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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 235 Documents
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Data Mining Method to Determine a Fisherman's Sailing Schedule Using Website Dwi Ayu Mutiara; Alung Susli; Didit Suhartono; Dani Arifudin; Imam Tahyudin
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1193

Abstract

Some of Cilacap people live in coastal areas as fishermen who utilize the seafood to meet the needs of life. One of the fishermen supporters in the cruise is the information of Meteorological, Climatological, and Geophysical Agency (BMKG). This information is important for safety such as wind speed and wave height. For addressing the problem, research is conducted to determine the sailing schedule of fishermen using data mining method with the website based. The proposed method is using Support Vector Machine (SVM) classification algorithm. This research uses data from BMKG Cilacap from 2015 until 2017. Test data is part of data that is 30% randomly fetched from the overall data used. From model testing, get value with performance results from datasets that generate accuracy of 88%, 87% precision and 89% recall. This solution is followed by constructing the website in order to easy to access of sailing information. Therefore, the researcher created a website of fisherman sailing scheduling system based on SVM algorithm.
CNN Architecture for Classifying Types of Mango Based on Leaf Images Nur Nafi'iyah; Jauharul Maknun
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1262

Abstract

In such conditions, it is necessary to have a system that can automatically classify plant species or identify types of plant diseases using either machine learning or deep learning. The plant classification system for ordinary people who are not familiar with the field of crops is not an easy job, it requires in-depth knowledge of the field from the experts. This study proposes a system for identifying mango plant species based on leaves using the CNN method. The reason for proposing the CNN method from previous research is that the CNN method produces good accuracy. Most previous studies to classify plant species use the leaves of the plant. The purpose of this study is to propose a CNN architectural model in classifying mango species based on leaf imagery. The input image of colored mango tree leaves measuring 224x224 is trained based on the CNN architectural model that was built. There are 4 CNN architectural models proposed in the study and 1 transfer learning InceptionV4. Based on the evaluation test results of the proposed CNN architectural model, that the best architectural model is the third. The number of parameters of the third CNN architecture is 1,245,989 with loss values and accuracy during evaluation are 1,431 and 0.55. The largest number of parameters is transfer learning InceptionV3 21,802,784, but transfer learning shows the lowest accuracy value and the highest loss, namely 0.2, and 1.61.
Smart Payment Application Security Optimization from Cross-Site Scripting (XSS) Attacks Based on Blockchain Technology Imam Riadi; Rusydi Umar; Tri Lestari
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1221

Abstract

The digital era is an era everyone has used technology and they are connected to each other very easily. The Smart Payment application is one of the applications that is developing in the digital era. This application is not equipped with security, so there is a concern that hackers will try to change user or even change user data. One of the possible attacks on this application is a cross-site attack (XSS). It is a code injection attack on the user side. Security in the Smart Payment application needs to be improved so that data integrity is maintained. In this research, security optimization is carried out by implementing blockchain. Blockchain has the advantage in terms of security with the concept of decentralization by utilizing a consensus algorithm that can eliminate and make improvements to data changes made by hackers. The result obtained from this study is the implementation of blockchain to maintain the security of payment transaction data on the Smart Payment application from XSS attacks. It is proven by the results of the vulnerability before and after blockchain implementation. Before the implementation of the vulnerability is found, 1 XSS vulnerability had a high level of overall risk. Meanwhile, the result of the vulnerability after blockchain implementation was not found from XSS attacks (the XSS vulnerability was 0 or not found).
The Evaluation of Image Messages in MP3 Audio Steganography Using Modified Low-Bit Encoding Ilham Firman Ashari
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1031

Abstract

Information security is an important aspect of maintaining the confidentiality of information. One type of document kept secret is images (.jpg, .png, .gif, dan .bmp). MP3 audio files are popular audio files that can be used as a medium for steganography. The method implemented in this study uses Base64 in the image and the insertion method used is using Low-Bit Encoding (LBE). In this study, the parameters of the value of LBE will be evaluated. The purpose of the evaluation is to compare the LBE parameter parameters that are the most optimal in securing the message while taking into account the quality of steganographic files. The results obtained from the study are the proposed method that supports the imperceptibility aspect seen from the image histogram and audio frequency spectrum. The fidelity aspect is seen from the PSNR and SNR values, where The optimal value on the LBE + 2 parameter is by considering the capacity of the picture message that can be inserted and audio quality. PSNR LBE + 2 values range from 50-60 dB with SNR different about 0.01% from LBE + 1. The proposed method does not support the robustness aspect because it is not resistant to attacks by bit rate manipulation and channel mode. The test results on the recovery aspect are worth 100%, meaning that the image's quality and size before and after extraction will be the same. And finally, the test results on the payload aspect, there is an increase in message capacity with LBE + 2 around 12.5% of LBE + 1, and using LBE + 3 will increase the maximum size around 25% of LBE + 1 and about 14% of LBE + 2. The insertion and extraction time for LBE + 3 is slower compared to the others.
Survey on Deep Learning Based Intrusion Detection System Omar Muhammad Altoumi Alsyaibani; Ema Utami; Anggit Dwi Hartanto
Telematika Vol 14, No 2: August (2021)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v14i2.1317

Abstract

Development of computer network has changed human lives in many ways. Currently, everyone is connected to each other from everywhere. Information can be accessed easily. This massive development has to be followed by good security system. Intrusion Detection System is important device in network security which capable of monitoring hardware and software in computer network. Many researchers have developed Intrusion Detection System continuously and have faced many challenges, for instance: low detection of accuracy, emergence of new types malicious traffic and error detection rate. Researchers have tried to overcome these problems in many ways, one of them is using Deep Learning which is a branch of Machine Learning for developing Intrusion Detection System and it will be discussed in this paper. Machine Learning itself is a branch of Artificial Intelligence which is growing rapidly in the moment. Several researches have showed that Machine Learning and Deep Learning provide very promising results for developing Intrusion Detection System. This paper will present an overview about Intrusion Detection System in general, Deep Learning model which is often used by researchers, available datasets and challenges which will be faced ahead by researchers
An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes Oman Somantri; Ratih Hafsarah Maharrani; Linda Perdana Wanti
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1307

Abstract

This research proposes a method to optimize the accuracy of the Naïve Bayes (NB) model by optimizing weight using a genetic algorithm (GA). The process of giving optimal weight is carried out when the data will be input into the analysis process using NB. The research stages were conducted by preprocessing the data, searching for the classic naïve Bayes model, optimizing the weight, applying the hybrid model, and as the final stage, evaluating the model. The results showed an increase in the accuracy of the proposed model, where the naïve Bayes classical model produced accuracy rate of 87.69% and increased to 88.65% after optimization using GA. The results of the study conclude that the proposed optimization model can increase the accuracy of the classification of early detection of diabetes.
Gaussian Pyramid Decomposition in Copy-Move Image Forgery Detection with SIFT and Zernike Moment Algorithms Firstyani Imannisa Rahma; Ema Utami; Hanif Al-Fatta
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1322

Abstract

One of the easiest manipulation methods is a copy-move forgery, which adds or hides objects in the images with copies of certain parts at the same pictures. The combination of SIFT and Zernike Moments is one of many methods that helping to detect textured and smooth regions. However, this combination is slowest than SIFT individually. On the other hand, Gaussian Pyramid Decomposition helps to reduce computation time. Because of this finding, we examine the impact of Gaussian Pyramid Decomposition in copy-move detection with SIFT and Zernike Moments combinations. We conducted detection test in plain copy-move, copy-move with rotation transformation, copy-move with JPEG compression, multiple copy-move, copy-move with reflection attack, and copy-move with image inpainting. We also examine the detections result with different values of gaussian pyramid limit and different area separation ratios. In detection with plain copy-move images, it generates low level of accuracy, precision and recall of 58.46%, 18.21% and 69.39%, respectively. The results are getting worse in for copy-move detection with reflection attack and copy-move with image inpainting. This weakness happened because this method has not been able to detect the position of the part of the image that is considered symmetrical and check whether the forged part uses samples from other parts of the image.
Website-Based Application for Flood Event Prediction Using Machine Learning Method In Cilacap District Imam Tahyudin; Faiz Ichsan Jaya; Nur Faizah
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1195

Abstract

Floods are the most common natural disasters, both in terms of their intensity at a place and the num- ber of locations of events in the amount of 40% among other natural disasters. The impact of flooding on the area in general is temporary housing in rural areas caused by flooding in addition to settlement as well as agriculture which can have an impact on the food security of the area and also a national level that is higher than the magnitude of the country. Based on data from the Central Statistics Agency of Cilacap Regency, the number of flood victims in Cilacap Regency in 2018 reached 771 people and arranged for them to flee from the flood. To solve this problem, do research to create a web-based application using the classification of the Support vector machine or Random Forest to predict flood events and compare the accuracy values of the two algorithms to get better prediction results.
Detection and Classification of Vehicles on the Bekasi Toll Road Using the Gaussian Mixture Models Method and Morphological Operations Rifki Kosasih; Hidayat Taufik Akbar
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1222

Abstract

Traffic surveillance was initially carried out directly using CCTV, but this kind of surveillance was not possible for a full day by the security forces. In addition, with the increasing growth of vehicles in Indonesia, a method is needed that can be used to assist security forces in monitoring traffic such as detecting and automatically counting the number of vehicles. Therefore, in our research, we propose a method that can detect vehicles, and count the number of vehicles from video recordings on the Bintara Bekasi toll road using background substraction methods such as gaussian mixture models and morphological operations. The results showed that the vehicle detection accuracy rate was 86.3636%, the precision was 89.0625%, and the recall was 96.6101%. In this study, vehicle classification was also carried out based on the detection results into two types of vehicles, namely cars and trucks. From the results of the research, the classification accuracy rate was obtained at 85.9649%.
Forest Fire Detector and Fire Fighting Monitoring System Using Solar Cell Based Internet of Things (IoT) Raden Jasa Kusumo Haryo; Budi Artono; Hanifah Nur Kumala Ningrum; Kumala Mahda Habsari; Isnani Mila Rahma
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1303

Abstract

Forest and land fires in Indonesia, based on National Board for Disaster Management (BNPB) are recorded that the burned area reached 328,724 hectares with 2,719 hotspots in the January – August 2019 period. The factors causing forest fires include climate change, human activities in forest areas, and forest officers that can’t be monitored in real-time. The internet of things (IoT) based solar cell-based forest fire detection using for a monitoring system in real-time. This research uses PV (Photovoltaic) as power on the system. This IoT is integrated with the android application so that the user can monitor forest conditions at any time. This research uses ESP8266 12 – F as an IoT module, Arduino Uno for controlling the device, and relays. This result data are based on laboratorium test. The result of this research is a monitoring system that can monitor smoke (MQ-2 sensor), temperature and humidity (DHT22 sensor), water pressure (pressure transmitter), current and voltage in the battery charging remotely and can be controlled automatically when the sensor detects the flame.

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