KARUNIA EKA LESTARI

@unsika.ac.id

Mathematics Education Departement
Universitas Singaperbangsa Karawang

KARUNIA EKA LESTARI


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https://researchid.co/karuniaekalestari

EDUCATION

2016-07-06 to 2021-02-16 | Doctor of Mathematics/Ph.D (Doctor of Mathematics), Institut Teknologi Bnadung, Indonesia
2011-07-09 to 2013-07-09 | Master of Education/M.Pd (Mathematics Education), Universitas Pendidikan Indonesia, Indonesia
2007-07-04 to 2011-07-07 | Bachelor of Mathematics Education/S.Pd. (Mathematics Education), Universitas Pendidikan Indonesia, Indonesia

RESEARCH INTERESTS

Mathematics Education
Education Statistics
Education Research Methodology
Correspondence Analysis
Categorical Data
24

Scopus Publications

6719

Scholar Citations

17

Scholar h-index

28

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.
  • ESTIMATION OF VALUE AT RISK FOR GENERAL INSURANCE COMPANY STOCKS USING THE GARCH MODEL
    Edwin Setiawan Nugraha, Agna Olivia, Fauziah Nur Fahirah Sudding, Karunia Eka Lestari
    Barekeng, 2025
    Investment plays a crucial role in supporting economic development by allocating funds to generate future profits. Among various investment options, stock investment is widely popular. However, investors face the challenge of developing strategies to maximize returns while minimizing risks. Effective investment requires understanding the potential maximum risk of loss, known as Value at Risk (VaR). This research focuses on estimating VaR for four top general insurance companies in Indonesia: PT Lippo General Insurance Tbk (LPGI), PT Asuransi Tugu Pratama Indonesia Tbk (TUGU), PT Victoria Insurance Tbk (VINS), and PT Asuransi Dayin Mitra Tbk (ASDM). These companies were selected due to their leading positions in the industry. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, an extension of the ARIMA method designed to handle volatility clustering, is utilized for VaR estimation. Results at confidence levels of 90%, 95%, and 99% reveal that VINS carries the highest risk, with a maximum VaR of IDR 2,848,710 at 99% confidence, while LPGI shows the lowest risk, with a maximum VaR of IDR 22,677. For TUGU, the maximum possible loss is IDR 517,589, and for ASDM, it is IDR 1,532,267. Backtesting confirms the reliability of the models, with some accepted at specific significance levels. Based on this analysis, the results can help investors make investment decisions that minimize potential losses, specifically in the four stocks analyzed.
  • 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
  • Clustering Company Profitability from the IDX30 Index Using K-Medoids and DBSCAN
    Edwin Setiawan Nugraha, Andreanne Intan Sulistyo Wardhani, Karunia Eka Lestari
    Icoait 2025 1st International Conference on Artificial Intelligence Technology Artificial Intelligence Driving Prosperity and Sustainability in the Modern World, 2025
    The capital market serves as an intermediary for investors and companies seeking capital. In the Indonesian capital market, the IDX30 is a stock price index consisting of 30 highly liquid companies that reflect market performance in Indonesia. An important factor that needs to be evaluated before considering investing is the company's profitability ratio, which reflects the company's ability to generate profits. This research seeks to find patterns in the profitability ratios of IDX30 companies through the application of clustering techniques, specifically by using K-Medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approaches. The exploited data are secondary data collected from the companies' annual reports for the period 2023, including Return on Assets (ROA), Return on Equity (ROE), Net Profit Margin (NPM), and Gross Profit Margin (GPM). Clustering analysis resulted in four clusters using K-Medoids and three clusters using DBSCAN. The results of the analysis show that the companies categorized in cluster 0 and 3 KMedoids and cluster 0 DBSCAN exhibit well-balanced profitability ratios, signifying their reliable ability to generate profits for investors. Simultaneously, DBSCAN effectively detects outlier companies that require more attention in investment considerations. Both approaches effectively identify data patterns based on their attributes, with DBSCAN having higher sensitivity to outliers. The findings of this study offer valuable insights for investors in identifying profitable companies and enhance the use of clustering algorithms in financial data analysis.
  • 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.
  • The Contribution Biplot on Correspondence Analysis to Investigate the Floricultural Crops Production in West Java
    Karunia Eka Lestari, Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara
    Aip Conference Proceedings, 2024
  • 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.
  • Elliptic Confidence on Correspondence Analysis Stainless Steels Corrosion Problem
    Engineering Letters, 2024
  • 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
  • REVEALING THE HIDDEN PATTERN OF UNDER-FIVE MALNUTRITION PREVALENCE DISTRIBUTION IN WEST JAVA-INDONESIA FROM CANONICAL CORRESPONDENCE ANALYSIS AND PREDICTIVE CLUSTERING PERSPECTIVE
    Communications in Mathematical Biology and Neuroscience, 2024
  • Digital Puzzle Worksheet for Identifying Metacognition Level of Students: A Study of Gender Differences
    Ramlah*, Agung, Agung Prasetyo, Dewi Siti, Karunia Eka, Mokhammad Ridwan
    European Journal of Educational Research, 2023
  • Sequential Exploratory Design by Performing Correspondence Analysis to Investigate Procedural Fluency of Undergraduate Student
    Karunia Eka Lestari, Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara
    Aip Conference Proceedings, 2023
  • NETWORK CLUSTERING METHOD FOR PREVENTING THE SPREAD OF COVID-19 IN INDONESIAN SCHOOLS
    Communications in Mathematical Biology and Neuroscience, 2023
  • POISSON REGRESSION MODELING OF AUTOMOBILE INSURANCE USING R
    Sandy Vantika, Mokhammad Ridwan Yudhanegara, Karunia Eka Lestari
    Barekeng, 2022
  • Exploratory Analysis on Adaptive Reasoning of Undergraduate Student in Statistical Inference
    Karunia Eka Lestari, , Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara, , and
    International Journal of Instruction, 2022
  • SIMPLE ALGORITHM TO CONSTRUCT CIRCULAR CONFIDENCE REGIONS IN CORRESPONDENCE ANALYSIS USING R
    Karunia Eka Lestari, Marsah Rahmawati Utami, Mokhammad Ridwan Yudhanegara
    Barekeng, 2022
  • Generating roots of cubic polynomials by Cardano's approach on correspondence analysis
    Karunia E. Lestari, Udjianna S. Pasaribu, Sapto W. Indratno, Hanni Garminia
    Heliyon, 2020
  • Clustering for multi-dimensional data set: A case study on educational data
    M R Yudhanegara, K E Lestari
    Journal of Physics Conference Series, 2019
  • Graphical depiction of three-way association in contingency table using higher-order singular value decomposition Tucker3
    K E Lestari, U S Pasaribu, S W Indratno
    Journal of Physics Conference Series, 2019
  • The comparative analysis of dependence for three-way contingency table using Burt matrix and Tucker3 in correspondence analysis
    K E Lestari, U S Pasaribu, S W Indratno, H Garminia
    Journal of Physics Conference Series, 2019
  • The Reliability of Crash Car Protection Level Based on the Circle Confidence Region on the Correspondence Plot
    K E Lestari, U S Pasaribu, S W Indratno, H Garminia
    Iop Conference Series Materials Science and Engineering, 2019

RECENT SCHOLAR PUBLICATIONS

  • 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
  • STRUKTUR ASOSIASI ANTARA KEMAMPUAN AWAL DAN KEMAMPUAN BERPIKIR KOMPUTASI MATEMATIS SISWA PADA SEKOLAH MENENGAH ATAS
    N Yasmin, KE Lestari
    Laplace: Jurnal Pendidikan Matematika 8 (2), 844-853 , 2025
    2025
    Citations: 1
  • Clustering Company Profitability from the IDX30 Index Using K-Medoids and DBSCAN
    ES Nugraha, AIS Wardhani, KE Lestari
    2025 1st International Conference on Artificial Intelligence Technology … , 2025
    2025
  • Efektivitas Pembelajaran Brain-Based Learning Terhadap Peningkatan Kemampuan Berpikir Logis Matematis Siswa
    KE Lestari
    Polinomial: Jurnal Pendidikan Matematika 4 (4), 883-891 , 2025
    2025
    Citations: 1
  • Model Persamaan Diskriminan untuk Prediksi Gaya Belajar Selmes Berdasarkan Kemampuan Metakognisi
    FN Arielhan, KE Lestari
    Polinomial: Jurnal Pendidikan Matematika 4 (3), 807-819 , 2025
    2025
    Citations: 1
  • Klasterisasi Siswa Sekolah Menengah Atas Berdasarkan Kemampuan Literasi Matematis dan Kemampuan Numerasi
    A Azkannaila, KE Lestari
    Polinomial: Jurnal Pendidikan Matematika 4 (3), 498-506 , 2025
    2025
    Citations: 1
  • Identifikasi Karakteristik Siswa Berdasarkan Kemampuan Representasi dan Pembuktian Matematis Menggunakan Pengelompokan K-Means
    DS Maharani, KE Lestari
    Polinomial: Jurnal Pendidikan Matematika 4 (3), 384-394 , 2025
    2025
    Citations: 1
  • 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
  • Estimation of Value at Risk for General Insurance Company Stocks Using the Garch Model
    ES Nugraha, A Olivia, FNF Sudding, KE Lestari
    Barekeng: Jurnal Ilmu Matematika dan Terapan 19 (2), 1071-1082 , 2025
    2025
    Citations: 2
  • 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
  • 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
  • Pemanfaatan Media Manipulatif “Mata Uanga” untuk Pembelajaran Siswa Di Sekolah Inklusi Karawang
    A Warmi, KE Lestari, DA Putri, M Kusumawardani, W Wiwin
    J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) 8 (2), 65-71 , 2024
    2024
    Citations: 1
  • 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 Kemampuan Pemecahan Masalah Matematis Siswa SMP Dalam Soal Sistem Persamaan Linear Dua Variabel
    Y Cahyana, KE Lestari, AP Abadi
    Jurnal Educatio FKIP UNMA 10 (3) , 2024
    2024
    Citations: 1
  • CORRESPONDENCE ANALYSIS ON STATISTICAL LITERACY AND GENDER: EMBEDDING E-CAMPUS PLATFORM WITH RANDOM ASSIGNMENT OF MATCHED SUBJECT IN EXPLANATORY ANALYSIS
    KE Lestari, R Risnawita, MR Yudhanegara, ES Nugraha, S Sylviani
    BAREKENG: Jurnal Ilmu Matematika dan Terapan 18 (3), 1975-1988 , 2024
    2024
  • Pengaruh Kemampuan Eksplorasi Matematis Terhadap Kemampuan Berpikir Kritis Matematis Siswa
    AA Rahman, KE Lestari
    Didactical Mathematics 6 (2), 212-221 , 2024
    2024
    Citations: 2
  • Pengaruh Kemampuan Elaborasi Terhadap Kemampuan Berpikir Kreatif Matematis
    N Istiqomah, KE Lestari
    Didactical Mathematics 6 (2), 164-170 , 2024
    2024
    Citations: 2
  • Elliptic Confidence on Correspondence Analysis Stainless Steels Corrosion Problem.
    YS Afrianti, US Pasaribu, H Ardy, KE Lestari
    Engineering Letters 32 (7) , 2024
    2024
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Penelitian Pendidikan Matematika
    KE Lestari, MR Yudhanegara
    Cetakan Pertama, Refika Aditama , 2015
    2015
    Citations: 5410
  • Implementasi Brain-Based Learning untuk meningkatkan kemampuan koneksi dan kemampuan berpikir kritis serta motivasi belajar siswa SMP
    KE Lestari
    Judika (Jurnal pendidikan UNSIKA) 2 (1) , 2014
    2014
    Citations: 255
  • Penelitian Pendidikan Matematika Edisi Kedua
    KE Lestari, MR Yudhanegara
    Bandung: PT. Refika Aditama , 2017
    2017
    Citations: 186
  • 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 motivasi belajar siswa terhadap hasil belajar matematika
    A Budiyani, R Marlina, KE Lestari
    Maju 8 (2), 502080 , 2021
    2021
    Citations: 68
  • Penelitian Pendidikan Matematika
    KE Lestari, MR Yudhanegara
    Cetakan Ketiga, Refika Aditama , 2018
    2018
    Citations: 68
  • 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
  • Analisis kemampuan pembuktian matematis mahasiswa menggunakan pendekatan induktif-deduktif pada mata kuliah analisis real
    KE Lestari
    MENDIDIK: Jurnal kajian pendidikan dan pengajaran , 2015
    2015
    Citations: 59
  • Penelitian Pendidikan Matematika
    KE Lestari, MR Yudhanegara
    Edisi Revisi, Refika Aditama , 2017
    2017
    Citations: 43
  • Penerapan model pembelajaran M-APOS untuk meningkatkan kemampuan pemecahan masalah matematis siswa SMP
    KE Lestari
    JUDIKA (JURNAL PENDIDIKAN UNSIKA) 3 (1) , 2015
    2015
    Citations: 39
  • Generating roots of cubic polynomials by Cardano's approach on correspondence analysis
    KE Lestari, US Pasaribu, SW Indratno, H Garminia
    Heliyon 6 (6) , 2020
    2020
    Citations: 34
  • Analisis kemampuan literasi matematis siswa dalam menyelesaikan soal pisa konten space and shape ditinjau dari level kemampuan spasial matematis
    AS Fitriana, KE Lestari
    JPMI (Jurnal Pembelajaran Matematika Inovatif) 5 (3), 859-868 , 2022
    2022
    Citations: 31
  • Efektivitas model connected mathematics project terhadap kemampuan penalaran matematis dan kecemasan matematika
    E Aprillia, KE Lestari
    Jurnal Educatio FKIP UNMA 8 (3), 873-882 , 2022
    2022
    Citations: 22
  • 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
  • Analisis Kemampuan Literasi Matematis Siswa Kelas X SMA dalam Menyelesaikan Soal PISA
    A Amelia, KN Effendi, K Lestari
    Majamath: Jurnal Matematika Dan Pendidikan Matematika 4 (2), 136-145 , 2021
    2021
    Citations: 18
  • Implementasi Brain-Based Learning untuk Meningkatkan Kemampuan Koneksi dan Kemampuan Berpikir Kritis Matematis Siswa Sekolah Menengah Pertama
    KE Lestari
    Jurnal Pendidikan Unsika 2 (1) , 2013
    2013
    Citations: 17
  • Analisis kelancaran prosedural matematis siswa berdasarkan kemandirian belajar
    A Safitri, KE Lestari
    Jurnal Educatio FKIP UNMA 8 (2), 444-452 , 2022
    2022
    Citations: 16
  • Digital puzzle worksheet for identifying metacognition level of students: A study of gender differences
    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