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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)

JITEKI
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Published by Universitas Ahmad Dahlan

ISSN : 23383070 EISSN : 23383062 DOI : -

Core Subject : Science, Engineering,

Computer Science & IT Electrical & Electronics Engineering

JITEKI (Jurnal Ilmiah Teknik Elektro Komputer dan Informatika) is a peer-reviewed, scientific journal published by Universitas Ahmad Dahlan (UAD) in collaboration with Institute of Advanced Engineering and Science (IAES). The aim of this journal scope is 1) Control and Automation, 2) Electrical (power), 3) Signal Processing, 4) Computing and Informatics, generally or on specific issues, etc.

Arjuna Subject : -

Articles 6 Documents

Search results for , issue "Vol 5, No 2 (2019): December" : 6 Documents clear

Identification of Bacterial Leaf Blight and Brown Spot Disease in Rice Plants with Image Processing Approach Ihsanuddin Ihsan; Eka Wahyu Hidayat; Alam Rahmatulloh
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

In agriculture, technology can provide benefits to farmers. However, at present there are still very few farmers who use technology, especially computerization in their agricultural processes, such as the identification of diseases in rice plants, there are still many rice farmers who cannot recognize and distinguish the types of diseases in rice plants. Research on the identification of bacterial leaf blight and brown spots on rice plants has carried out before, but the accuracy rate is only 70%. This research developed a system to identify bacterial leaf blight and brown spot in rice plants through leaf images with an image processing approach. Image of affected rice leaves is segmented first using K-Means Clustering, then the texture features are extracted using the Gray Level Co-Occurrence Matrix (GLCM) with features extracted in the form of energy, contrast, correlation, homogeneity and shape pattern characteristics using metric and eccentricity features, then identified using Euclidean Distance. The training data used 40 images for each disease and 12 images for each disease. The test results show that the system has a better level of accuracy than previous studies that reached 100% with a Mean Squared Error (MSE) value of 0.007282214.

Mobile Forensics for Cyberbullying Detection using Term Frequency - Inverse Document Frequency (TF-IDF) Imam Riadi; Sunardi Sunardi; Panggah Widiandana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

The case of cyberbullying in Indonesia was ranked third in the world in 2015 and as much as 91% was experienced by children [1]. RSA Anti Fraud Command Center (AFCC) report reports that in 2015 45% of transactions were carried out through mobile channels, while 61% of fraud occurred through mobile devices [2]. WhatsApp in July 2019, 1.6 billion users access the WhatsApp messenger on a monthly basis [10]. The data opens a reference for investigators to better anticipate cybercrime actions that can occur in the whatsapp application because more users are using the application. In this study using the TF-IDF method in detecting cyberbullying that occurs in order to be able to add a reference for investigators. The conclusions that have been obtained from the simulation of conversations between four people in a whatsapp group get the results of the cyberbullying rate that the user "a" has a cyberbullying rate of 66.80%, the user "b" has a cyberbullying rate of 50%, the user "c" has a level cyberbullying is 33.19%, user "c" has a cyberbullying rate of 0% from the data proving that the TF-IDF method can help investigators detect someone who will commit cyberbullying actions but in its development a better way is needed when preprocessing so that the abbreviation or changing words can still be detected perfectly.

Text Classification Using Long Short-Term Memory With GloVe Features Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations with regard to large-scale dataset training. Deep Learning is a proposed method for solving problems in text classification techniques. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95

Machine Vision-based Obstacle Avoidance for Mobile Robot Nuryono Satya Widodo; Anggit Pamungkas
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

Obstacle avoidance for mobile robots, especially humanoid robot, is an essential ability for the robot to perform in its environment. This ability based on the colour recognition capability of the barrier or obstacle and the field, as well as the ability to perform movements avoiding the barrier, detected when the robot detects an obstacle in its path. This research develops a detection system of barrier objects and a field with a colour range in HSV format and extracts the edges of barrier objects with the FindContoure method at a threshold filter value. The filter results are then processed using the Bounding Rect method so that the results are obtained from the object detection coordinate extraction. The test results detect the colour of the barrier object with OpenCV is 100%, the movement test uses the processing of the object's colour image and robot direction based on the contour area value> 12500 Pixels, the percentage of the robot making edging motion through the red barrier object is 80% and the contour area testing <12500 pixel is 70% of the movement of the robot forward approaching the barrier object.

Prototype of Smart Lock Based on Internet Of Things (IOT) With ESP8266 Firza Fadlullah Asman; Endi Permata; Mohammad Fatkhurrokhman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

This study aims to design a prototype house that has an automatic safety system installed that has two inputs for its control. The developed system can be controlled via an Android or IOS smartphone that has the Blynk application installed, or by using a PIR sensor installed in the circuit. The output in this series of systems is solenoid lock which is used as a door lock for the house later. This research refers to the ADDIE development model with details: (1) Analyze, (2) Design, (3) Development, (4) Implementation and (5) Evaluate. Based on the testing of each component used, the results are obtained in the form of PIR sensor readings with a radius of 90cm with an angle of 120o with a voltage of 5V, a solenoid lock that uses a 12V voltage, a pin from the Wemos D1 R1 board that has errors on its 2 input / output pins, and a battery that can be used as a backup power source when the main power source goes out for more than 5 hours.

The Combination of Naive Bayes and Particle Swarm Optimization Methods of Studentâ€™s Graduation Prediction Evi Purnamasari; Dian Palupi Rini; Sukemi Sukemi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 5, No 2 (2019): December
Publisher : Universitas Ahmad Dahlan

This research conducted classification testing on the study case of student graduation prediction in a university. It aims to assist the university in maintaining academic development and in finding solutions for improving timely graduation. This study combined two methods, i.e., Naive Bayes and Particle Swarm Optimization, to produce a better level of accuracy. The Naive Bayes method is a statistical classification method used to predict a student's graduation in this study. That will be further enhanced using the Particle Swarm Optimization method to produce a better level of accuracy. There are 10 (ten) samples in this study randomly selected from the alumni data of UIGM students in 2011-2014. From the test results, this research resulted in an accuracy value of 90% from the Naive Bayes algorithm testing, after testing the Naive Bayes with Particle Swarm Optimization, which produced an accuracy value of 100%. The conclusion obtained from the results is the Naive Bayes method has a higher accuracy value if combined with Particle Swarm Optimization. Thus the university can more easily predict whether or not the students graduate on time for the upcoming graduation period. The results of this test prove that to predict student graduation using the Naive Bayes method with Particle Swarm Optimization is appropriate.

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Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA