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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Nasional Teknologi Komputer
Salwa Nur JB
Universitas Pembangunan Panca Budi
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Klasifikasi Tingkat Demensia Alzheimer’s Berbasis Machine Learning dan Deep Learning Salwa Nur JB; Fachrurazy; Irwansyah Putera Sitorus; Katharina Tyas Aprilia
Jurnal Nasional Teknologi Komputer Vol 6 No 3 (2026): Juli 2026
Publisher : CV. Hawari

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Abstract

Alzheimer’s disease is a progressive neurodegenerative disorder characterized by cognitive decline, particularly in memory and reasoning abilities [1]. Early detection of disease severity plays a critical role in improving clinical decision-making [4]. This study aims to classify Alzheimer’s dementia levels using a public MRI dataset from Kaggle consisting of four classes: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented [11]. This research compares conventional machine learning methods, namely Decision Tree and Random Forest, with a deep learning approach using Convolutional Neural Network (CNN) [4][8]. The experimental stages include preprocessing, data splitting, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix [4][10]. The results show that Random Forest outperforms Decision Tree with an accuracy of 89%, while CNN achieves the highest performance at 92% [2][3][4]. These findings indicate that CNN is more effective in extracting spatial features from MRI images compared to traditional machine learning methods [2][3][9].
OPTIMIZING PATIENT SERVICE PRIORITY DETERMINATION IN THE EMERGENCY DEPARTMENT USING THE K-MEANS ALGORITHM AT RSUD LANGSA ACEH Salwa Nur JB; Muhammad Irfan Sarif; Lola Astri Nadita; Fachrurazy; Lewika Tampubolon
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6232

Abstract

Abstract: Emergency Department (ED) services require speed, accuracy, and coordination among medical staff. Langsa Aceh Regional General Hospital, a Type B hospital, faces a high volume of patient visits that could lead to inefficiencies, particularly in determining service priorities. This study aims to optimize ED patient segmentation to support service prioritization using the K-Means clustering algorithm. The data used consists of 31,761 ED patient records from Langsa Regional General Hospital in 2025; after preprocessing, 24,062 records meeting the analysis criteria were obtained. Research variables included visit frequency, visit interval, type of service, duration of service, and patient urgency level. The research method employed a quantitative approach using data mining techniques with the K-Means algorithm. The results showed the formation of three clusters with a Silhouette Score of 0.6213, indicating good clustering quality. The resulting clusters represent the categories of non-urgent, semi-urgent, and complex care needs patients. These segmentation results can support more systematic service prioritization, improve efficiency, accelerate response times, and support a data-driven triage system at the Langsa Regional General Hospital Emergency Department in Aceh. Keywords: K-Means Clustering, Medical Informatics, Patient Care Prioritization, Patient Segmentation, Healthcare Data Mining   Abstrak: Pelayanan pada Instalasi Gawat Darurat (IGD) menuntut kecepatan, ketepatan, dan koordinasi antar tenaga medis. RSUD Langsa Aceh sebagai rumah sakit tipe B menghadapi tingginya jumlah kunjungan pasien yang berpotensi menimbulkan ketidakefisienan, khususnya dalam penentuan prioritas pelayanan. Penelitian ini bertujuan mengoptimalkan segmentasi pasien IGD untuk mendukung penentuan prioritas pelayanan menggunakan algoritma K-Means clustering. Data yang digunakan merupakan data pasien IGD RSUD Langsa tahun 2025 sebanyak 31.761 record, dan setelah preprocessing diperoleh 24.062 data yang memenuhi kriteria analisis. Variabel penelitian meliputi frekuensi kunjungan, interval kunjungan, jenis layanan, durasi pelayanan, dan tingkat urgensi pasien. Metode penelitian menggunakan pendekatan kuantitatif melalui teknik data mining dengan algoritma K-Means. Hasil penelitian menunjukkan terbentuk tiga cluster dengan nilai Silhouette Score sebesar 0,6213 yang mengindikasikan kualitas pengelompokan baik. Cluster yang dihasilkan merepresentasikan kategori pasien non-urgensi, semi-urgensi, dan kebutuhan pelayanan kompleks. Hasil segmentasi ini dapat mendukung penentuan prioritas pelayanan secara lebih sistematis, meningkatkan efisiensi, mempercepat waktu respon, serta mendukung sistem triase berbasis data di IGD RSUD Langsa Aceh. Kata Kunci: K-Means Clustering, Informatika Medis, Prioritas Pelayanan Pasien, Segmentasi Pasien, Data Mining Kesehatan
Co-Authors Fachrurazy Irwansyah Putera Sitorus Katharina Tyas Aprilia Lewika Tampubolon Lola Astri Nadita Muhammad Irfan Sarif