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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Masykur, Fauzan Adi, Kusworo Nurhayati, Oky Dwi Diponegoro University Doctoral Program of Information System School of Postgraduate Studies Semarang Indonesia Muhammadiyah University of Ponorogo Department of Informatics Engineering Ponorogo Indonesia

ISBN: (纸本)9781665453899

Paddy is a rice-producing plant as a staple food raw material for most of the population in Indonesia. The rice planting process cannot be separated from pests and diseases that can cause crop failure and impact on rice production stocks. Currently, the process of monitoring the development of rice plants is done conventionally by direct observation by farmers. Meanwhile, in this paper, a classification model and determination of object detection in rice plants infected with pests and diseases is proposed based on leaf color. Leaf classification applies convolution conditional network algorithm with mobilenet as the architecture. The leaf image dataset was taken from public data from *** by involving 4 classes, namely healthy leaf images, brownspot, hispa and leafblast images. In each class the leaf images are grouped into 2 groups, namely 70% training and 30% validation. On the mobilenet backbone, it produces 97% accuracy and 22% loss with the time required for 195s 19s/step in 1 epoch round. © 2022 IEEE.

关键词： Object detection

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Multi-Step-Ahead Prediction of User Movement on 5G Networks Using the Long Short-Term Memory Method

Multi-Step-Ahead Prediction of User Movement on 5G Networks ...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Adriel Timoteo Fabian Rafinanda Benyamin Naufal Bhanu Anargyarahman Iskandar Hajiar Yuliana School of Electrical Engineering and Informatics Telecommunications Engineering Program Bandung Institute of Technology Bandung Indonesia Department of Electrical Engineering Faculty of Engineering Jenderal Achmad Yani University Cimahi Indonesia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

Since the initial launch of 5G networks in 2018, 5G technology has rapidly expanded to cover about 40% of the world's population, offering higher bandwidth, faster connectivity, and lower latency than 4G. However, due to its higher frequency operation, 5G necessitates more frequent handovers, making efficient handover performance crucial. This research focuses on enhancing handover efficiency by predicting user movement using Long Short-Term Memory (LSTM) models for multi-step-ahead prediction. The model created will utilize 60 seconds of historical data to predict 10 seconds ahead, with a target to achieve an average prediction accuracy of less than 20 meters with a latency under 100 ms. The study employs TensorFlow for model training, leveraging the Grab-Posisi dataset, and utilizes Plotly and TensorBoard for result visualization. Six model variations were trained, with the 16-unit model trained for 3106 epochs performing best, achieving 16.99 meters accuracy and 31 ms latency. The findings suggest that LSTM is effective for predicting user movement in 5G networks, with potential accuracy improvements through extended training epochs.

关键词： Training Meters Accuracy 5G mobile communication Quality of service Predictive models Handover Data models Telecommunications Long short term memory

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Oxygen Availability Control and Monitoring System in Hospitals using IoT 4

Oxygen Availability Control and Monitoring System in Hospita...

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4th International Conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2021

作者： Nugraha, Nikko Aji Bayu Mashoedah Post Graduate Program of Electronic and Informatics Engineering Education Faculty of Engineering Universitas Negeri Yogyakarta Indonesia

Medical Gas is an important component in the treatment of patients with COVID-19 disease. Medical gas is used to help COVID-19 patients to reduce the effects of respiratory disorders by providing oxygen ventilators to patients. With the surge in typical COVID-19 sufferers as of July 2021, the need for Oxygen in hospitals is getting higher. This is when the control and monitoring of medical gases in hospitals are late because the integrated system is very dangerous. Therefore, a system that can be used to control and monitor medical gases in hospitals that are integrated and automated and can be monitored by the Government and Medical Gas Producers. This is useful for anticipating the lack of availability of Medical Gas in hospitals. In this study, the system used IoT systems as a base in delivery and control. This system uses Arduino as a minimum system that reads the press sensor on the Medical Gas tube and regulates the valve. The data obtained is then sent to the Local Server to be processed and delivered to the Hospital Officer. Local Servers also send the data to cloud servers to be monitored by the government funds of several medical gas producers. This design can help in the process of controlling and monitoring Medical Gases in hospitals in hopes of minimizing the risk of delays in supplying medical gases to hospitals. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Hospitals

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Application of the K-Medoids Clustering Model and Borda Decision Group in Viewing Flood Area Groupings in North Aceh Regency

Application of the K-Medoids Clustering Model and Borda Deci...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Wahyu Fuadi Ilham Sahputra Dedi Fariadi Agustinar Informatics Engineering Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia Information Systems Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia Electrical Engineering Study Program Faculty of Engineering Universitas Malikussaleh Lhokseumawe Indonesia

The importance of the K-Medoid model in viewing disaster-prone areas in each sub-district in North Aceh. The advantage of the k-medoid model results is that it has better cluster centers than other models. The novelty of this research is the cluster results are assessed by each decision maker using the Borda group model in providing flood disaster assistance or areas that are a priority for handling, which are included in the flood-prone cluster. Therefore, this research is important because it can be used to identify areas most severely affected by flooding. This research aims to analyze and identify the impact of flood hazards based on grouping and decision-making using the Borda group model in each sub-district in North Aceh. The results of the k-medoids model show 3 clusters that can see potential areas for flood disasters, namely very vulnerable. There are six sub-districts. For the k-medoids results, ten sub-districts are vulnerable. Lastly, the safe zones are 11 sub-districts. This research’s k-medoid model analysis is more precise in seeing clusters of places that are potentially affected by flooding based on spatial maps of their nearest neighboring areas. The percentage of each cluster is as follows: Cluster 1 is 4%, Cluster 2 is 55%, and Cluster 3 is 41%. The k-medoid model provided this result after three iterations, with a closeness difference of 1.594510537. The results of the Borda model group research can be used as a recommendation for the areas that receive the highest priority in disaster management.

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AlexNet Architecture Based Convolution Neural Network for Realtime Audio to Text Translator of Bisindo Hand Sign

AlexNet Architecture Based Convolution Neural Network for Re...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Sujatmiko, Dhiki Sari, Christy Atika Rachmawanto, Eko Hari Krismawan, Andi Danang Altamer, Bilal R. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Animation Semarang Indonesia Mosul University Presidency University of Mosul Department of Computer Science Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Deafness is a condition that results in the loss of hearing function, hindering the reception of information such as oral communication that relies on auditory senses. Consequently, individuals with hearing impairment experience communication barriers and may have limited or no ability to respond. One solution is the use of sign language. In Indonesia, there are two known sign languages: Sibi and Bisindo. Both serve the same function but differ in their style of movement and expression. Bisindo is considered more flexible as it conveys meaning based on the Indonesian language. However, the universal understanding of this language solution is still limited among many people. Therefore, a program is needed to facilitate translation between deaf individuals who use sign language and their counterparts who do not communicate through sign language. CNN (Convolutional Neural Network) is a deep learning algorithm used for training visual input data recognition by computer systems. There are various CNN-based architectures, and one of them is AlexNet. Based on the author's testing, the AlexNet architecture proves to be suitable for real-time sign language translation. The evaluation of the system involved 7,800 datasets and 520 testing instances, with an average accuracy of 468 correct translations. When averaged, the system achieved a 90% accuracy rate, representing a 100% increase in accuracy compared to previous approaches. © 2023 IEEE.

关键词： Audition

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

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

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Information Technology International Seminar (ITIS)

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

ISBN: (数字)9798331521295

ISBN: (纸本)9798331521301

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.

关键词： Temperature measurement Temperature sensors Support vector machines Accuracy Prototypes Soil Nutrients Real-time systems Monitoring Random forests

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Innovative web-based microteaching model: To improve the teaching skills of prospective informatics teachers in vocational high schools

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Social Sciences and Humanities Open 2025年 11卷

作者： Sugihartini, Nyoman Elmunsyah, Hakkun Nurhadi, Didik Rahmawati, Yuni Vocational Education Doctoral Program Post Graduate School Universitas Negeri Malang Malang Indonesia Informatics Education Study Program Department of Informatics Faculty of Engineering and Vocational Universitas Pendidikan Ganesha Bali Indonesia

Prospective informatics teachers in vocational schools (VS) must master content knowledge, pedagogical skills, and technology integration to achieve learning goals. Therefore, informatics teachers in VS need to innovate to increase students' creativity. However, many prospective informatics teachers experience difficulties in this regard. This research aims to develop and implement the Web-Based Microteaching (WBM) model to improve the teaching skills of prospective informatics teachers at VS. This study uses research and development methods with the ADDIE model approach. The research results show that the WBM model effectively improves prospective informatics teachers' teaching skills, especially in planning lessons and practising basic teaching skills. This innovation is a relevant learning solution in the digital era and positively contributes to the professional development of informatics teachers. The WBM model is an innovation to provide a more intensive and structured practical experience by utilizing web technology as the main media. Through web-based learning, prospective teachers can access material, exercises and feedback independently, which is expected to accelerate pedagogical mastery. © 2025 The Authors

关键词： Microteaching model Prospective informatics teacher Teaching skills Vocational high schools Web-based

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Development of attitude assessment instrument in engineering mathematics 1 course to assess discussion on MOOC platform 7

Development of attitude assessment instrument in engineering...

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7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020

作者： Yuniwati, I. Yustita, A.D. Hardiyanti, S.A. Suardinata, I.W. Mechanical Engineering Program Mechanical Engineering Department Politeknik Negeri Banyuwangi East Java Banyuwangi Indonesia Management Business of Tourism Program Informatics Engineering Department Politeknik Negeri Banyuwangi East Java Banyuwangi Indonesia Civil Engineering Program Civil Engineering Department Politeknik Negeri Banyuwangi Kabat East Java Banyuwangi Indonesia Informatics Enggineering Program Informatics Engineering Department Politeknik Negeri Banyuwangi East Java Banyuwangi Indonesia

Affective assessment instruments have been developed, especially those related to attitudes. In engineering mathematics learning 1 through the MOOC platform, it was carried out with discussion. The development of attitude instrument assessment to assess student activity was carried out after learning was complete. This development research used the Plomp Model. The Plomp Model consisted of 5 stages. They were the initial investigative stages, design stage;construction stage;the test, evaluation, revision stages;and implementation stages. Two experts and trials carried out validation were carried out on 100 students. The result of developing an attitude assessment instrument was a questionnaire. The difference from other questionnaires lies in paying attention to the internal continuum interval of attitudes developed by Bloom's Taxonomy. They were Responding, Valuing, and Organization. Afterward, The instrument is tested for reliability and validity until it is declared to validate each item and the instrument's reliability in the excellent category. The value of Cronbach's Alpha instrument is 0.848. The value indicates that the attitude assessment instrument is classified as having good criteria. Then the instrument is implemented in discussion on the MOOC Platform. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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RFI measurements at Bosscha observatory to prepare a new VGOS station

RFI measurements at Bosscha observatory to prepare a new VGO...

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2023 International Symposium From the Universe Back to Earth: Developing Astronomy to Meet Today's Natural Challenges

作者： Ramadhan, S. Hidayat, T. Suryana, J. Romadhoni, A. Kurniasari, M. Bosscha Observatory Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Astronomy Study Program Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Laboratory of Radio and Microwave Telecommunication School of Electronics and Informatics Engineering Institut Teknologi Bandung Indonesia

A new VGOS (VLBI Global Observing System) radio telescope will be constructed at Bosscha Observatory Lembang, West Java, in cooperation between Shanghai Astronomical Observatory (SHAO) and Institut Teknologi Bandung. One of the preparation steps is to conduct RFI (Radio Frequency Interference) measurements to obtain the RFI environment from the vicinity of the observatory. In this work, we report preliminary measurements of the RFIs that were done for several days in August and September 2023. Instruments used includes: A-INFOTM LB-20180 horn antenna, Wright TechnologiesTM low noise amplifier, and AaroniaTM Spectran V5 80200 OEM spectrum analyzer. The frequency range of 2.0 - 18.0 GHz is targeted referring to VGOS frequencies. Measurements in one day are carried out for about 24 hours in eight cardinal directions and two polarizations. The results and analysis are presented in spectral power flux density plots. © Published under licence by IOP Publishing Ltd.

关键词： Observatories

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Performance Evaluation of Coverage Prediction in 4G Networks Using Machine Learning Classification Algorithm

Performance Evaluation of Coverage Prediction in 4G Networks...

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International Conference on Wireless and Telematics (ICWT)

作者： Hajiar Yuliana Iskandar Hendrawan Azwar Mudzakkir Ridwan Atik Charisma Nivika Tiffany Somantri Department of Electrical Engineering Faculty of Engineering Universitas Jenderal Achmad Yani Doctoral Program of School of Elecrtical Engineering and Informatics Institut Teknologi Bandung School of Electrical Engineering and Informatics (STEI) Institut Teknologi Bandung Bandung Indonesia Department of Electrical Engineering UIN Sunan Gunung Djati Bandung

ISBN: (数字)9798350377057

ISBN: (纸本)9798350377064

Network coverage prediction is an important aspect of 4G network planning and optimization. In this study, we conducted a comprehensive analysis of the performance of various machine learning algorithms in predicting 4G network coverage. The data collected includes information on location, signal intensity, topography, and other important variables. Data pre-processing is performed to clean, normalize, and select relevant features. Next, we compared the performance of several machine learning algorithms, including Logistic Regression, KNN, Naïve Bayes, Decision Tree, SVM, XGBoost, AdaBoost, and Bayesian Network Classifier. The model training process is performed using subsets of training, validation, and testing data, with evaluation performed using various evaluation metrics such as accuracy, precision, recall, F1-score, RMSE, MAE, and R-squared. The experimental results show that Random Forest stands out with excellent performance, while Naïve Bayes tends to produce lower performance. The conclusion of this study provides valuable insights into the potential applications of machine learning algorithms in predicting 4G network coverage with accuracy and reliability.

关键词： Training Wireless communication Support vector machines Machine learning algorithms Accuracy Surfaces Telematics

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