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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal International Journal of Applied Sciences and Smart Technologies Jurnal Consulenza : Jurnal Bimbingan Konseling dan Psikologi Jurnal Pedagogi dan Pembelajaran Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Yayasan Cita Cendikiawan Al Khwarizmi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Bangkit Indonesia Jurtik STMIK Bandung JURTIK JURTIK: Jurnal Penelitian dan Pengembangan Teknologi Informasi dan Komunikasi
Siti Yuliyanti, Siti
Program Studi Informatika, Universitas Siliwangi, Tasikmalaya-Indonesia
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Flood mapping using Res-Q and machine learning on imbalanced data Yuliyanti, Siti; Purwayoga, Vega; Rachman, Andi Nur; Gusnadi, Zakwan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10374

Abstract

Flood disaster mapping requires accurate methods to support early warning and mitigation planning. To address common issues such as imbalanced data distribution and limited attribute handling, this study proposes an improved approach. The methodology includes: i) modification of the spatial sort filter skyline method with reverse normalization based on attribute preferences, applied when an attribute has minimal preference to ensure balanced consideration during skyline filtering; ii) data labeling and balancing, where initial flood potential labeling is generated using Res-Q, followed by K-Means clustering to group data into four classes (low, moderate, high, and very high) and SMOTE to further balance the dataset with 558 data points per class; iii) model evaluation using the C5.0 algorithm under three schemes, showing high and consistent accuracy with 89.24% on imbalanced data (Schema 2) and 93.3% on balanced data (Schema 3), while Schema 1 shows overfitting due to extreme imbalance; and iv) the main contribution, integrating reverse normalization with skyline filtering combined with clustering and resampling, enhancing both accuracy and robustness in identifying flood-prone areas. This structured approach highlights methodological improvements, reliable results, and practical contributions for effective flood disaster management.
Co-Authors Abdul Latif Ace Usman Somantri Acep Irham Gufroni Aep Saepudin AHMAD RIZKY FAUZI Al-Husaini, Muhammad Andi Nurkholis Asep Mulyana BISRY, AHMAD Dani Pradana Dede Suhendar Dedy Apriadi Dedy Apriyadi Dewi Lestari Dewi, Euis Nur Fitriani Doni Hermana Doni Hermana Ermanu Azizul Hakim Erna Yayuk Fiitriani Dewi, Euis Nur Fitriani Dewi, Euis Nur Gini Abdul Azis Hadraeni, Sopi Nuryani Hamdan Arsyad Haris Irnawan Haris Irnawan Haris Irnawan Harry Gunawan Herlina, Anita Hermansyah, Aam Heru Sukoco Kartini, Nuri Linda Apriyanti Linda Apriyanti Linda Apriyanti Lukmaba, Hen Hen Maarip, Syamsul Miqdad, Salman Mira Mustika Febikusuma Muhamad Lutfirrahman Muhamad Luthfir Rahman Muhammad Lutfirrahman Muhammad Salman Alfarisi Nopebrian, Adliani Awalia Nur Widiyasono, Nur Nuryadin, Ardhan Dimas Purwayoga, Vega PUSPA CITRA Rachman, Andi Nur Radityo Yoga Pratama Rahmat Zaenal Ramdani, Cecep Muhamad Sidik Ramdhani, Gilang Ria Arista Asih Rizal, Randi Rizky Sartika, Tika Septi Asriani, Aveny Shofa, Rahmi Nur SITI SHOLIHAH Sokid, Sokid Syifa Hasni Taufik Djatna Ula, Diah Hidayatul Uro Abdrohim Wardani, Yusrima Syamsina Wulan Suryani Wulan Suryani Yoga Helfiyan Yoga Helfiyan Zakwan Gusnadi