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All Journal Jurnal Gaussian TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Abmas Negeri (JAGRI)
Muh Hijrah
Program Studi Statistika, Universitas Sulawesi Barat, Majene, Indonesia
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The Application of The Neighborhood Cleaning Rule in Conjunction with Random Forest, K-Fold Cross-Validation, and Grid Search for Addressing Imbalanced Datasets Laila Qadrini; Muh Hijrah; Laelatul Hikmah; Handayani Handayani
TIN: Terapan Informatika Nusantara Vol 3 No 8 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v3i8.4124

Abstract

Finding a model that explains and separates data classes is the process of classification in data mining, which is used to guess the class of an item with an unknown class. Numerous strategies have been developed since categorization can be applied in a wide range of applications. But a common issue with classification is class imbalance. Data predictability suffers as a result of the issue of unbalanced classes. There are typically not an equal number of examples in each class in real-world categorization datasets. Class imbalance is not a problem when there are not significant differences in how the classes are distributed. Due to class imbalance, prediction models may skew in favor of the majority class, with the minority class contributing little to the model. One often used strategy for addressing class imbalance is the resampling technique. This study's objective is to put the Resampling Algorithm into practice. Neighborhood Cleaning Rule Random Forest K-Fold Tune Grid Search was carried out on a dataset that includes cases of Low Birth Weight Infants (BBLR) in Majene Regency and breast cancer diagnoses, which was posted on the UCI website. The Neighborhood Cleaning Rule (NCL), a data processing method, eliminates noise or other disturbances from datasets used for modeling or analysis. The F1-Score, G-Mean, Accuracy, and Sensitivity values from the model are good.
Pembuatan dan Pemanfaatan Website sebagai Pendukung Terwujudnya Desa Cantik Ahmad Ansar; Muh. Hijrah; Putri Indi Rahayu; Muh. Riswan Kadir; Nilawati Nilawati
Jurnal Abmas Negeri (JAGRI) Vol. 4 No. 2 (2023): Volume 4 Nomor 2 Desember 2023
Publisher : Sarana Ilmu Indonesia (salnesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36590/jagri.v4i2.698

Abstract

Website desa memegang peranan penting sebagai sistem informasi desa dan mensosialisasikan program kerja desa kepada masyarakat. Pengabdian kepada masyarakat ini bertujuan untuk memberikan pemahaman terkait pengelolaan website kepada perangkat Kelurahan Totoli sebagai upaya mendukung Desa Cinta Statistik (Desa Cantik). Pelaksanaan pengabdian kepada masyarakat ini dilakukan dalam beberapa tahap yaitu tahap perencanaan meliputi komunikasi dan diskusi dengan mitra terkait permasalahan yang dihadapi. Tahap selanjutnya berupa tahap persiapan, tahap pelaksanaan pelatihan pembuatan dan pemanfaatan website, tahap pendampingan dan evaluasi kegiatan. Pada tahap akhir dilakukan peluncuran website secara resmi. Hasil dari kegiatan ini berupa meningkatnya pemahaman aparat desa terkait pengelolaan website desa untuk mendukung berbagai program desa. Luaran lain yang berhasil dicapai dari kegiatan ini adalah website resmi Kelurahan Totoli sudah dapat diakses dan dimanfaatkan oleh masyarakat.
PREDIKSI KEKERINGAN DENGAN MENGGUNAKAN RANDOM FOREST DAN XGBOOST SEBAGAI STRATEGI MITIGASI BENCANA DI KABUPATEN MAJENE Muh. Hijrah; Putri Indi Rahayu; Wahyudi Wahyudi
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.98-109

Abstract

The growing incidence of drought attributable to climate change poses serious challenges to food systems and agricultural livelihoods in Majene Regency, West Sulawesi. To address this, the present study compares the predictive accuracy of Random Forest and XGBoost in classifying drought occurrences from meteorological variables, using 300 monthly observations recorded by BMKG between 2000 and 2024. Exploratory data analysis revealed that 60% of drought events are concentrated in August and the target variable is severely imbalanced (16.3% drought vs. 83.7% non-drought). At the default threshold of 0.50, Random Forest outperforms XGBoost in all metrics. However, because imbalanced data causes both models to underdetect drought at the default threshold (sensitivity = 0.267), threshold optimization was applied by lowering the decision threshold to τ = 0.15. The result of threshold optimalization was significantly improved Random Forest sensitivity from 0.267 to 0.867, detecting 13 out of 15 drought events in the test set while maintaining an AUC-ROC of 0.916. Feature importance analysis consistently identifies X3 as the dominant predictor in both models. Random Forest with threshold τ = 0.15 is recommended as the primary model for drought early warning systems in Majene.
Co-Authors Ahmad Ansar Handayani Handayani Laelatul Hikmah Laila Qadrini Muh. Riswan Kadir Nilawati Nilawati Putri Indi Rahayu Putri Indi Rahayu Wahyudi Wahyudi