cover
Contact Name
Risanuri Hidayat
Contact Email
risanuri@ugm.ac.id
Phone
+62274-552305
Journal Mail Official
jnteti@ugm.ac.id
Editorial Address
Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
Location
Kab. sleman,
Daerah istimewa yogyakarta
INDONESIA
Jurnal Nasional Teknik Elektro dan Teknologi Informasi
ISSN : 23014156     EISSN : 24605719     DOI : 10.22146/jnteti
Topics cover the fields of (but not limited to): 1. Information Technology: Software Engineering, Knowledge and Data Mining, Multimedia Technologies, Mobile Computing, Parallel/Distributed Computing, Artificial Intelligence, Computer Graphics, Virtual Reality 2. Power Systems: Power Generation, Power Distribution, Power Conversion, Protection Systems, Electrical Material 3. Signals, Systems, and Electronics: Digital Signal Processing Algorithm, Robotic Systems and Image Processing, Biomedical Instrumentation, Microelectronics, Instrumentation and Control 4. Communication Systems: Management and Protocol Network, Telecommunication Systems, Wireless Communications, Optoelectronics, Fuzzy Sensor and Network
Articles 13 Documents
Search results for , issue "Vol 9 No 2: Mei 2020" : 13 Documents clear
Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network I Md. Dendi Maysanjaya
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1190.795 KB) | DOI: 10.22146/jnteti.v9i2.66

Abstract

Pneumonia is a lung disease that could be caused by bacteria, viruses, fungi, or parasites. The pulmonary cysts are filled with fluid, causing croup and mucus cough. Usually, observation of the patient's lung condition is performed through X-rays. However, the quality of X-ray images tends to be less than optimal. Therefore, a CAD-based automation system was developed. In this paper, a new chest X-rays dataset for pneumonia cases is classified by using Convolutional Neural Network (CNN). This study examines the CNN performance in handling the new dataset. The data were obtained from the Kaggle platform. In total, there were5,840 images occupied in this study, consisting of 1,575 normal lung images and 4,265 pneumonia lung images. The data were divided into training and testing data, with the amount of data 5,216 and 624 images on each, respectively. The CNN activation function applied the Rectifier Linear Unit (ReLU) function, Adam optimization function, and epoch as many as 200times. Based on the test results, the average accuracy and loss values are sequentially at 89.58% and 47.43%. The results of this test indicate that the CNN method is quite capable of classifying the pneumonia cases.
MPPT Menggunakan Algoritme Particle Swarm Optimization dan Artificial Bee Colony Ermanu Azizul Hakim; Tamadar Al Ghufran; Machmud Effendy; Novendra Setyawan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1172.478 KB) | DOI: 10.22146/jnteti.v9i2.81

Abstract

Solar power plant is a renewable electricity generator that utilizes heat from sunlight. However, because the intensity of light received by the solar cell and the temperature in the solar cell is always changing, the power generated is not optimal. To optimize the output power of the solar cell, a Maxi-mum Power Point Tracking (MPPT) system is needed. Solar cells can be optimized by looking for MPPT and also by using a DC-DC converter. In this study, boost converter is optimized using Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The results show that the highest efficiency obtained from boost converter is 78.25%,using duty cycle of 20%. For the overall system testing conducted at 09:00 WIB until 11:10 WIB, the average power obtained without using MPPT is 12.55 W, the average power of MPPT system using boost converter with PSO algorithm is 16.79 W, and average power of MPPT system using boost converter with ABC algorithm is 14.52 W. From the results, it was concluded that the output power of MPPT system using boost converter with PSO algorithm is more optimal than the MPPT system using boost converter with ABC algorithm.
Komparasi Term Weighting dan Word Embedding pada Klasifikasi Tweet Pemerintah Daerah Pande Made Risky Cahya Dinatha; Nur Aini Rakhmawati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1071.916 KB) | DOI: 10.22146/jnteti.v9i2.90

Abstract

The emergence of social media encourages the government to use social media to diseminate information to its people. The information must be beneficial for the people to maintain government to citizen relationships. Classification on social media post is possible to categorize the types of posts. The study was conducted on the local government`s social media accounts, yet the text processing in theresearch needsto be explored. Term weighting and word embedding are implemented in this research. The purpose is to compare term weighting term frequency-inverse document frequency, Okapi BM25, and word embedding doc2vec in producing features for the problem of short text classification.This study representsfeature selection process, how to assessclassification model, and to find the best model to overcome short text classification problem. There are six classes to categorize 1,000 short texts from 91 accounts. The measurements, i.e.precision, recall, f-1, macro-averages, micro-averages, and AUC,were calculated on each model. The result shows that the SVM linear kernel with TF-IDF performs best and slightly better than the logistic regressionwith 0.572 and 0.766 on macro-averagerecall and micro-average recall,respectively.
Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization Valentino Kevin Sitanayah Que; Ade Iriani; Hindriyanto Dwi Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1270.746 KB) | DOI: 10.22146/jnteti.v9i2.102

Abstract

Phenomenon of online transportation with some problems like crime and fraud in Indonesia triggers pros and cons to Twitter users. This study aims to find out sentiments of the society on online transportation and compare the accuracy of SVM and SVM-PSO with default parameters value. The proposed solution divided the dataset into training and testing data, because some researches only used one dataset that had already been classified. The research data is tweet data, which is obtained through scraping method using Octoparse. A total of 1,852 tweets from 1/1/2019 to 15/10/2019 were divided into 1,130 tweet testing data and 722 tweet training data. Then, RapidMiner was used for analysis process. Analysis positive sentiment using SVM is 62% and negative sentiment is 38%, while in SVM-PSO, positive opinion is 53% and negative opinion is 47%. The results of research using 10 k-fold CV produce accuracy on SVM is 95.46% and AUC is 0.979 (excellent classification), while in SVM-PSO accuracy is 96.04% and AUC is 0.993 (excellent classification). The results show that use of training and testing data on this study can be done and prove that SVM-PSO is better than ordinary SVM, although the parameters value is default.
Sistem Monitoring Temperatur Tuang Logam dan Penggunaan Energi Berbasis IoT di MIDC Anugrah Erick Eryantono; Muhammad Nauval Fauzi; Muhammad Fathurrohman
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1497.423 KB) | DOI: 10.22146/jnteti.v9i2.106

Abstract

Tough competition among metal casting industries requires the use of technology to produce high-quality productsandlarge quantities with optimal production costs. Parameters that affect casting products are temperature at pouring time and pouring rate. The heat loss caused by the transfer of metal through the ladle and the influence of the surrounding environment that absorbs heat can cause a significant temperature drop. Therefore, a temperature monitoring system to monitor and record the rate of temperature decrease in real-time to obtain optimal casting results is needeed, as well as a monitoring system for energy usage to reduce the cost of the production. Industrial revolution technology 4.0 in the metal casting industry,especially carbon steel and cast iron,can help operators to display data in real-time, store,process,and evaluate the data on cloud/server. This system is integrated using Internet of Things (IoT) technology. Database of casting parameters (pouringtemperature and energy usage) obtained from measurements of sensors and stored in cloud is used for analysis and evaluation of the research. The results of the monitoring system analysis was expected to help reduce production cost in MIDC.
Antena SIW dengan Defected Ground Structure pada Frekuensi L-Band Mia Maria Ulfah; Achmad Munir
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1094.459 KB) | DOI: 10.22146/jnteti.v9i2.122

Abstract

In this paper, a bandwidth enhancement technique for Substrate Integrated Waveguide(SIW) antenna is attempted using Defected Ground Structure(DGS)by modifying the shape and geometry of the slot on the ground plane. In order to achieve wide bandwidth response of the antenna, a rectangular split ring incorporated with across shapes lots are employed as DGS. The quality factor (Q) of antenna increases as the substrate thickness decreases, which leads to narrow down bandwidth response of the SIW antenna. Therefore, the SIW antenna is simulated using multiple layers of FR4epoxydielectric substrate in overcoming the problem. The SIW antenna has a dimension of 171 mm×160.5mm, 4.905mm-height,and is intended to work at the center frequency of 2 GHz. Furthermore, the SIW antenna is excited by proximity coupling method which is connected to a 50Ω connector. Simulation result shows that the 575 MHz bandwidth could be achieved by the antenna with the proposed technique, in which the -10dB bandwidth response is ranged from the frequency of 1.675 GHz –2.25 GHz and the maximum gain is6.03 dBi at the frequency of 1.85 GHz,with bidirectional radiation pattern.
Optimasi Algoritme Perkalian Karatsuba dengan Menggunakan Metode Nikhilam II Felix
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (994.066 KB) | DOI: 10.22146/jnteti.v9i2.148

Abstract

Multiplication is an essential operation in informatics engineering,for example in cryptography, cryptanalysis, and image processing fields.Researchesabout multiplication algorithmhave been conducted and improved by experts in numerous field,ranging from mathematics, informatics engineering, to electrical engineering. The most popular multiplication algorithm is created by Anatoly Karatsuba in 1960 in Soviet Union. Although it is old and many new multiplication algorithms arise, still this algorithm is chosen for middle to large size number category. Divide and conquer technique is implemented in this algorithm to speed up the multiplication process. The weakness of Karatsuba algorithm is the excessive recursive process,causing a longer execution time. Nikhilam II method is an algorithm founded in India and is included in Vedic Mathematics. Usually,Nikhilam II method is used by common people in India to ease daily multiplication calculation. This method can replace some of the multiplication operationswith addition, therefore it can be more optimum. In this paper, Nikhilam II method is implemented in the base case part of Karatsuba algorithm to reduce the recursive call. Hence, Karatsuba algortihm can be optimized from time execution point of view. As the result, this new algorithm can optimize time execution up to thrice faster than the original algorithm.
Penentuan Kemampuan Motorik Halus Anak dari Proses Menulis Hanacaraka Menggunakan Random Forest Nurul Zainal Fanani; Adri Gabriel Sooai; Surya Sumpeno; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1328.116 KB) | DOI: 10.22146/jnteti.v9i2.153

Abstract

The children's Fine Motor Skill Assessment (FMS) at the beginning of school age is essential to get information about children's school readiness. The process of measuring FMS has been carried out by observing children, both directly and from the results of sketches or children's writing. This observation process is very dependent on the observer's perception. This study aims to determine the children's FMS using Javanese script. This research develops a new method for determining children's FMS from the process of writing the Javanese script. The system was recording data directly when the child is writing the Javanese script. Retrieval of data recording from the writing process involved 14 students in 1st grade and 2nd grade from three elementary schools in Jember district. The process of recording data from each student produces a large enough raw data. Therefore, this study uses random forest classification method,because this method can carry out the classification process on large amounts of data by combining several decision trees. Other classification methods, including naïve Bayes and k-NN, were used as a comparison. The experiment results show that the random forest classification method is the bestwith an accuracy of 98.7%.
Kinerja Micro Grid Menggunakan Photovoltaic-Baterai dengan Sistem Off-Grid Adhi Kusmantoro; Ardyono Priyadi; Vita Lystianingrum Budiharto Putri; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1149.52 KB) | DOI: 10.22146/jnteti.v9i2.155

Abstract

Renewable energy based micro grid planning is perfect for delivering electricity to rural areas, as an uninterruptible resource. In this paper, a DC micro grid system is designed. The system consists of several PVs and batteries which are connected to each other through a network. PV grids A and C deliver 1,904 watts of power in the micro grid system, while the battery contributes 784 watts of power. The system has a load of 730 watts. The purpose of this study is to improve the performance of micro grid with off-grid systems. The performance of the designed system is quite good because there sources of the grid A and grid C systems are sufficient to meet load demands and to charge batteries. When solar radiation is low, the battery meets load demands. To make the system more reliable, although it will increase system costs, a battery with a larger capacity can be used. The proposed system maintains the voltage at 12V with a change of only ± 10%.
Pemetaan Emosi Dominan pada Kalimat Majemuk Bahasa Indonesia Menggunakan Multinomial Naïve Bayes Wisnu Agastya; Aripin
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1248.65 KB) | DOI: 10.22146/jnteti.v9i2.157

Abstract

This study aimed at mapping Indonesian sentences into emotion classes based on the classification process in those sentences. The results of emotion mapping can be applied in various fields, such as production of animated films and games, analysis of facial expressions, human-computer interactions, and development of other expressive virtual characters, specifically to produce facial expressions that match the spoken sentences. The method used for the emotion mapping process was the text classification using multinomial naïve Bayes model that was accompanied by dominant boundary equation. Multinomial naïve Bayes model in the text classification is used to determine the types and the emotional intensity of Indonesian sentences, whereas dominant boundary equation iss used to determine the threshold in order to identify the dominant classes. The emotion classes used as references are six basic emotion classes according to Paul Ekman, i.e., happiness, sadness, anger, fear, disgust, and surprise. The experiment on the process of mapping emotions used Indonesian single and compound sentences. The experimental results show that the text classification using multinomial naïve Bayes model accompanied by dominant boundary equation can map compound sentences into several classes of dominant emotions.

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