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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 16, No 2: August (2023)" : 5 Documents clear
Classification of COVID-19 Cough Sounds using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Support Vector Machine Mafazy, Muhammad Meftah; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Saragih, Triando Hamonangan
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2569

Abstract

A lot of research has been carried out to detect COVID-19, such as swabs, rapid antigens, and using x-ray images. However, this method has the disadvantage that it requires taking samples through physical contact with the patient. One way to avoid physical contact is to use audio through coughing with the aim of reducing the transmission of COVID-19. Audio feature extraction such as the Mel Frequency Cepstral Coefficient (MFCC) has often been used in audio classification research, such as the classification of musical genres and so on. This study aims to compare more or less the features of audio classification performance through coughing sounds for early detection of COVID-19 using a Support Vector Machine based on the Linear and Radial Basis Function (RBF). The dataset used is the COVID-19 Cough audio dataset, before classifying, the audio data is processed into a spectrogram and then feature extraction is carried out. Classification is divided into 2 schemes, using default parameters, then using the specified configuration parameters. From the research results, the highest AUC is 0.572266 in the linear kernel-based SVM classification. Meanwhile, when using the RBF kernel, the highest AUC is 0.560181.
A Comparative Study on the Combination of Classification Algorithm and Language Model Implementation for Smart Accounting System Makayasa, Bagas Adi; Fatwanto, Agung
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2258

Abstract

Micro, Small, and Medium Enterprises (MSMEs) normally dealing with financial documentation and reporting problems due to in sufficient budget for hiring professional accounting services. Although some of them might have utilized off the shelf accounting softwares, they still face many obstacles in compiling a proper financial documentation because the employed software do not have an automatic transaction classification capability to assist users in recording any transactions. This study was aimed to investigate the opportunity of implementing automatic transaction classification for accounting system by using a Natural Language Processing (NLP) approach to automatically interpret the suitable account for any financial transactions based on the text written on the transaction forms. An experiment was conducted to compare the performance of eight combinations comprising of four classification algorithms (i.e. SVM, KNN3, KNN5, and NB) with two language models (i.e. TF-IDF and BoW). The result showed that KNN5 and TF-IDF pair gave highest performance with accuracy 82,5%, precision 82,54%, recall/sensitivity 83,7%, specificity 92,06%, and F1 Score 81,5%.
Convolutional Neural Networks for Classification of Lung Cancer Based on Histopathological Images Agustiani, Sarifah; Pribadi, Denny; Junaidi, Agus; Wildah, Siti Khotimatul; Mustopa, Ali; Arifin, Yoseph Tajul
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2356

Abstract

Lung cancer is one of the deadliest types of cancer characterized by the uncontrolled growth of cancer cells in the lung tissue due to the accumulation of carcinogens. Lung cancer ranks second in the most cases with 2.206 million new cases and ranks first in deaths. This lung cancer often does not cause symptoms in the early stages, because it only appears after the tumor is large enough or the cancer has spread to surrounding tissues or organs, so it is necessary to have early detests to prevent severity and determine follow-up treatment. This study aims to classify lung cancers using digital pathology images with data of 15000 images obtained from the LC25000 dataset containing 5,000 images for each class. The method used in this classification process uses convolutional neural networks (CNN) which is one of the implementations of Deep Learning used for digital image processing. Using this method, the doctor can diagnose and find out the type of lung cancer quickly without spending much time. Thus, the faster the prediction results received by the doctor / health expert, the faster the next action or handler will be, this study produces a fairly accurate accuracy value even though it uses a shallow CNN architecture because it only consists of 5 layers with 3 convolution layers and 2 fully connected layers, with the resulting accuracy value of 98.53%.
Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods Lestari, Mega; Kartini, Dwi; Budiman, Irwan; Faisal, Mohammad Reza; Muliadi, Muliadi
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2548

Abstract

The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.
Modification CNN Transfer Learning for Classification MRI Brain Tumor Wardhani, Retno; Nafi'iyah, Nur
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2272

Abstract

Identification, or detecting the infected part of a brain tumor on an MRI image, requires precision and takes a long time. MRI (Magnetic Resonance Imaging) is a magnetic resonance imaging technique to examine and take pictures of organs, tissues, and skeletal systems. The brain is essential because it is the center of the nervous system, which controls all human activities. Therefore, MRI of the brain has an important role, one of which is used for analysis or consideration before performing surgery. However, MRI images cannot provide optimal results when analyzed due to noise, and the bone and tumor (lumps of flesh) have the same appearance. AI (artificial intelligence), or digital image processing and computer vision, can analyze MRI images to detect or identify tumors correctly. This study proposes changes to the last layer of CNN (Convolution Neural Network) transfer learning (VGG16, InceptionV3, and ResNet-50) to identify brain tumor disease on MRI. Data were taken from Kaggle with types of glioma, meningioma, no tumor, and pituitary, with a total of 5712 training images and 1311 testing images. The proposed changes include a flattening layer and a pooling layer. The result is that replacing the flatten layer further improves accuracy, and the accuracy of the transfer learning CNNs (VGG16, InceptionV3, and ResNet-50) is 0.918, 0.762, and 0.934, respectively.

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