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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi JOIN (Jurnal Online Informatika)
Sanjaya, I Made Wisnu Adi
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Computer Science & IT Mathematics

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Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection Setiawan, Yohanes; Al Faroby, Mohammad Hamim Zajuli; Ma’ady, Mochamad Nizar Palefi; Sanjaya, I Made Wisnu Adi; Ramadhani, Cisa Valentino Cahya
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1495

Abstract

In Indonesia, the stunting rate has reached 36%, significantly higher than the World Health Organization's (WHO) standard of 20%. This high prevalence underscores the urgent need for effective early detection methods. Traditional data mining approaches for stunting detection have primarily focused on unimodal data, either tabular or image data alone, limiting the comprehensiveness and accuracy of the detection models. Modality-based modeling, which integrates image and tabular data, can provide a more holistic view and improve detection accuracy. This research aims to analyze modality-based modeling for the early detection of stunting. Two modalities, unimodal and multimodal, are used in this study. The main contributions of this research are the development of a comprehensive framework for modality-based analysis, the application of advanced data preprocessing techniques, and the comparison of various machine learning algorithms to identify the best model for stunting detection. The dataset, comprising images and tabular data, is sourced from Posyandu in Sidoarjo, Indonesia. Image data undergoes preprocessing, including background segmentation and feature extraction using the Gray Level Co-occurrence Matrix (GLCM), while tabular data is processed through categorical encoding. The Synthetic Minority Oversampling Technique (SMOTE) addresses class imbalance, and Principal Component Analysis (PCA) is used for dimensionality reduction. Unimodal modeling uses tabular or image data alone, while multimodal modeling combines both before classification. The study achieves the best F1 scores of 0.96, 0.91, and 0.90 for tabular-only, image-only, and image-tabular modalities, respectively, demonstrating the effectiveness of data balancing and dimensionality reduction techniques.
Optimizing K-Means Clustering through Distance Metric Simulation for Strategic Enrollment Segmentation in Private Universities Permata, Regita Putri; Alifah, Amalia Nur; Sanjaya, I Made Wisnu Adi
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33089

Abstract

K-Means clustering is a widely used unsupervised learning technique for identifying patterns and grouping data based on feature similarities. However, the effectiveness of K-Means significantly depends on the choice of distance metric. This study conducts a comprehensive simulation to evaluate and compare the performance of four distance metrics—Euclidean, Cityblock (Manhattan), Canberra, and Mahalanobis—in the context of strategic market segmentation for private universities. The dataset includes simulated and institutional data incorporating variables such as account creation, registration, graduation, student performance (social, science, and scholastic scores), income, and geographic distance. The results indicate that Euclidean and Cityblock distances yield efficient and interpretable clusters with low computational costs, whereas Mahalanobis distance, despite its capacity to model covariance, introduces computational overhead without proportional improvement in segmentation quality. Interestingly, Canberra distance produces compact clusters but offers no significant gain in separability. From the resulting segmentation, two clusters emerge as high-potential targets for marketing strategies: Cluster 0 (high-income and distant students) and Cluster 1 (diverse academic and socioeconomic profiles). The findings highlight the importance of aligning distance metric selection with specific clustering objectives and offer practical insights for data-driven strategic enrollment planning in private higher education institutions.
Co-Authors Alifah, Amalia Nur Mochamad Nizar Palefi Ma’ady Mohammad Hamim Zajuli Al Faroby Ramadhani, Cisa Valentino Cahya Regita Putri Permata Yohanes Setiawan