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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 30 Documents
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Search results for , issue "Vol. 9 No. 3 (2023): September" : 30 Documents clear
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Nafiz, Muhammad Fauzan; Kartini, Dwi; Faisal, Mohammad Reza; Indriani, Fatma; Saragih, Triando Hamonangan
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.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Vessel Tracking System Based LoRa SX1278 Apriani, Yosi; Oktaviani, Wiwin A; Sofian, Ian Mochamad
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.v9i3.26385

Abstract

This research presents a vessel tracking system that provides real-time coordinate and speed information. The idea behind the development of this system originated from Automatic Identification System (AIS) technology, which functions as a vessel monitoring system in maritime areas. The system aims to improve navigation safety, monitor vessel traffic, and maritime security. In Indonesia, AIS is regulated by the Ministry of Transportation. However, this technology has not yet been implemented in river waters. In addition, AIS is a complex and expensive system. In this research, geographic location detection information in the form of a vessel tracking system is obtained using the UBlox Neo-6M GPS module based on LoRa technology. The LoRa mechanism periodically sends location data and vessel speed from the node to the gateway. The data is then sent to the ThingSpeak server using the MQTT protocol. On the server, the data can be accessed for further analysis. The developed system shows that the research can be realized and the system functions properly through a series of experimental tests. While in the in situ test, the system displayed good performance on LoRa SF 7 configuration with a signal strength of -118 dBm within the communication range of 1000 meters. This result can be improved by considering the MAPL value of -138 dBm.
Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing Handoko, Wahyu Tri; Muladi, Muladi; Handayani, Anik Nur
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.v9i3.26395

Abstract

Sunlight is an alternative energy source that can be used as a substitute for fossil fuels. Renewable energy potential has not been widely utilized, especially in Indonesia. Utilization of sunlight, one of which is done indoors to save electricity and the source is not limited. This study aims to predict solar irradiance to determine the value of sunlight intensity in an area as the main source of the utilization of renewable electrical energy through the solar tube system with the LSTM method. This low-cost system offers a renewable way and considers the potential for solar radiation as an energy-efficient alternative based on the intensity of light captured by the solar tube. This research uses two methods. The LSTM method is a recurrent neural network forecasting technique that can study deeply and extract temporal relationships in data because of its large architecture. The exponential smoothing method is part of the time series forecasting technique and is used when the dataset has no cyclic variance and trend. Data collection was carried out in sunny conditions because it represents a stable condition in sunlight. The results obtained from the two methods are evaluated with RMSE and MAE values to choose the optimal approach. Due to lower RMSE and MAE values in this comparison, LSTM performs better than Multiple Repeat and Exponential Smoothing in terms of performance.
Photovoltaic Generator Approach Model for Characteristic Estimation I-V Suwarno, Suwarno; Sadiatmi, Rini; Dewi, Aminah Asmara; Birje, Herman
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.v9i3.26440

Abstract

Modeling of the photovoltaic (PV) approach is generally described by nonlinear equations with solving the equation using the steps iteratively.  However, this proposed research discusses the monocrystal type PV module approach model to estimate the characteristics of a photovoltaic generator, because it has the advantage that it is good enough to operate in Indonesia. This approach model takes into account the relationship between power, energy, and current to obtain the performance characteristics of the PV generator. This PV generator approach model is compared with PV generator manufacturer data and analyzed to validate the proposed approach model. Approach model with simulation hope this helps to find out IV characteristics according to the data recorded on the PV module and can save time or reduce the time to measurement results. The simulation results obtained the amount of power, energy, and current, 200.475W, 133.65 W/m2, and 7.9 Amperes, respectively with a simulation time of about 1.5 milliseconds. In addition to the above results, a comparison between the current and the power of the PV modules under study is also given and it gives the result that at a certain current the power level will no longer increase, but the power will decrease drastically due to heat from the PV panel module, because the module has the ability to receive heat from outside.
Certainty Factor-based Expert System for Meat Classification within an Enterprise Resource Planning Framework Kusnadi, Adhi; Arkeman, Yandra; Syamsu, Khaswar; Wijaya, Sony Hartono
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.v9i3.26443

Abstract

The demand for halal products in the Islamic context continues to be high, requiring adherence to halal and haram laws in consuming food and beverages. However, individuals face the challenge of distinguishing between haram meat and permissible halal meat. This study aims to answer these challenges by designing an expert system application within the ERP framework to increase the usability functionality of the system that can differentiate between beef, pork, or a mixture of both based on the physical characteristics of the meat. The aim is to determine halal products permissible for consumption by Muslims. The research methodology includes a data collection process that involves taking 30 meat samples from various sources, and the criteria used to classify the meat will be determined based on an analysis of the physical characteristics of the meat. System administrators use expert systems to ensure proper treatment of meat during administration processes, including separating halal beef from pork and implementing different inventory procedures. The Certainty Factor (CF) inference engine deals with uncertainty even though the expert system's accuracy level is relatively good with several rules. However, these results must be studied further because the plan relies on expert opinion. Therefore, it is necessary to set the correct CF value for accurate height classification. The CF inference engine facilitates reasoned conclusions in meat classification. Functional testing confirms the smooth running of the system, validating its reliability and performance. In addition, the expert system accuracy assessment produces a commendable accuracy rate of 90%. In addition, the expert system works powerfully on various meat samples, accurately classifying meat types with high precision. This study explicitly highlights the expert system's design for meat classification in determining halal products by using the Expert System Certainty Factor. In conclusion, this expert system provides an efficient and reliable approach to classifying meat and supports the production and consumption of Halal products according to Islamic principles.
Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models Geni, Lenggo; Yulianti, Evi; Sensuse, Dana Indra
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.v9i3.26490

Abstract

General election is one of the crucial moments for a democratic country, e.g., Indonesia. Good election preparation can increase people's participation in the general election. In this study, we conduct a sentiment analysis of Indonesian public opinion on the upcoming 2024 election using Twitter data and IndoBERT model. This study is aimed at helping the government and related institutions to understand public perception. Therefore, they could obtain valuable insights to better prepare for elections, including evaluating the election policies, developing campaign strategies, increasing voter engagement, addressing issues and conflicts, and increasing transparency and public trust. The main contribution of this study is threefold: (i) the application of state-of-the-art transformer-based model IndoBERT for sentiment analysis on political domain; (ii) the empirical evaluation of IndoBERT model against machine learning and lexicon-based models; and (iii) the new dataset creation for sentiment analysis in political domain. Our Twitter data shows that Indonesian public mostly reacts neutrally (83.7%) towards the upcoming 2024 election. Then, the experimental results demonstrate that IndoBERT large-p1 is the best-performing model that achieves an accuracy of 83.5%. It improves our baseline systems by 48.5% and 46.49% for TextBlob, 2.5% and 14.49% for Multinomial Naïve Bayes, and 3.5% and 13.49% for Support Vector Machine in terms of accuracy and F-1 score, respectively.
Design of Automatic Organic Fertilizer Processing Tools by Utilizing the Internet of Things (IOT) as a Monitoring System Rahmaniar, Fadhila Desti; Endri, Jon; Salamah, Irma
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.v9i3.26528

Abstract

This paper aims to design an automatic organic fertilizer processing tool by utilizing the Internet Of Things (IOT) as a monitoring system. The proposed tool system aims to make it easier to sort organic waste or inorganic waste which can then also automatically process organic waste into organic fertilizer. The method used is a literature survey conducted to solve relevant problems. At this stage, a data collection survey is carried out by utilizing previous research sources from books, journals, and the internet. We used this literature review to enable us to update the tool from previous research. The proposed system uses ESP 32 module as a microcontroller that can connect to WiFi. In addition, it has also been introduced in the Internet of Things (IOT) as an automatic monitoring system for organic fertilizer processing plants. Garbage download information is sent via the internet and returned to the Android application. However, in this study, one application, namely WhatsApp, was used. Garbage has long been a social problem, especially in big cities, polluting the environment, endangering public health, and reducing environmental aesthetics. Therefore, waste management is important to minimize the negative impact of waste. Finally, the research results obtained can be useful as technological development in the future of automatic organic waste processing equipment.
Cat Feeding Using Microcontroller Arduino Uno TCS3200 Sensor and Internet of Things Wardana, Handava; Salamah, Irma; Taqwa, Ahmad
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.v9i3.26529

Abstract

Petting animals is one way for humans to reduce stress levels and entertain themselves. When coming home from work, a person needs entertainment at home, namely by keeping cute animals; one of the attractive and widely kept pets is a cat. The presence of cats in the house can help restore mood or feelings, and animals that like to be invited to play. For that, the owner must love his own pet without reducing affection for his pet; a cat's diet must be maintained even though the owner is busy working, especially outside the city, because cats need a good diet. Therefore, the purpose of doing this research is to facilitate cat owners in feeding while doing other activities outside the home. In addition, previous research still cannot feed the cat automatically but can monitor the state of cat activity. This tool uses several sensors, namely the RTCDS3231 sensor, TCS3200, Load Cell, HX711, ESP32CAM. The results obtained are Cat Feeding Using Arduino Uno Microcontroller TCS3200 Sensor, and Internet of Things is a tool system that can notify that the feed has run out, can feed the cat automatically, and can find out the activity of the cat by using the sensor. Several pet shop parties strongly agree that this tool is very helpful, namely to reduce the worry of the owner in feeding the cat. 
Hate Speech Detection Using Convolutional Neural Network and Gated Recurrent Unit with FastText Feature Expansion on Twitter Wijaya, Kevin Usmayadhy; Setiawan, Erwin Budi
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.v9i3.26532

Abstract

Twitter is a popular social media for sending text messages, but the tweets that can send are limited to 280 characters. Therefore, sending tweets is done in various ways, such as slang, abbreviations, or even reducing letters in words which can cause vocabulary mismatch so that the system considers words with the same meaning differently. Thus, using feature expansion to build a corpus of similarity can mitigate this problem. Two datasets constructed the similarity corpus: the Twitter dataset of 63,984 and the IndoNews dataset of 119,488. The research contribution is to combine deep learning and feature expansion with good performance. This study uses FastText as a feature expansion that focuses on word structure. Also, this study uses four deep learning methods: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and a combination of the two CNN-GRU, GRU-CNN classification with boolean representation as feature extraction. This study uses five scenarios to find the best result: best data split, n-grams, max feature, feature expansion, and dropout percentage. In the final model, CNN has the best performance with an accuracy of 88.79% and an increase of 0.97% from the baseline model, followed by GRU with an accuracy of 88.17% with an increase of 0.93%, CNN-GRU with an accuracy of 87.47% with an increase of 1.86%, and GRU-CNN with an accuracy of 87.55% with an increase of 1.32%. Based on the result of several scenarios, the use of feature expansion using FastText succeeded in avoiding vocabulary mismatch, proven by the highest increase in accuracy of the model than other scenarios. However, this study has a limitation is that the dataset is used in Indonesian.
Network Slicing Using FlowVisor for Enforcement of Bandwidth Isolation in SDN Virtual Networks Monita, Vivi; Wendanto, Wisnu; Anggiratih, Endang
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.v9i3.26533

Abstract

Software-defined networking (SDN) is becoming increasingly popular because of features such as programming control, embedded monitoring, fine-grained control, flexibility, support for many tenants, and scalability. Problems with the prior design, known as the conventional network, include the need to configure each network device individually, decentralized control, and a persistent issue with tenant enforcement for multitenant support. Tenants are unable to administer their networks without disturbing their neighbours. In this research, network slicing on SDN will ensure tenant isolation using FlowVisor and an SDN controller. Flowspace, which is part of FlowVisor capable of implementing network isolation, is for isolation in this research. Multitenancy is supported in SDN via the network slicing technique. Two types of renters were employed, and two testing procedures connectivity and functionality were run to meet the research objectives. This research produced several findings, including that all hosts were correctly linked, and the connection was achieved without turning on FlowVisor. The host function can only send and receive data from hosts with the same tenant. The research results show that FlowVisor can be applied for isolation enforcement. As a result of each tenant utilising their slice of the network without being interrupted by other slices, this research finds that utilising FlowVisor to construct Flowspace can segment the network to allow multitenancy. Expanding the number of slices for more study and testing in a real-world setting is possible.

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