Mathematics, Statistics and Probability, Applied Mathematics, Education
23
Scopus Publications
6019
Scholar Citations
12
Scholar h-index
17
Scholar i10-index
Scopus Publications
Tucker3 Tensor Decomposition for the Standardized Residual Hypermatrix on Three-Way Correspondence Analysis Karunia Eka Lestari, Mokhammad Ridwan Yudhanegara, Edwin Setiawan Nugraha, Sisilia Sylviani Journal of the Indonesian Mathematical Society, 2025 This study investigates the theoretical and practical mathematical aspects of Tucker3 tensor decomposition from the three-way correspondence analysis point of view. Since the standardized residual hypermatrix represents the association of the three categorical variables, this study focused on (1) Tucker3 tensor decomposition for the standardized residual hypermatrix, (2) some mathematical properties of Tucker3 tensor decomposition, and (3) constructing the correspondence plot via Tucker3 tensor decomposition. Some mathematical results are presented in lemmas, theorems and algorithms, while a practical result is exhibited at the end of the discussion.
CLUSTERING BASED ON BETWEENNESS CENTRALITY IN PERIOD: TRANSFORMATION OF CORRELATION COEFFICIENT VALUE INTO DISTANCE IN MATRIC SPACE Mokhammad Ridwan Yudhanegara, Edwin Setiawan Nugraha, Sisilia Sylviani, Karunia Eka Lestari, Ebenezer Bonyah Barekeng, 2025 The main information of this research is the transformation of the correlation coefficient value for stock price into the distance. It is done to create a representation in metric space that can be used in cluster analysis on the correlation network, which is a dynamic network. The dynamic network is generated from the weighted edges in the form of distances in each period. Finding the cluster members of the network can be analyzed using a simple technique called a minimum spanning tree. The central cluster member is the vertex betweenness. Vertex betweenness represents banking companies with a high degree of proximity and correlation. It means that the banks that are members of the central cluster are banks with high investment value. Clustering based on betweenness centrality in the case study of stock price correlation becomes useful when transforming the value of the correlation coefficient to distance. The effort to build a network with the edge weight being the distance makes the minimum spanning tree a simple, valuable method for cluster analysis on bank stock prices. In particular, the benefit to investors, i.e., it can reveal which assets are closely correlated, indicating that they may respond to market events in a similar way and make decisions in stock purchases
Predicting Loan Approval Decisions Using Logistic Regression with SMOTE for Imbalanced Data Edwin Setiawan Nugraha, Ni Kadek Gita Maharani, Mokhammad Ridwan Yudhanegara Icoait 2025 1st International Conference on Artificial Intelligence Technology Artificial Intelligence Driving Prosperity and Sustainability in the Modern World, 2025 Credit is an important part of an economy by giving individuals and businesses some access to capital and borrowing. Whenever loans are taken, it usually involves significant risk, especially when the approval decision is made on poor or very weak judgement. One common difficulty with predicting loan approvals is the number of customer rejections is often much larger than the approvals. These cases can bias in the prediction model. This study is a simple logistic regression model prediction on whether a person's credit application will be approved or rejected. The dataset consists of 45,000 loan records from Kaggle. The dataset shows a very big imbalance in that only 10,000 were approved and 35,000 were rejected. This study utilized SMOTE to help balance the dataset. The data preprocessing involved dealing with missing values, the use of label encoder, and min-max normalization. SMOTE was utilized only on the training set after splitting the training and test data. Model performance was evaluated with a confusion matrix and metrics. The results showed an accuracy of 85% with a recall of 91%, precision of 62%, and f1-score of 74%. This study demonstrates the usefulness of combining SMOTE with logistic regression for automating loan approval decisions.
NEUROCOGNITIVE PREDICTION OF DYSLEXIC HANDWRITING PATTERN USING AN EXPLAINABLE AI-DRIVEN CUSTOM LITEBINARYNET-CNN Karunia Eka Lestari, Sri Winarni, Aditya Prihandhika, Edwin Setiawan Nugraha, Mokhammad Ridwan Yudhanegara Communications in Mathematical Biology and Neuroscience, 2025 Artificial intelligence (AI) based on deep learning, particularly convolutional neural networks (CNNs), shows strong potential in handwriting recognition. However, their limited transparency constrains use in sensitive domains such as dyslexia prediction. From a neurocognitive standpoint, dyslexia stems from atypical neural processing reflected in handwriting irregularities, making handwriting prediction a neurocognitive inference task. This study introduces a neurocognitively informed framework, Custom LiteBinaryNet, a lightweight CNN integrated with Explainable AI (XAI) for transparent dyslexia prediction from handwriting. Custom LiteBinaryNet-CNN was evaluated in baseline and tuned configurations, the latter optimized through aggressive augmentation and hyperparameter tuning. Compared to LeNet-5 (60.49% accuracy, AUC 0.56), the baseline achieved 78.73% accuracy and AUC 0.87, while the tuned model reached 83.36% accuracy and AUC 0.91. Loss analysis confirmed improved stability and generalization. XAI methods, including Grad-CAM and Occlusion Sensitivity, revealed neurocognitive interpretability by highlighting handwriting traits, such as letter reversals and spatial inconsistencies, which linked to dyslexic motor patterns. These results align computational predictions with cognitive evidence, enhancing transparency and diagnostic value. The proposed model offers a practical and explainable approach for early neurocognitive prediction of dyslexia through handwriting analysis.
RETROSPECTIVE ANALYSIS IN HYPOTHESIS TESTING TO EVALUATE INDONESIA'S GINI RATIO AFTER COVID-19 PANDEMIC Karunia Eka Lestari, Fitriani Agustina, Mokhammad Ridwan Yudhanegara, Edwin Setiawan Nugraha, Sisilia Sylviani Barekeng, 2024 The study highlighted three essential roles of retrospective analysis in hypothesis testing, particularly as a priori analysis, post hoc analysis, and sensitivity analysis. These approaches were applied to the Gini ratio data sourced from the National Socioeconomic Survey Indonesia 2023 to examine the income inequality level in Indonesia. The sample size, statistical power, and effect size for the one-sample t-test are evaluated by aid G*Power software. The test results show that for a sample size of 10, at the 95% confidence interval, there is not enough evidence to show that the Gini ratio in 2023 is smaller than 0.4. A retrospective analysis using G*power software reveals that for a sample size of 20 at the same confidence interval, there is enough evidence to suggest that the Gini ratio is statistically significant at less than 0.4 with a power of analysis of 90.8% and an effect size of 0.76. This study has important implications in hypothesis testing, especially in retrospective analysis, since understanding the effect of sample size and effect size makes it possible for academics or practitioners to optimize hypothesis testing and generate more accurate and reliable test results.
CORRESPONDENCE ANALYSIS ON STATISTICAL LITERACY AND GENDER: EMBEDDING E-CAMPUS PLATFORM WITH RANDOM ASSIGNMENT OF MATCHED SUBJECT IN EXPLANATORY ANALYSIS Karunia Eka Lestari, Risnawita Risnawita, Mokhammad Ridwan Yudhanegara, Edwin Setiawan Nugraha, Sisilia Sylviani Barekeng, 2024 This study aims to evaluate the embedding of e-campus platforms during the pandemic in dealing with gender disparities in statistical literacy and shed light on the association structure between statistical literacy and gender disparities. A mixed methods approach with sequential explanatory analysis was performed among 42 pairs (man-woman) sample of sophomore students enrolled in the Inferential Statistics course selected from a random assignment of matched subjects. The two main instruments, the placement test, and the statistical literacy test, were analyzed quantitatively using the Mann-Whitney test and correspondence analysis, followed by qualitative analysis using image and text analysis. The findings reveal that the e-campus platform has increased women's statistical literacy. Specifically, there is a statistically significant difference (1) between men's and women's statistical literacy scores, (2) an association between statistical literacy level and gender, and (3) different tendencies between men's and women's statistical literacy in various ways. The e-campus platform is an excellent solution for the teaching and learning process during the COVID-19 pandemic and beyond. Likewise, it can overcome gender disparities in literacy statistics. Since these findings lead to a higher statistical literacy rate for women than men, this could break the stereotype that women are less statistically literate than men.
PREDICTIVE DISTRIBUTION TO DETERMINE LEARNING MODEL AT THE STRATEGIC COMPETENCE LEVEL OF STUDENTS IN STATISTICS GROUP COURSE Mokhammad Ridwan Yudhanegara, Karunia Eka Lestari Barekeng, 2024 The problem of this research comes from a situation or condition that is not static. The description of these problems is the condition of the learning system, which tends to change due to the Covid-19 pandemic, causing learning conditions to be dynamic. From a statistical perspective, the dynamic situation can be modeled using a predictive distribution approach, so its characteristics can be studied. The purpose is to provide policy recommendations on appropriate learning models for lecturers in improving students' strategic competence, which is an ability that students need to master in solving various mathematical problems. The main discussion of this paper consists of three parts: clustering, predictive distribution, and statistical inference. The purpose of clustering is to group students based on test results to determine the level of strategic competence. In addition, clustering is also used as an initial process to predict students' strategic competence level if the learning used is still the same. The benefits of statistical inference in the distribution procedure in this study are used to determine the type of data distribution from each arrival of new information or data. The results of the statistical inference determine whether or not it is necessary to update the learning model of the lecturer. This research produce a new alternative statistical inference needed to make decisions. Based on the simulation results and discussion, the use of a predictive distribution approach to predict dynamic data is very appropriate. Distribution approach can use for detecting changes in new data distribution with historical data for the dynamic condition. If the changes are insignificant, direct instruction can still be used for the learning model in statistics course. A new learning model is recommended for the statistics group course at a higher level when the changes are significant.
Empirical Study of Mathematical Investigation Skill on Graph Theory Mathematics Teaching Research Journal, 2024
NETWORK PREDICTION BASED ON CLUSTERING: CASE STUDY FOR HUMAN SETTLEMENTS ALONG URBAN ROADS Communications in Mathematical Biology and Neuroscience, 2024 Indonesia is in a category with a very dense population of 279,918,617 people in July 2024. Population density can lead to a decrease in the quality of health services. One of the causes is the high rate of transmission of viral diseases in densely populated areas. For this reason, an innovative strategy is needed to inhibit the spread of the virus. Network clustering and predictive distribution techniques in delivering health logistics in densely populated areas can improve the efficiency and effectiveness of health logistics delivery. The techniques in delivering health logistics in densely populated areas can improve efficiency and effectiveness in dealing with the rate of virus spread. Based on these methods, the problem of delivering health logistics will be easy because zone predictions from network clustering results provide information on the location and density of the area. The method allows medical officers to prioritize the delivery zone for health logistics. This method can also overcome the next wave of viruses, such as COVID-19 and other infectious diseases.
Neurocognitive prediction of dyslexic handwriting pattern using an explainable AI-driven custom LiteBinaryNet-CNN KE Lestari, S Winarni, A Prihandhika, ES Nugraha, MR Yudhanegara Commun. Math. Biol. Neurosci. 2025, Article ID 141 , 2025 2025 Citations: 1
Predicting Loan Approval Decisions Using Logistic Regression with SMOTE for Imbalanced Data ES Nugraha, NKG Maharani, MR Yudhanegara 2025 1st International Conference on Artificial Intelligence Technology … , 2025 2025
K-MEANS CLUSTERING ANALYSIS OF THE RELATIONSHIP BETWEEN CRITICAL AND METAPHORICAL THINKING ABILITIES AM Putri, MR Yudhanegara EMTEKA: Jurnal Pendidikan Matematika 6 (2), 587-602 , 2025 2025 Citations: 1
Analisis Klaster terhadap Hubungan Kemampuan Berpikir Kreatif dan Komunikasi Matematis Siswa dengan Metode K-Means N Latifa, MR Yudhanegara Prosiding Sesiomadika 6 (3), 294-305 , 2025 2025
Analisis Cluster terhadap Hubungan Kemampuan Penalaran Matematis dan Kemampuan Berpikir Kreatif Matematis Siswa pada Materi Lingkaran Menggunakan Metode K-Mean S Legina, MR Yudhanegara Prosiding Sesiomadika 6 (2), 59-68 , 2025 2025
Analisis Klaster terhadap Hubungan Kemampuan Representasi Matematis dengan Kemampuan Pemecahan Masalah Matematis Menggunakan Metode K-Means Clustering pada Materi Matriks MRS Ramadhan, MR Yudhanegara Didactical Mathematics 7 (2), 286-298 , 2025 2025
An Analysis of Investment Decision-Making for IDX30 Stocks (2022–2023) Using the Capital Asset Pricing Model (CAPM) ES Nugraha, JC Cecilia, MR Yudhanegara FIRM Journal of Management Studies 10 (1), 200-219 , 2025 2025
KLASTERISASI TINGKAT KEMAMPUAN PENALARAN MATEMATIS DAN PEMECAHAN MASALAH MATEMATIS PADA SISWA SMP BESERTA HUBUNGANNYA RA Hafizd, MR Yudhanegara Laplace: Jurnal Pendidikan Matematika 8 (1), 144-159 , 2025 2025
THE RELATIONSHIP BETWEEN MATHEMATICAL EXPLORATION ABILITY AND STUDENTS MATHEMATICAL PROBLEM SOLVING ABILITY USING K-MEANS CLUSTERING D Gayatri, MR Yudhanegara EMTEKA: Jurnal Pendidikan Matematika 6 (1), 520-532 , 2025 2025
Pendampingan Pembuatan Flipbook E-Komik Numerasi Dengan Konteks Aljabar Pada Monumen Rawagede HN Sopiany, DL Hakim, MR Yudhanegara, Y Ardiyanti, H Kartika, ... BERNAS: Jurnal Pengabdian Kepada Masyarakat 6 (2), 1305-1312 , 2025 2025
Tucker3 Tensor Decomposition for the Standardized Residual Hypermatrix on Three-Way Correspondence Analysis KE Lestari, MR Yudhanegara, ES Nugraha, S Sylviani Journal of the Indonesian Mathematical Society 31 (2), 1491-1491 , 2025 2025
CLUSTERING BASED ON BETWEENNESS CENTRALITY IN PERIOD: TRANSFORMATION OF CORRELATION COEFFICIENT VALUE INTO DISTANCE IN MATRIC SPACE MR Yudhanegara, ES Nugraha, S Sylviani, KE Lestari, E Bonyah BAREKENG: Jurnal Ilmu Matematika dan Terapan 19 (2), 1109-1118 , 2025 2025 Citations: 1
IMPLEMENTATION OF K-MEANS CLUSTERING FOR STUDENT GROUPING IN THE RELATIONSHIP BETWEEN CONCEPTUAL UNDERSTANDING AND PROBLEM-SOLVING ABILITY R Dermawan, MR Yudhanegara EMTEKA: Jurnal Pendidikan Matematika 6 (1), 21-36 , 2025 2025 Citations: 1
Penerapan Tpack: Modul Ajar dan Media Digital yang Berfokus pada Kemampuan Literasi dan Numerasi MRY Siswadi, MR Yudhanegara, KE Lestari, HI Umam Jurnal Pengabdian Kepada Masyarakat 6, 461-473 , 2025 2025 Citations: 2
Forecasting weekly stock price of PT. Aneka Tambang Tbk (ANTM) using ARIMA Box-Jenkins method AIS Wardhani, MR Yudhanegara Journal of Actuarial, Finance and Risk Management (JAFRM) 3 (2), 20-31 , 2024 2024 Citations: 12
Revealing the hidden pattern of under-five malnutrition prevalence distribution in West Java-Indonesia from canonical correspondence analysis and predictive clustering perspective KE Lestari, A Warmi, S Winarni, S Sylviani, ES Nugraha, ... Commun. Math. Biol. Neurosci. 2024, Article ID 132 , 2024 2024
Analysis of Value at Risk of Optimal Portfolio Forming Using Single Index Model on LQ45 Stock Index in 2018 and 2019 ES Nugraha, T Savera, FNF Sudding, MR Yudhanegara FIRM Journal of Management Studies 9 (2), 170-184 , 2024 2024
The contribution biplot on correspondence analysis to investigate the floricultural crops production in West Java KE Lestari, MR Utami, MR Yudhanegara AIP Conference Proceedings 2867 (1), 020007 , 2024 2024 Citations: 1
RETROSPECTIVE ANALYSIS IN HYPOTHESIS TESTING TO EVALUATE INDONESIA'S GINI RATIO AFTER COVID-19 PANDEMIC KE Lestari, F Agustina, MR Yudhanegara, ES Nugraha, S Sylviani BAREKENG: Jurnal Ilmu Matematika dan Terapan 18 (4), 2517-2530 , 2024 2024
Analisis Cluster Untuk Hubungan Kemampuan Pemahaman Konsep Matematis Dengan Kemampuan Pemecahan Masalah Pada Materi Relasi Dan Fungsi Menggunakan Metode K-Means R Dermawan, MR Yudhanegara Prosiding Sesiomadika 5 (4), 598-609 , 2024 2024 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Penelitian pendidikan matematika KE Lestari, MR Yudhanegara Bandung: PT Refika Aditama , 2015 2015 Citations: 5350
Penelitian pendidikan matematika: panduan praktis menyusun skripsi KE Lestari, MR Yudhanegara Tesis, dan Laporan Penelitian dengan Pendekatan Kuantitatif, Kualitatif dan … , 2015 2015 Citations: 172
Meningkatkan kemampuan representasi beragam matematis siswa melalui pembelajaran berbasis masalah terbuka MR Yudhanegara, KE Lestari Majalah Ilmiah Solusi 1 (03) , 2014 2014 Citations: 96
Analisis kemampuan representasi matematis mahasiswa pada mata kuliah geometri transformasi berdasarkan latar belakang pendidikan menengah KE Lestari, MR Yudhanegara Jurnal Matematika Integratif 13 (1), 28-33 , 2017 2017 Citations: 62
Mathematics education research KE Lestari, MR Yudhanegara Refika Aditama , 2015 2015 Citations: 61
Clustering for item delivery using rule-k-means MR Yudhanegara, SW Indratno, RRKN Sari Journal of the Indonesian Mathematical Society 26 (2), 185-191 , 2020 2020 Citations: 21
Clustering for multi-dimensional data set: a case study on educational data MR Yudhanegara, KE Lestari Journal of Physics: Conference Series 1280 (4), 042025 , 2019 2019 Citations: 21
Exploratory Analysis on Adaptive Reasoning of Undergraduate Student in Statistical Inference. KE Lestari, MR Utami, MR Yudhanegara International Journal of Instruction 15 (4) , 2022 2022 Citations: 20
Digital Puzzle Worksheet for Identifying Metacognition Level of Students: A Study of Gender Differences Ramlah, AP Abadi, DS Aisyah, KE Lestari, MR Yudhanegara European Journal of Educational Research 12 (2), 795-810 , 2023 2023 Citations: 14
How to Develop Students' Experience on Mathematical Proof in Group Theory Course by Conditioning-Reinforcement-Scaffolding MR Yudhanegara, KE Lestari 5th SEA-DR (South East Asia Development Research) International Conference … , 2017 2017 Citations: 14
Analisis kemampuan representasi matematis mahasiswa pada mata kuliah sistem geometri berdasarkan latar belakang prestasi belajar mata kuliah geometri transformasi MR Yudhanegara, KE Lestari JP3M (Jurnal Penelitian Pendidikan Dan Pengajaran Matematika) 3 (2), 83-88 , 2017 2017 Citations: 13
Forecasting weekly stock price of PT. Aneka Tambang Tbk (ANTM) using ARIMA Box-Jenkins method AIS Wardhani, MR Yudhanegara Journal of Actuarial, Finance and Risk Management (JAFRM) 3 (2), 20-31 , 2024 2024 Citations: 12
Clustering for Items Distribution Network MR Yudhanegara, SW Indratno, R Sari Journal of Physics: Conference Series 1496 (1), 012019 , 2020 2020 Citations: 12
Evaluasi Penyelenggaraan Pendidikan Di Perguruan Tinggi: Relevansi Bidang Pekerjaan Dengan Program Studi KE Lestari, MR Utami, MR Yudhanegara JUDIKA (Jurnal Pendidikan Unsika) 9 (2), 149-164 , 2021 2021 Citations: 11
Sequential exploratory design by performing correspondence analysis to investigate procedural fluency of undergraduate student KE Lestari, MR Utami, MR Yudhanegara AIP Conference Proceedings 2588 (1), 050004 , 2023 2023 Citations: 10
Penerapan Pembelajaran Berbasis Masalah Terbuka Terhadap Kemampuan Representasi Matematis dan Kecemasan Siswa MR Yudhanegara Jurnal Mendidik 2 (2), 119-130 , 2016 2016 Citations: 10
Pengaruh Minat Belajar Siswa Terhadap Hasil Belajar Matematika SH Hotimah, MR Yudhanegara Jurnal Didactical Mathematics 5 (2), 432-439 , 2023 2023 Citations: 7
Pengaruh Kemampuan Berpikir Kreatif Terhadap Hasil Belajar Matematika Siswa Pada Materi Lingkaran F Firdaus, MR Yudhanegara, L Roesdiana Judika (Jurnal Pendidikan Unsika) 12 (1), 13-24 , 2024 2024 Citations: 6
Portfolio Optimization Analysis Using Markowitz Model on Idx30 Stock Index in 2022 and 2023 E Nugraha, CJ Lantang, MR Yudhanegara FIRM Journal of Management Studies 9 (1), 97-107 , 2024 2024 Citations: 6
