

The Covid-19 pandemic that occurred throughout the world caused tremendous losses and reduced the intensity of human activities, especially in cities that had the potential to reduce the number of pollutants in the atmosphere. The purpose of this article is to see the relationship between pollutant fluctuations with weather factors and the reliability of weather models. Elaboration of pollutant data, weather models, and government policies is used to see the extent to which a decrease in pollutant concentration is detected by a weather model. The results obtained show that a decrease in pollutant intensity is very visible in almost all European cities, but for Indonesia, a significant decrease is only in Jakarta and Tangerang, while in Semarang City an increase has been detected. Sophisticated weather modeling can be used to monitor pollutant fluctuations, but this model still requires field observation data. The same condition certainly applies to other parameters, especially maritime observations for which data have not been obtained.
Trigonometric Finger Learning Media to Improve Mathematics Learning Outcomes of High School Students Bilqis, Sayyidah; Aswad A, Muhammad Hajarul; T, Irma
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.212

Abstract

Learning media in mathematics education is one of the determining factors for students' learning outcomes in mathematics. This study developed the Trigonometric Finger Learning Media (Jargonometry) to improve the mathematics learning outcomes of high school students. The research design an R&D approach using the ADDIE model. The study was conducted at a high school in East Luwu Regency, with Class X.8 from the 2023/2024 academic year as the research subjects. Data were collected through observation, interviews, product validation, response questionnaires, and learning outcome tests. The findings indicate that the Jargonometry learning media is valid, practical, and effective in improving high school students' mathematics learning outcomes.
Principal Component Analysis: Identifiying Underlying Issues that Lead to Divorce in South Sulawesi Purnama, Eka
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.234

Abstract

This study aims to analyze the various factors contributing to divorce in South Sulawesi, with the goal of identifying and understanding the underlying issues that lead to marital breakdowns in the region. Utilizing Principal Component Analysis (PCA) to reduce the variables associated with divorce, thirteen factors were examined. The analysis revealed five key components: moral issues, personal will, differences of opinion, economic challenges, and criminality. Notably, these components accounted for 82% of the variance in the data, highlighting their significance in understanding the dynamics of divorce in South Sulawesi. This research provides valuable insights for policymakers and practitioners working to enhance marital stability in the region.
Sentiment Analysis of Bapenda South Sulawesi Mobile Application on Google Play Store Using Support Vector Machine Burhan, Muhammad Ikhwan; Ali, Andi Nurfadillah; Auliyah, A. Inayah; Hading, Muhaimin
Journal of Mathematics and Applied Statistics Vol. 2 No. 2 (2024): December 2024
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v2i2.244

Abstract

This study analyzes user sentiment toward the Bapenda Sulsel Mobile application, an e-government platform developed by the Regional Revenue Agency of South Sulawesi, Indonesia. The research aims to evaluate user feedback and identify areas for improvement to enhance user satisfaction. Using sentiment analysis, user reviews from Google Play Store were collected and classified into positive, negative, and neutral sentiments through the Support Vector Machine (SVM) algorithm. Preprocessing steps such as tokenization, stopword removal, and stemming were applied to prepare the data. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction to enhance classification accuracy. The SVM model demonstrated an overall accuracy of 80%, achieving a high recall of 98% for positive reviews but only 40% for negative reviews, reflecting challenges in handling class imbalance. Results show that 72% of users expressed positive sentiment, praising the app’s functionality and ease of use. However, 28% of reviews were negative, citing issues like technical bugs and usability challenges The findings highlight the app’s strengths in delivering e-government services and its role in improving tax management. However, the significant proportion of negative feedback emphasizes the need for addressing user concerns. Recommendations include balancing the dataset, refining the SVM model, and prioritizing improvements based on user feedback. This study contributes to the broader understanding of applying sentiment analysis in evaluating e-government platforms and offers actionable insights for enhancing the user experience.
Time Series Forecasting in Finance Using Convolutional Neural Network (CNN) model Kalange, Dhananjay
Journal of Mathematics and Applied Statistics Vol. 3 No. 1 (2025): June 2025
Publisher : Yayasan Insan Literasi Cendekia (INLIC) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/mathstat.v3i1.285

Abstract

This study investigates the effectiveness of a one-dimensional Convolutional Neural Network (1D-CNN) model in forecasting stock prices of selected companies listed on the Bombay Stock Exchange (BSE). Due to the highly volatile and non-linear nature of financial time series, traditional models like ARIMA often fail to capture hidden patterns. To address this challenge, this research employs a CNN model trained on historical stock data (2015–2023) from five prominent Indian companies: SBI, Reliance, TCS, Infosys, and HDFC Bank. The data pre-processing included handling missing values, applying Min-Max normalization, and using a sliding window of 60 days to predict the next day's closing price. The CNN model, structured with convolutional and pooling layers followed by a dense network, was trained and validated using 80/20 data splits. Model performance was evaluated using MAE, MSE, RMSE, and MAPE. Results revealed that the CNN model achieved MAPE values between 1.5% and 4.2%, demonstrating high accuracy. Compared to ARIMA and LSTM models, CNN provided competitive predictive performance with faster training time. Visual comparisons and performance metrics confirmed the model's ability to capture both upward and downward market trends effectively. This research contributes to the growing field of deep learning in financial forecasting and supports the utility of CNNs in modeling complex time-series data.

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