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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
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 505 Documents
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Optimization of Distributed Generation Placement and Capacity Using Flower Pollination Algorithm Method Trio Putra, Jimmy; Istiqomah, Istiqomah; Shaddiq, Syahrial; Diantoro, Agus
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 7 No. 1 (2021): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v7i1.20586

Abstract

The need for energy, especially electricity is increasing along with the development of technology. An increase in electrical load and the location of the powerplant far causes voltage drops and causes power line losses. One solution can be chosen by adding a distributed generation (DG) to the distribution network. This study aims to enhance the voltage profile and reduce power losses based on the optimal placement and capacity of DG-based photovoltaic (PV) in the Bantul Feeder 05 distribution network. The flower pollination algorithm (FPA) method is used to determine the optimal DG placement and capacity. The study was conducted using three additional DG scenarios, namely scenario 1 with single DG and scenario 2 with multi-DG (2 DG and 3 DG). The results showed that the optimal placement and capacity of DG were on buses 9, 19, and 33 with DG sizes of 1.880 kW, 2.550 kW, and 2.300 kW, respectively. This placement can increase the voltage profile and reduce the active power loss from 439.8 kW to 77.5 kW. The research also considers the increase in the reliability of the distribution system observe by the energy not supplied and cost of energy not supplied index.
Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price Wati, Masna; Haviluddin, Haviluddin; Masyudi, Akhmad; Septiarini, Anindita; Hatta, Heliza Rahmania
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.22286

Abstract

Crude oil is the main commodity of the global economy because oil is used as an ingredient for many industries globally and is the price base used in the state budget. Indonesian Crude Price (ICP) fluctuates following developments in world crude oil prices. A significant increase in crude oil prices will certainly disrupt the economy. Thus, the movement or fluctuation of ICP is essential for business players in the energy market, especially domestically. Therefore, crude oil price forecasting is needed to assist business people in making decisions related to the energy market. This study aims to find a suitable forecasting model for Indonesian crude oil prices using the Autoregressive Integrated Moving Average (ARIMA) method. The forecasting process used ICP time-series data per month for 50 types of crude oil within five years or 63 months. Based on the experimental results, it was found that the most fit ARIMA models were (0,1,1), (1,1,0), (0,1,0), and (1,2,1). The test results for April to September 2020 have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts. The ARIMA error rate is very dependent on the value of the data before it is predicted and external factors, the more unstable the data value every month, the higher the error rate.
Double Face Masks Detection Using Region-Based Convolutional Neural Network Carita, Sa'aadah Sajjana; Hadiprakoso, Raden Budiarto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.23902

Abstract

Because of the fast spread of coronavirus, the globe is facing a significant health disaster of COVID-19. The World Health Organization (WHO) released many suggestions to combat the spread of coronavirus. Wearing a face mask in public places and congested locations is one of the most effective preventive practices against COVID-19. However, according to recent research wearing double face masker even provide better protection than just one mask. Based on this finding, various public places require double masks to proceed more. It is pretty tricky to monitor individuals in crowded public places personally. Therefore, a deep learning model is suggested in this paper to automate recognizing persons who are not wearing double face masks. A faster region-based convolutional neural network model is developed using the picture augmentation approach and deep transfer learning to increase overall performance. We apply deep transfer learning by fine-tuning the low level pre-trained Visual Geometry Group (VGG) Face2 model. This study used the publicly accessible VGGFace2 dataset and the self-processed dataset. The findings in this study show that deep transfer learning and image augmentation can increase detection accuracy by up to 11%. Consequently, the created model achieves 93.48% accuracy and 93.19% F1 score on the validation dataset, demonstrating its excellent performance. The test results show the proposed model for further research by adding the predicted dataset and class.
Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective N, Naveen; G Cholli, Nagaraj
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.25148

Abstract

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinicians
Comparison of Support Vector Machine (SVM) and Random Forest Algorithm for Detection of Negative Content on Websites Syahputra, Hermawan; Wibowo, Aldiva
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25861

Abstract

The amount of negative content circulating on the internet can damage people's morale so that social conflicts arise in society that threaten national sovereignty. Detecting negative content can help identify and prevent harmful events before they occur. This can lead to a safer and more positive online environment. Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm for Detection of Negative Content on Websites. The research contributions are 1) detect negative content on the internet with random forest and SVM, 2) comparing SVM and RF algorithms for detecting negative content on websites, 3) detection of negative content based on text focusing on the categories of fraud, gambling, pornography and Whitelist. The stages of this research are preparing a text content dataset on a website that has been labeled, preprocessing (duplicated data, text cleansing, case folding, stopward, tokenize, label encoding, data splitting, and determine the TF-IDF), finally performing the classification process with SVM and Random Forest. The dataset used in this study is a structured dataset in the form of text obtained from emails that have been registered on the TrustPositive website as negative content.  Negative content includes fraud, pornography and gambling. The results show the accuracy of the SVM is 97%, Precision 90% and Recall 91%, while for Accuracy in Random Forest is 92%, Precision 71%, and Recall 86%. The value obtained is the result of testing using 526 website URLs. The test results show that the Support Vector Machine is better than the Random Forest in this study.
Implementation Of Fuzzy Logic Control Method On Chilli Cultivation Technology Based Smart Drip Irrigation System Umam, Faikul; Dafid, Ach.; Cahyani, Andharini Dwi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25878

Abstract

Herbal chili plants are very beneficial from a health and economic perspective. In the process of cultivating herbal chili plants, there are still many problems that need to be faced, including unfavorable climatic conditions and less intensive cultivation processes. Based on this description, to overcome these problems, technological innovation is needed that can be implemented directly in the cultivation of herbal chili plants. This situation can be achieved by applying a drip irrigation system. This system makes it possible to control the water supply requirements of chili herbs efficiently. System stability can run optimally when combined with a method that can make a decision quickly. Fuzzy logic is used in research because it is able to provide appropriate decisions on temperature and soil moisture data in chili herbs. This research is expected to overcome the problem of water shortages in barren areas. And increase people's interest in the cultivation of herbal chili plants. This research is also an overview and framework for developing the agricultural sector in Madura in the technology field. The results of this study indicate that technology can be designed and integrated with the fuzzy logic control method, then the results of testing the tool also show a 99,98% success rate. This is shown by the results of testing in the morning, afternoon, and evening. The contribution of this study is the control of temperature and humidity which in other studies only focused on the soil, not on the temperature and humidity of the air around the herbal chili plants with a system that has been controlled using the fuzzy method.
Dynamic Voltage Restorer for Mitigation of Voltage Sags Due to 3 Phase Motor Starts Based on Artificial Neural Networks Sujito, Sujito; Gumilar, Langlang; Ridzki, Imron; Syah, Abdullah Iskandar; Falah, Moh. Zainul
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25897

Abstract

The Direct On-Line (DOL) process of starting a high-power 3-phase induction motor causes voltage sags in the distribution system that is connected to one point of common coupling (PCC). Voltage sag can cause damage and failure of sensitive loads. This article analyzes and proposes a simulation of voltage sag recovery using a Dynamic Voltage Restorer (DVR) based on an Artificial Neural Network (ANN). ANN is used as a detector and regulator of the voltage compensation value. In this study, a 3-phase induction motor will be connected to a sensitive load, and the DVR will be placed in series with a voltage source or PCC with a sensitive load. The simulation test system uses Simulink-Matlab R2016a with different configurations of induction motor parameters. Based on the simulation results show that the parameters of the 3-phase induction motor cause the depth and duration of the voltage sag. DVR with ANN control can detect and compensate for a voltage sag of 0.5 pu so that the voltage will be normal to 1 pu.
Optimization of Heavy Point Position Measurement on Vehicles Using Support Vector Machine Melky, Franky; Sendari, Siti; Elbaith, Ilham Ari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26261

Abstract

During this time, weight point testing is still done manually using a jack until now it has begun to be replaced with hydraulic equipment namely Lift Table Hydraulic (LTH) which is a portable table with a hydraulic system equipped with sensors (Loadcell and LVDT), powerpack control panel, powerpack, relay module and solenoid valve to adjust the table height. This portable table is one component of the heavy point measurement equipment system used for mining and plantation vehicles such as tractors, buses, trucks which are required to have a safe structure in heavy road conditions with rough or uneven surfaces with slopes up to an angle of 15 ° to 20 °. This emphasized research contributes to more accurate testing. Based on these problems, this research was conducted using Support Vector Machine (SVM) for the optimization of heavy point position measurement. The objects used are minibuses with 1 and 19 passengers and buses with 29 and 36 passengers on the proportion of datasets (training: testing) of 80% and 20% using linier kernel. From the experimental results, the accuracy in the condition of 1 passenger is 94.7%; minibus 19 passengers 98%; bus 29 passengers 98.1% and bus 36 passengers 97.4%. The highest accuracy obtains on 29 passengers minibus. 
Circuit Modeling of Dual Band MIMO Diversity Antenna for LTE and X-Band Applications Gambo A., Aminu; Kolawale, S. F.; Saminu, Sani; Danladi, Ali; Jabire, Adamu Halilu
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26272

Abstract

This paper presents a study on developing a dual-band antenna equivalent circuit model for X-Band and LTE applications. MIMO antennas play a crucial role in modern wireless communication systems, and understanding their impedance behavior is essential. This work proposes a dual-band lumped equivalent circuit model, utilizing gradient optimization based on antenna-simulated S-parameters in Advanced Design System (ADS). The four radiating elements of the MIMO antenna are accurately modeled, considering their geometry and the defected ground structure (DGS) effect, which enhances the antenna's isolation and low correlation coefficient (ECC). The calculated lumped equivalent circuit model is validated through rigorous simulation and measurement data, demonstrating consistency with the expected results. The experimental measurements show measured isolation exceeding 20 dB while achieving a maximum realized gain of 5.9 dBi and an efficiency of 87%. The developed model holds promise for improving the design and performance of MIMO antennas for various applications.
Development of a Remote Straw Mushroom Cultivation System Using IoT Technologies Azman, Novi; Habiburrohman, Muhammad; Nugroho, Endang Retno
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26280

Abstract

Indonesia's tropical climate creates vast potential for straw mushroom cultivation. However, crop failures are frequent during the rainy season due to lower temperatures. To address this challenge, this paper presents an innovative, IoT-based system designed to remotely control and monitor temperature and humidity in mushroom cultivation sites, thereby minimizing crop failure and optimizing production. The proposed system employs a DHT11 sensor to measure temperature and humidity levels accurately. A DS3231 module is incorporated to schedule automatic watering procedures, ensuring adequate hydration for the mushrooms without manual intervention. For real-time monitoring, an ESP32-Cam is used to capture images of the mushroom cultivation site. The core of this system is a NodeMCU microcontroller, which processes environmental data and automatically adjusts the cultivation conditions. The system triggers a heater if the temperature falls below 30°C, or an exhaust fan if it exceeds 35°C. Similarly, a humidifier activates if humidity falls below 80%, and an exhaust fan turns on when humidity exceeds 90%. To provide users with instant updates, the system integrates with the Blynk application, sending notifications when these specified conditions are met. This feature allows for prompt intervention when necessary, facilitating optimal growth conditions at all times. During testing, the proposed system demonstrated its effectiveness, enabling successful straw mushroom cultivation within nine days. Furthermore, it achieved this with modest power consumption, using a total of 661.608Wh. This system offers a promising solution to improve straw mushroom farming in regions with similar climates to Indonesia.

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