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Jurnal Nasional Teknik Elektro dan Teknologi Informasi

JNTETI
Website

Published by Universitas Gadjah Mada

ISSN : 23014156 EISSN : 24605719 DOI : 10.22146/jnteti

Core Subject : Science, Engineering,

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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

Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro

Articles 644 Documents

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Penyisipan Kode Dalam Sinyal Iklan Radio Siaran Niaga Sebagai Penanda Identitas Kepemilikan Wahyu Dewanto; M. Farid Susanto; Sujoko Sumaryono
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 1 No 1: Februari 2012
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Advertisements broadcasting service on radio is required to report data after performing broadcast correctly and should be accountable to the owners of ads on the date, hours of broadcasting and the number of ads that aired under the contract. Therefore, it is necessary to monitor the ads in accordance with the contract between the ads owner and the radio broadcaster. The monitoring can be done by code marking inside the ads as the identity of the owner. The insertion of the Custom DTMF code through the mixing signal was done using the principle of steganography in the time domain. This research was conducted with a variety of ads audio signal that mixed with noise signal and then analyzed regarding on the placement of the custom DTMF code. For detecting on the receiver side, the DTMF Custom signal must have the magnitude level properly. The results showed that the ads audio signal and the inserted custom DTMF codes can be read well by the detector and it wasn’t interfere the ads signal heard by the audience.

Analisis Kinerja Aplikasi Pemantauan dan Pengendalian Smart Agriculture Berbasis Android Helmy; Fenny Rahmasari; Arif Nursyahid; Thomas Agung Setyawan; Ari Sriyanto Nugroho
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

The ever-evolving digital era leads to an industrial revolution in the internet of things (IoT)-based smart agriculture and smart farm. Of many uses is the use of an Android-based app that monitors and controls parameters in the cultivation process in this digital era. An unstable internet connection can interfere with the monitoring process. For this reason, a system integration into a single app running even in an offline condition is needed; therefore, the user can monitor and control the Android-based smart agriculture app in two modes, namely online and offline. A performance analysis is also necessary to know the app's reliability in sending and receiving data. This system integration used two modes of operation, i.e. online and offline, wherein the online mode, the app will communicate with the server when connected with the internet using representational state transfer application programming interface (REST API). Meanwhile, the app will communicate directly with the system through a local access point in the offline mode. This app interacts with the system with the MQTT protocol where the app acts as an MQTT client. The performance analysis was conducted in the black box test, load activity test, and app performance test from the Android profiler. The acquired test from the app functionality test (black box) showed that the user could monitor and control the smart agriculture in online and offline mode through the app. The average load time for all the activities was 3.507 seconds with a network bandwidth of 4.54 Mbps. At the same time, the average load time in a network bandwidth of 35.35 Mbps was 1.4 seconds. The system performance test indicated the app was relatively light as the CPU usage for the app was 31%, with a memory usage of 453.8 MB.

Review: Analisis Fitur Deteksi Aritmia dan Metode Deep Learning untuk Wearable Devices Ratna Lestari Budiani Buana; Imroatul Hudati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Arrhythmia is one of the heart abnormalities which probably not a life threat in a short time but could cause a long-term interference in electricity of the heart. Even so, it should be detected earlier to have proper treatment and suggest a better lifestyle. Arrhythmia diagnosis is usually made by performing a long recording ECG by using Holter monitoring then analyzing the rhythm. Nevertheless, the observation takes time, and using Holter in several days may affect the patient’s physiological condition. Previous research has been conducted to build an auto-detection of arrhythmia by using various datasets, different features, and detection methods. However, the biggest challenges faced by the researcher were the computation and the complex features used as the algorithm input. This study aims to review the latest research on the data used, features, and deep learning methods that can solve the time computation problem and be applied in wearable devices. The review method started by searching the related paper, then studied on the data used. The second step was to review the used ECG features and the deep learning method implemented to detect arrhythmia. The review shows that most researchers used the MIT-BIH database, even it requires a lot of effort on the pre-processing. The CNN is the most used deep learning method, but time computation is one of the considerations. The ECG interval features in the time domain are the best feature analysis for rhythm abnormality detection and have a low computation cost. These features will be the input of the deep learning process to reduce computation time, especially on wearable device applications.

Desain Konseptual Internet Accelerator Laboratory (IAL) untuk Siklotron DECY-13 Frida Iswinning Diah; Idrus Abdul Kudus; Suharni; Fajar Sidik Permana; Taxwim
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Research and Technology Center for Accelerator of Research Organization for Nuclear Energy of National Research and Innovation Agency (PRTA-ORTN – BRIN) has become the Center for Excellence in Science and Technology (PUI), especially in the particle accelerator field. One of the studies is the DECY-13 cyclotron for radioisotopes production used in the medical field for cancer diagnostics. Currently, the DECY-13 cyclotron R&D is in the final stages of the design process. A Cyclotron internet laboratory facility will be implemented to expand the benefits of this R&D. This facility will support the cyclotron training to be more widely accessible and increase capacity building and human resources programs in the nuclear field. Therefore, a preliminary study for DECY-13 cyclotron Internet Accelerator Laboratory (IAL) is needed, which includes the preparation of long-term concepts and the design of IAL. The formulation of the long-term idea follows the established cyclotron road map. In the conceptual design, the users at the early stages are students and cyclotron operators in hospitals. In order to determine the design requirement, the next step is a literature study on online laboratories and the accelerator learning concept that has been applied in other countries. The IAL apparatus identification is based on a review of the DECY-13 cyclotron laboratory's current conditions and future research plans. The conceptual design that has been successfully developed consists of a research roadmap on IAL for long-term research planning and a material syllabus for two target users, namely students and cyclotron operators in hospitals. While the employed IAL component identification is CompactRIO as the primary controller on the cyclotron operating system and as a liaison with the network system. Supporting the network includes database systems, servers, LAN, internet, webcams, and websites or an application to be accessed by users.

Analisis Kualitas Jagung Berbasis IoT dengan Penerapan Model SSD Mobilenet dan Histogram Audy; Zaini
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Corn is one of the main comestibles in our society. In these last few years, the production of comestible in Indonesia has decreased, including corn. For this reason, besides increasing the production of corn, it is necessary to research corn quality so that corn has a competitive advantage. The research aims to monitor the corn’s growth and classify the corn’s quality. The research was divided into two aspects: the aspect of good corn growth, with the internet of things (IoT) monitoring, and the classification of the corn quality based on the RGB intensity pattern of digital and TensorFlow using SSD Mobilenet models. On the corn growth, the research observed temperature, humidity, and plant distance based on two kinds of corn plant diseases (blight leaf and rotten knob), using a microcontroller (Arduino Uno), DHT11 sensor, VL53L0X sensor, and ESP8266 for accessing data to a website. The quality of corn was classified into three groups, namely rotten, moldy, and normal. The classification was carried out using Python programming on Raspberry Pi with open-source library TensorFlow using SSD Mobilenet models (as the primary option for classifying the quality of corn) and Delphi 7 on a computer (as an additional option). The number of samples used was 180 sample corn seeds tested ten times for each type of quality. The results showed that the recognition of normal corn quality was nine times correct, moldy corn quality was seven times correct, and rotten corn quality was six times correct with an accuracy rate of 73.3%.

Implementasi Laboratorium Komputer Virtual Berbasis Cloud – Kelas Pemrograman Berorientasi Obyek Dwi Susanto; Ridi Ferdiana; Selo Sulistyo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

The COVID-19 pandemic that has been occurred since March 2020 has forced learning activities to be carried out online. Online learning activities can generally be done using a learning management system (LMS) and video conference applications. However, in some subject topics, practicum activities are needed, such as in practicum using a computer laboratory. To accomplish computer practicum activities during the pandemic, a computer laboratory that can be accessed online is required. One of the online practicum solutions is a virtual laboratory (Vlab), which is a virtual computer laboratory that uses virtualization technology. Vlab provides a virtual machine (VM) that is accessed online with a remote access application (Remote Desktop Protocol/RDP, Virtual Network Computing/VNC, Secure Shell/SSH). Vlab infrastructure can either use on-premise or public cloud infrastructure. Compared to on-premise infrastructure-based Vlab, public cloud-based Vlab does not require an expensive initial investment and eliminates routine complex hardware maintenance. This study proposes a cloud-based Vlab application with Azure Lab Services in the case of an Object-Oriented Programming class. Vlab was designed based on the technical needs of the programming practicum, which included VM specifications (CPU, RAM, and storage), operating system, and software that must be installed up to the number of VMs in one class. Based on the total cost of ownership analysis, the cost of providing cloud-based Vlab was potentially up to 26% cheaper than on-premise infrastructure-based Vlab. A cloud-based Vlab installation performed using a Powershell script could be completed in six interactions and an installation time of 132 minutes. Vlab access could be done with a standard computer/laptop with an internet connection and an RDP client application. The bandwidth required to access a cloud-based Vlab ranged from 0.13 Mbps to 3.09 Mbps. The bandwidth range is still within the average speed range of the 4G networks available in Indonesia.

Pengembangan Hypermedia Learning Environment (HLE) untuk Meningkatkan Self-Regulated Learning Berdasarkan Kemampuan Self-Monitoring Intan Sulistyaningrum Sakkinah; Rudy Hartanto; Adhistya Erna Permanasari
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

The use of learning media is currently growing rapidly. Today, many studies use computers as adaptive learning media for students; one example is the hypermedia learning environment (HLE). HLE media was developed to assist students in learning, such as the current situation of the Corona Virus Disease 2019 (COVID-19) pandemic which requires all learning activities to be carried out online. One of those affected fields is the education field, where all learning activities are transferred online, so HLE web-based learning can help students to keep learning from home. HLE is currently being developed to improve students’ abilities in the self-regulated learning (SRL) process. In SRL, there is an important component in it, namely self-monitoring. However, in its development, the developed HLE is not based on self-monitoring. In this study, an adaptive HLE was developed based on students’ self-monitoring abilities. In its development, the HLE system used the agile development method, namely Scrum. The initial data collection for student classification was the self-regulatory inventory (SRI). SRI was used as an instrument to measure students’ self-monitoring ability. The data were then processed to classify students into three classes, namely high, medium, and low. Subsequently, the results of the classification of student abilities were used to develop learning aids in HLE. The development assistance provided was in the form of text and videos that were adjusted to the level of student self-monitoring. From the results of the development, it was found that all HLE functions could run well. The system was tested on twelve students to determine the level of usability by using the system usability scale (SUS). The results were classified as good category, with a score of 72.92. Further research can apply this method to students and measure the effectiveness of the system that has been developed.

Pemanfaatan Metode Smoothing Whittaker-Henderson untuk Meningkatkan Akurasi Neural Network Forecasting Hans Pratyaksa; Adhistya Erna Permanasari; Silmi Fauziati
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 1: Februari 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Health institutions need to ensure the availability of drug stocks for patients. There are challenges related to the uncertainty of the amount of drug use for the next period. Uncertainty can be reduced by analysing historical drug data to predict future demand. Time series can contain spikes or fluctuation pattern which spikes can disguise the main information. Hence, it can affect the accuracy of the prediction model. One widely used forecasting method in the time series data is the artificial neural network (ANN) method. The ANN method requires the pre-processing stage of the data before the training process. The pre-processing stage is essential to obtain information or knowledge. This study focused on applying smoothing methods at the pre-processing stage of the ANN method. The application of the smoothing method was expected to improve the quality of ANN learning data that would lead to better predictive accuracy. This research focuses on implementing the smoothing method in data pre-processing step for ANN method. Smoothing methods used in this research were exponential smoothing (ES) and Whittaker-Henderson (WH) smoothing applied to two time series datasets. The refining method used in this study was the WH method, which was tested on two time series datasets of medicine. The results show that the mean square error (MSE) obtained by applying the WH method was lower than the non-smoothing ANN for both datasets. Evaluation results revealed that implementing WH smoothing method in data pre-processing step for ANN (WH+ANN) provided MSE significantly lower than ANN results with a confidence level of 94% for dataset 1 and 85% for the dataset 2.

Analisis Sentimen terhadap Penggunaan Aplikasi MySAPK BKN di Google Play Store Raksaka Indra Alhaqq; I Made Kurniawan Putra; Yova Ruldeviyani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

In realizing the Electronic-based Government System (Sistem Pemerintahan Berbasis Elektronik - SPBE) policy, the National Civil Service Agency (Badan Kepegawaian Nasional - BKN), as the fostering government agency for the State Civil Aparatus (Aparatur Sipil Negara - ASN), needs to carry out an accurate, up-to-date, and integrated data management through an Android-based application named MySAPK. Over time, users encounter various troubles operating this application and writing down their reviews on the Rating & Review feature in Google Play Store. From May 9, 2017, until October 18, 2021, reviews written by users amounted to 4,778. This paper conducted a sentiment analysis on MySAPK users’ reviews. Stages carried out included data collection, data labeling (annotation), data preprocessing, word feature extraction, classification modeling, modeling evaluation, sentiment analysis, and recommendation result preparation. The classification modeling of the sentiment using the naïve Bayes and support vector machine (SVM) resulted in an accuracy level of 92.47% and 94.14%, respectively. The sentiment measurement results showed that users who wrote reviews with positive sentiments amounted to 2,118 (44.3%), and negative sentiments amounted to 2,660 (55.7%). Factors that prompted users to leave positive sentiment reviews are the excellent application quality, providing benefits, making it easier to fill out and store ASN data, and gratitude comments for BKN. On the other hand, factors causing users to leave negative sentiments reviews include requesting to fix the application, having difficulty accessing the application, failing to fill out and update data, and encountering an error server. To address these issues, this paper suggests that BKN could increase supporting server capacity and update the most recent version to fix the bugs. The research result is expected to serve as a reference for BKN in evaluating and improving the quality of ASN services via the MySAPK app.

Deteksi Dini Penyakit Diabetes Menggunakan Machine Learning dengan Algoritma Logistic Regression Erlin; Yulvia Nora Marlim; Junadhi; Laili Suryati; Nova Agustina
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 2: Mei 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Diabetes is one of the deadliest diseases in the world, including in Indonesia. It can cause complications in numerous body parts and increase the overall risk of death. One way to detect diabetes is to use machine learning algorithms. Logistic regression is a classification model in machine learning widely used in clinical analysis. In this paper, a predictive model was created in Python IDE using logistic regression to conduct an early detection if a person has diabetes or not depending on the initial data provided. The experiment was carried out using a dataset from the Pima Indians Diabetes Database, which consisted of 768 patient data with eight independent variables and one dependent variable. Exploratory data analysis was applied to obtain maximum insight of the datasets owned by using statistical assistance and presenting them through visual techniques. Some dataset variables contained incomplete data. Missing data values were replaced with the median value of each variable. Unbalanced data was handled using the synthetic minority over-sampling technique (SMOTE) to increase the minority class through synthetic data sampling. The model was evaluated based on the confusion matrix, which showed a reasonably good performance with an accuracy value of 77%, precision of 75%, recall of 77%, and F1-score of 76%. In addition, this paper also used the grid search technique as a hyperparameter tuning that could improve the performance of the logistic regression model. The primary model performance with the model after applying the grid search technique was tested and evaluated. The experimental results showed that the hyperparameter tuning-based model could improve the performance of the logistic regression algorithm for prediction with an accuracy value of 82%, precision of 81%, recall of 79%, and F1-score of 80%.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

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

Abstract

Abstract

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Abstract

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Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

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

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

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