Model Development Procedure Research Articles

예비상담자인 상담심리 전공 학부생을 위한 그릿 향상 프로그램 개발 및 효과성 검증

Objectives The purpose of this study was to develop a grit improvement program and verify its effectiveness to help prospective counselors increase their mental strength to flexibly respond to various stresses they may experience in the process of building their capabilities as counselors. Methods The grit improvement program was developed according to the ADDIE model development procedure and its validity was confirmed by two relevant experts. The grit improvement program was operated for a total of 6 sessions (12 sessions), each time lasting 2 hours (100 minutes). A grit measurement tool was used to confirm the direct effectiveness of the program, and a resilience measurement tool was used to confirm the indirect effectiveness. The Mann-Whitney U test was performed to verify the homogeneity of the experimental group and the control group. The Wilcoxen Matched Pairs Signed-Rank test was performed to compare the pre- and post-score changes of the experimental group and the control group. For comparison between groups, the Wilcoxen Matched Pairs Signed-Rank test was performed. Analysis was performed using the Mann-Whitney U test. Results As a result of analyzing the effectiveness of the grit improvement program for prospective counselors, the post-average for both grit and resilience increased statistically significantly compared to the pre-average. Through this, it can be seen that the grit improvement program has a positive effect on the grit and resilience of prospective counselors. Conclusions The grit strengthening program will help prospective counselors control the various stresses they may experience during the difficult training process and reach their goal of becoming a professional counselor. Furthermore, it will help them achieve their goal without giving in to bigger challenges in life. It will help you have the strength to do it.

Cognitive States Classification Analysis

Alzheimer's disease is a chronic, prolonged, and irreversible neurodegenerative disease of unknown cause. In recent years growing research interest assumes that by processing data of essential factors effective models can be defined for recognizing and predicting the disease development. The present article aims to propose classification models for the diagnosis of Alzheimer's disease cognitive states. For this aim medical data of biomarkers and cognitive assessment data are used. The novelty of the paper is to explore both the Amyloid/TAU/ Neurodegeneration framework and the biologically determined process of delay between the brain impairment and visibility of its appearances by incorporating these concepts in the model development procedure. The study explores the ability of three classifiers – Random Forest, Extreme Gradient Boosting, and Logistic Regression. Conclusion results have been done by comparison of the grouping abilities in different data spaces. The practical result of the study is helping to determine medical examinations that give accurate results for the diagnosis and prediction of the progression of the disease in possible earlier stages of the disease development.

A Prediction of Heart Disease using IoT based ThingSpeak Basis and Deep Learning Method

Developing an IoT-based system with enhanced deep learning to predict heart disease aims to revolutionize healthcare. The system, by continuously monitoring vital signs, utilizes advanced machine learning for early detection of potential heart issues, emphasizing innovation for timely interventions and potentially life-saving outcomes. Select appropriate sensors for monitoring vital signs such as heart rate, blood pressure, ECG (Electrocardiogram), oxygen saturation, and temperature. Connect these sensors to an IoT platform using wireless communication protocols like Wi-Fi. Predicting heart disease using an IoT-based ThingSpeak framework is a practical and efficient way to monitor and predict heart-related issues in real-time. ThingSpeak is an IoT platform that allows you to collect, store, analyse, and visualize data from IoT sensors. Here's a step-by-step guide on how to develop a heart disease prediction system using ThingSpeak: Deploy the trained deep learning model to make real-time predictions based on the IoT sensor data. Implement strong security regulations. The input dataset is pre-processed approach for resolving error data and missing values. On ThingSpeak, we can apply preprocessing functions to your data. This might include data smoothing, outlier removal, or missing data handling. Pre-processed data values features are then classified into normal and abnormal by Convolution Neural Network-CNN. A 10-fold cross-validation tactic was employed during the model development procedure. In the experimental result comparisons of MCC calculation, the MD2N model attained the 0.86201 and DCNN perfect reached as 0.84111 and then DBN reached as 0.91157 and then AE model rate as 0.88662 and then proposed model reaches as 0.93291 correspondingly.

Interactive Learning Media Assisted by Adobe Flash to Improve Students’ Motivation and Story-reading Skills at the Third Grade of Elementary School

The absence of learning media that is appropriate to the objectives of learning reading skills is a problem that exists in elementary schools. Based on these problems, the motivation and story reading skills of students are low. This research aims to create interactive learning media assisted by Adobe Flash that is valid, feasible, and effective in increasing third-grade elementary school students' motivation and story-reading skills. The research method uses RnD with the ADDIE model development procedure. The research subjects included material and media experts, teachers and third grade elementary school students. Data collection used scales, questionnaires and tests. The data analysis technique is quantitative analysis which is explained by qualitative analysis. The research results were the average validation results by media experts 99.2%, material experts 73.75%, teacher responses 94.05% and student responses 97.31%. The independent t-test and MANOVA research results show significant differences and influence on motivation and story reading skills between students who use and do not use interactive learning media assisted by Adobe Flash. It was concluded that creating interactive learning media assisted by Adobe Flash that was valid, practical and effective could increase the motivation and story reading skills of third grade elementary school students.

Development of STEM-based Virtual Laboratory on Global Warming Topic for Junior High School Student

Our study aimed to develop a feasible STEM-based virtual laboratory on global warming topics for junior high school students. We used the development research type by adopting Thiagarajan's 4D model development procedure, which was limited to the development stage. The data collection methods we used were questionnaires and interviews, while the research instruments were interview sheets and questionnaires. Product validation involved a lecturer as a subject matter expert and a lecturer as a learning media expert. In addition, a small group evaluation sample with 10 students was selected using a simple random sampling technique to be involved in the product trial. Qualitative and quantitative data analysis techniques were used. The results showed that the assessment by subject matter experts received a feasibility percentage of 84%, including the criteria worth using, and the assessment by learning media experts received a feasibility percentage of 85%, including the criteria worth using. In addition, the small group student response trial showed 91%, which means the developed product is very practical. Thus, the STEM-based virtual laboratory on global warming topics was declared feasible for junior high school students to use in science learning

Bilateral Renal Ischemia-Reperfusion Model for Acute Kidney Injury in Mice.

Acute kidney injury (AKI) is defined as a rapid decline in renal function, in which persistent kidney dysfunction gradually progresses to chronic kidney disease (CKD) due to the irreversible loss of nephrons and their maladaptive repair. In recent years, the incidence of AKI has been increasing concerning diverse etiologies, including volume depletion, sepsis, nephrotoxicity, muscle injury, and major trauma, in which ischemia-reperfusion injury (IRI)accounts for most episodes. Development of the IRI model in mice is induced by surgical clamping of the renal pedicles, which provides powerful and controllable tools for preclinical models of AKI. Importantly, the IRI model is deployed at different stages of the AKI development, especially in the processes of AKI to CKD. Despite the IRI model being widely practiced in many laboratories, a series of variables still influence the results of this model. Here, we describe the procedure of IRI model development to provide a repeatable and reliable method for researchers to explore the underlying pathogenesis in the development of AKI and the progression of AKI to CKD.

Pengembangan Media Ludo Digital Materi Tanggung Jawab Siswa Pada Mata Pelajaran PPKn Kelas V SD

This research is motivated by technological developments in the world of education today, namely in the era of revolution 4.0. This of course makes teachers have to be more creative in carrying out learning. This digital learning media is very relevant to the age development of elementary school children, especially grade V. The development of this digital ludo media uses a platform that geniuses use the Design and Development method. The research procedure to be used in research is the ADDIE model development procedure (Analysis, Design, Development, Implementation, Evaluation). The validation test procedures and responses of material experts, media experts and linguists according to indicators refer to BSNP 2017 and are modified based on research needs. This received a very good response and deserves to be tested on elementary school students with a percentage of results of 96.02%. The response of students, users and teachers received a positive response with an accumulation percentage of 96.65%. Based on several responses, this digital ludo media obtained results that are very worthy of use in learning and received positive responses from students, users and teachers in teaching and learning activities

E-Modul Pembelajaran Mata Kuliah Peminatan Kecerdasan Buatan Program Studi Teknik Informatika IIB Darmajaya Berbasis Android

The problem of this research is that the modules available in courses specializing in artificial intelligence are still in printed form, so it is necessary to develop learning media to adapt to technological developments that can be used as facilities to facilitate the learning process, especially in material that requires a certain understanding. The purpose of this research is to build an android-based learning e-module application that is more practical and easy to use in the subject of specialization in artificial intelligence, IIB Darmajaya informatics engineering study program. This research method uses Research and Development (R&D) with the ADDIE model development procedure which consists of 6 stages, namely analysis, design, development, implementation, evaluation. The results of this study are an e-module application for learning subjects in the specialization of artificial intelligence which can make it easier for students and lecturers of the IIB Darmajaya informatics engineering study program to carry out learning activities.

Evaluation of the flammability characteristics of alkyl esters: New QSPR models

In the present study, new quantitative structure–property relation (QSPR) models have been developed to predict different flammability characteristics (i.e., Lower flammability temperature (LFLT), Upper flammability temperature (UFLT), Lower flammability percent (LFL(V%), and Upper flammability percent (UFL(V%)) of pure Alkyl esters. In this regard, new data sets containing 179 different alkyl esters from 10 different chemical categories were used. In the model development procedure, the appropriate molecular descriptors were selected for each property using the enhanced replacement method (ERM). Afterward, a multivariable linear model and three nonlinear models based on genetic programming (GP), random forest regression (RFR), and support vector regression (SVR) were developed using the molecular descriptors as input variables. The implementation of different internal and external validation methods confirmed the acceptable prediction capability of the developed models. In this regard, the average absolute error for the best models were 7.61 K for LFLT, 7.96 K for UFLT, 0.10 % for LFL (V%), and 1.10 for UFL(V%) over the whole dataset. A comparison between the prediction capabilities of the developed QSPR models with previous models also confirmed the superiority of the developed models in this study. Therefore, the developed QSPR models can be employed in the evaluation of the flammability characteristics of new alkyl esters such as new fatty acid alkyl esters in the biodiesel.

Artificial intelligence model of fuel blendings as a step toward the zero emissions optimization of a 660 MWe supercritical power plant performance

AbstractAccurately predicting fuel blends' lower heating values (LHV) is crucial for optimizing a power plant. In this paper, we employ multiple artificial intelligence (AI) and machine learning‐based models for predicting the LHV of various fuel blends. Coal of two different ranks and two types of biomass is used in this study. One was the South African imported bituminous coal, and the other was lignite thar coal extracted from the Thar Coal Block‐2 mine by Sind Engro Coal Mining Company, Pakistan. Two types of biomass, that is, sugarcane bagasse and rice husk, were obtained locally from a sugar mill and rice mill located in the vicinity of Sahiwal, Punjab. Bituminous coal mixture with other coal types and both types of biomass are used with 10%, 20%, 30%, 40%, and 50% weight fractions, respectively. The calculation and model development procedure resulted in 91 different AI‐based models. The best is the Ridge Regressor, a high‐level end‐to‐end approach for fitting the model. The model can predict the LHV of the bituminous coal with lignite and biomass under a vast share of fuel blends.

Machine learning for construction crew productivity prediction using daily work reports

Construction productivity estimation lacks a comprehensive, standard, and task-type-independent framework to generate and serialize Machine Learning (ML) models. This research aims to develop an ML management framework for estimating the work crew productivity (crew outputs over their working hours) by addressing various operation and project types. The framework takes advantage of historical data, including information regarding operations' progress, weather conditions, the number of resources, and their composition in a work crew. Daily work reports are used as a principal source of historical data. Various hyperparameters-tuned ML algorithms are adopted and ranked based on their computational complexity and prediction accuracy. The generated productivity prediction models have the flexibility to be reused for the effective planning of various construction projects. Applying the proposed framework to a case study of nine disciplines provided estimation models with high accuracies. This study also discusses the theoretical and practical implications of the presented model development procedure.

Developing GAN-boosted Artificial Neural Networks to model the rate of drilling bit penetration

The goal of achieving a single model for estimating the rate of drilling bit penetration (ROP) with high accuracy has been the subject of many efforts. Analytical methods and, later, data-based techniques were utilized for this purpose. However, despite their partial effectiveness, these methods were inadequate for establishing models with sufficient generality. Based on deep learning (DL) concepts, this study has developed an innovative approach that produces general and boosted models capable of more accurately estimating ROPs compared to other techniques. A vital component of this approach is using a deep Artificial Neural Network (ANN) structure known as the Generative Adversarial Network (GAN). The GAN structure is combined with regressor (predictive) ANNs, to boost their performance when estimating the target parameter. The predictive ANNs of this study include Multi-Layer Perceptron Neural Network (MLP-NN) and 1-Dimensional Convolution Neural Network (1D-CNN) structures. More specifically, the key idea of our approach is to utilize GAN’s capability to produce fake samples comparable to true samples in predictive ANNs. Therefore, the proposed approach introduces a two-step predictive model development procedure. As the first step, the GAN structure is trained with the target of the problem as the input feature. GAN’s generator part, which can produce fake ROP samples similar to the true ones after training, is frozen and then replaces the output layer’s neuron of the predictive ANNs. In the second step, final predictive ANNs carrying frozen trained-generator called GAN-Boosted Neural Networks (GB-NNs) are trained to make predictions. Because this approach reduces the computational load of the predictive model training process and increases its quality, the performance of predictive ANN models is improved. An additional innovation of this research is using the residual structure during 1D-CNN network training, which improved the performance of the 1D-CNN by combining the input data with those features extracted from the inputs. This study revealed that the GB-Res 1D-CNN model, a GAN-Boosted 1-Dimensional Convolutional Neural Network with a Residual structure, results in the most accurate prediction. The validity of the GB-Res 1D-CNN model is confirmed by its successful implementation in blind well. As the final step of this study, we conducted a sensitivity analysis to identify the effect of different parameters on the predicted ROP. As expected, the DS and DT parameters significantly affect the model-estimated ROP.

Needs Analysis for the Development of Student Worksheets on Heat Material in the Context of a Solar Energy Desalinator

Before using student worksheets, so that they can be used legally, they should be tested first on at least 3 experts and tested on students. The type of research used is descriptive qualitative. This needs analysis aims to determine what teaching materials are needed in the heat material at Junior High School 4 Bengkulu City. The method in this study, researchers used the Research and Development (R&D). ADDIE model development procedure. The research was carried out in September 2022. The research sample consisted of 40 students who were in 8th grade. The research instruments were in the form of interviews and student response questionnaires which consisted of 18 question items regarding the needs of integrated worksheets for solar energy-based desalinator designs which contained 4 aspects namely indicator aspects, teaching material needs aspects, learning media aspects, and interest aspects. The results of student response data based on aspects of the average indicator of 80% do not use Student Worksheets on heat material, the aspect of teaching material needs on average is 80% of students find it difficult to learn science on heat material with teaching materials that used by teachers, the learning media aspects of students on average 77% rarely use laboratories to conduct experiments or practicums, so teachers rarely use and provide Student Worksheets to students in science lessons, and the motivational aspects of student interest on average on average 82% are quite interested, quite enthusiastic about learning science so what are they interested in developing Student Worksheets teaching materials. From the data obtained, some students agree that it is necessary to develop Student Worksheets.

Development Of Android-Based Digital Maps for Learning the Mapking Process

Educational media is an important component in education for making the Jambi Province Map which must be presented in an innovative way. Media that is currently known is a digital map that is not easy to read and the position and location or color errors, this situation will lead to errors in meaning and purpose. This study aims to make a product in the form of a digital map and test its validity based on media experts. This study uses the ADDIE model development procedure which is modified into 4 stages, namely analysis, design, development and implementation. The Likert scale method was used as an analysis to retrieve information from 20 class VII students of SMPN I Jambi City as respondents, while the validation method was used as an analysis to test product validity. The results of the development in this study are in the form of digital map images designed through the Android-based UMapper and WhatsApp Plus Aero applications. Test results from two material experts show that this product gets a score of 47 and 46 out of a maximum score of 50 which means very good. This shows that the development of this media is very good in meeting the needs of students in the field of education for making Jambi Province maps, being creative and innovating in using media in the form of android-based applications.

Testing-Oriented Development and Open-Source Documentation of Interoperable Benchmark Models for Energy Systems

This article provides novel fundamental tools for the open-source design and documentation of benchmark network models. The procedure for model development is motivated and described in detail in all its different phases: after an initial discussion on the relevance of open-source benchmark models, particularly in the context of an interoperable power system, the general conceptual framework for model documentation, based on the holistic test description and PreCISE paradigms, is presented. Building up on this theoretical basis, the procedure for model development is structured according to a testing-oriented approach, providing clear examples on how to map and translate the proposed conceptual framework into a practical and complete model description. Finally, to demonstrate the applicability and usefulness of the proposed method, a complete example of model documentation for a developed low-voltage network is described and validated in simulation. The complete model description is publicly available online as a guide and reference for the proposed model documentation procedure.

The Prototype of ACCDR Model: M-Learning Multimedia Development Design Model Stem-Based for the Online Science Learning

Online learning is learning carried out without face-to-face but supports the development of virtual academic mobility. It can be integrated with multimedia either in e-learning or m-learning. M-learning is a learning paradigm that takes advantage of developments in wireless and mobile technology. It is flexible, independent, and accessible anywhere and anytime. Integration of science and technology in multimedia m-learning becomes more comprehensive when combined with the principles of Science, Technology, Engineering, and Mathematics. STEM-based multimedia m-learning is developed using a specific development design model. The multimedia m-learning development design model is a structured framework or reference for developing multimedia m-learning. Some of the existing m-learning multimedia development design models do not fully cover the criteria for a suitable model for developing STEM-based multimedia m-learning. One solution that can be given is to develop a new model concept using the F2-O1-S1-A2 type model development procedure through literature studies. The instrument consisted of the model development matrices. Afterward, a STEM-based multimedia m-learning development design model for science learning consisted of five syntaxes, namely: analysis, concepting, design, development, and review. The ACDDR model is a linear diagrammatic-narrative procedural model which is then shortened to the ACDDR model and presented in a prototype form. The ACDDR prototype model is expected to be able to be used for the development of STEM-based multimedia m-learning in the 21st century learning.

Pengembangan nilai-nilai pendidikan Islam pada bahan ajar ilmu pengetahuan sosial berbasis Learning Clinic di MI NU Al Huda 02 Kecamatan Gebog Kabupaten Kudus

The purpose of this study was to develop the Development of Islamic Education Values in Learning Clinic-Based Social Science Teaching Materials for fifth-grade teachers of Madrasah Ibtidaiyah (MI) MI NU Al Huda 02, Gebog District, Kudus Regency. This research method uses Research and Development (R & D) from Borg and Gall. The model development procedure uses the main stages, namely introduction, design and development, and validation. Sources of data in the form of documentation of the performance of learning assistance by teachers, questionnaires, observations, interviews, and model trials. The results of the study show that learning clinic-based online learning assistance is very important and needed, it is proven that many students experience difficulties in understanding the subject matter due to limited interaction and communication with the teacher so the learning outcomes are low on average of 67.33 or below the KKM score of 75.00 and there is no learning clinic-based online learning model that can help students with learning difficulties. Based on the results of limited trials and expanded trials of using the product, it was described that there was an increase in the quality of teachers' ability to carry out mentoring by 27.50 from limited trials of 60.00% up to 84.50% so that the product could be categorized as effective and suitable for use as a guide by class V teacher. The development product is in the form of a learning clinic-based learning assistance guidebook. The results of the due diligence guidebook from experts and practitioners obtained a value of 83.33 in the very feasible category. Test the effectiveness with the SPSS Paired sample Test t-test in improving student learning outcomes from pretest and post-test data obtained t count 7.434, while p = 0.00 <0.05, then Ha is accepted and Ho is rejected. The conclusion from these results is that there is a real significant difference between learning outcomes in the pretest and posttest. This means that the Development of Islamic Education Values in Learning Clinic-Based Social Science Teaching Materials is effective in increasing student learning outcomes at MI NU Al Huda 02 Padurenan, Gebog District, Kudus Regency

ANALISIS VALIDASI TERHADAP PENGEMBANGAN BUKU AJAR DIGITAL DISERTAI NILAI-NILAI ISLAM UNTUK MENINGKATKAN NILAI PROFETIK PADA MATA KULIAH KEWIRAUSAHAAN

Muhammadiyah Metro University is one of the Islamic campuses in Lampung Province which has the vision of "Center of Professional, Modern and Enlightening Professional Excellence". Therefore, as a UM Metro lecturer, you are required to develop and apply technology-oriented science based on Islamic values that can be useful and applicable in the business and industrial world. This research is a development research, the product produced in this research is the Digital Textbook of Entrepreneurship which is accompanied by Islamic values to increase the prophetic value in entrepreneurship courses. The development of this digital textbook follows the ADDIE model development procedure. Based on research on validation analysis on the development of digital textbooks accompanied by Islamic values to increase the prophetic value in entrepreneurship courses developed using the ADDIE method through validation tests with the assessment sheet instrument, the average score is 88.5% with valid categories based on the value aspect. Islamic, media aspects and material aspects and are suitable for use in learning.Keywords: Research and Development, Digital Textbook, Entrepreneurship

Pengembangan Soal Hots Pada Materi Sistem Pencernaan Manusia Kelas VIII SMP/MTs

The problem in this research is motivated by data about daily assessment questions that still use low-level abilities (LOTS), while 21st century learning requires students to have higher-order thinking skills. This research aims to develop and determine the theoretical and empirical validity of the HOTS questions on the human digestive system for class VIII SMP/MTs. The research conducted is a type of research development, this research produces HOTS questions on the Human Digestive System. The development of this HOTS question refers to the ADDIE model development procedure which consists of five stages, (analyze), (design), (development), (implementation) and (evaluation). This development research produces a product HOTS questions on the material of the human digestive system class VIII, which consists of 8 questions. The results of the average score percentage of theoretical validity by material experts, construction experts, and science teachers are 95.3%, 94.7%, and 98.7%, very valid categories. and empirical validity which contains the validity of the items with 8 questions declared valid, with an average score of 0.597, Reliability resulting in an average score of 0.627 being declared reliable, Distractor producing an average score of 18.7% with poor criteria, and discriminating power produce an average score of 0.35% with sufficient criteria. This shows that the product of HOTS questions that have been developed is feasible to use.

Machine learning coupled with group contribution for predicting the density of deep eutectic solvents

Deep Eutectic Solvents (DESs) are a recently introduced class of green solvents with unique and favorable characteristics. Despite their recent debut, the scientific community has begun to place greater emphasis on them as alternatives to ionic liquids (ILs). Knowledge of the various physical properties of DESs is essential for various applications in the chemical industries and related fields. In this study, a comprehensive database including 1410 density data points, from 166 different DESs at various temperatures and atmospheric pressure, were retrieved from open literature to develop models to increase the accuracy of density predictions. The densities of DESs were used to develop two commonly used machine learning models, namely Multilayer Perceptron Artificial Neural Network (MLPANN) and Least Square Support Vector Machine (LSSVM), in conjunction with the group contribution (GC) method. Based on the GC method, each fragment of a compound contributes a specific amount to the physical property's value. By considering this, the prediction ability was improved by applying the GC method in the model development procedure. Both models predict the DES densities by taking into account the effect of 35 functional groups, the temperature, and the HBA/HBD molar ratios. The optimum MLPANN model structure consists of a single hidden layer with five neurons and a logarithmic sigmoid transfer function. By employing this MLPANN-GC model, the values of the squared correlation coefficient, R2, and absolute average relative deviation percent, AARD%, were 0.99 and 0.61%, respectively, while for the LSSVM-GC model (with the radial basis function (RBF) kernel), they were 0.99 and 0.56%, respectively. Also, K-fold cross-validation was used to assess the performance of the LSSVM-GC model. The presented machine learning models in this study were found to perform more accurately than those obtained using the best current correlations and GC models for DES densities in the open literature. The more accurate results, in addition to the enhanced predictability behavior of the developed models, give these models a preference for use in industrial and academic applications.