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IoT: Monitoring Temperature, Water pH, and Automatic Water Drainage Systems for Gurami Cultivation

IoT: Monitoring Temperature, Water pH, and Automatic Water D...

引用

2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology, IConNECT 2023

作者： Sulistiani, Heni Saputra, Wijaya Darwis, Dedi Isnain, Auliya Rahman Rimanda Putra, Ardy Khairunisa, Yosi Universitas Teknokrat Indonesia Study Program of Accounting Information System Lampung Indonesia Universitas Teknokrat Indonesia Study Program of Computer Engineering Lampung Indonesia Universitas Teknokrat Indonesia Study Program of Informatics Lampung Indonesia Regional Research and Development Agency Lampung Indonesia

ISBN: (纸本)9798350331172

Gurami is a native fish from Indonesian waters that is much-loved by Indonesian people for daily food consumption. The gurami inhabits still waters such as swamps, lakes, and ponds. Besides that, carp cannot live in a water content that is too alkaline or acidic, the oxygen content in the water must be stable. Water quality is a very important factor and can affect the survival of cultivated fish. Several parameters must be considered in improving the development of carp, one of which is the temperature and pH of the water and automatic drainage of water. In this paper, we conduct IoT-based research that focuses on controlling water temperature and pH and automatically draining pool water. Water temperature and pH control such as a pH sensor temperature sensor and a water turbidity sensor will later be connected to the ESP 32 microcontroller which already has a wi-fi module. A system that is linked to electrical power and the internet then the sensor will read the water quality including the pH temperature value and water turbidity from inside the fish pond, after getting the data, the data will go to ESP 32. This research can help gurami cultivating farmers to make it easy to control the pH temperature and perform automatic pool water draining. The results of sensor testing with actual data are pretty accurate with the average error value of the ds18b20 sensor being 0.05% while the value of the pH sensor itself is 0.07%. © 2023 IEEE.

关键词： Temperature sensors

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Eye Disease Detection Model Using Convolutional Neural Network with RMSprop, SGD, and Adam Optimizers 11

Eye Disease Detection Model Using Convolutional Neural Netwo...

引用

11th International Conference on ICT for Smart Society, ICISS 2024

作者： Kurniadi, Dede Mulyani, Asri Riefky Rafliana Suwandy, Moch Institut Teknologi Garut Department of Computer Science Kabupaten Garut Indonesia Institut Teknologi Garut Informatics Study Program Kabupaten Garut Indonesia

ISBN: (纸本)9798350389654

Eye disorders such as cataracts, glaucoma, and diabetic retinopathy can cause abnormalities in eye function, including blindness. This research aims to develop a machine-learning model and find the best model for detecting eye diseases. Deep Learning methods based on the Convolutional Neural Network (CNN) model will be used to create eye disease detection models by adding the RMSProp, SGD, and Adam optimizers techniques. The dataset used consists of 4 classes, namely normal (1074 data), glaucoma (1007 data), cataract (1038 data), and diabetic retinopathy (1098 data), totaling 4217 fundus images. The evaluation results reveal the CNN model with the RMSProp optimizer has better model performance with a value of 88% accuracy, 88% precision, 87% recall, and 88% f1-score, and based on the AUC value on the ROC-AUC curve for each class is above 0.90, which means that the model can differentiate data between classes well and is included in the Excellent classification category. © 2024 IEEE.

关键词： Convolutional neural networks

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Urgency Detection of Events Through Twitter Post: A Research Overview 4

Urgency Detection of Events Through Twitter Post: A Research...

引用

4th International Conference on Electrical Engineering and Computer Science, ICECOS 2024

作者： Edlim, Frederick William Edo, Gregorius Wiratama, Rangga Kurnia Putra Mahmudin, Riyan Solihin, Andi Ariyanto, Amelia Devi Putri Purwitasari, Diana Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Widya Husada University Information Technology and Systems Study Program Semarang Indonesia

ISBN: (纸本)9798350368253

Indonesia is located in volatile Pacific fire ring and stands as one of the most global disaster-prone areas. The government and public are in constant vigilance of the potential threats. Social media generates large amounts of data from its users and is one of the prime candidates to be used as a disaster monitoring and response system. Several studies have demonstrated the effectiveness of using such an approach for developing disaster monitoring systems. but due to the number of messages generated, filtering is needed to extract useful information from the message. Hence, it's needed to detect an urgency from a message. This review, which was carried out following the PRISMA model, focused on the detection of the urgency of Indonesian Twitter during crisis events. From studies selected, several recommendations are suggested for future endeavor in this topic. Future systems are advised to employ three or four class schemes rather than binary class to gain more nuanced insights into tweet urgency. On-Event training and testing approaches are suitable for frequently occurring events, whereas out-of-event approaches offer broader applicability for real-world scenarios. Both machine learning and deep learning methods, including multimodal approaches, have demonstrated efficacy in urgency detection. Addressing pre-processing challenges, such as abbreviations and slang, is critical, and ensuring consistency in manually annotated data is essential. Additionally, the selection of appropriate word embeddings, such as fastText, IndoBERT, or crisis-specific embeddings, is crucial for achieving optimal model performance. © 2024 IEEE.

关键词： Tweets

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Automated Test on Multiple Platform Framework Development 7

Automated Test on Multiple Platform Framework Development

引用

7th International Conference on Sustainable Information Engineering and Technology, SIET 2022

作者： Ubaidilah, Muhammad Fadhli Permatasari, Desy Intan Hardiansyah, Fadilah Fahrul Informatics Engineering Study Program Politeknik Elektronika Negeri Surabaya Indonesia

ISBN: (纸本)9781450397117

Software testing is a phase in software development to ensure software quality. As more and more software is developed for web and mobile platforms, Software Quality Assurance (SQA) Engineer is responsible to test software on both platforms to ensure functionality on each platform. While automated test can be implemented to reduce the workload of SQA Engineer, it has its setbacks as automated test needs a huge effort to maintain it and available tools to develop automated test currently only support one platform. In this research, an automated test framework is proposed and developed that can test both web and mobile platforms. Compared to an automated test project that uses separate tools, a project that uses the proposed framework has 2.57% higher maintainability index. A user test to 3 SQA Engineers has also been conducted and the proposed framework has generally been given high maintainability rating. © 2022 ACM.

关键词： Software testing

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IoT-Integrated Machine Learning for Real-Time Soil Nutrient Monitoring and Horticultural Crop Recommendations 10

IoT-Integrated Machine Learning for Real-Time Soil Nutrient ...

引用

10th IEEE Information Technology International Seminar, ITIS 2024

作者： Wahid, Abdul Parenreng, Jumadi Mabe Aswandi Alwi, Ananta Dwi Prayoga Zain, Satria Gunawan Setialeksana, Wirawan Universitas Negeri Makassar Informatics and Computer Engineering Department Makassar Indonesia Politeknik Negeri Lhokseumawe Network Engineering Technology Study Program Lhokseumawe Indonesia

ISBN: (纸本)9798331521295

Farmers and agricultural newcomers often rely on intuition for plant nutrient management, leading to inaccuracies. This study presents a real-time soil nutrient monitoring and plant recommendation system using IoT and machine learning. The prototype integrates an ESP32 microcontroller, NPK sensor, soil pH sensor, and DHT11 with an application and a machine learning model. The model employs a hard voting method combining random forest, LightGBM, and support vector machine algorithms, deployed via Flask API and hosted on Vercel. Testing showed the system effectively monitors soil nutrients in real-time, with a pH sensor error margin of ±0.51 and NPK sensor readings displaying a non-linear increase in levels. The hard voting method achieved 98.5% accuracy, with recommendation processing taking less than one second (371.17ms) and CPU usage at 7%. System performance depends on internet speed, while recommendation accuracy is influenced by temperature and humidity. This system offers a practical solution for precision agriculture by optimizing nutrient application and plant recommendations. © 2024 IEEE.

关键词： pH sensors

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case study UCI Machine Learning Dataset) 4

Finding the Best Techniques for Predicting Term Deposit Subs...

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4th International Conference on Sustainable Engineering and Creative Computing, ICSEC 2022

作者： Setiyani, Lila Indahsari, Ayu Rosalina Wansen, Tjong President University Master of Science Information Technology Study Program Bekasi Indonesia President University Informatics Study Program Bekasi Indonesia

ISBN: (纸本)9798350322002

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%). © 2022 IEEE.

关键词： Forecasting

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Development Of Scholarship Sustainability Analysis Application Every Semester On STMIK Pelita Nusantara Students 4

Development Of Scholarship Sustainability Analysis Applicati...

引用

4th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2022

作者： Br Tarigan, Nera Mayana Sinaga, Bosker Panggabean, Erwin Sagala, Jijon Raphita Informatics Engineering Study Program Stmik Pelita Nusantara Medan Indonesia Software Engineering Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (纸本)9798350399073

Decision making in an organization is needed a system. Decision support system is a system that helps company management and education in decision making that provides information, modeling and analyzing data with the application of a method. The management, especially higher education, must maximize sophisticated technology. For example, to analyze scholarship data. Problems are found in manual data analysis so that human errors often occur in addition to using more time than the target, losing old data, resulting in inaccurate analysis of scholarship sustainability every semester. The research was conducted to overcome the problems that have been found by analyzing the existing data with the simple additive weighting method, building a system by applying the method and testing the system. The result of this research is to produce an application for the development of a decision support system in managing student data to determine the sustainability of the scholarship every semester. Applications that are built can help the management in managing scholarships. © 2022 IEEE.

关键词： Decision support systems

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Online Handwritten Recognition for Alphabet Writing Practice 4

Online Handwritten Recognition for Alphabet Writing Practice

引用

4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Santiari, Ni Putu Linda Rahayuda, I Gede Surya Institute of Technology and Business STIKOM Bali Information System Study Program Faculty of Informatics and Computer Denpasar Indonesia Informatics Study Program Faculty of Math and Natural Sciences Udayana University Mangupura Indonesia

ISBN: (纸本)9781665453950

Learning to write in children requires the child's habit of using a pencil to write on paper. The manual method has drawbacks such as the use of a lot of paper when children have to study harder to keep repeating the exercises and also cannot be done online remotely. In addition to this, writing exercises on the touchscreen can also train children to be more skilled at using the touchscreen. To overcome these problems, the author will discuss the use of handwriting recognition technology to practice writing capital letters (A-H), where the technology will be built on a web-based basis. The recognition algorithm used is K-NN. Testing the use of the website was carried out at Tunas Mekar Sari Kindergarten, Denpasar. The identification number and result will be stored on the web server. Based on the experiment, it was found that from 80 image data, the K-NN algorithm succeeded in providing accurate recognition results on 60 image data, and obtained an accuracy of 78.75%. © 2022 IEEE.

关键词： Websites

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The Improvement of Personal Service in the MSMEs Marketplace Using a Rank-Based Collaborative Filtering Approach

The Improvement of Personal Service in the MSMEs Marketplace...

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2022 International Conference on Electrical and Information Technology, IEIT 2022

作者： Lestari, Sri Yulmaini Aswin Sulyono Nul Fikri, Ruki Rizal Pratama, Yan Aditiya Informatics Engineering Study Program Faculty of Computer Science Lampung Bandar Indonesia Management Study Program Faculty of Business Economics Lampung Bandar Indonesia

ISBN: (数字)9781665453035

ISBN: (纸本)9781665453035

Indonesian MSMEs in 2022 will begin to enter the transformational recovery phase. Various preparations must be made in order to adapt. One that has been done is to increase the number of MSMEs entering the digital ecosystem, such as the MSME marketplace. The MSME Marketplace is used for marketing various MSME products online. The MSME Marketplace must be supported by methods that are able to provide good and fast personal services, considering the types and variety of products offered are increasing in number and varying from time to time. Therefore, this study proposes the Porat_Rank method to produce product recommendations that match user interests quickly so that users are served well. The experimental results show that the Porat_Rank method is superior to the Copelan Score method both in producing recommendations and in processing speed. © 2022 IEEE.

关键词： Collaborative filtering

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Internet of Things for Real-Time Multi Room Monitoring System 1

Internet of Things for Real-Time Multi Room Monitoring Syste...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Tae, Kim Sung Suhartomo, Antonius Vincent President University Study Program of Electrical Engineering Bekasi Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350306156

Electronic devices have become an integral component in our daily activities. In fact, the consumption of electrical energy in households reached 49% in 2018, and is expected to increase to 58% in 2050. Concurrently, the LPG is the second highest consumed energy in the household and is expected to increase to 37% in 2050. However, the devices utilizing these energies could lead to a danger such as housefire if not operated and treated carefully. An Internet of Things (IoT)-based smart home monitoring system would solve this problem, especially by effectively monitoring several parameters in the house simultaneously. In this study, a system is built to monitor and control three rooms in the house independently and simultaneously. The sensors and/or actuators in all rooms are controlled by NodeMCU ESP8266s. All microcontrollers are linked via the Internet to a local server, where all data is analyzed, saved, and transmitted to the user using the MQTT protocol. The monitoring is done by the user via MQTT dashboard in the smartphone. Following testing, the three microcontrollers are fully capable of exchanging data with the user's smartphone. The humidity changed by 1%, while the temperature changed by 0.1 °C. Perfect data interchange is possible between the relays, flame, gas, and movement activity sensor. © 2023 IEEE.

关键词： Informatization

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