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Jurnal Nasional Teknik Elektro dan Teknologi Informasi

JNTETI
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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 10 Documents

Search results for , issue "Vol 11 No 2: Mei 2022" : 10 Documents clear

Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management Widi Hastomo; Nur Aini; Adhitio Satyo Bayangkari Karno; L.M. Rasdi Rere
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

Indonesia is committed to reducing greenhouse gas (GHG) emissions through a nationally determined contribution (NDC) scheme. The target to reduce GHG emissions is 29% through the business as usual (BAU) scheme or 41% with international aid. These ambitious targets require transformations in energy, food, and land-use systems, which need to cope with the potential trade-offs among many targets, such as food security, energy security, avoided deforestation, biodiversity conservation, land use competition, and freshwater use. Mitigation and adaptation have complementary roles in responding to climate change at both temporal and spatial scales. This paper aims to perform simulations and predictions on manure management emissions producing CO2eq using machine learning methods of long short-term memory (LSTM) and gated recurrent unit (GRU). The hidden layer architecture used was six combinations, while the dataset was obtained from the fao.org repository. The optimizer used in this paper was RMSprop, with a graphical user interface using the Streamlit dashboard. The results of this study are (a) cattle with fifteen epochs using hidden layer four combinations (LSTM, GRU, LSTM, GRU) yielded RMSE 450,601; (b) non-dairy cattle with fifteen epochs and one hidden layer (GRU, GRU, GRU, GRU) yielding RMSE 361.421; (c) poultry birds with twelve epoch values and three hidden layers (GRU, GRU, LSTM, LSTM) resulted in an RMSE value of 341.429. The challenges faced were the determination of epochs, the combination of hidden layers, and the characteristics of the relatively small number of datasets. The results of this study are expected to provide added value for developing better decision support tools and models to assess emission trends in the livestock sector and develop CO2eq emission mitigation strategies that lead to sustainable fertilizer management practices.

Modifikasi Convolutional Neural Network Arsitektur GoogLeNet dengan Dull Razor Filtering untuk Klasifikasi Kanker Kulit Sofia Saidah; I Putu Yowan Nugraha Suparta; Efri Suhartono
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

Skin is the widest external organ covering the human body. Due to a high intensity exposure to the environment, the skin can experience various health problems, one of which is skin cancer. Early detection is needed so that further medication for patients can be done immediately. In this regard, the use of artificial intelligence (AI)-based solutions in detecting skin cancer images can be used to detect skin cancer potentials. In this study, the classification of benign and malignant skin cancer types was carried out by utilizing GoogLeNet Convolutional Neural Network (CNN) method. The GoogLeNet architecture has the advantage of having an inception module, allowing the convolution and pooling processes to run in parallel terms that can reduce computing time and speed up the classification process without lowering the system accuracy. This study consisted of several stages, starting from the data acquisition of 600 skin cancer images from Kaggle.com to the uniformity of the input size that allows the system to work faster. There was also a utilization of dull razor filtering to reduce input image noise due to hair growing along the epidermis. After the preprocessing process was complete, GoogLeNet architecture processed the image input before categorizing the input into benign or malignant skin cancer. The system’s performance was then evaluated using performance parameters such as accuracy, precision, recall, and F-1 score, and it was compared to other methods. The system managed to obtain satisfactory results, including the accuracy of 97.73% and the loss of 1.7063. As for precision, recall, and F-1 score parameters, each received an average value of 0.98. The system performance proposed by the authors successfully have better accuracy compared to the previous study with much less use of datasets. The test results show that CNN method is able to detect and classify skin cancer accurately, so it is expected that this method could help medical workers in diagnosing the community.

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%.

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.

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%.

Technology Acceptance Model (TAM) untuk Sistem Smart Lighting di PT. XYZ Teja Laksana; Novian Anggis Suwastika; Muhammad Al Makky
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

This research was conducted to identify and measure the significance of the factors or variables that influence technology acceptance for a smart lighting system built based on the internet of things (IoT) and artificial intelligence (AI) technology implemented in XYZ company. The smart lighting system implemented was a dedicated smart lighting system for office space (more than 20 m2 to 60 m2) to sense the conditions and make automatic adjustments to room conditions. Before mass production, the smart lighting system would be reviewed for its technology acceptance by users using the technology acceptance technology model (TAM). TAM is a method used to identify factors that affect the technology acceptance based on the functionality of the smart lighting system. Based on the smart lighting purposes and conditions from the XYZ company, six variables influencing the acceptance of smart lighting systems, namely reliability and accuracy (RA), perceived ease of use (PEOU), perceived usefulness (PU), attitude toward using (ATU), behavior intention (BI), and actual system use (AU) were proposed. These variables influenced each other and formed eight hypotheses, namely H1, H2, H3, H4, H5, H6, H7, and H8. Using the purposive sampling technique, validity test with product-moment correlation, and Cronbach’s alpha validity test, five hypotheses had a positive and significant effect, namely H1, H4, H5, H6, and H7. The RA variable influenced the PU variable, the PU variable influenced the ATU variable, the PEOU variable affected the ATU variable, the ATU variable influenced BI, and the PU variable affected BI. Meanwhile, the three hypotheses had negative and insignificant impacts, namely H2, H3, and H8. The RA variable did not affect the PEOU, the PEOU variable did not affect the PU, and the BI variable did not affect the AU variable.

Sistem Pengenalan Penggunaan Masker dan Pemantauan Suhu Penumpang Pesawat Menggunakan Sensor Fusion Feni Isdaryani; Noor Cholis Basjaruddin; Aldi Lugina
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

Transportation is currently an unavoidable necessity. However, the COVID-19 pandemic has impacted all lines of industry, including the Indonesian aviation transportation industry. Technology is one of the solutions to deal with these problems. The monitoring system of masked face recognition and body temperature detection for the check-in process of passengers at the airport is aimed to be developed in this research. The contribution of this research is that the system can distinguish the type of face mask used. Therefore, this monitoring system classified only medical masks and N95/KN95 respirator masks as ‘Good Masked’. IP camera and thermal camera are used to identify a masked face and body temperature, respectively. The sensor fusion method was used for decision-making on passengers whether they can be departed or not. The decision was taken based on the measured body temperature, the use of standardized face masks, and the face recognition of the airport passengers. Convolutional neural network (CNN) method was used for face and face mask recognition. The CNN model training was conducted four times according to the four proposed scenarios. The CNN model that has been trained can distinguish a masked face and a face without a mask. The best results were obtained in the fourth scenario with the comparison of the training dataset to the testing dataset was 9:1 and the epoch was 500 times. The basic deep learning model used for face detection was the single shot multibox detector (SSD) using the ResNet-10 architecture. Meanwhile, the CNN method with the MobileNetV2 architecture was used to detect the use of masks. The accuracy of the CNN model for face recognition and mask recognition was 100%. All check-in monitoring and verification process data were displayed on the web application which was built on the localhost.

Optimasi Sistem Pembangkit Listrik Tenaga Hybrid di Pulau Enggano Dyah Ayu Kartika Sari; Fransisco Danang Wijaya; Husni Rois Ali
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

Enggano Island is one of the outermost regions using diesel power plants (Pembangkit Listrik Tenaga Diesel - PLTD) as their source of electrical energy. PLTD, which began its operations in 2017, consists of three units of generator machines capable of generating 730 kW of power, with a total of 1,050 customers and electricity needs of 1,097,883 kWh/year. Although power plants are readily available, in reality, the electricity problem is still a fundamental unresolved issue on the island. The average fuel consumption to operate a PLTD is 21 tons/month or Rp582,757,000.00 per month, assuming the fuel price is Rp9,800.00 per liter. The high operating expenses resulted in electricity only being supplied for sixteen hours per day. The utilization of PLTD also produces very high carbon dioxide (CO2) emissions. It is not in line with the government's commitment to transition to net zero emissions by 2060. The utilization of new renewable energy (Energi Baru dan Terbarukan - EBT), targeted at 23% by 2025, is still not optimal. The paper aims to discover Enggano Island's optimal hybrid power plant configuration in terms of technicality and economic feasibility. Economic feasibility is reviewed using the net present cost (NPC), and cost of economic (COE) approaches. In addition, sustainability analysis is also carried out from environmental aspects. From this study, the most optimal configuration based on the lowest system cost was configuration 2 of scenario 1, consisting of photovoltaic (PV) 1,005 kW, diesel of 250 kW, and 594 battery units. This configuration can produce electricity of 1,576,115 kWh/year with an NPC value of Rp31.7 billion rupiah and a COE value of Rp1,998.75 per kWh. This configuration also has good environmental sustainability because it has a renewable fraction value of 91%.

Exploratory Data Analysis untuk Pembelajaran Daring Sinkron Berdasarkan Gambar Digital AFEA Syefrida Yulina; Mona Elviyenti
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 spread of COVID-19 throughout the world has affected the education sector. In some higher education institution, such as Polytechnic Caltex Riau (PCR), it is mandatory for students to participate in synchronous or asynchronous learning activities via virtual classroom. Synchronous online learning is usually supported by video conferencing media such as Google Meeting or Zoom Meeting. The communication between lecturers and students is captured as an image as evidence of students’ interaction and participation in certain learning subjects. These images can provide information for lecturers in determining students’ internal feelings and measuring students’ interest through facial emotions. Taking this reason into account, the current research aims to analyze the emotions detected in facial expression through images using automatic facial expression analysis (AFEA) and exploratory data analysis (EDA), then visualize the data to determine the possible solution to improve the educational process’ sustainability. The AFEA steps applied were face acquisition to detect facial parts in an image, facial data extraction and representation to process feature extraction on the face, and facial expression recognition to classify faces into emotional expressions. Thus, this paper presents the results obtained from applying machine learning algorithms to classify facial expressions into happy and unhappy emotions with mean values of 5.58 and 2.70, respectively. The data were taken from the second semester of 2020/2021 academic year with 1,206 images. The result highlighted the fact that students showed the facial emotion based on the lecture types, hours, departments, and classes. It indicates that there are, in fact, several factors contributing to the variances of students’ facial emotions classified in synchronous online learning.

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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

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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