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Latif, Nuraida
Universitas Teknologi Akba Makassar
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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Explainable Machine Learning for Long-Term Monthly Hydroclimatic Forecasting and Extreme-Event Detection Arif Fadillah; Markani Pato; Nuraida Latif; Benny Leornard Encrico Panggabean; Muhammad Rizal; Mursalim Mursalim; Muhajirin Muhajirin
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2741

Abstract

Long-term hydroclimatic prediction in arid urban environments remains methodologically demanding because monthly records are often intermittent, highly seasonal, zero-inflated, and dominated by rare but consequential extreme events. Using a 121-year monthly hydroclimatic record for Makkah, Saudi Arabia, spanning January 1901 to December 2021, this study develops an explainable hybrid machine-learning framework for monthly forecasting, seasonal diagnostics, and extreme-event detection. The dataset contains 1,452 monthly observations with a mean value of 6.19, median of 3.00, standard deviation of 8.05, and maximum of 52.00, indicating a strongly skewed distribution. Exploratory analysis reveals pronounced seasonality: November, December, and January exhibit the highest hydroclimatic values, whereas June is consistently dry across the full record. A temporal feature set was constructed using lag variables, rolling statistics, annual seasonal memory, cyclical month encodings, and trend indicators. Several predictive models were evaluated, including Random Forest, Extra Trees, Histogram Gradient Boosting, XGBoost, and a hybrid SARIMA–Random Forest residual-correction model. Extra Trees achieved the best forecasting performance on the holdout period, with MAE = 2.997, RMSE = 5.603, sMAPE = 57.669%, and R² = 0.518. Extreme-event detection was performed using a 90th-percentile threshold of 17.68, identifying 146 extreme months over the full record. The best classification trade-off was obtained by Histogram Gradient Boosting, while Random Forest produced the highest ROC-AUC. SHAP-based interpretation demonstrates that seasonal phase variables and annual memory features dominate model behaviour, especially month_cos, month_sin, same_month_last_year, and lag_12. The findings show that interpretable ensemble learning can provide a more transparent and operationally relevant framework than accuracy-only forecasting for arid-region hydroclimatic risk assessment.
Waste Service Retribution Management in Selayar Islands Regency through Website-Based Solutions Wisda; Nuraida Latif; Rezki Apriana; Neneng Awaliah; Kumar Vivek; Kamaruddin
Ceddi Journal of Information System and Technology Vol. 3 No. 1 (2024): April
Publisher : Yayasan Cendekiawan Inovasi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i1.78

Abstract

The waste retribution management system at the Environmental Service office in Selayar Islands Regency is currently suboptimal. Customers are required to register for waste collection in person at the office, and the payment process must be done through a collection officer who visits the customer's location. Additionally, there is no formal channel for customers to provide feedback on the waste services they receive. To address these issues, an application is proposed to streamline waste service usage for the community and facilitate reporting for Environmental Service employees. The study aims to design and implement an Application for Data Collection and Reporting of Waste Service Retribution at the Environmental Service of Selayar Islands Regency. The research utilized field research, library research, interviews, and documentation to gather relevant data. The research methodology employed was Rapid Application Development (RAD), which involves stages such as requirements planning, user design, development, and implementation. The findings of the study suggest that the proposed application is highly feasible, based on the results of the respondents' assessments. This application is envisioned to significantly improve waste service management and customer experience in Selayar Islands Regency.
Co-Authors - STMIK AKBA Makassar, Wisda A.Yulia Muniar Agus Halid Agus Halid, Agus Ali, Lutfi Ananda, Restu Andani Achmad Andi Diaunnajiyah Andi Sumardin Andi Yulia Muniar Arif Fadillah Arif Fadillah Ashari Ashari Ashari Askar, Muhammad Ichfan Asrul Awaliyah, Neneng Ayu Astuti Benny Leornard Encrico Panggabean Butsiarah Dhea Monica Taiboko Dyah Utari Yusa Wardhani Elly Warni Elly Warni Hasriana Kamaruddin Khaidir Rahman Nasir Kumar Vivek Markani, Markani Marwa Sulehu Mashud, Mashud Muhajirin, Muhajirin MUHAMMAD ICHFAN ASKAR Muhammad Nuhwaqif MUHAMMAD RIZAL MUHAMMAD RIZAL H Mursalim Mursalim Mursalim Mursalim Mursalim mursalim mursalim nasir, khaidir rahman Neneng Awaliah Neneng Awaliah Neneng Awaliah Neneng Awaliyah Nur Rifkah Rusdianto Nurhajar Anugraha NURZAENAB, NURZAENAB Qadri, Muhammad Ramlah P Ratnawati Ratnawati Ratnawati, Ratnawati Rezki Apriana Rizal, Muh Rohayati - Arifin Rohayati Arifin Rohayati Arifin Rohayati Arifin, Rohayati Rusdianto, Nur Rifkah Sabir, Fitriana M. Saputra, Febri Hidayat Sulehu, Marwa Syaharullah Disa, Syaharullah Taiboko, Dhea Monica TAMRAH Tamsir, Tamsir Umar, Muh Yusuf, Andi M Zahir Zainuddin