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All Journal Journal of Applied Data Sciences Solidaritas: Jurnal Pengabdian
Arif Mu'amar Wahid
Universitas Amikom Purwokerto
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Social Sciences

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GAN-Enhanced Radial Basis Function Networks for Improved Landslide Susceptibility Mapping Chyntia Raras Ajeng Widiawati; Ika Maulita; Yuli Purwati; Arif Mu'amar Wahid
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1035

Abstract

Landslide susceptibility modeling is a critical task for disaster mitigation, yet it is frequently undermined by a severe class imbalance inherent in landslide datasets, where non-landslide instances vastly outnumber actual landslide events. This imbalance leads to biased machine learning models with poor predictive power for the minority (landslide) class, resulting in unreliable hazard maps. This study, focusing on the high-risk area of Malang Regency, Indonesia, addresses this challenge by proposing an innovative framework that integrates a Generative Adversarial Network (GAN) for synthetic data augmentation with a Radial Basis Function Network (RBFN) for classification. A highly imbalanced dataset with a 1:10 ratio of landslide to non-landslide points was constructed to establish a realistic baseline. On this data, the RBFN model, while theoretically powerful for capturing non-linear relationships, failed completely, achieving a Recall of 0.00 for the landslide class. The novelty of this research lies in the specific application of a GAN, trained for 15,000 epochs, to generate high-fidelity synthetic landslide data, thereby creating a perfectly balanced training set. After retraining on this augmented data and undergoing a systematic hyperparameter tuning process, the RBFN’s performance was dramatically transformed. The optimized model achieved an F1-Score of 0.9333 and a Recall of 0.8750, elevating its performance from total failure to a level competitive with the robust Random Forest benchmark. This work validates that the integrated GAN-RBFN approach is a highly effective methodology for overcoming the data imbalance problem in geospatial hazard modeling. By turning a previously unreliable classifier into a powerful predictive tool, this method has significant practical implications for developing more accurate landslide susceptibility maps, which are crucial for informed spatial planning and enhancing early warning systems.
Pelatihan Pengelolaan Keuangan dan Pemasaran Digital bagiPengelola Warung Kopi Berbasis Masjid Abdul Jahir; Amin Nasyith; Arif Mu'amar Wahid
Solidaritas: Jurnal Pengabdian Vol. 6 No. 1 (2026): Solidaritas: Jurnal Pengabdian
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat UIN Prof. K.H. Saifuddin Zuhri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/0f5e3258

Abstract

Pengelola warung kopi berbasis masjid yang bermitra dengan LTMNU Kabupaten Banyumas menghadapi persoalan berupa pencatatan transaksi yang belum tertib, pemisahan dana usaha dan dana masjid yang belum konsisten, serta promosi yang masih mengandalkan pelanggan di sekitar masjid. Program pengabdian ini menawarkan pelatihan pengelolaan keuangan dan pemasaran digital untuk membantu pengelola memahami alur pencatatan serta menyiapkan bahan promosi awal. Kegiatan diikuti oleh 50 peserta, termasuk 30 delegasi dari 10 masjid dan 20 peserta dari LTMNU serta badan otonom Nahdlatul Ulama. Metode pelaksanaan menggunakan pendekatan partisipatif melalui penjelasan, demonstrasi, praktik kelompok, umpan balik, dan pendampingan. Peserta mempraktikkan pemisahan dana, pencatatan transaksi menggunakan CasirQu, pengelolaan stok, dan pembacaan laporan laba rugi sederhana. Sesi pemasaran mencakup fotografi produk, penyusunan teks promosi, dan pengenalan WhatsApp Business. Evaluasi dilakukan secara deskriptif berdasarkan catatan fasilitator, hasil simulasi, bahan latihan, dan umpan balik peserta. Hasil kegiatan menunjukkan bahwa kelompok peserta dapat menyelesaikan simulasi transaksi, menampilkan laporan laba rugi sederhana, serta menghasilkan foto produk dan teks promosi dengan arahan dari fasilitator. Capaian tersebut terbatas pada kemampuan menyelesaikan praktik terbimbing. Penggunaan aplikasi secara mandiri, konsistensi pencatatan setelah pelatihan, jangkauan promosi, dan pesanan digital 
GAN-Enhanced Radial Basis Function Networks for Improved Landslide Susceptibility Mapping Chyntia Raras Ajeng Widiawati; Ika Maulita; Yuli Purwati; Arif Mu'amar Wahid
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1035

Abstract

Landslide susceptibility modeling is a critical task for disaster mitigation, yet it is frequently undermined by a severe class imbalance inherent in landslide datasets, where non-landslide instances vastly outnumber actual landslide events. This imbalance leads to biased machine learning models with poor predictive power for the minority (landslide) class, resulting in unreliable hazard maps. This study, focusing on the high-risk area of Malang Regency, Indonesia, addresses this challenge by proposing an innovative framework that integrates a Generative Adversarial Network (GAN) for synthetic data augmentation with a Radial Basis Function Network (RBFN) for classification. A highly imbalanced dataset with a 1:10 ratio of landslide to non-landslide points was constructed to establish a realistic baseline. On this data, the RBFN model, while theoretically powerful for capturing non-linear relationships, failed completely, achieving a Recall of 0.00 for the landslide class. The novelty of this research lies in the specific application of a GAN, trained for 15,000 epochs, to generate high-fidelity synthetic landslide data, thereby creating a perfectly balanced training set. After retraining on this augmented data and undergoing a systematic hyperparameter tuning process, the RBFN’s performance was dramatically transformed. The optimized model achieved an F1-Score of 0.9333 and a Recall of 0.8750, elevating its performance from total failure to a level competitive with the robust Random Forest benchmark. This work validates that the integrated GAN-RBFN approach is a highly effective methodology for overcoming the data imbalance problem in geospatial hazard modeling. By turning a previously unreliable classifier into a powerful predictive tool, this method has significant practical implications for developing more accurate landslide susceptibility maps, which are crucial for informed spatial planning and enhancing early warning systems.
Co-Authors Abdul Jahir Amin Nasyith Chyntia Raras Ajeng Widiawati Ika Maulita Yuli Purwati