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Risanuri Hidayat
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Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No 2. Kampus UGM Yogyakarta 55281
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Jurnal Nasional Teknik Elektro dan Teknologi Informasi
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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Stochastic Unit Commitment dalam Berbagai Ukuran Sistem di bawah Ketidakpastian Peramalan PLTS yang Tinggi Muhammad Yasirroni; Lesnanto Multa Putranto; Sarjiya; Husni Rois Ali; Indra Triwibowo; Qiangqiang Xie
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.5281

Abstract

This paper proposes a stochastic unit commitment (SUC) approach to solve a day-ahead unit commitment (UC) problem in a system with high uncertainty net load which is caused by photovoltaic (PV) power plants. In contrast with robust unit commitment (RUC) which only considers the worst-case scenario, SUC considers every possible scenario with its probability. Multiple possible PV curves were obtained using k-means clustering on historical data. The proportion of cluster members was used as a weight factor representing the occurrence probability of PV curves. The test was separated into two-step tests, namely day-ahead and real-time markets, using IEEE 10 generating unit system and solved using CPLEX. The results showed that in a day-ahead UC, SUC ($539,896) had lower cost than RUC ($548,005). However, when the total energy generated was considered, the SUC (20.78 $/MWh) cost higher compared to RUC (20.75 $/MWh). It is because the solution proposed by SUC is as robust as the RUC, but the generation cost formulation also considers over-commitment. Thus, SUC produced a fairer price for the independent power producer and electric utility in the day-ahead calculation. The results also showed that in the test environment of the real-time market, SUC was able to produce a robust solution without going into over-commitment. It is clearly shown in a 30 units system test with 10 centroids, in which SUC had a cheaper solution (20.7253 $/MWh) compared to RUC (20.7285 $/MWh), without violating power balance or going to load shedding.
Optimasi Klasifikasi Fonem Menggunakan Backpropagation Neural Network dan Principal Component Analysis Clara Maria Livia Suitela; Aripin; Erika Dina Permata; Muzalfa Nakiatun Niza; Naeli Laelal Khiaroh
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.5674

Abstract

A phoneme is the smallest sound in a sentence that has no meaning but plays the most important role in meaning formation. Phoneme identification from a video that shows an actor speaking Indonesian sentences is an important part of developing visual-to-text applications. This application can translate mouth movements from a video into a series of Indonesian texts so that it can facilitate communication for the deaf. This study aims to optimize the performance of the classification process on image data, including as many as 32 phonemes from video extraction results so that they can be used to support the phoneme identification process to realize visual-to-text applications in Indonesian. The classification algorithm used in this study was neural network backpropagation. Some of the proposed efforts to optimize the performance of the classification process included using a comparison of the proportion of datasets, estimating the number of hidden layers, and reducing the dimensions of the dataset using the principal component analysis (PCA) method to reduce the amount of data that is considered less important without reducing the level of information. The dimensions of the data before reduction were 1280 × 7100 data matrices and 1280 × 50 data matrices after reduction. The accuracy results obtained in data optimization using the PCA were equal to 87.16% with a data proportion of 8 : 2 and fifty important data points were used in the data optimization process using the PCA.
Sistem Monitoring Cairan Infus Berbasis IoT Menggunakan Protokol MQTT Nur Afiyat; Raizly Helmi Navilla; Mohamad Hariyadi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.5862

Abstract

One of the medical equipment that is often used in hospitals is infusion; however, the infusion monitoring system is generally still manual so that medical staff must constantly monitor the infusion device condition, which is requiring more effort and time. In this study, a 500 ml Ringer’s lactate infusion fluid monitoring system was developed based on the internet of things (IoT) using the message queuing telemetry transport (MQTT) communication protocol and an IoT cloud server system using the MQTT Dash. This infusion fluid was chosen as it is the most widely used infusion fluid. The HX711 load cell was used as a sensor to determine the weight of the infusion fluid. In addition, the NodeMCU ESP8266 microcontroller was used since it is equipped with a WiFi module called the ESP8266 module, hence it can support the implementation of IoT systems. The MQTT protocol was used to send the data to users. It was then connected to the MQTT Dash application as a monitoring medium for medical personnel. The system’s performance based on the performance of the HX711 load cell sensor in terms of the accuracy of reading the weight of infusion fluids was very good. It is evidenced by the average error percentage value of 0.08% to 0.64%, meaning that load cell sensor works well as it remains within the error tolerance limit of 5%. Meanwhile, the quality of service (QoS) test revealed that the results of the average delay on all systems ranged from 0.014 ms to 0.023 ms. According to the telecommunications and internet protocol harmonization over networks (TIPHON) standards, these values are considered very good. After that, the average of packet loss testing results on all systems were 0% to 0.01%. According to the TIPHON standards, these values are likewise classified as very good.
Model Berbasis CNN untuk Estimasi dan Autentikasi Copy Detection Pattern Syukron Abu Ishaq Alfarozi; Azkario Rizky Pratama
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 1: Februari 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i1.6205

Abstract

Counterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount of information loss is used to distinguish between original and fake CDPs. This paper proposed a CDP estimation model based on the convolutional neural network (CNN), namely, CDP-CNN. The CDP-CNN addresses the spatial dependency of the image patch. Thus, it should be better than the state-of-the-art model that uses a multi-layer perceptron (MLP) architecture. The proposed model had an estimation bit error rate (BER) of 9.91% on the batch estimation method. The error rate was 9% lower than the previous method that used an autoencoder MLP model. The proposed model also had a lower number of parameters compared to the previous method. The effect of preprocessing, namely the use of an unsharp mask, was tested using a statistical testing method. The effect of preprocessing had no significant difference except in the batch estimation scheme where the unsharp mask filter reduced the error rate by at least 0.5%. In addition, the proposed model was also used for the authentication method. The authentication using the estimation model had a good separation distribution to distinguish the fake and original CDPs. Thus, the CDP can still be used as the authentication method with reliable performance. It helps anti-counterfeiting on product distribution and reduces negative impacts on various sectors of the economy.
Desain Integrasi Sistem Instrumentasi dan Kendali untuk Commissioning Siklotron DECY-13 Frida Iswinning Diah; Fajar Sidik Permana; Emy Mulyani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.5509

Abstract

A cyclotron is a particle beam accelerator used for various applications, one of which is to produce medical radioisotopes. Research Center for Accelerator Technology, Research Organization for Nuclear Energy - National Research and Innovation Agency (Pusat Riset Teknologi Akselerator, Organisasi Riset Tenaga Nuklir - Badan Riset dan Inovasi Nasional, PRTA ORTN-BRIN) is conducting research and development (R&D) for the DECY-13 cyclotron that has reached the stage of testing its primary components. The primary components of the DECY-13 cyclotron consist of seven systems. In commissioning, it is necessary to prepare an integrated instrumentation and control system (ICS) design that unites the operation of all DECY-13 cyclotron’s primary components to produce a particle beam as desired. The integration process was carried out in two stages: determining operating procedures during commissioning and identifying operating parameters; and designing the ICS integration. The process of identifying parameters and determining operating procedures was carried out by studying test data and operating standards for each component to obtain important parameters in operation. Next, the ICS architecture was developed by integrating the operating system on the primary components using the distributed control system (DCS) method. The DCS configuration consisted of three layers, namely the operator, the main control, and the device layers. Communication between the device and the main control layers was carried out using the RS-232 serial communication, while the communication between the main control and the operator layers used the Ethernet. The RS-232 communication between the device and main control layers was used to manage data acquisition, data logging, and operation control. At the operator layer, there was a host-PC that functioned as a data viewer and data logging. This design is expected to be a guide in the implementation of ICS improvements, and realizing ease during commissioning.
Deep Transfer Learning untuk Meningkatkan Akurasi Klasifikasi pada Citra Dermoskopi Kanker Kulit Qorry Aina Fitroh; Shofwatul 'Uyun
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.6502

Abstract

Benign and malignant cancers are the most common skin cancer types. It is essential to know skin cancer symptoms with an early diagnosis to provide an appropriate treatment and reduce the mortality rate. Dermoscopic image is one of the diagnostic media that many researchers have developed. It provides more optimal results in computational-based diagnosis than visual detection. Deep learning and transfer learning are two models that have been used successfully in computational-based analysis, although optimization is still needed. In this study, transfer learning was used to separate dermoscopic images of skin cancer into two categories: benign and malignant. This study used 2,000 images to increase previous research’s accuracy conducted on the Kaggle public dataset containing 3,297 images. Two pretrained models, namely VGG-16 and residual network (ResNet)-50, were compared and used as feature extractors. Fine-tuning was conducted by adding a flatten layer, two dense layers with the ReLU activation function, and one dense layer with the Softmax activation function to classify images into two categories. Hyperparameter tuning on the optimizer, batch size, learning rate, and epoch were performed to get each model’s best performance parameter combination. Before hyperparameter tuning, the model was tested by resizing the input image using different sizes. The results of model testing on the VGG-16 model gave the best performance at an image size of 128 × 128 pixels with a combination of Adam parameters as an optimizer, batch size of 64, learning rate of 0.001, and epoch of 10 with an accuracy value of 91% and loss of 0.2712. The ResNet-50 model yielded better accuracy of 94% and a loss of 0.2198 using the optimizer parameter RMSprop, batch size of 64, learning rate of 0.0001, and epoch of 100. The results indicate that the proposed method provides good accuracies and can assist dermatologist in the early detection of skin cancer.
Algoritma Genetika dalam Penentuan Alokasi Biaya Wheeling Menggunakan LRMC dan MW-Mile Angga Cahya Putra; Sasongko Pramonohadi; Sarjiya
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.4755

Abstract

Electricity deregulation has occurred in many countries. This deregulation primarily aims to introduce competitions to increase the efficiency and quality of service in the electricity supply industry. Generation values and transmission line functions will change significantly. Customers will welcome the free market, causing many companies to build their own generators in a wheeling operation scheme to meet their needs. Wheeling is the solution to this problem. The power flow method was used after adding wheeling to the system. This method was used to determine the system conditions after wheeling was added, considering that power flow map will change when there is a wheeling costumer. The study of the power flow method provides information on the amount of total power generated by the generator yet does not provide information on the power supplied by the generator in each transmission network. To address this shortcoming, the power tracing method was used. This method can provide information on the allocation of power supplied by generators in each transmission network in the system. This research discusses the power tracing method using the genetic algorithm (AG) method. AG is one of several optimization methods; it assumes the allocation of power flowing by the generator as the problem to be optimized. The wheeling pricing used the long run marginal cost (LRMC) method. This method projects future costs by taking into account changes in expenses that occur at any time within a specified period. In this study, the LRMC method was compared with another wheeling costing method, namely the MW-Mile method. The results showed that the LRMC method was cheaper than the MW-Mile method. From an economic perspective, the wheeling costs determination using the LRMC method is 14%-20% cheaper than the MW-Mile method.
Fast Charging pada Baterai Li-Ion dengan Kontrol ANFIS Renny Rakhmawati; Zhafira Rana Khalisa Permana; Rachma Prilian Eviningsih; Suhariningsih
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.5143

Abstract

Most electrical energy used today comes from fossil fuels, which can deplete and contribute to air pollution. In Indonesia, the sun can be used as an alternative energy source and converted into electrical energy utilizing solar panel technology. The voltage generated by the solar panel is relatively high, so it needs to be lowered using a DC-DC converter type buck converter. This electrical energy can be stored using a battery which can be charged in a fast-charging mode to shorten the charging time. The most suitable battery type for fast charging is the lithium-ion (Li-ion) type for its capability to receive large current as big as 1C or equal to the battery capacity. Due to the temperature and solar irradiance effects, the output generated by the solar panel source is not constant. Moreover, to prevent overcharging, a constant current (CC) method with a constant current of 10 A and a constant voltage of 14.4 V was used which the PWM duty cycle driver was controlled using the adaptive neuro-fuzzy inference system (ANFIS) algorithm to obtain a faster response to reach the specified set point. ANFIS is a combination of two algorithms, i.e., artificial neural network (ANN) and fuzzy inference system (FIS). This research was conducted in simulation, the charging current results at the CC method of 10.01A were obtained and would move from the CC method to constant voltage (CV) when the state of charge (SoC) was 85% and the voltage reached 14.4 V. Then, the charging method would change to CV with a constant charging voltage of 14.4 V. When compared to the previous research using fuzzy control, the time required for ANFIS controls to reach set points was 3.2 ms, which is 2.3 ms faster than fuzzy controls, and ANFIS controls can reach set points with fewer errors.
Model Library Support Vector Machine (LibSVM) untuk Sentiment Review Penilaian Pesisir Pantai Oman Somantri; Ratih Hafsarah Maharrani; Santi Purwaningrum
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.6367

Abstract

Improving services as an effort to provide the convenience of tourist destinations, especially on the south coast of Java, is a demand placed on tourism managers, which in the long run will yield positive impacts. The assessment is conducted to determine whether the tourism destination give positive impressions to the tourists. The application of machine learning-based text mining technology, especially a sentiment review, is one of the solutions proposed to overcome this problem, therefore predictions of coastal tourism potential can be known beforehand. This research proposed a coastal sentiment review model using the library support vector machine (LibSVM) method. The process proposed a model optimization based on feature weights using the particle swarm optimization (PSO) algorithm as a model optimization to increase the accuracy level. Efforts to improve the accuracy of the proposed model are the main contribution of this research. The results of research and experiments on the proposed model produced the best model named LibSVM_IG+PSO using the information gain (IG). On the other hand, PSO-based LibSVM method generated an accuracy level of 88.97%. The model proposed in this research is expected to serve as a decision support for tourists, government, and tourism managers in assessing sentiment towards the coastal maritime tourism.
Sistem Estimasi State of Charge untuk Aplikasi Sistem Tertanam Berbasis Neural Networks Muhammad Adib Kamali; Wansu Lim
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 2: Mei 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i2.6632

Abstract

The conventional state of charge (SOC) estimation model has several concerns, such as accuracy and reliability. In order to realize robust SOC estimation for embedded applications, this study focuses on three concerns of the existing SOC estimation model: accuracy, robustness, and practicality. In improving the estimation accuracy and robustness, this study took into account the dynamic of the actual SOC caused by the dynamic charging and discharging process. In practice, the charging and discharging processes have characteristics that must be considered to realize robust SOC estimation. The model-based SOC estimation developed based on the virtual battery model causes difficulties for real-time applications. Additionally, model-based SOC estimation cannot be reliably extrapolated to different battery types. In defining the behavior of various types of batteries, the model-based SOC estimation must be updated. Hence, this study utilized data-driven SOC estimation based on an artificial neural network (ANN) and measurable battery data. The ANN model, which has excellent adaptability to nonlinear systems, is utilized to increase accuracy. Additionally, using measurable battery data such as voltage and current signals, the SOC estimation model is suitable for embedded applications. Results indicate that estimating SOC with the proposed model reduced errors with respect to actual datasets. In order to verify the feasibility of the proposed model, an online estimation was out on the embedded system with the use of C2000 real-time microcontrollers. Results show that the proposed model can be executed in an embedded system using measurable battery data.

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