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Fadzilla Kusuma Ningrum
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Penerapan Model SARIMAX untuk Peramalan Curah Hujan dengan Variabel Eksogen Indian Ocean Dipole di Kota Pariaman Taura Davin Santosa; Fadzilla Kusuma Ningrum; Amalia Khoirum Mazidah; Nasrudin Nasrudin
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2762

Abstract

Rainfall is seasonal, fluctuating, and influenced by external factors, requiring a method capable of capturing these patterns while incorporating exogenous variables. In this study, the Seasonal Autoregressive Integrated Moving Average with Exogenous Variable (SARIMAX) model is applied and compared with the SARIMA model to assess the extent to which the inclusion of the exogenous IOD variable improves rainfall forecasting accuracy in Pariaman City. The data used consist of monthly secondary data for rainfall and the IOD from January 2010 to December 2024. The results indicate that the SARIMAX model outperformed the SARIMA model in forecasting performance. The best-performing model was SARIMAX(0,0,0)(1,1,1)12 with a MAPE value of 36.25% and an RMSE of 174.03. The forecasting results are expected to serve as a reference for formulating regional economic development policies and managing climate-sensitive sectors, thereby enhancing the economic resilience of Pariaman City to climate variability.
Analisis Sentimen Kebijakan Kementerian Energi dan Sumber Daya Mineral Terkait Bahan Bakar Minyak pada Media Sosial X Fadzilla Kusuma Ningrum; Muhammad Muhlis Aditya Nur Wahid; Muhammad Halim; Rizal Fakhruddin Nasution; Syadza Khumairah Akmul; Lya Hulliyyatus Suadaa
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2763

Abstract

Throughout 2024, policies issued by the Ministry of Energy and Mineral Resources (ESDM) concerning fuel triggered diverse public responses on the social media X. This study aims to analyze public sentiment on X toward the policy using 763 tweets collected through web scraping (after removing duplicates from the 913 tweets). Data were labeled using Gemini AI and validated by three annotators, achieving a Fleiss' Kappa of 0.66, indicating substantial agreement. To address class imbalance, back translation was applied as a data augmentation technique, and sentimen classification was performed using Naïve Bayes and Support Vector Machine (SVM) algorithms with N-Gram features. The results show that Naïve Bayes with back translation achieved the best performance, with an F1-score of 85%, compared with 82% for SVM. Topic modeling using Latent Dirichlet Allocation (LDA) identified three main topics with a coherence score of 0,46: the single-gate import policy, fuel scarcity at private gas stations, and the role of the House of Representatives. The dominant negative sentiment indicates an unfavorable public perception of the policy.
Co-Authors Amalia Khoirum Mazidah Muhammad Halim Muhammad Muhlis Aditya Nur Wahid Nasrudin Nasrudin Rizal Fakhruddin Nasution Suadaa, Lya Hulliyyatus Syadza Khumairah Akmul Taura Davin Santosa