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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Informatika (JUTIF)
Nur Risal, Andi Akram
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Computer Science & IT Engineering

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PCA and t-SNE Implementation for KNN Hypertension Classification Visualization Cahyana Resky, Andi Aulia; Lapendy, Jessica Crisfin; Nur Risal, Andi Akram; Surianto, Dewi Fatmarani; Wahid, Abdul
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6208

Abstract

Hypertension is a condition that, if allowed to increase, can significantly injure internal organs due to high blood pressure. The objective of this study is to use the K-Nearest Neighbor (KNN) algorithm along with PCA and t-SNE to accurately identify four categories of Hypertension, Normal, Hypertension, Stage 1 Hypertension, and Stage 2 Hypertension. After establishing the scope, a dataset consisting of 7,794 samples was sourced from Labuang Baji Regional General Hospital, Makassar, and contained age, weight, and systolic and diastolic blood pressure parameters. The class distribution is Normal (36.3%), Hypertension (43.12%), Stage 1 Hypertension (8.29%), and Stage 2 Hypertension (12.31%). Experimental results show that the KNN base model achieved 99% accuracy, KNN with PCA reached 100%, and KNN with t-SNE attained 99%. Cross-validation was used to evaluate model generalization, yielding accuracies of 91%, 94%, and 91%, respectively. These findings suggest that KNN, particularly when integrated with t-SNE, is highly effective in visualizing and classifying non-linear data structures. Furthermore, this study demonstrates that incorporating dimensionality reduction techniques enhances the interpretability of classified hypertension data, which is crucial for informed decision-making by mental health committees.
Clustering and Modeling of Daily Weather Pattern Distribution in Makassar City Using Hybrid DBSCAN-Gaussian Mixture Model Risaldi, Muhammad; Safitri, Ayu; Nur Risal, Andi Akram; Surianto, Dewi Fatmarani; Andayani, Dyah Darma; Edy, Marwan Ramdhany; Firdaus, Firdaus; Parenreng, Jumadi M
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5254

Abstract

Dynamic and irregular daily weather changes present major challenges in understanding seasonal patterns. Data uncertainty, outliers, and inter-season variability further complicate weather analysis using conventional methods. To address this issue, this study integrates Density-Based Spatial Clustering of Application with Noise (DBSCAN) and Gaussian Mixture Model (GMM) to analyze daily weather patterns in Makassar City. A total of 2,192 daily records from 2019 to 2024, including rainfall, specific humidity, atmospheric pressure, and wind speed, were examined. DBSCAN detected one dominant cluster (2019 data) and 173 outliers. The main cluster was further partitioned by GMM into three sub-clusters representing the wet (511 records, 13.39 mm rainfall), dry (633 records, 0.15 mm), and transition (875 records, 2.53 mm) seasons. GMM identified 1,764 fixed clusters and 255 ambiguous data points, with a log-likelihood of 5091.22 and the highest Silhouette Score of 0.188. Comparative evaluation demonstrated that the hybrid DBSCAN-GMM achieved superior performance (Silhouette Score = 0.1434) compared to DBSCAN or GMM individually. The novelty of this research lies in applying the DBSCAN-GMM integration, which is rarely used in tropical weather analysis, to capture seasonal structure and anomalies adaptively. This study contributes methodologically to clustering-based weather modeling and practically supports applications such as agricultural planning, disaster mitigation, and adaptive climate strategies in tropical regions.
Co-Authors Abdul Wahid Ayu Safitri Cahyana Resky, Andi Aulia Dewi Fatmarani Surianto Edy, Marwan Ramdhany Firdaus Firdaus Jumadi Mabe Parenreng Lapendy, Jessica Crisfin Risaldi, Muhammad