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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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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 5 Documents
 1 
Search results for , issue "Vol 15, No 2: August (2022)" : 5 Documents clear
SEIHR Model on Spread of COVID-19 and Its Simulation Rois, Muhammad Abdurrahman; Tafrikan, Mohamad; Norasia, Yolanda; Anggriani, Indira; Ghani, Mohammad
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.v15i2.1141

Abstract

The modified SEIR model of the COVID-19 spread is divided into five compartments: susceptible, exposed, infected, and recovered. Based on the results, two equilibrium points were obtained: the disease-free equilibrium point and the endemic equilibrium point. The existence of an equilibrium point depends on the value of the basic reproduction number R0, as well as on stability. The endemic equilibrium point exists if it is satisfied R0>1. Then, the disease-free equilibrium point is said to be locally asymptotic stable if R0<1, and the endemic equilibrium point is locally asymptotic stable if R0>1. Sensitivity analysis was performed to determine the most influential parameters in the spread of the virus. Finally, the numerical simulations determine the behavior of the model and support the results of the dynamic analysis.
Stacked LSTM-GRU Model for Traffic Anomalies Detection Alsyaibani, Omar Muhammad Altoumi; Utami, Ema; Raharjo, Suwanto; Hartanto, Anggit Dwi
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.v15i2.1855

Abstract

This study aims to improve the accuracy of the intrusion detection system model. It focused on LSTM and GRU methods proposed by several previous studies. The bidirectional layer was also tested to see if it improves model performance. Dataset used in the study was CIC IDS 2017. The dataset was divided into 3 parts, for training, validation, and testing purposes. Validation data was used to evaluate model performance in every training iteration. It helped to make the model would not overfit the training data. Furthermore, Dropout layer and L2 regularization were also added to the model architecture. The training model was done in a binary classification approach with a learning rate of 0.0001. We found that the stacked method reached accuracy 98.1087% in 100 iteration training. This result is slightly higher than LSTM, GRU, Bidirectional LSTM, and Bidirectional GRU. The method which contains LSTM layer performed its best accuracy using Tanh activation. Differently, GRU and Bidirectional GRU performed the best performance with Lrelu and Prelu activation function, respectively. All models could reach the plateau in the first 20 iterations, while in the next 80 iterations the model performance still could be fluctuately improved. Even though the model already reached the plateau in 20 iteration training, it is still possible for the model to slowly improve by using a small learning rate and by implementing Dropout layer and L2 regularization. Fluctuation of model performance implies that the highest model performance was not always reached in the last training iteration. ModelCheckPoint could help to overcome the issue. In addition, the Bidirectional layer increased the complexity of the model which certainly increased training duration. The bidirectional layer improved the performance of the GRU method, but it did not improve the performance LSTM method.
Effect of Macroprudential Loan to Value (LTV) Policy using the Support Vector Regression (SVR) Approach Saadah, Siti; Purnomo, Muhammad Ridaffa
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.v15i2.1882

Abstract

Macroprudential policy has a goal to confine the risk and price from crises systemic, especially in managing financial stability amidst the COVID-19 pandemic. One of its instruments is Loan to Value (LTV). Ratio of LTV is a ratio between value of credit or cost that can gave from Bank Conventional or Syariah towards collateral value as property. This study aims in getting to know about its influence on citizen to take Kredit Kepemilikan Rumah (KPR). Based on the data from Central Bank Indonesia (BI) would be found about the increasing ratio of LTV yoy. The data set in this study derived from five bank with the data range being from 2014 to 2020. According to the characteristic data that will be used, thus one of the algorithm machine learning that is Support Vector Regression (SVR) was chosen as an approach to observe this trend. By using this method, the result indicated which bank that had been influenced by LTV ratio. Category of the bank who got impact are the bank that had the reverse influence between credit value of home ownership, they are Foreign Bank, Mixed Bank, Bank Persero, Bank Swasta, and Bank Perkreditan Rakyat.
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.
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.

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