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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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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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Web-Based Incentive Calculations System at CV Karya Prima using Prototyping Method

Web-Based Incentive Calculations System at CV Karya Prima us...

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Conference on New Media Studies (CoNMedia)

作者： Monica Cornelia Suryasari Information Systems Study Program Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia

Consumption is an essential aspect of daily life, and fulfilling customer needs requires efficient distribution of goods. Therefore, distributors are needed and they frequently set targets for their employees to achieve, and motivation plays a significant role in employee performance. An organization that distributes goods called CV Karya Prima motivates its employees by offering incentives. However, manual incentive calculation sets out problems such as a lack of progress tracking, irregular payout dates, and probable inaccuracies. This study aims to develop a web-based incentive calculation system using the analytical hierarchy process method and a database for data retrieval. The system utilizes PHP, CodeIgniter, and MySQL and adopts the SDLC prototyping method. The developed system is focused on the sales division and variables like total sales, effective calls, calls, and outlets. The web-based system is capable of solving the issues using the features that were built based on user needs and have already been tested through user acceptance testing.

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Advanced Data Instrumentation for Stunting Analysis in Physical-Cyber Architecture 17

Advanced Data Instrumentation for Stunting Analysis in Physi...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Nugraha, Daniel Nurtam, M. Riza Mistialustina, Hartuti Saepudin, Didin Surjono, Welly Prihatmanto, Ary Setijadi Institut Teknologi Bandung Doctoral Program of School of Electrical Engineering and Infomatics Bandung Indonesia Universitas Widya Dharma Pontianak Kalimantan Barat Indonesia Universitas Sangga Buana Faculty of Engineering Electrical Engineering Department Bandung Indonesia Universitas Sangga Buana Bandung Indonesia Institut Teknologi Bandung School of Electical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350309164

In Indonesia, stunting is a serious problem that has drawn a lot of attention from the government. This study offers a novel strategy for preventing stunting by combining the 5-Desk Model used in integrated health posts (Posyandu) with the Cyber-Physical System (CPS) architecture. Chronic malnutrition and childhood diseases can cause stunting, which can result in long-Term health issues and cognitive deficits. Despite advancements, it still poses a serious threat to almost every Indonesian province. For the purpose of gathering data in Posyandu, such as registration, growth measurement, recording, counseling, and health services, the 5-Desk Model provides an organized framework. This promotes the well being of the community and gives healthcare professionals the ability to thoroughly monitor important health indicators. This strategy is improved with CPS integration, which results in a four-layered, comprehensive system that includes gamification, business intelligence, information systems, and instrumentation. To maximize efforts to avoid stunting, this architecture facilitates accurate monitoring, real-Time data collecting, data analysis, and stakeholder communication. This study imagines a data-driven future for Indonesian stunting prevention, where cooperation, technology, and data are combined to address this pressing problem and safeguard the future prospects and wellbeing of Indonesia's youth. Implementing this goal and expanding stunting prevention initiatives will be the main goals of future research. © 2023 IEEE.

关键词： Cyber Physical System

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Modified genetic algorithm for employee work shifts scheduling optimization 3

Modified genetic algorithm for employee work shifts scheduli...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Saraswati, N.W.S. Artakusuma, I.D.M.D. Indradewi, I.G.A.A.D. Study Program of Informatics Engineering Stmik Stikom Indonesia Denpasar-Bali80225 Indonesia

Arranging an employee shift work's schedule requires high accuracy. It is because we have to pay attention to several constraints simultaneously. The genetic algorithm presents as a method which can automatize the process of arranging the schedule as well as optimizing the result of the schedule. The Shala Bali is a hospitality business whose scheduling was complicated because of the number of the employes. This research aimed at producing a shift work schedule of the employees in a week and to know the optimum genetic algorithm parameter in this case. The constraints that were taken into account in the arrangement of the schedule included the schedule conflict of the employees in one shift, schedule conflict of employees in 1 day, the same composition of employees per shift, employees should not get morning shifts after being in night shift the night before, each shift has at least 1 employee in the front office, and each employee is required to get 1 day off within 1 schedule period. This study was able to produce an optimal work schedule of employees with crossover probability (Pc) of 0.6, and mutation probability (Pm) of 0.3. The modification algorithm in chromosome generation and chromosome structure in this study results that changes in gene length (additional number of employees) do not have to be followed by an increase in the number of chromosome populations to get optimum results. © Published under licence by IOP Publishing Ltd.

关键词： Chromosomes

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Double exponential smoothing brown method towards sales forecasting system with a linear and non-stationary data trend 3

Double exponential smoothing brown method towards sales fore...

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3rd International Conference on Vocational Education and Technology, IConVET 2020

作者： Dharmawan, P.A.S. Indradewi, I.G.A.A.D. Study Program of Informatics Engineering Stmik Stikom Indonesia Denpasar-Bali80225 Indonesia

UD Parama Store is a trading company engaged in selling retail goods that sell various types of daily necessities retail. The major problems occurred are the difficulty in predicting the sales due to the maturity level of experience, customer demand changes, and the owner's limited memory. Therefore, there should be an increase of merchandise stock to prevent any sudden decrease in sales and overcome the stock shortage when there is an increase in sales. In this current research, a sales forecasting web-based system was designed and built to assist the owner in predicting the number of sales in the next period. As a result, decisions can be made in determining the number of goods to be provided. The forecasting method used was double exponential smoothing brown by improving forecasting, averaging (smoothing) the past value of a time coherent data, and decreasing (exponential), which requires one parameter only. It was used to increase and decrease the linear and non-stationary data. The calculation of forecasting accuracy using the MAPE (Mean Absolute Percentage Error) method in forecasting sales of merchandise produces the smallest error rate ranging from 7.99% to 32.42% for 10 different items. © Published under licence by IOP Publishing Ltd.

关键词： Sales

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm 8

Image Processing Implementation to Classify Coffee Fruit Rip...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Hussaini Dermawan, M. Farhan Witarsyah, Deden Syamsuar, Dedy Fakhrurroja, Hanif Luthfi, Ahmad Herdiansyah, Muhammad Izman Telkom University Faculty of Industrial Engineering Bandung Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Islamic University of Indonesia Faculty of Industrial Technology Department of Informatics Jogjakarta Indonesia Universitas Bina Darma Informatics Study Program Faculty of Science and Technology Palembang Indonesia

ISBN: (纸本)9798350326093

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level. © 2023 IEEE.

关键词： Classification (of information)

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A Review of Coverage Predictions in Mobile Communication Systems using Machine Learning Algorithm

A Review of Coverage Predictions in Mobile Communication Sys...

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

作者： Hajiar Yuliana Iskandar Hendrawan Doctoral Program of School of Electrical Engineering and Informatics (STEI) Institut Teknologi Bandung Bandung Indonesia School of Electrical Engineering and Informatics (STEI) Institut Teknologi Bandung Bandung Indonesia

The needs for best signal coverage and high performance cellular network quality is critical in planning, designing, and operating 5G cellular networks due to the maturity of Internet penetration in today’s society. In the process of cellular network planning and optimization, accurate predictions are required. With this accurate coverage prediction, mobile network operators can identify areas that require network infrastructure improvements, as well as optimize network resources to improve the quality of service provided. There are several factors that influence coverage analysis and prediction, including the distance between the transmitter and receiver, topographic depiction of the area modeled in the propagation path loss model, weather, signal interference, and other factors. There are several methods for predicting coverage or coverage of signal power levels at the receiver. These methods have been developed in previous research including model-based prediction methods and data-driven prediction methods. Model-based prediction methods are based on empirical models of network characteristics and environmental parameters, while data-driven prediction methods are based on processing signal data and environmental data that have been collected from cellular networks and simulated using machine learning algorithms. In addition to these two methods, there are also studies that use a combination of the two methods to be used in predicting coverage or predicting other functions in cellular communication systems. This paper will present a review of research papers related to these two methods, including the development of the methods.

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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Biomedical Instrumentation and Technology Conference (IBITeC), International

作者： Arief Purnama Muharram Farhan Hilmi Taufikulhakim Ayu Purwarianti Faculty of Medicine Universitas Indonesia Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Master Program of Informatics Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.

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