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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Global Science: Journal of Information Technology and Computer Science
Moh. Khoridatul Huda
Universitas Negeri Surabaya
Computer Science & IT Mathematics

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Design of Fuzzy Logic Controller Based on Gustafson-Kessel Clustering for Quadcopter Altitude Control Belgis Ainatul Iza; Heri Purnawan; Moh. Khoridatul Huda; Nurul Laili
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.41543

Abstract

Quadcopter altitude control is a critical issue in unmanned aerial vehicle systems due to its nonlinear dynamics and impact on operating conditions. Various fuzzy logic-based approaches have been used to address these nonlinear characteristics. However, in many previous studies, the rule structure and membership function were determined heuristically or based on expert knowledge, resulting in a less systematic design process that did not fully represent the distribution of system dynamics data.This study proposes a data-driven fuzzy control approach using the Gustafson–Kessel clustering algorithm to automatically generate fuzzy rules from system data. This algorithm is used to identify an ellipsoidal cluster structure in the input space formed by the error variable (e) and the error change (Delta e). The cluster center parameters are used to construct Gaussian membership functions and a rule base for the fuzzy inference system that generates control signals for the quadcopter's vertical dynamics.Evaluation is conducted through quadcopter altitude control simulations using several error metrics. The simulation results show Integral Squared Error value of 0.363789, Integral Absolute Error of 0.554409, Root Mean Square Error of 0.190637, and Mean Absolute Error of 0.055386, with a maximum error of 0.999600 in the beginning of the system response.
Predictive Modelling of Disease Patterns Using Time-Series Patient Data in Primary Healthcare Moh. Khoridatul Huda; Jerhi Wahyu Fernanda; Darmatasia
Global Science: Journal of Information Technology and Computer Science Vol. 2 No. 2 (2026): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v2i2.204

Abstract

Understanding and predicting disease distribution patterns in primary healthcare settings require models capable of integrating both spatial and temporal dimensions. Traditional statistical approaches often fail to capture complex non-linear relationships across locations and time, leading to delayed detection of disease clusters. Objective: This study aims to develop a spatiotemporal machine learning framework to identify and forecast potential disease hotspots using electronic primary care records from 2024. Methods: The dataset comprised 5,343 patient visit records containing temporal, geographic (village-level), and clinical attributes. Data preprocessing included temporal aggregation, spatial encoding, and feature normalization. Three models—Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot—were trained and evaluated. Model performance was assessed using AUC, F1-score, and spatial accuracy metrics to ensure both predictive precision and spatial coherence. Results: Analysis revealed a clear seasonal pattern, with disease incidence peaking between April and August. Spatial mapping identified consistent hotspots in Sungai Asam and Beringin, accounting for over 70% of total cases. Among all tested models, Multi-EigenSpot achieved the best performance (AUC = 0.91; F1 = 0.86), effectively capturing multi-cluster spatial variability across months. Conclusions & Implications: The findings demonstrate that spatiotemporal learning models can significantly enhance disease surveillance and early warning capabilities in primary healthcare systems. Integrating spatial intelligence with explainable machine learning improves predictive accuracy, supports evidence-based policy, and enables targeted interventions for emerging disease hotspots in resource-limited settings.
Co-Authors Darmatasia Heri Purnawan Iza, Belgis Ainatul Jerhi Wahyu Fernanda Nurul Laili