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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
Hate Speech Detection Using Convolutional Neural Network and Gated Recurrent Unit with FastText Feature Expansion on Twitter Kevin Usmayadhy Wijaya; Erwin Budi Setiawan
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.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Tiara Lailatul Nikmah; Jumanto Jumanto; Budi Prasetiyo; Nina Fitriani; Much Aziz Muslim
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.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Design of Automatic Organic Fertilizer Processing Tools by Utilizing the Internet of Things (IOT) as a Monitoring System Fadhila Desti Rahmaniar; Jon Endri; Irma Salamah
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.
Development of a Remote Straw Mushroom Cultivation System Using IoT Technologies Novi Azman; Muhammad Habiburrohman; Endang Retno Nugroho
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.
Cat Feeding Using Microcontroller Arduino Uno TCS3200 Sensor and Internet of Things Handava Wardana; Irma Salamah; Ahmad Taqwa
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.
Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing Wahyu Tri Handoko; Muladi Muladi; Anik Nur Handayani
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.
Design and Implementation of IoT-Based Monitoring Battery and Solar Panel Temperature in Hydroponic System Rizky Rahmatullah; Trie Maya Kadarina; Bagus Bhakti Irawan; Reza Septiawan; Arief Rufiyanto; Budi Sulistya; Arief Budi Santiko; Puput Dani Prasetyo Adi; Nicco Plamonia; Ravindra Kumar Shabajee; Suhardi Atmoko; Dendy Mahabror; Yudi Prastiyono
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.v9i3.26729

Abstract

Hydroponics is currently widely used for the effectiveness of farming in narrow areas and increasing the supply of food, especially vegetables. This hydroponic technology grew until it collaborated with the internet of things technology, allowing users to monitor hydroponic conditions such as temperature and humidity in the surrounding environment. This technology requires electronic systems to obtain cost-effective power coverage and have independent charging systems, such as power systems using solar panels, where the power received by solar panels from the sun is stored in batteries. It must ensure that the condition of the battery and solar panels are in good condition. The research contribution is to create a solar panel temperature monitoring system and battery power using Grafana and Android Application. Apart from several studies, solar panels are greatly affected by temperature, which can cause damage to the panels. If the temperature is too high, the battery and panel temperature monitoring system can help monitor the condition of the device at Grafana and Android application with sensor data such as voltage, current, temperature and humidity that have been tested for accuracy. Accuracy test by comparing AM2302 sensor with Thermohygrometer and INA219 sensor with multimeter and clampmeter, both of which have been calibrated. The sensor data gets good accuracy results up to 98% and the Quality-of-Service value on the internet of things network is categorized as both conform to ITU G.1010 QOS data based on network readings on the wireshark application. QOS results are 0% Packet loss with very good category, 14ms delay with very good category and Throughput 71.85 bytes/s.  With the results of sensor accuracy and QOS, the system can be relied upon with a high level of sensor accuracy so that environmental conditions are monitored accurately and good QOS values so data transmission to the server runs smoothly.
Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price Masna Wati; Haviluddin Haviluddin; Akhmad Masyudi; Anindita Septiarini; Heliza Rahmania Hatta
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.
Network Slicing Using FlowVisor for Enforcement of Bandwidth Isolation in SDN Virtual Networks Vivi Monita; Wisnu Wendanto; Endang Anggiratih
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.
Certainty Factor-based Expert System for Meat Classification within an Enterprise Resource Planning Framework Adhi Kusnadi; Yandra Arkeman; Khaswar Syamsu; Sony Hartono Wijaya
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.

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