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All Journal Teorema: Teori dan Riset Matematika Journal of Soft Computing Exploration Scientific Journal of Informatics
Hasnataeni, Yunia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics

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Perbandingan Metode Klastering K-Means dan DBSCAN dalam Identifikasi Kelompok Rumah Tangga Berdasarkan Fasilitas Sosial Ekonomi di Jawa Barat Mutiah, Siti; Hasnataeni, Yunia; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Teorema: Teori dan Riset Matematika Vol 9, No 2 (2024): September
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/teorema.v9i2.16290

Abstract

Penelitian ini bertujuan untuk membandingkan efektivitas dua metode klastering, yaitu K-Means dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN), dalam mengelompokkan rumah tangga berdasarkan karakteristik sosial ekonomi di Jawa Barat. Perbandingan kedua metode ini penting karena masing-masing metode memiliki kelebihan dan keterbatasan yang berbeda, K-Means unggul dalam menangani data dengan klaster yang lebih seragam, sedangkan DBSCAN lebih fleksibel dalam mengelola outlier dan klaster tidak teratur yang sering muncul dalam data sosial ekonomi. Data yang digunakan meliputi empat kategori: Fasilitas Rumah Tangga, Ketersediaan dan Kualitas Air, Bantuan Sosial dan Ekonomi, serta Kesejahteraan Ekonomi. Hasil analisis menunjukkan ketimpangan dalam akses fasilitas, air bersih, dan bantuan sosial ekonomi di berbagai wilayah, di mana wilayah seperti Bandung dan Garut lebih unggul dibanding Indramayu dan Cirebon. Motode terbaik dilihat dari nilai silhouette tertinggi. Metode K-Means menghasilkan segmentasi yang lebih terstruktur dengan skor silhouette 0,69, menunjukkan performa yang baik dalam mengelompokkan data dengan karakteristik yang lebih seragam. Sebaliknya, metode DBSCAN, yang lebih fleksibel dalam menangani outlier, menghasilkan 7 klaster dengan 248 noise points dan skor silhouette yang lebih rendah yaitu 0,398, mengindikasikan struktur klaster yang kurang kuat. Perbandingan kedua metode ini relevan dalam konteks klastering rumah tangga di Jawa Barat, di mana K-Means lebih efektif untuk wilayah dengan akses fasilitas yang seragam, sedangkan DBSCAN lebih baik dalam menangkap variasi yang tidak beraturan dan outlier. Penjelasan perbandingan kedua metode ini telah diperinci lebih lanjut untuk mencakup bagaimana variasi akses sosial ekonomi di berbagai wilayah memengaruhi efektivitas masing-masing metode sehingga memberikan pemahaman yang lebih mendalam tentang keunggulan dan keterbatasan keduanya dalam menangani heterogenitas data social ekonomi di Jawa Barat.Kata kunci: DBSCAN, K-Means, Klastering, Susenas
Comparison of clustering analysis of K-means, K-medoids, and fuzzy C-means methods: case study of school accreditation in west java Hasnataeni, Yunia; Nurhambali, M Rizky; Ardhani, Rizky; Hafsah, Siti; Soleh, Agus M
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.575

Abstract

This research aims to analyze school accreditation data in West Java using clustering methods: K-Means, K-Medoids, and Fuzzy C-Means, to identify patterns and groups of schools based on similar characteristics. K-Means, known for its simplicity, suggests an optimal two-cluster solution based on silhouette values but employs three clusters for detailed analysis. K-Medoids, noted for its robustness against outliers, achieves the best clustering with a lowest Davies-Bouldin Index (DBI) of 0.8 and the highest Silhouette Information (SI) value of 0.46. Fuzzy C-Means, which assigns membership degrees to each data point across clusters, performs reasonably well with a DBI of 0.87 and an SI value of 0.40, while K-Means shows the highest DBI of 0.9 and the lowest SI value of 0.39. The findings highlight K-Medoids as the superior method for clustering. Regions with lower educational quality, such as Bekasi and Cianjur regions, require priority interventions, whereas areas with better quality, like Bandung and Bekasi regions, can serve as models. Data-driven approaches, inter-regional collaboration, and continuous monitoring and evaluation are recommended to optimize educational policies and enhance overall educational quality in West Java.
Comparison of Ensemble Forest-Based Methods Performance for Imbalanced Data Classification Hasnataeni, Yunia; Saefuddin, Asep; Soleh, Agus Mohamad
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24269

Abstract

Purpose: Classification of imbalanced data presents a major challenge in meteorological studies, particularly in rainfall classification where extreme events occur infrequently. This research addresses the issue by evaluating ensemble learning models in handling imbalanced rainfall data in Bogor Regency, aiming to improve classification performance and model reliability for hydrometeorological risk mitigation. Methods: Four ensemble methods: RF, RoF, DRF, and RoDRF were applied to rainfall classification using three resampling techniques: SMOTE, RUS, and SMOTE-RUS-NC. The data underwent preprocessing, stratified splitting, resampling, and 5-fold cross-validation. Performance was evaluated over 100 iterations using accuracy, precision, recall, and F1-score. Result: The combination of DRF with SMOTE-RUS-NC yielded the most balanced results between accuracy (0.989) and computation time (107.28 seconds), while RoDRF with SMOTE achieved the highest overall performance with an accuracy of 0.991 but required a longer computation time (149.30 seconds). Feature importance analysis identified average humidity, maximum temperature, and minimum temperature as the most influential predictors of extreme rainfall. Novelty: This research contributes a comprehensive comparison of ensemble forest-based methods for imbalanced rainfall data, revealing DRF-SMOTE as an optimal trade-off between performance and efficiency. The findings contribute to improved rainfall classification models and offer practical insight for disaster mitigation planning and resource management in tropical regions.
Co-Authors Agus Mohamad Soleh Ardhani, Rizky ASEP SAEFUDDIN Erfiani Erfiani Fitrianto, Anwar Jumansyah, L.M. Risman Dwi mutiah, siti Nurhambali, M Rizky Siti Hafsah