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Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 473 Documents
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Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features Heriyanto Heriyanto; Tenia Wahyuningrum; Gita Fadila Fitriana
JURNAL INFOTEL Vol 13 No 2 (2021): May 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i2.657

Abstract

This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% than other frames.
Acceptance of e-learning system at private university in Indonesia during the covid-19 pandemic: students' perspectives Achmad Solichin; Riki Wijaya
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.662

Abstract

The Covid-19 pandemic in Indonesia has an impact on changes in policies and the learning process at Budi Luhur University (UBL). The complete online learning policy has been implemented since the 2nd semester of 2019/2020, which began in March 2020. Students and lecturers carry out teaching and learning activities through an e-learning system developed in 2005. Although it has been implemented for a long time, the level of acceptance has never been measured comprehensively. This research has a contribution in measuring the level of acceptance of the e-learning. In addition, before the Covid-19 pandemic, the use of the e-learning system was still partially implemented and only for a few courses. In this study, an analysis of the student acceptance of the UBL e-learning system was carried out by involving respondents and a more comprehensive acceptance model. The modeling used in this study refers to the Comprehensive Technology Acceptance Model (CTAM) with seven exogenous variables and five endogenous variables. Testing and analysis are based on variant-based structural equation models, namely Partial Least Square (PLS) using the SmartPLS application. The results show that nine main factors influence student acceptance of the e-learning system: system quality, content quality, information quality, accessibility, enjoyment, perceived ease of use, perceived usefulness, and student attitudes towards applications and behavioral intention to use. This research is helpful for UBL and other educational institutions as material for developing a quality e-learning system accepted by its users.
Multi-aspect sentiment analysis on netflix application using latent dirichlet allocation and support vector machine methods Attala Rafid Abelard; Yuliant Sibaroni
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.670

Abstract

Among many film streaming platforms that have sprung up, Netflix is ​​the platform that has the most subscribers compared to the other platforms. However, not all reviews provided by the Netflix users are good reviews. These reviews will later be analyzed to determine what aspects are reviewed by the users based on reviews written on the Google Play Store, using the Latent Dirichlet Allocation (LDA) method. Then, the classification process using the Support Vector Machine (SVM) method will be carried out to determine whether each of these reviews is included in the positive or negative class (Sentiment Analysis). There are 2 scenarios that were carried out in this study. The first scenario resulted that the best number of LDA topics to be used is 40, and the second scenario resulted that the use of filtering process in the preprocessing stage reduces the score of the f1-score. Thus, this study resulted in the best performance score on LDA and SVM testing with 40 topics, and without running the filtering process with the score of 78.15%.
Usability and user satisfaction rate evaluation on e-learning application from student's perspective using Nielsen usability method Alexander J P Sibarani
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.673

Abstract

Covid-19 is a new and contagious disease that has spread throughout the world community. This disease has spread to Indonesia since early March 2020. One way to prevent the spread of this virus is by implementing activities from home. Budi Luhur University is one of the educational institutions that has implemented work and learning from home during the Covid-19 pandemic. In the teaching and learning process, the E-Learning application is the main tool used both from the faculty and student sides. The total and urgent implementation of E-Learning application which began in March 2020 caused several problems that reduced the comfort of students in carrying out learning process. The purpose of this study is to measure the level of student satisfaction and the level of use of the E-Learning application and its features. This study used a survey sampling method by distributing questionnaires and getting results from 115 respondents. The method used in this research is the Nielsen Usability method. Measurements were made using five criteria: Learnability, Memorability, Efficiency, Errors, and Satisfaction. Result show, although the average of each dimension is in the satisfied interpretation, there are significant differences in the level of satisfaction in each dimension. The dimension with the lowest average rating is the Errors dimension. This shows that respondents still find some errors when using the E-Learning application so that respondents do not show significant satisfaction in terms of the Errors dimension. In other side, result shows that the respondent thinks face-to-face meetings are still needed.
The Modelling of Nonlinear Distance Sensor Using Piecewise Newton Polynomial with Vertex Algorithm Gutama Indra Gandha
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.678

Abstract

The Sharp GP2Y0A02YK0F is categorized as a nonlinear sensor for distance measurement. This sensor is also categorized as a low-cost sensor. The higher resolution, cheap, high accuracy and easy to install are the advantages. The accuracy level of this sensor depends on the type of the measured object materials, requires an additional device unit and further processing is required since the output is non-linear. The distance determination is not easy for this type of sensor since the characteristic of this sensor fulfills non-injective function. The modelling process is one of methods to convert the output voltage of the sensor to a distance unit. The advantages of polynomial modelling are simple form model, moderate in flexibilities of shape, well known and understood properties, and easy to use for computational matters. The obstacle of polynomial-based modelling is the presence of Runge’s phenomenon. The minimization of Runge’s phenomenon can be done with decreasing the model order. The piecewise Newton polynomials with vertex determination method have been succeeded to generate a nonlinear model and minimize the occurrence of Runge’s phenomenon. The low level of MSE by 0.001 and error percentage of 2.38% has been obtained for the generated model. The low MSE level leads to the high accuracy level of the generated model.
Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting Dhanar Bintang Pratama; Favian Dewanta; Syamsul Rizal
JURNAL INFOTEL Vol 13 No 3 (2021): August 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i3.683

Abstract

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.
Automatic detection of covid-19 based on CT Scan images using the convolution neural network Mawaddah Harahap; Masdiana Damanik; Linda Wati; Wahyudi Valentino Simamora; Isnaeni Khairani Sipahutar; Amir Mahmud Husein
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.689

Abstract

The 2019 coronavirus pandemic (Covid-19) has been declared a health emergency by WHO with the death rate steadily increasing worldwide, various efforts have been made to deal with this pandemic, from prediction to receiving medical imaging. CT Scan and chest X-Ray images have been proven to be accurate to help medical personnel diagnose COVID, in this paper, we propose a convolutional neural network (CNN) approach and the DenseNet transfer learning model series which aims to understand and find the best classification for COVID or Non-COVID detection. On CT scan chest images, we made two special models in the Descent series, then compared the CNNs in both models by calculating the Accuracy, Precision, Recall, and F1-Score values and presented the results in the confusion matrix. The testing framework is carried out on CNN and the first model of the DenseNet series uses adam optimization, the input function is 244x244x3, the soft-max function is applied as an activity with losses across entropy categories, epoch 50, and batch size for training and testing 16 while validation uses batch size 8, the EarlyStopping function also determined, From the test results, the CNN model is superior to the Densenet series of the first model with an accuracy of about 0.76 (76%), when testing the second model, we carried out the shifting, zooming process and changed the input function to 64x64x3, epoch 30 by adding 4 layers. The second model approach produces better accuracy than CNN and the first DenseNet series, but not as good as expected, based on the test results on the second model produces an accuracy of 0.90 (90%) on Densenet169, Densenet121 around 0.88 (88%) and last Densenet201 is about 0.83 83%), so it is superior to simple CNN models
Evaluation of MVNO model implementation in remote and border areas using the consistent fuzzy preference relations method Anggun Fitrian Isnawati; Ridwan Pandiya; Ade Wahyudin
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.691

Abstract

Law No. 36 of 1999 concerning Telecommunication has brought many changes, especially in the development of telecommunications infrastructure in Indonesia. However, the penetration of telecommunications services in the forefront, outermost, and backward regions is still relatively low. The government has made various efforts in terms of minimizing the gap in telecommunication services between urban and rural areas through various programs. However, an acceleration is needed so that the service disparity can be immediately overcome. One of the telecommunications products that can be applied to overcome these barriers is the Mobile Virtual Network Operator (MVNO). This study evaluates the most appropriate type of MVNO model to be applied in Indonesia by implementing the Consistent Fuzzy Preference Relations (CFPR) method. This method is able to accommodate expert opinion through a series of scientific steps so as to produce weights for each alternative type of MVNO model. The results obtained are that the most appropriate model to be applied in Indonesia by taking into account the criteria given. The implementation of this model is expected to be able to encourage the optimization of BTS USO that has been declared by the government.
Breast cancer recurrence prediction system using k-nearest neighbor, naïve-bayes, and support vector machine algorithm I Ketut Agung Enriko; Melinda Melinda; Agnesia Candra Sulyani; I Gusti Bagus Astawa
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.692

Abstract

Breast cancer is a serious disease and one of the most fatal diseases in the world. Statistics show that breast cancer is the second common cancer worldwide with around two million new cases per year. Some research has been done related to breast cancer, and with the advancements of technology, breast cancer can be detected earlier by using artificial intelligence or machine learning. There are popular machine learning algorithms that can be used to predict the existence or recurrence of breast disease, for example, k-Nearest Neighbor (kNN), Naïve Bayes, and Support Vector Machine (SVM). This study aims to check the prediction of breast cancer recurrence using those three algorithms using the dataset available at the University of California, Irvine (UCI). The result shows that the kNN algorithm gives the best result in terms of accuracy to predict breast cancer recurrence.
All-in-one computation vs computational-offloading approaches: a performance evaluation of object detection strategies on android mobile devices Muhammad Abdullah Rasyad; Favian Dewanta; Sri Astuti
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.700

Abstract

Object detection gives a computer ability to classify objects in an image or video. However, high specified devices are needed to get a good performance. To enable devices with low specifications performs better, one way is offloading the computation process from a device with a low specification to another device with better specifications. This paper investigates the performance of object detection strategies on all-in-one Android mobile phone computation versus Android mobile phone computation with computational offloading on Nvidia Jetson Nano. The experiment carries out the video surveillance from the Android mobile phone with two scenarios, all-in-one object detection computation in a single Android device and decoupled object detection computation between an Android device and an Nvidia Jetson Nano. Android applications send video input for object detection using RTSP/RTMP streaming protocol and received by Nvidia Jetson Nano which acts as an RTSP/RTMP server. Then, the output of object detection is sent back to the Android device for being displayed to the user. The results show that the android device Huawei Y7 Pro with an average FPS performance of 1.82 and an average computing speed of 552 ms significantly improves when working with the Nvidia Jetson Nano, the average FPS becomes ten and the average computing speed becomes 95 ms. It means decoupling object detection computation between an Android device and an Nvidia Jetson Nano using the system provided in this paper successfully improves the detection speed performance.

Page 28 of 48 | Total Record : 473

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