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All Journal Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) UNP Journal of Statistics and Data Science
Amelia Susrifalah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Mathematics Medicine & Pharmacology Social Sciences

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Peramalan Curah Hujan Sebagai Upaya Mitigasi Bencana Menggunakan Seasonal Autoregressive Integrated Moving Average Fayyadh Ghaly; Amelia Susrifalah; Yenni Kurniawati
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 1 (2025): VOLUME 13 NO 1 TAHUN 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i1.55289

Abstract

Rainfall prediction is important in disaster mitigation to reduce impacts such as drought, flood, and landslide. Rainfall data that has a seasonal pattern requires an appropriate forecasting method, one of which is SARIMA. This study predicts rainfall at the Deli Serdang Climatology Station, North Sumatra, based on monthly observation data for 2018–2023, showing a seasonal pattern with a 12-month cycle. The best model obtained is SARIMA (0,0,1) (0,0,1)12 with a MAPE of 19.5%, indicating a prediction accuracy of 80.5%. The forecasting results indicate a decrease in rainfall in the first semester of 2024, which is in the medium rainfall category. These findings can support disaster risk mitigation strategies and natural resource management planning related to climate change. The SARIMA model also has the potential to be applied in further climatology studies.
Penerapan Algoritma Extreme Gradient Boosting dengan ADASYN untuk Klasifikasi Rumah Tangga Penerima Program Keluarga Harapan di Provinsi Sumatera Barat Amelia Susrifalah; Dodi Vionanda; Yenni Kurniawati; Dwi Sulistiowati
UNP Journal of Statistics and Data Science Vol. 3 No. 2 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss2/369

Abstract

Program Keluarga Harapan (PKH) is a form of social protection provided by the government to overcome poverty in Indonesia. However, challenges remain in accurately predicting eligible households. Therefore, a data-based classification method is needed to identify PKH recipients based on their factors. This research was conducted in West Sumatra Province using variables from the Data Terpadu Kesejahteraan Sosial (DTKS) variable group contained in SUSENAS 2024. Based on data from Badan Pusat Statistik (BPS) of West Sumatera Province, there are 1.790 PKH recipient households and 9.810 non-recipient households, indicating a class imbalance. Considering the large amount of data and complex variables, PKH can be analyzed using the Extreme Gradient Boosting (XGBoost) algorithm because of its ability to handle large-scale data and produce high classification performance. To address data imbalance, Adaptive Synthetic (ADASYN) was applied before analysis. The application of XGBoost with the scale_pos_weight parameter shows low classification performance, with sensitivity value of 12.3% and balanced accuracy of 55.2%. To overcome this, unbalanced data was handled using the ADASYN method. The application of XGBoost after data balancing with ADASYN showed significant performance improvement, with sensitivity value 80.4% and balanced accuracy 88.1%. In classifying PKH recipient households, the variables that make an important contribution are the age of the head of household, floor area, diploma of the head of household, floor material and number of household Members. This research shows that the combination of XGBoost and ADASYN is effective in overcoming data imbalance and improving PKH recipient classification performance.
Co-Authors Dodi Vionanda Dwi Sulistiowati Fayyadh Ghaly Yenni Kurniawati