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INCODING: Journal of Informatics and Computer Science Engineering
Published by Mahesa Research Center
ISSN : -     EISSN : 2776432X     DOI : 10.34007
Core Subject : Science,
Computer Science & IT
INCODING: Journal of Informatics and computer science engineering, is a journal of informatics is the study of the structure, behavior, and interactions of natural and engineered computational systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 65 Documents
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Deteksi Pola Kunjungan Pasien Berdasarkan Status Kesehatan Menggunakan Algoritma DBSCAN Razaq, Faisal; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.979

Abstract

This study identified eight visit clusters grouped into four service profiles: Acute (Clusters 1 5; 1,186/3,000 ≈ 39.5%; mean age 22.8 years; peaks on Saturday at 19:00 and Thursday at 08:00; predominant diagnoses: dengue fever, typhoid, acute respiratory infection, influenza, and gastroenteritis), Chronic (Clusters 3 4; 924/3,000 ≈ 30.8%; mean age 66–67 years; peaks on Thursday at 08:00 and Friday at 13:00; predominantly COPD, type 2 diabetes mellitus, heart failure, hypertension, and kidney failure), Routine Follow-up (Clusters 2 7; 590/3,000 ≈ 19.7%; mean age 41–42 years; peaks on Thursday at 11:00 and Friday at 15:00; including post-operative follow-up, annual check-ups, adult vaccination, cholesterol screening, and nutrition counseling), and Emergency (Clusters 0 6; 300/3,000 = 10%; mean age 44–46 years; peaks at 22:00 on Thursdays and Sundays; predominantly ischemic stroke, myocardial infarction, road-traffic injuries, appendicitis, and asthma exacerbations). The age–time–diagnosis patterns indicate a distinct segmentation of service needs: acute cases are concentrated among younger patients and peak on weekends and weekday mornings; chronic cases cluster among older adults with morning–midday weekday peaks.
Penerapan Algoritma K-Means Untuk Klasterisasi Pasien Berdasarkan Riwayat Kesehatan dan Jenis Layanan Kesehatan Putri, Riza Dwi; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.973

Abstract

The digital transformation in the healthcare sector has led to the generation of large and complex datasets, requiring appropriate analytical techniques to extract meaningful information. This study aims to implement the K-Means algorithm to cluster patients based on their health history and the types of healthcare services they use, in order to support data-driven decision-making in hospital management. The dataset consists of 1,459 patient records from Sapta Medika Hospital, covering attributes such as age, gender, chronic disease history (diabetes, hypertension, heart disease), visit frequency, medical costs, and healthcare service types including outpatient, inpatient, emergency (ER), and telemedicine. The research stages involved data preprocessing, transformation, categorical data encoding, numerical data normalization, and clustering using the K-Means algorithm. The optimal number of clusters was determined using the Elbow Method, which identified K = 3. The clustering results revealed three distinct patient groups: chronic patients with high treatment costs and frequent inpatient services, routine patients with stable conditions mostly using outpatient services, and general patients, usually younger with mild conditions. Principal Component Analysis (PCA) was used to visualize the cluster separation, while the clustering quality was evaluated with a Silhouette Score of 0.47. These results conclude that the K-Means algorithm is effective in producing meaningful and practical patient segmentation, which can be used to design more adaptive, efficient, and patient-centered healthcare service strategies.
Implementasi Algoritma K-Nearest Neighbors (KNN) dalam Deteksi Dini Hipertensi berdasarkan Analisis Tekanan Darah Siregar, Ary Prandika; Al Idrus, Said Iskandar; Indra, Zulfahmi; Taufik, Insan
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.1018

Abstract

This study aims to develop a web-based hypertension detection system using the K-Nearest Neighbors (KNN) algorithm and to analyze its accuracy in classifying hypertension status. The dataset was obtained from 447 patient medical records at RSKG Rasyida, consisting of eight variables: gender, age, systolic blood pressure, diastolic blood pressure, height, weight, body mass index (BMI), and hypertension status. The preprocessing stage involved three main steps—feature selection (age, systolic and diastolic blood pressure, BMI), data balancing using undersampling, and data normalization through the Min-Max method—resulting in 425 balanced data samples with five hypertension categories. The web application includes modules for login, dashboard, data input, detection results, and detection history, and has been evaluated using black box testing. The best KNN performance was achieved at k = 13 with 92.94% accuracy, 94% precision, 93% recall, and 93% F1-score. These results indicate that the proposed system can accurately classify hypertension and serve as an effective, data-driven screening tool for healthcare professionals.
Penerapan Arsitektur EfficientNet dalam Model CNN untuk Optimalisasi Klasifikasi Gambar Fashion pada Dataset Fashion MNIST Panggabean, Shimon Abert; Susilawati, Susilawati
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.980

Abstract

This study evaluates the performance of the EfficientNet architecture for image classification on Fashion-MNIST (70,000 grayscale images, 10 classes). The training/testing split follows the standard 60,000/10,000 scheme, with an internal validation subset drawn from the training data. Preprocessing resizes images to match EfficientNet’s input requirements. The model is trained with the Adam optimizer (learning rate 0.001), batch size 32, for 20 epochs, with data augmentation and metric monitoring. Evaluation on the test set employs accuracy, precision, recall, F1-score, and the confusion matrix. The results show accuracy = 0.9429, precision = 0.9426, recall = 0.9429, and F1-score = 0.9425. Per-class analysis indicates that Trouser and Bag achieve the highest performance, while T-shirt/top and Shirt are most challenging due to visual similarity, as reflected in the confusion matrix. Compared with several baselines standard CNN, CNN-3-128, VGG16, XG-ViT (Vision Transformer), and DRQCNN EfficientNet attains the best overall score, although its advantage is relatively marginal; hence, practical significance depends on application goals.
Analisis Persebaran Penyakit di Wilayah Menggunakan Algoritma K-Means Berbasis Data Kunjungan Fasilitas Kesehatan Suhaira, Zatin; Muliono, Rizki
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.983

Abstract

This study aims to analyze the distribution of diseases based on patient visit data to various healthcare facilities using the K-Means clustering method. The research data were obtained secondarily from the Kaggle platform, namely the ‘Healthcare Dataset’, which contains patient information, including healthcare facility attributes, medical conditions, and other related data. The determination of the optimal number of clusters was carried out using the Elbow Method, while the quality of clustering was evaluated with two internal metrics, namely the Silhouette Score and the Davies–Bouldin Index (DBI). The clustering results produced three main clusters with distinct characteristics. The first cluster was dominated by patients diagnosed with arthritis in the age group of 55–59 years with blood type O+. The second cluster showed a predominance of obesity in the age group of 35–39 years with blood type AB+, while the third cluster indicated cancer cases in the age group of 65–69 years with blood type O-. The evaluation resulted in a Silhouette Score of 0.5349 and a DBI of 0.5830, indicating that the clustering quality is fairly good, with compact and well-separated clusters. These findings not only highlight variations in disease distribution across healthcare facilities but also provide a foundation for mapping disease patterns and supporting strategic decision-making in public health..

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