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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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Jawa tengah
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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 235 Documents
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Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients Tikaningsih, Ades; Lestari, Puji; Nurhopipah, Ade; Tahyudin, Imam; Winarto, Eko; Hassa, Nazwan
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2816

Abstract

Cardiovascular disease (CVD) stands as the foremost contributor to worldwide mortality, with strokes as part of significant CVD. Research on potential mortality risks and hospitalizations for stroke patients became crucial as a basis for evaluation to improve the quality and control of stroke patient services. Although machine learning technology has been widely used in health data analysis, understanding the relative performance and characteristics of machine learning (ML) models is still limited. Therefore, the study aims to broaden this understanding by comparing five ML models, namely XGBoost, Random Forest, Decision Trees, CatBoost, and Extra Trees, using stroke patient data from RSUD Banyumas Neural Poliklinik Indonesia. The model performance improvement process is the main focus, involving adjustments using the Optuna tuning library. Through this tuning approach, the key parameters of each ML model are optimally adjusted to improve their performance in predicting mortality risk and the duration of hospitalization for stroke patients. As a result, the XGBoost algorithm proved superior in predicting mortality (accuracy 86%, AUC 0.87) and the duration of hospitalization (accuracy 82%, AUC 0.79). This research has great potential to help hospitals identify high-risk stroke patients and plan more efficient treatment. This approach allows hospitals to use their resources better, improve medical services, and reduce unnecessary treatment costs.
CNN and SVM Combination for Multi-Class Classification of Diabetic Retinopathy Based on Fundus Imaging Agustin, Tinuk; Purwidiantoro, Moch. Hari; Utami, Ema; Fatta, Hanif Al
Telematika Vol 15, No 2: August (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i2.2086

Abstract

Diabetic Retinopathy (DR) is a cause of blindness. Early detection has the potential to save the patient's vision. Reading fundoscopic photos requires more expertise and effort by the ophthalmologist. There are many visual similarities in lesions and only minor differences in the spatial domain. A computer-assisted automatic detection system is needed to assist medical experts in DR diagnosis and can reduce costs. This study proposes a combination method of Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for the automatic classification of Diabetic Retinopathy (DR). The pre-train architecture Inception-V3 uses for feature extraction of input data. After training and getting the best model, the next is classification with SVM. Data augmentation techniques use to multiply and add variations to the dataset. Before the feature extraction stage, the dataset will process by separating the green channel from the RGB image. Next, the CLAHE will require increasing the contrast of the picture. This study aims to improve the performance in multi-class DR classification. The proposed model uses four classes of unbalanced and small datasets from retinal fundus images. This paper also compares the combined performance of CNN SVM with CNN Softmax based on the accuracy value to validate the results. Our experiments show that the combination of CNN SVM can increase the accuracy of auto-detection of DR severity up to 11.48% better than CNN softmax. The results showed that the pre-trained architectural model from the combination of Inception-V3 with SVM classification improves the accuracy extensively, even on small and unbalanced datasets.
Effectiveness of Pickup and Delivery Services in Logistics Companies with Route Optimization using the A* Algorithm Prianto, Cahyo; Adiningrum, Nur Tri Ramadhanti
Telematika Vol 17, No 2: August (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i2.2860

Abstract

Logistics is situated at the epicenter of both production and consumption, its role in the economy is becoming more and more significant. A logistics company is a business that specializes in offering logistics services; an example of such a business in Bandung is a logistics company that offers pickup and delivery services. Of the many locations that will be visited by couriers every day, of course, effective vehicle route management is needed to minimize costs, time, and vehicle efficiency. Therefore, the goal is to find the shortest route from one location to another based on the distance factor. To achieve this goal, the A* algorithm is used using Python as a solution to find the shortest route and Dijkstra as a comparison of route search algorithms. The study's findings demonstrated that the A* algorithm, with a calculation time of 0.0004022 ms, was efficient in finding the shortest path while requiring the least amount of CPU processing at 5.56%. While Dijkstra took 7.29% of the computation and produced a time of 0.033026 ms. A* proved effective in finding the shortest route by producing a distance of 3.11 km. While other routes produced distances of 3.34 km, 4.54 km, and 4.77 km. In addition, the use of a GUI has been successfully implemented as an interactive visualization so that couriers can easily find the shortest route along with the distance traveled. The logistics company can use the A* algorithm and the GUI developed to improve the efficiency of delivery and pickup of goods. By utilizing optimized shortest route searches, companies can save time and increase customer satisfaction through faster and more efficient delivery.
Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification Agustiani, Sarifah; Rianto, Yan
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2831

Abstract

Colon cancer is a type of cancer that attacks the last part of the human digestive tract. Factors such as an unhealthy diet, low fiber consumption, and high animal protein and fat intake can increase the risk of developing this disease. Diagnosis of colon cancer requires sophisticated diagnostic procedures such as CT scan, MRI, PET scan, ultrasound, or biopsy, which are often time-consuming and require particular expertise. This study aims to classify colon cancer based on histopathological images using a dataset of 10,000 images. This data is divided into 7,950 images for training, 2,000 for testing, and 50 for validation, aiming to achieve effective generalization. The Convolutional Neural Network (CNN) method was applied in this research with a relatively shallow architecture consisting of 4 convolution layers, 2 fully connected layers, and 1 output layer. Research results were evaluated by looking at the accuracy value of 99.55%, precision value of 99.49%, recall of 99.59%, prediction experiments on several images, and loss and accuracy graphs to detect signs of overfitting. However, this research has limitations in determining hyperparameters and layer depth, which was only tested from 1 to 5 convolution layers. Therefore, there are still opportunities for further development, such as applying unique feature extraction before the classification process.
An Improved K-NN Algorithm and Bagging for Liver Disease Classification Wardhani, Anindya Khrisna; Lakhmudien, Lakhmudien; Putri, Astrid Novita; Ashour, Salim Fathi Salim
Telematika Vol 15, No 2: August (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i2.1247

Abstract

The function of the liver is to detoxify toxins in the human body and control cholesterol and fat in the human body. If the liver is damaged, health will be disturbed, even death. A lot of data on the liver disease can be used to predict liver disease. This study aims to improve the accuracy of liver disease classification using K-NN and bagging methods. The experimental results in this study are the bagging method can improve the performance accuracy of the K-NN prediction model even though it is based on the T-test even though there is only a slight change in accuracy. In this study, the accuracy value using the K-NN method was 78.56%. For the highest accuracy value of 99.83% using the K-NN model which is integrated with bagging. From the results of experiments carried out in this study, the K-NN model with bagging can certainly improve performance on the prediction model of liver disease classification. So that the predictions made can be more accurate and can be used to predict liver disease.
Comparison of Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in Forecasting Commodity Prices Mujiyanto, Mujiyanto; Nurindahsari, Susi; Nurul Izza, Rahmafatin
Telematika Vol 17, No 2: August (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i2.2932

Abstract

In this study, we compare the performance of both hybrid and non-hybrid forecasting models, explicitly focusing on Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in predicting commodity prices within the volatile market of Central Java, Indonesia. The primary objective is to evaluate which hybrid and non-hybrid models provide the most accurate and reliable forecasts under various conditions. Analyzing daily price data from the SiHaTi platform, an official service provided by Bank Indonesia, the Hybrid ARIMA-LSTM model emerges as the most accurate, achieving a forecast accuracy of 92.5%, compared to the 78.3% and 84.7% accuracies of Linear Regression and ARIMA, respectively. These findings underline the potential advantages of combining machine learning with statistical methods to improve predictions in dynamic market conditions, providing invaluable insights for policymakers and market analysts. However, it should be noted that only one hybrid model was compared, and future research should explore multiple hybrid models to ensure a comprehensive evaluation of their effectiveness.
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.

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