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The Telematika, with registered number ISSN 2442-4528 (online) ISSN 1979-925X (print) is a scientific journal published by Universitas Amikom Purwokerto. The journal registered in the CrossRef system with Digital Object Identifier (DOI) prefix 10.35671/telematika. The aim of this journal publication is to disseminate the conceptual thoughts or ideas and research results that have been achieved in the area of Information Technology and Computer Science. Every article that goes to the editorial staff will be selected through Initial Review processes by the Editorial Board. Then, the articles will be sent to the Mitra Bebestari/ peer reviewer and will go to the next selection by Double-Blind Preview Process. After that, the articles will be returned to the authors to revise. These processes take a month for a minimum time. In each manuscript, Mitra Bebestari/ peer reviewer will be rated from the substantial and technical aspects. The final decision of articles acceptance will be made by Editors according to Reviewers comments. Mitra Bebestari/ peer reviewer that collaboration with The Telematika is the experts in the Information Technology and Computer Science area and issues around it.
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Kab. banyumas,
Jawa tengah
INDONESIA
Telematika
Published by Universitas Amikom Purwokerto
ISSN : 1979925X     EISSN : 24424528     DOI : 10.35671/telematika
Core Subject : Education,
Education
Jl. Letjend Pol. Soemarto No.126, Watumas, Purwanegara, Kec. Purwokerto Utara, Kabupaten Banyumas, Jawa Tengah 53127
Arjuna Subject : -
Articles 6 Documents
 1 
Search results for , issue "Vol 18, No 1: February (2025)" : 6 Documents clear
Optimization of the XG-Boost Algorithm for Predicting Stroke Patient Care Outcomes Lestari, Puji; Tahyudin, Imam; Tikaningsih, Ades; Nurhopipah, Ade
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2817

Abstract

Stroke is a critical health issue in Indonesia, contributing to high mortality rates. At Banyumas District Hospital, stroke is the fourth most common condition, presenting significant challenges in both clinical care and financial management. The purpose of this study is to enhance the quality of services and optimize treatment costs for stroke patients by developing a predictive model using the XGBoost algorithm. This study employs the XGBoost algorithm to develop predictive models, which are then implemented within a web-based machine learning application using the Paython Flask framework. The models predict patient mortality and hospitalization duration. The results indicate that the XGBoost algorithm predicts patient mortality with 86% accuracy and hospitalization duration with 82% accuracy. The developed application significantly enhances care quality and resource management at Banyumas District Hospital by providing accurate predictions to support healthcare decision-making. The use of this application can significantly improve the management of stroke patient care at Banyumas District Hospital, thus maximizing service quality and optimizing treatment costs. By integrating accurate predictive modeling into healthcare decision-making processes, the application facilitates more effective allocation of resources and timely medical interventions, ultimately contributing to better patient outcomes.
Comparative Analysis of Distance Metrics in KNN and SMOTE Algorithms for Software Defect Prediction Maulidha, Khusnul Rahmi; Faisal, Mohammad Reza; Saputro, Setyo Wahyu; Abadi, Friska; Nugrahadi, Dodon Turianto; Adi, Puput Dani Prasetyo; Hariyady, Hariyady
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3008

Abstract

As the complexity and scale of projects increase, new challenges arise related to handling software defects. One solution uses machine learning-based software defect prediction techniques, such as the K-Nearest Neighbors (KNN) algorithm. However, KNN’s performance can be hindered by the majority vote mechanism and the distance/similarity metric choice, especially when applied to imbalanced datasets. This research compares the effectiveness of Euclidean, Hamming, Cosine, and Canberra distance metrics on KNN performance, both before and after the application of SMOTE (Synthetic Minority Over-sampling Technique). Results show significant improvements in the AUC and F-1 measure values across various datasets after the SMOTE application. Following the SMOTE application, Euclidean distance produced an AUC of 0.7752 and an F1 of 0.7311 for the EQ dataset. With Canberra distance and SMOTE, the JDT dataset produced an AUC of 0.7707 and an F-1 of 0.6342. The LC dataset improved to 0.6752 and 0.3733 in tandem with the ML dataset, which climbed to 0.6845 and 0.4261 with Canberra distance. Lastly, after using SMOTE, the PDE dataset improved to 0.6580 and 0.3957 with Canberra distance. The findings confirm that SMOTE, combined with suitable distance metrics, significantly boosts KNN’s prediction accuracy, with a P-value of 0.0001.
CNN-Based for Skin Cancer Classification with Dull Razor Filtering and SMOTE KZ, Widhia Oktoeberza; Prasetio, Wahyu Dwi; Ramadhan, Adam Idham; Putra, Adde Nanda C.; Eliora, Firsti; Mainil, Afdhal Kurniawan; Susanto, Agus
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3048

Abstract

Skin cancer is usually diagnosed by dermatologists through biopsy, which can be a time-consuming process due to limited resources. Early detection of skin cancer can increase the survival rate to over 99%, but if it's detected late, the rate drops to around 14%. This finding highlights the need for a rapid and accurate computing system for early cancer detection, which can prevent severe consequences. The purpose of this study is to classify skin cancer images into benign and malignant classes based on their nature. To facilitate the classification of CNN-based skin cancer, this research employs dull razor filtering. Additionally, the SMOTE method handles the unbalanced dataset. The classification results indicate that the proposed approach has an accuracy of 88.54%, a precision of 88%, and a sensitivity of 88%. These findings suggest that CNN-based methods can aid dermatologists in the diagnosis of skin cancer.
Guava Disease Detection and Classification: A Systematic Literature Review Kurniawan, Muhammad Bayu; Utami, Ema
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2901

Abstract

Guavas (Psidium guajava) are nutrient-rich fruits that provide significant health benefits. However, guava cultivation faces persistent threats from various diseases affecting both leaves and fruits, leading to substantial yield and quality losses. The early and accurate detection of these diseases is crucial but remains challenging due to economic constraints and limited infrastructure. While plant pathologists employ various diagnostic methods, these approaches are often time-consuming, costly, and sometimes inconsistent. Recent advancements in deep learning (DL) and machine learning (ML) have introduced innovative techniques for guava disease identification. This study conducts a Systematic Literature Review (SLR) to evaluate the existing research on guava leaf and fruit disease detection, focusing on dataset sources, identified disease categories, preprocessing and augmentation techniques, applied algorithms, and reported evaluation metrics. A comprehensive search was conducted across multiple databases, covering publications from 2017 to 2023, leading to the identification of 47 relevant studies. After applying exclusion criteria, 16 studies were selected for in-depth analysis. The findings highlight the most commonly used datasets, the predominant classification techniques, and the effectiveness of various deep learning models based on multiple performance metrics, providing insights into current research trends, existing limitations, and potential directions for future studies. This review serves as a valuable reference for researchers aiming to enhance the accuracy and efficiency of guava leaf and fruit disease diagnosis through data-driven approaches.
Comparative Analysis of Green Snake Identification using Head Structure and Body Patterns with Vision Transformer Putriany, Eva; Ariatmanto, Dhani
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.2992

Abstract

Snakebites remain a major global health concern, with over 4.5 million cases annually, primarily affecting rural populations in tropical regions. Accurate snake species identification is critical for proper treatment, yet challenges persist due to morphological similarities, particularly among visually similar green snake species. We test five Vision Transformer (ViT)-based models to see how well they can classify snakes based on pictures of their heads and bodies. The models are ViT-B16, DeiT, PoolFormer, Swin-T, and CaiT. Results indicate that head structure classification achieved higher accuracy than body pattern classification due to more distinct morphological features. CaiT outperformed other models, achieving 87% accuracy, particularly when trained on RGB images. These findings highlight the importance of model selection and dataset characteristics in improving snake species classification, especially for species with high visual similarity.
Improving Alzheimer's Disease Prediction Accuracy using Feature Selection, K Fold Cross Validation, and KNN Imputer Techniques Kirso, Kirso; Anasanti, Mila Desi
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3055

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss; it accounts for 60–70% of dementia cases. Early diagnosis remains challenging due to the subtlety of its symptoms. This study explores the effectiveness of ensemble methods, feature selection techniques, and imputation strategies in enhancing the accuracy of AD diagnosis. We applied an ensemble method with Chi-Square feature selection, achieving a high accuracy of 95.733% with 7 optimal features. The combination of classifiers, including Gradient Boosting (GB), Support Vector Machine (SVM), and Logistic Regression (LR), contributed to the high performance. Additionally, the use of KNN Imputer and K-Fold Cross Validation significantly improved accuracy, regardless of whether feature selection was employed. Notably, feature selection slightly reduced model complexity but resulted in a marginal decrease in accuracy. The study highlights the importance of these methods in achieving reliable AD predictions, though dataset dependency and potential biases from methodological choices are acknowledged. Future work may involve exploring alternative classifiers and validating findings across diverse datasets to enhance generalizability and address these limitations.

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