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All Journal Jurnal ULTIMATICS ALMUISY: Journal of Al Muslim Information System
Naella Nabila Putri Wahyuning Naja
Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Integrasi Sistem Cerdas Berbasis AI untuk Penyaluran Bantuan Langsung Tunai yang Tepat Sasaran Wresti Andriani; Naella Nabila Putri Wahyuning Naja
ALMUISY: Journal of Al Muslim Information System Vol. 4 No. 1 (2025): ALMUISY: Journal of Al Muslim Information System
Publisher : LPPM STMIK AL MUSLIM

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Abstract

Penelitian ini mengembangkan sistem cerdas berbasis AI untuk penyaluran Bantuan Langsung Tunai (BLT) yang tepat sasaran menggunakan algoritma Decision Tree, K-Nearest Neighbors (KNN), dan Naive Bayes. Evaluasi awal menunjukkan akurasi rata-rata model berada di bawah 50%, dengan AUC terbaik sebesar 0.47 pada Naive Bayes. Setelah optimasi menggunakan Particle Swarm Optimization (PSO), algoritma KNN menunjukkan peningkatan terbaik dengan AUC sebesar 0.51, sementara Decision Tree mencapai AUC sebesar 0.49. Sistem ini memanfaatkan data seperti penghasilan, kondisi kesehatan, dan status tempat tinggal untuk menentukan kelayakan penerima BLT. Penelitian ini membuktikan bahwa penggunaan metode AI dengan optimasi mampu meningkatkan efisiensi dan akurasi dalam mendistribusikan BLT secara lebih tepat sasaran, memberikan kontribusi signifikan pada perbaikan sistem bantuan sosial.
Machine Learning for Chili Pepper Price Forecasting Using Exogenous Public-Attention Signals and Bayesian Hyperparameter Optimization Wresti Andriani; Gunawan Gunawan; Naella Nabila Putri Wahyuning Naja
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4439

Abstract

Chili prices in Indonesia are highly volatile due to seasonal production, fragile supply chains, and shocks in public perception. This study improves short-run forecast accuracy by adding public-attention signals (Google Trends and news volume) as exogenous features summarized in a Shock Index. Evaluation metrics are sMAPE (primary), RMSE, and MASE; hyperparameters are tuned via Bayesian HPO. Empirically, the attention-augmented configuration (S4: +Trends +News +Shock) is best. Post-HPO (average across horizons), S4 attains sMAPE 12.47%, RMSE 3,433 IDR/kg, and MASE 0.87. By horizon, S4’s sMAPE is 9.8% (H=1), 12.0% (H=2), 15.6% (H=4); RMSE 2,550/3,350/4,400 IDR/kg; MASE 0.78/0.86/0.96. Compared with the price-only (S1) baseline, S4 is already better pre-tuning and becomes even stronger after HPO (average sMAPE reduction ≈ −6.2% relative). These findings show that incorporating the intensity of public issues enhances predictive value—especially at longer horizons when uncertainty rises—and that the approach is ready for operational use in nowcasting and early-warning.
Co-Authors Gunawan Gunawan Wresti Andriani Wresti Andriani