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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 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): December 2024" : 5 Documents clear
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.
Designing a Web Application for Recognizing Past Learning Using the Laravel Framework Jaya, Arsan Kumala; Hanif, Abdullah; Triadi, Fara; Biabdillah, Fajerin
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.v2i2.239

Abstract

This study aims to provide information on the application design process using the Laravel framework. This study aims to design a web application that can help higher education institutions manage students who take prior learning recognition (RPL) classes effectively and efficiently. The problem often faced by universities is the difficulty in recording the formal/non-formal education history of RPL students. This application is expected to provide a solution by providing features such as recording education history, training history, conference history, award history, organizational history, and employment history. The system development method used in the design is the System Development Life Cycle (SDLC) by utilizing the Laravel framework as a framework for the system development process. The expected results of this study are a web application that is user-friendly, reliable, and able to increase the efficiency of student data collection in universities.

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