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All Journal Journal of Information Systems and Technology Research Jurnal Informatika Teknologi dan Sains (Jinteks)
Amri Muhaimin
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Robust Large-Scale Poverty Prioritization Using a-Cut Fuzzy AHP and Fuzzy WASPAS Hauzan Hanifah Zahra; Amri Muhaimin; Sugiarto
Journal of Information Systems and Technology Research Vol. 5 No. 2 (2026): May 2026
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v5i2.1560

Abstract

Prioritizing poverty alleviation programs remains challenging due to multidimensional indicators and uncertainty in expert judgment. This study purposes a robust decision support framework by integrating -cut Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Fuzzy Weighted Aggregated Sum Product Assessment (Fuzzy WASPAS) for large-scale poverty prioritization. The -cut mechanism was incorporated into the Fuzzy AHP weighting process to improve flexibility and robustness under uncertainty, while Fuzzy WASPAS was employed to rank 20,000 household alternatives based on 14 poverty indicators derived from DTKS and BPS Data. Sensitivity analysis was conducted using several  values to evaluate ranking stability under varying confidence levels. The results demonstrate that the proposed framework produces highly stable rankings, with an average maximum rank shift of 143 positions (0.7%) and a median shift of 81 positions (0.4%) across all alternatives. Futhermore, the model achieved an average Spearman rank correlation of 0.9996, indicating strong consistency in poverty prioritization outcomes despite variations in fuzzy defuzzification parameters. The findings confirm that the integration of α-cut Fuzzy AHP and Fuzzy WASPAS provides a reliable and robust approach for evidence-based poverty targeting and social assistance allocation. The proposed framework can support policymakers in improving the accuracy, transparency, and consistency of poverty intervention strategies.
PERAMALAN MENGGUNAKAN HYBRID SEASONAL ARIMA DAN EXTREME LEARNING MACHINE: STUDI KASUS JUMLAH PRODUKSI BERAS DI PROVINSI JAWA TIMUR Vera Febrianti Pakpahan; Amri Muhaimin; Wahyu Syaifullah
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6673

Abstract

Penelitian ini mengevaluasi performa metode hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Extreme Learning Machine (ELM) untuk peramalan data deret waktu. Metode SARIMA digunakan untuk menangkap pola musiman dan linier, sedangkan ELM diaplikasikan pada residual prediksi SARIMA untuk mendeteksi pola non-linier yang sulit ditangkap oleh model tradisional. Studi kasus difokuskan pada prediksi produksi beras bulanan di Provinsi Jawa Timur, salah satu lumbung beras nasional dengan fluktuasi produksi yang memengaruhi perencanaan distribusi dan kebijakan pangan. Hasil evaluasi menunjukkan bahwa model hybrid SARIMA–ELM mencapai nilai MAPE sebesar 9,01% dan RMSE sebesar 38.639,93, menunjukkan akurasi prediksi yang baik. Temuan ini menegaskan bahwa kombinasi SARIMA dan ELM dapat menjadi pendekatan yang efektif untuk peramalan deret waktu dengan pola linier dan non-linier, serta memiliki potensi untuk diterapkan pada dataset atau sektor lain yang memiliki karakteristik serupa.
Co-Authors Hauzan Hanifah Zahra Sugiarto, Sugiarto Vera Febrianti Pakpahan Wahyu Syaifullah