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All Journal Jurnal Inovasi Teknologi Pendidikan AKSIOMA: Jurnal Program Studi Pendidikan Matematika JTAM (Jurnal Teori dan Aplikasi Matematika) Sinar Sang Surya: Jurnal Pusat Pengabdian kepada Masyarakat Enthusiastic : International Journal of Applied Statistics and Data Science Jurnal Mahasiswa Ilmu Komputer Sciencestatistics: Journal of Statistics, Probability, and Its Application Journal of Mathematics, Computation and Statistics (JMATHCOS)
Wardhani Utami Dewi
Universitas Muhammadiyah Metro
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Physics

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IMPLEMENTATION OF DECISION TREE AND SUPPORT VECTOR MACHINE ON RAISIN SEED CLASSIFICATION Wardhani Utami Dewi; Khoirin Nisa; Mustofa Usman
AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol 12, No 1 (2023)
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.668 KB) | DOI: 10.24127/ajpm.v12i1.6873

Abstract

In everyday life there are many complex and global problems, especially in terms of decision making. Machine learning (ML) which is built from the concepts of computer science statistics and mathematics can automatically solve problems without guidance from ordinary users. Decision tree (DT) and support vector machine (SVM) are two supervised learning methods among several classification algorithms in ML. Both algorithms are the most popular classification techniques due to their ability to change a complex decision-making process into a simple process. In this study, the accuracy of the DT and SVM algorithms is studied on classifying raisin seeds into the Besni class and the Kecimen class based on existing features. The raisin data are divided into training and testing data, and the evaluation of the two methods is done using the testing data. The results of the evaluation are compared based on the accuracy, sensitivity, specificity, and kappa levels of the DT and SVM algorithms. The results on classifying raisin seeds data show that the SVM algorithm is superior to DT, therefor the number of positive observations is more precise in the prediction.
IMPELEMENTASI K-NEAREST NEIGHBORS, DECISION TREE DAN SUPPORT VECTOR MECHINE PADA DATA DIABETES Irfan, Miftahul; Dewi, Wardhani Utami; Nisa, Khoirin; Usman, Mustofa
Jurnal Mahasiswa Ilmu Komputer Vol. 4 No. 2 (2023): Jurnal Mahasiswa Ilmu Komputer October 2023
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ilmukomputer.v4i2.4007

Abstract

Diabetes merupakan salah satu penyakit yang menjadi penyebab kematian terbesar didunia. Kasus kematiannya pun tercatat lebih dari 4 juta pada tahun 2019. Diabetes juga dapat menyebabkan timbulnya penyakit lainnya. Bahaya diabetes ini menjadi perhatian khusus WHO. Seiring dengan perkembangan teknologi ini, banyak sekali kolaborasi antara bidang kesehatan, statistic dan computer untuk menanggulangi berbagai macam penyakit. Algortima machine learning menjadi popular dalam proses klasifikasi data dan sudah banyak diterapkan pada data kesehatan. Dengan begitu pada artikel ini akan dilakukan perbandingan algoritma machine learning KNN, Decision Tree, dan SVM untuk melihat algortima mana yang paling cocok untuk klasifikasi data diabetes. Hasil menunjukkan bahwa KNN dan SVM memiliki akurasi yang cukup besar yaitu 81,13%. Sehingga kedua algortima tersebut dapat menjadi rekomendasi proses klasifikasi data diabetes sehingga dapat membantu dokter dalam menanggulangi penyakit diabetes. Hasil ini juga menunjukkan bahwa 8 variabel yang digunakan berpengaruh terhadap resiko diabetes
Structural Equation Modeling on Data on Students' Knowledge and Interest in Entrepreneurship in Lampung Sholiha, Sangidatus; Vahia, Ira; Dewi, Wardhani Utami
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.26557

Abstract

Entrepreneurship plays a crucial role in economic growth and reducing unemployment, particularly in regions like Lampung, Indonesia, which face challenges such as limited entrepreneurial resources and low interest in entrepreneurship. This research aims to explore the relationship between entrepreneurial knowledge and entrepreneurial interest among students in Lampung, using Structural Equation Modeling (SEM) for analysis. A quantitative approach with a cross-sectional design was applied, involving 300 students randomly selected using simple random sampling from Lampung. The study focuses on entrepreneurial knowledge as the independent variable and entrepreneurial interest as the dependent variable. Data were collected using a questionnaire and analyzed with R Studio 4.2.1 using the lavaan package for SEM. The results show that entrepreneurial knowledge significantly influences entrepreneurial interest, explaining 86.10% of its variation. These findings suggest that strengthening entrepreneurial knowledge through curriculum development and innovative learning approaches can boost students’ entrepreneurial interest. Higher education institutions in Lampung can improve entrepreneurial education by integrating practical knowledge, case studies, and mentorship programs to foster entrepreneurial attitudes. This research contributes to the growing field of entrepreneurship education and offers actionable insights for policymakers and educators to develop sustainable entrepreneurs in Lampung.
PCA-SVM Classification: Motor Ability of Down Syndrome Based on Education, Economics And Physiotherapy Therapy Bota Muhammad Akbar; Al Um Aniswatun Khasanah; Sangidatus Sholiha; Wardhani Utami Dewi
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7261

Abstract

The classification of motor abilities of individuals with Down Syndrome is essential to identify more effective developmental patterns. However, research that integrates educational, economic, and physiotherapy factors in the classification model is still limited, especially in the application of machine learning-based methods. The purpose of this study is to classify using PCA-SVM on the motor ability of DS based on education, economics, and physiotherapy therapy. PCA is used to reduce the dimensions of the dataset by extracting the main features that have the greatest variation, thereby increasing the efficiency and accuracy of classification. Meanwhile, SVM with Radial Base Function RBF Kernel is applied to build a classification model capable of handling non-linear data and finding optimal hyperplanes as the separation boundary between classes. This study used 50 samples obtained from POTADS in Lampung Province, Indonesia. The results showed that PCA successfully extracted three main components that explained 80.2% of the variance of the data. The SVM model achieved an overall accuracy of 80.2%, with the highest classification success rate at Level 1 (100%) and Level 3 (75%), while Level 2 had some classification errors due to a wider variation in sample characteristics. This study implies that the resulting model can be used to identify individuals at risk of motor difficulties, allowing for earlier and targeted behavior. In addition, the results of this study can be a reference for medical practitioners and educators in developing therapy and education strategies that are more in line with the needs of each individual.
Naïve Bayes Algorithm: Analysis of Student Group Assignment Project Patterns in Mathematics Learning Dewi, Wardhani Utami; Vahlia, Ira; Linuhung, Nego
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.30459

Abstract

Effective collaboration in mathematics learning is essential for developing students' critical thinking and problem-solving skills; however, identifying patterns that lead to successful group collaboration remains challenging. This study aims explicitly to identify and classify the patterns of student group assignment completion in the Logic and Sets course using the Naïve Bayes algorithm. Survey data from 65 mathematics education students were analyzed using a quantitative approach and machine learning techniques. Attributes such as group size, task completion time, participation, contribution strategies, and communication effectiveness were collected via structured questionnaires. Data analysis involved preprocessing, model training using Naïve Bayes, and validation through accuracy and posterior probability analysis. Results indicated that the Naïve Bayes model accurately distinguished groups with very good (A) and fairly good (B) performance, achieving 84.62% accuracy. Groups achieving an A grade typically featured balanced participation and open communication strategies, whereas groups graded B exhibited uneven participation and passive members. This research significantly contributes by demonstrating how data-driven predictive analytics can support instructors in monitoring and enhancing collaborative learning processes in mathematics courses. Future research could further refine predictive accuracy by incorporating additional factors such as leadership style and collaborative technologies, potentially integrating the model into learning management systems for real-time evaluation and intervention.
Analysis of Food Security Factors in Indonesia using SEM-GSCA with the Alternating Least Squares Method Dewi, Wardhani Utami; Nisa, Khoirin; Usman, Mustofa
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.v8i2.20378

Abstract

An economic recession, characterized by prolonged economic decline, increased unemployment, and decreased spending, is projected to occur globally in 2023, potentially impacting production capacity within the food sector. Experts have identified various contributing factors such as shifts in global trade dynamics and geopolitical tensions, highlighting the need to understand the broader global economic context leading to this recession. To achieve this goal, in this research SEM is used to analyze the relationship between variables that influence food security. Furthermore, GSCA is used to handle complex structural models and non-normal data distribution. Special considerations include the use of ALS methods to estimate parameters effectively and consistently. The findings of this research are the important role of availability, access and utilization in shaping food security in Indonesia, with a contribution of 98% of the overall influence shown by the model. These insights help governments design targeted interventions to improve food security, especially amidst challenges posed by a potential global economic downturn. Implementing strategies to increase availability, increase access and optimize utilization is very important in maintaining food security amidst economic uncertainty.
Confirmatory Factor Analysis to Reduce the Knowledge and Economic Dimensions of the Behavior of Cerebral Palsy Parents Khasanah, Al Um Aniswatun; Yuliana, Sri; Ikrom, Dhofirul Fadhil Dzil; Sholiha, Sangidatus; Dewi, Wardhani Utami
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.v8i3.22952

Abstract

This research is important to understand the behavior of parents of Cerebral Palsy (CP) children by reducing the complexity in understanding knowledge and economic dimensions, thus providing a structured approach to identifying factors that influence the psychology of children with CP. This study aimed to identify and reduce the dimensions that influence the behavior of parents of children with CFA.This study aimed to reduce the socio-economic dimensions and knowledge of the behavior of parents of Cerebral Palsy (CP) children using CFA. A quantitative approach with a cross-sectional design was used in this study to comprehensively examine the behavior of parents of children with CP. Purposive sampling was used to select 200 participants from various backgrounds. The instrument used was a 28-item questionnaire which was distributed online to collect data. The questionnaire has gone through rigorous testing, including CFA, determinant KMO test, Bartlett test PCA, and grouping of variables based on identified factors.Research methods , a quantitative approach with cross-sectional design, and purposive sampling were used to select 200 parents of children with CP from various backgrounds. The instrument in the form of a questionnaire with 28 statement items was distributed online. Data analysis was carried out using CFA, including the determinant test, Kaiser-Meyer-Olkin (KMO) test, Bartlett test, Principal Components Analysis (PCA), and grouping variables based on factors. Research in Lampung shows that the number of boys with CP is slightly higher, with the majority aged 1-5 years. Factor analysis identified three main dimensions: parental knowledge about CP, family economic situation, and parental behavior. Better knowledge and a stable economic situation are positively correlated with better parental behavior in caring for children with CP. Many parents experience high levels of stress due to the physical, emotional, and financial burden of caring for a child with CP. The research conclusion shows that the 28 statement items were successfully reduced to three main dimensions (knowledge, economics, and behavior) which have a significant relationship in the care of children with CP. These findings suggest the need for interventions to increase parental knowledge about CP and family economic stability to increase parental participation in child care.Findings the sex distribution of children with cerebral palsy in Lampung showed a slight excess in the number of boys, with the majority of children being in the age range of 1-5 years. Factor analysis identified three main dimensions: parental knowledge of CP, family economic situation, and parental behavior, where better knowledge and stable economic situation positively correlated with better parental behavior in caring for children with CP. Many parents experience high levels of stress in caring for a child with CP because of the physical, emotional, and financial burden that the condition brings. In conclusion, the 28 items of the statement are reduced to 3 dimensions or factors, including knowledge, economics, and behavior. Each dimension has a relationship with each other significantly to the behavior of parents of CP children.
PENDAMPINGAN PARENTAL TRADITIONAL THAI MASSAGE UNTUK MENINGKATKAN KESADARAN SENSORI PADA KOMUNITAS PEDULI AUTIS LAMPUNG (KOPALA) Yudistira, Efraldo; Khasanah, Al Um Aniswatun; Soliha, Sangidatus; Vahlia, Ira; Dewi, Wardhani Utami
SINAR SANG SURYA Vol 9, No 2 (2025): Agustus 2025
Publisher : UM Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/sss.v9i2.4090

Abstract

Pengabdian ini bertujuan untuk memberikan pendampingan Parental Thai Massage. permasalahan yang diterapkan dalam komunitas ini adalah Parental Traditional Thai Massage, yang bertujuan untuk mengurangi stres, meningkatkan relaksasi, serta meningkatkan kesadaran sensorik pada anak autisme.Parental Traditional Thai Massage ini memberikan stimulasi pada sistem saraf pusat, membantu meredakan ketegangan otot, dan menciptakan efek relaksasi yang bermanfaat bagi anak. Metode pengabdian ini yaitu pengumpulan data, perencanaan, pelaksanaan dan evaluasi. Hasil pengabdian menunjukkan mitra sangat antusias dalam mengikuti pendampingan pendampingan Parental Thai Massage. Sebelum pendampingan dilakukan, hampir secara keseluruhan mitra belum mengetahui manfaat pendampingan Parental Thai Massage untuk meningkatkan kesadaran sensoris pada anak autis. Setelah diberikan pendampingan Parental Thai Massage 90% anggota KOPALA memahami dalam pelaksanaan teknik Parental Thai Massage. Dalam kegiatan tersebut, KOPALA sangat antusias dalam mengikuti pendampingan pembuatan Parental Thai Massage. Sebelum pendampingan dilakukan, hampir secara keseluruhan KOPALA belum mengetahui bahwasannya Parental Thai Massage memberikan efek stimulasi untuk meningkatkan kesadaran sensoris. Setelah diberikan pendampingan teknik Parental Thai Massage 90% orang tua KOPALA memahami teknik Parental Thai Massage 
Log-Linear Analysis of the Association among Hematological Variables in Dengue Hemorrhagic Fever Cases Irfan, Miftahul; Hayati, Ma’rufah; Madonna, Nora; Dewi, Wardhani Utami
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art6

Abstract

Health data are often analyzed in their continuous form through approaches such as linear, logistic, or survival models. In this study, hematological variables were dichotomized based on established clinical cut-offs to enable log-linear analysis of associations among categorical variables, acknowledging the potential loss of information from this transformation. A log-linear model was applied to evaluate independence, dependence, and interaction patterns among leukocyte, hemoglobin, and hematocrit categories in a dengue hemorrhagic fever (DHF) patient dataset. Previous analyses using survival models identified these variables as factors associated with recovery rates; however, these models did not capture their interaction structure. Log-linear analysis was therefore employed to examine these associations more comprehensively. The best-fitting model was identified as , which included two-factor interactions between leukocyte–hematocrit and hemoglobin–hematocrit. This model demonstrated a good fit (Pearson , , ), including a three-factor interaction resulted in a saturated model (= 0) and did not improve model performance. These findings highlight significant interaction patterns among hematological variables in DHF patients, providing a more detailed understanding of their joint associations.
An OBE-based e-module integrated with prophetic values: Enhancing statistical understanding, engagement, and character development Vahlia, Ira; Sholiha, Sangidatus; Suprihatin, Siti; Utami Dewi, Wardhani
Jurnal Inovasi Teknologi Pendidikan Vol. 13 No. 1 (2026): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v13i1.90807

Abstract

Many students struggle to understand abstract concepts in Statistics because of limited contextual and interactive learning resources that connect theory to practice. Moreover, there is a lack of e-modules integrating prophetic values to support the vision of Universitas Muhammadiyah Metro. However, existing studies have not sufficiently integrated OBE with prophetic values in digital learning media for statistics, indicating a clear research gap. This study aims to develop an OBE-based e-module integrated with prophetic values for Statistics courses. The research employed an R&D method using the ADDIE model (analysis, design, development, implementation, and evaluation). The subjects were students of the Economic Education Study Program. Data were collected through interviews, expert validation and student response questionnaires, and analysed using descriptive quantitative and qualitative techniques. The results show that the e-module is highly valid (91.6%) and very practical (mean score = 4.3/5). Learning outcomes improved from an average of 62.5 to 83.7 (p < 0.05), indicating positive learning gains. This study provides contextual and methodological contributions by integrating OBE principles with prophetic values in digital learning design, and theoretical contributions in linking cognitive and character development in statistics learning. Future research should involve larger samples and longer implementation periods to assess affective outcomes better.
Co-Authors AL UM ANISWATUN KHASANAH Al Um Aniswatun Khasanah Bota Muhammad Akbar Efraldo Yudistira Hayati MT, Ma’rufah Ikrom, Dhofirul Fadhil Dzil Ira Vahlia Ira Vahlia Irfan, Miftahul Khasanah, Al Um Aniswatun Khoirin Nisa Khoirin Nisa Madonna, Nora Miftahul Irfan Mustofa Usman Mustofa Usman Nego Linuhung Sangidatus Soliha Sholiha, Sangidatus Siti Suprihatin Soliha, Sangidatus Sri Yuliana Tri Cahyo Wahyudi Vahia, Ira Warsono Warsono Warsono Yudistira, Efraldo