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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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Kab. banyumas,
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INDONESIA
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 6 Documents
 1 
Search results for , issue "Vol 15, No 1: February (2022)" : 6 Documents clear
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.
Performance Evaluation of Naive Bayes Algorithm for Classification of Fertilizer Types Rastri Prathivi; April Firman Daru; Sara Sharifzadeh
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.v15i1.1410

Abstract

Determining the right fertilizer is very important to get optimal plant growth results. Each plant requires different nutrient requirements. Different soil types cause the soil's nutrient content and PH value to differ from one type to another. Regional conditions in a place will also cause the need for plant absorption of nutrient content to be more varied. By using the classification of the problems that have been mentioned, it can be solved by studying patterns from existing fertilizer use data into knowledge that can be used to determine decisions. In this study, modeling with the Naïve Bayes algorithm has been applied to the existing fertilizer use data where the probability value of each class has been calculated to get the highest probability value of a class. The measurement of the accuracy value of the modeling used is measured using the Split Validation method, where the training data will be divided into training data and testing data so that the accuracy value of the model is obtained. From the applied modeling, an accuracy value of 60% is obtained, which shows the level of accuracy of the model obtained from the classification results in the form of the name of the fertilizer, which is expected to help in determining the name of the fertilizer that needs to be used.

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