Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://turcomat.org/index.php/turkbilmat <h2 class="py-3 bg-white text-dark" style="background-color: white; padding: 10px;">Turkish Journal of Computer and Mathematics Education (TURCOMAT)</h2> <p style="background-color: white; padding: 10px;"><strong>e-ISSN</strong> 1309-4653 | <strong>Period</strong> Tri-annual | <strong> Starting Year: </strong> 2009 |<strong>Format:</strong> Online | <strong>Language:</strong> ENGLISH | <strong>Publisher:</strong> <a href="https://nnpub.org" target="_blank" rel="noopener"><strong>NINETY NINE PUBLICATION</strong></a></p> <div class="row"> <div class="col-md-4"><img style="background-color: white; padding: 10px; display: block; margin-left: auto; margin-right: auto;" src="https://turcomat.org/public/site/images/admin_turcomat/black-and-white-simple-company-cover-journal.png" alt="" width="200" height="259" /><br /> <p style="background-color: white; padding: 10px;"><strong>Citation Analysis: </strong><br /><br /><a href="https://scholar.google.co.in/citations?hl=en&amp;user=mELVS0AAAAAJ&amp;view_op=list_works&amp;sortby=pubdate" target="_blank" rel="noopener"><strong>Google Scholar</strong></a><br /><strong>Citations: 18638 <br />h-index: 54<br />i10 -index: 438</strong></p> <p> </p> </div> <div class="col-md-8"> <p style="background-color: white; padding: 10px; text-align: justify;"><strong>Announcement:</strong>We are excited to announce that Turkish Journal of Computer and Mathematics Education (TURCOMAT) is now under the new management of <strong>Ninety Nine Publication</strong>, effective since November 2023. We are proud to launch our first issue with the new team, Volume 15, Issue 1, for the year 2024. This issue marks a new chapter in the journal's history and is now available on our website. For detailed information and to access the latest issue, please visit our <a href="https://turcomat.org/index.php/turkbilmat ">journal's website</a></p> <p style="background-color: white; padding: 10px; text-align: justify;">The Turkish Journal of Computer and Mathematics Education, known as TURCOMAT, is a globally acknowledged journal notable for its comprehensive peer-review process and open access availability. This journal publishes three issues a year, in the periods of January-April, May-August, and September-December. TURCOMAT primarily focuses on sharing scholarly research in the fields of mathematics education and computer science. For more detailed insights into its areas of interest, readers are encouraged to refer to the journal's focus and scope section.</p> </div> </div> <div class="row"> <div class="jumbotron" style="padding: 10px; margin-bottom: 5px;"> <p>Call for Papers: Jan-April 2024 Issue of TURCOMAT</p> <ul class="list-group"> <li class="list-group-item"> Submission Deadline: April 30, 2024</li> <li class="list-group-item">Publication Model: Continuous</li> <li class="list-group-item">Scope: Encourages exchange of ideas in mathematics and computer science, covering both theoretical and applied research.</li> <li class="list-group-item">Focus Areas: Mathematical theories, computational algorithms, data science, and their applications in various domains.</li> <li class="list-group-item">Submission Encouragement: Innovative, interdisciplinary research and comprehensive reviews contributing to mathematical and computational sciences.</li> <li class="list-group-item">Journal Characteristics: International, scholarly, refereed, and editor-organized.</li> <li class="list-group-item">TURCOMAT's Evolution: Dynamic, adapting to changes and developments in the field.</li> <li class="list-group-item">Participation Invitation: Enthusiastic call for manuscripts for future issues, highlighting enjoyment in engaging with new authors and their research.</li> </ul> <p> </p> </div> </div> Ninety Nine Publication en-US Turkish Journal of Computer and Mathematics Education (TURCOMAT) 1309-4653 <h3>Licensing </h3> <p>TURCOMAT publishes articles under the <a title="Creative Commons Attribution 4.0 International License (CC BY 4.0)" href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International License (CC BY 4.0)</a>. This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge. </p> <h4>Detailed Licensing Terms </h4> <p>Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use. </p> <p>No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. </p> Prediction of Type 2 Diabetes using logistic regression techniques https://turcomat.org/index.php/turkbilmat/article/view/13875 <p><strong>Abstract</strong></p> <p>Diabetes is recognized as a significant public health concern and a global epidemic. It is a chronic condition resulting from insufficient insulin production by the pancreas. The long-term elevated blood sugar levels associated with diabetes lead to chronic damage and impaired function in multiple tissues, such as the eyes, kidneys, heart, blood vessels, and nerves.</p> <p>The objective of this study is to demonstrate the utilization of machine-learning algorithms, specifically logistic regression, in predicting an individual's likelihood of having diabetes based on medical data. Furthermore, the study aims to develop a prediction model that determines whether a patient has diabetes by analyzing specific diagnostic measurements included in the dataset. Various techniques will be explored to enhance the performance and accuracy of the prediction model.</p> <p>&nbsp;</p> <p><strong>Results</strong>: The logistic regression algorithm for the dataset containing various patient data, found that the algorithm predicted whether people would be diagnosed with diabetes with an 82 percent success rate.</p> Hussein Al-Rimmawi Copyright (c) 2024 Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-11 2024-01-11 15 1 1 13 10.61841/turcomat.v15i1.13875 STOCK SELECTION USING SEMI-VARIANCE AND BETA TO CONSTRUCT PORTFOLIO AND EFFECT MACRO-VARIABLE ON PORTFOLIO RETURN https://turcomat.org/index.php/turkbilmat/article/view/14350 <p>This research has aims to construct portfolio by varying method and using semi-variance and Beta for selection stocks. This research found 28 stocks to become member portfolio. Equal Weighted, Market Capitalization Weighted, Markowitz Method and Elton Gruber is used to construct portfolio.&nbsp; This research found that the efficient frontier similar to Markowitz Method. Roy Criterion found the portfolio return varying from 2.2% to 9.65% but Kataoka Criterion found the portfolio return varying from 5.4% to 11.12%. This research found that Elton Gruber has the highest portfolio return compared to others portfolio. There is no difference of average return for four portfolios.&nbsp; Market return significant affect to all portfolio return but the interest rate significant affect portfolio returns for equal weighted portfolio and Elton Gruber Method.</p> Prof. Adler Haymans Manurung Amran Manurung Nera Marinda Machdar Jadongan Sijabat Copyright (c) 2024 Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-15 2024-01-15 15 1 14 25 10.61841/turcomat.v15i1.14350 GENDER AND OTHER SIGNIFICANT FACTORS CAUSING DISPARITIES IN SENIOR HIGH SCHOOL STUDENTS’ MATHEMATICS PERFORMANCE https://turcomat.org/index.php/turkbilmat/article/view/14020 <p>Research findings on gender and other student related, teacher related and school related factors affecting students’ performance in mathematics are still debatable. With the recent trend of poor performance in mathematics recorded in both district and national performance statistics in the Assin North District, this present study examined gender factor and other significant factors causing disparities in mathematics performance among high school students. A mathematics achievement test and questionnaires were employed to collect data from a representative sample of 500 final-year students from three public senior high schools in the Assin North District, Ghana. Data were analysed descriptively and quantitatively using independent t-test and probit regression. Results show that male students did better than female students in the mathematics achievement test. The differences were statistically significant at .05 significance level. Aside gender, self-assurance and self-regard were identified as significant student related factors affecting the mathematics performance among senior high school students in the Assin North District. Teacher subject matter knowledge, teacher methods and teacher-student interaction were also significant teacher related factors affecting performance in mathematics. Finally, teacher motivation and school environment were identified as significant school related factors affecting mathematics performance among the senior high school. Other factors such as students’ socioeconomic background and teaching resources had effect on students’ performance but they were not statistically significant. The study recommends that senior high school mathematics teachers should employ gender responsive pedagogies in their teaching practices. It is also recommended that professional learning communities should also be formed at school levels to enable mathematics teachers improve upon their knowledge, motivation and teaching styles.</p> Emmanuel Amoah Copyright (c) 2024 Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-16 2024-01-16 15 1 26 33 10.61841/turcomat.v15i1.14020 From Detection to Prediction: AI-powered SIEM for Proactive Threat Hunting and Risk Mitigation https://turcomat.org/index.php/turkbilmat/article/view/14393 <p><span class="fontstyle0">The evolution of cybersecurity has witnessed a transformative shift from reactive defense measures to proactive threat-hunting and risk-mitigation strategies. In response to the rapidly evolving threat landscape, the integration of Artificial Intelligence (AI) into Security Information and Event Management (SIEM) tools has emerged as a crucial solution. Historically, SIEMs primarily aggregated security data but struggled to analyze the vast, complex datasets effectively. The integration of AI, especially Machine Learning (ML) and Deep Learning (DL), revolutionized these systems. AI algorithms enable SIEMs to extract meaningful insights from massive datasets, allowing for the identification of subtle anomalies and hidden threats that may not be detected by traditional detection methods. This transition marks a fundamental shift from simple data aggregation to intelligent analysis, empowering SIEMs to move beyond detection toward<br>proactive threat hunting. This paper highlights the role of AI in predicting threats, leveraging historical data to forecast potential risks, and continuously learning to adapt to evolving threat landscapes. It also explores the real-world use cases of AI-powered SIEMs in proactive threat hunting and risk mitigation.</span> </p> Srinivas Reddy Pulyala Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-11 2024-01-11 15 1 34 43 10.61841/turcomat.v15i1.14393 Unveiling Hidden Threats with ML-Powered User and Entity Behavior Analytics (UEBA) https://turcomat.org/index.php/turkbilmat/article/view/14394 <p><span class="fontstyle0">The ever-growing cost of cybercrime has created the need for proactive solutions for organizations seeking to protect their digital assets. While traditional security systems struggle to detect anomalies buried within vast datasets, new solutions like User and Entity Behavior Analytics (UEBA) emerge as a game-changer. By leveraging the power of machine learning, UEBA analyzes diverse data sources like user logins, file accesses, event logs, business context, external<br>threat intelligence, and network activity, to unveil hidden threats most traditional methods could miss. The ability to analyze multiple data sources enables UEBA solutions to effectively detect malicious insiders, compromised users, Advanced Persistent Threats (APTs), and zero-day attacks. By using various analytics techniques like supervised learning, unsupervised learning, and statistical modeling, UEBA solutions can detect subtle anomalies that deviate from<br>established behavior baselines. Despite the many benefits, UEBA solutions still have limitations like data quality concerns, high implementation costs, and the need for model maintenance. Integration with System Information and Event Management (SIEM) systems helps mitigate some of these challenges to further enhance UEBA's capabilities and provide a unified platform for threat identification and response. This paper provides a detailed insight into the capabilities of<br>UEBA, its three pillars, significance, and limitations.</span> </p> Avinash Gupta Desetty Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-11 2024-01-11 15 1 44 50 10.61841/turcomat.v15i1.14394 Exploring Factors Contributing to Indifference Towards Learning Mathematics Among Secondary School Students in Nepal https://turcomat.org/index.php/turkbilmat/article/view/14355 <p>Mathematics is a compulsory subject at the school level in Nepal, deemed essential for everyday life and higher studies, particularly in the fields of science and technology. However, there is a noticeable apathy among students when it comes to learning mathematics. This qualitative research aims to identify the factors that contribute to this indifference towards learning mathematics. Data was collected through in-depth interviews with four participants from both public and private schools, all enrolled in the tenth grade. Analysis and interpretation of the data revealed several factors that lead to this indifference. These factors can be classified as student-related, school-related, and home and society-related. Student-related factors include mathematics anxiety, negative perceptions, insufficient effort, poor academic achievements, limited real-world applications, low self-efficacy, and perpetuation of misconceptions about mathematics. School-related factors encompass teaching practices, teacher qualifications, traditional methods focused on rote learning, impractical curriculum and courses, inadequate school administration, and subpar physical facilities. Home and society-related factors have a negative effect on mathematics engagement, such as unfavorable home environments, low socioeconomic status, and parental education. Together, these factors contribute to the observed indifference towards learning mathematics.</p> <p><strong>Keywords: Indifference, Qualitative, Mathematics, Factors, Home, Students, School </strong></p> Maheshwor Pokhrel Madhav Prasad Poudel Copyright (c) 2024 Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-22 2024-01-22 15 1 51 60 10.61841/turcomat.v15i1.14355 POLYMER FLAT PLATE SOLAR COLLECTOER: A REVIEW https://turcomat.org/index.php/turkbilmat/article/view/14103 <p>A brief description on polymer flat plat solar collector manufacturing, design, and applications are given in this work. The main obstacles that face these collectors type, and how can be processed are also discussed. It is found that polymer low thermal conductivity, and degradation are the most essential difficulties in this industry, and increase heat transfer area and additives are the best common solutions. While stabilizers can be added to increase polymer life time.</p> Noora Hashim Ruaa Daham Angham Abed Copyright (c) 2024 Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-01-27 2024-01-27 15 1 61 69 10.61841/turcomat.v15i1.14103 Lagrange formula conjugate third order differential equation https://turcomat.org/index.php/turkbilmat/article/view/14372 <p>The paper considers a boundary value problem for a third order with no smooth coefficients and pure derivatives. Odds. This is due to the fact to introduce the concept of the conjugate Green's function. It is very difficult to write the form of the conjugate differential operator corresponding to equation in the Lagrange sense. Therefore, in this work, without using strict conditions smoothness under the conditions and boundedness, an explicit form is found conjugate operator since the initial-boundary value problem for integral-differential equations has been studied based on the introduction special conjugate systems in the form of an integral-algebraic equations’ system. In this article, it can be said that Green's function is considered based on Lagrange's formula for the third-order differential equation with boundary conditions and its conjugate.</p> Farhad Nasri Ghulam Hazrat Aimal Rasa Copyright (c) 2024 Turkish Journal of Computer and Mathematics Education (TURCOMAT) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-02-05 2024-02-05 15 1 70 74 10.61841/turcomat.v15i1.14372 The Impact Of Using 5G Technology In The Development Of Information Technology Applications https://turcomat.org/index.php/turkbilmat/article/view/14437 <p><span class="fontstyle0">Our study aims To apply 5G technology in the fields of information technology, where the study includes the application of 5G technology in information technology applications and artificial intelligence applications, where an algorithm is created to determine the extent of development taking place in developing programs over the Internet and managing them remotely and controlling them as a result of the speed of 5G technology, which is equivalent to hundreds of times from previous technologies (4G / 3G / 2G) Heading over the advantages and disadvantages of using 5G technology, through the use of the algorithm, the extent of development is evaluated and the use of this evaluation in developing other areas of technology to create a technological environment that can be controlled remotely that carries out all electronic governance activities as well as multiple areas of life.</span></p> Yasir khudheyer Abass Aloubade Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/deed.en 2024-01-11 2024-01-11 15 1 75 84