bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple Network and Double System Orthogonal Low-Ranking Training Melanoma Image Grouping
Authors: Sureshkumar R , Yuvaraj S A , Kuppusamy P G , Dilliraj E
Published: July 2021

A significant method to identify and track early cancer of the breast in clinical practise is histopathological image analysis. The diagnosis of breast cancer is still facing issues with an open in healthcare sector, however, with a limited quality. We are creating a classification system based on histological picture pictures, integrating deep learning with mechanical methodologies of learning, in sequence to enhance the prediction of early recognized breast cancer and to minimize the work pressure of physicians. In particular, through pre-trained Deep Convection Neural Networks, we build a multi-network extraction model, create an efficient method of reducing features and train an ecosystem supporting vector machine (E-SVM). Next, we use scale transformation and colour improvement approaches to prepare histological pictures. Second, four pre-trained DCNNs extract the multi-network functionality. Thirdly, the Dual Network Orthogonal Low-Rank Learning (DOLL) role selection approach is further introduced to increase efficiency and to reduce unnecessary efficiencies. An E-SVM is at last instructed by melded usefulness and casting a voting procedure for characterization, what isolates the pictures into four gatherings considerate, in situ carcinomas, obtrusive carcinomas, and ordinary. The suggested procedure is tested by us for the public ICIAR 2018 Challenges Data Set on histology photographs of breast cancer. Our approach can offer very promising productivity and underperform province approaches through analytical outcomes.
Implementation of Quantum-Dot Cellular Automata Based Efficient N-Bit BCD Adders Using Verilog
Authors: Qureshi Vaseem Ahmed , Lavanya L , Goel Anuj Kumar
Published: July 2021

Quantum Dot Handheld Automatics is one of the best technologies in recent days for introducing a high speed and ultra-low powered digital circuit. Few binary and decimal arithmetic circuits were designed using an efficacious QCA-based algorithm. However, there is a substantial enhancement required yet if there is inbuilt availability of logic gates inside the QCA. Therefore, this article introduced innovative binary coded decimal (BCD) adders using QCA methodology. Simulation analysis discloses that proposed BCD adders obtained higher computational speed with lower delays and lesser area than conventional CMOS approaches.
A Comprehensive Review on Machine Learning Based Optimization Algorithms for Antenna Design
Authors: Seshu Kumar N , Yalavarthi Usha Devi
Published: July 2021

Machine learning has become a great attention to find optimize solutions in different areas and is anticipated to play a vital role in our upcoming technologies. This paper presents a comprehensive review on basic optimization algorithms for micro-strip patch antenna design using machine learning. Classification of machine learning based algorithms: deterministic, stochastic and surrogate model assistant is discussed. Further machine learning models training for optimizing output and for prediction of antenna parameters is presented in this paper. This paper is useful to the readers who work on a particular antenna using the Machine Learning Techniques.
A Numerical Analysis of Blood Flow in Clogged Artery
Authors: Sahu Nilesh , Sharama Ayush , Kumar Pankaj
Published: July 2021

The artery wall gets stressed due to the variation in pressure of the blood flow passing through it. When this pressure subsides, these arterial walls dilate in reaction to increasing blood pressure and contract. The additional simulation focused on determining when there is a blockage inside an artery, the velocity and wall shear stress. The current simulation focused on determining the velocity, pressure and wall shear stress when there is a different stages of blockage present inside an artery. This paper has extended the simulation with the blockage developed at the symmetric or asymmetric condition inside the arteries. The analysis of blockage expressed with flow contour, pressure, wall shear stress and velocity. This work explains completed simulations with 10% to 80% blockage at different Reynolds number as 50,100,500,1000, and 4000. More particularly, focused on understanding the functioning and morbidity related to vital organs of the human anatomy with maximum shear stress. The result suggested that how these parameters vary according to the above stated cases and the stage of critical situation. The parameters set in the study at hand aim to resolve the complexity of the procedure by eons and can be utilized in placing a design of set apparatus which will greatly help in increasing the mortality among ailing humans.
Band gap engineering on CdS quantum dots through dual doping of Zn2+ and of Ni2+ ions
Authors: Sakthivel P. , Devadoss I. , Krishnamoorthy A.
Published: July 2021

The CdS Quantum dots (QDs) have been synthesized without using any capping agent via a co-precipitation chemical route. The structural and optical investigations of the Ni2+-doped Cd0.9Zn0.1S QDs have been made. XRD patterns revealed that the synthesized QDs exhibit cubic structure and the particle size was measured ∼ 2 nm. EDX spectra showed that the existence of cadmium, zinc, sulphide, and nickel in the synthesized samples. FTIR spectra verified the vibrational peaks related to available molecules of elements in the samples. The absorbance rate of Ni2+-doped Cd0.9Zn0.1S QDs doped was more than Cd0.9Zn0.1S QDs in the visible region. The introduction of Ni2+ ions into CdZnS QDs improved the optical transmittance in the visible spectrum and the band gap was blue-shifted for Ni = 6 at.% composition. The quantum confinement effect played an effective role in the band gap tailoring of CdS QDs via particle size tuning mechanism.
Analysis of Numerical Method on Inverse Kinematics of Robotic Arm Welding with Artificial Intelligence
Authors: Yaseen Syed , Prakash Jaya
Published: July 2021

The key component of versatile production processes is robots. They are found in diverse systems where it is possible to bypass and optimize human function. I simulated a remote-control trickster in MATLAB with 6 degrees of freedom in this research. A robot is used in numerous applications, such as decoration, carpentry and equipment testing. In hardware verification labs, robotic arms are used to hold passive and power rail probes that connect from instruments like scopes and power supplies to PCB boards to protect the PCB layout from rip off due to sudden movement of the probes. Robot kinematics uses the geometry (position and orientation) of rigid bodies (links) and joints to control the movement of the robot. In this project, to control its movement, I have illustrated either forward or reverse enzyme kinetics of a device. Through Kinematics uses the positions of a joint to measure the end-effector position of the robot. The angles of the joints with the end-effector position are determined by inverse kinematics as the reference. There are several methods to calculate the forward and inverse kinematics such as analytical methods, numerical hit and trial, and iterative methods.
Comprehensive Analysis of Machine Learning Algorithms Used in Robo-Advisory Services
Authors: Potdar Akhilesh , Pande Mandaar
Published: July 2021

Adoption of Robo advisory is rapidly increasing among retail investors. Approximately 10% of all assets under management will be managed by them by 2025. Researchers continuously work on finding machine learning and deep learning algorithms which will accurately predict the stock price movement with less error. Various Robo advisor platform providers are working on different approaches like NLP (Natural language algorithm) based sentiment analysis, hybrid time series algorithms to limit human bias, which existed in the current financial advisory services. This research compared the performance of various forecasting algorithms, which can be used in the Robo advisory framework. RMSE (Root mean square value) value is taken into consideration in this research to compare the performance of algorithms as it is a good and simple loss function. It has been observed that newer algorithms like those proposed by Facebook are able to predict stock prices pretty well, but ARIMA outperformed all the algorithms which are considered in this research.
Synthesis of Images from SMRI through Extreme Integrand Unstable Learning
Authors: Tamilselvi M , Dappuri Bhasker , Tapas Bapu B R , Baburaj E
Published: July 2021

In conventional deep learning medical imagery research, appropriate healthcare images are often invaluable. Still, it can limit acquisitions of such texture features because of many concerns, such as high cost, patient problems, etc. However, due to recent developments in profound education techniques that can greatly ease the challenge mentioned above, synthesizing medical photographs has already synthesized different modalities such as MRI images, PEI images, heart infrared detector, retinal images, etc. Unfortunately, a synthesis picture of the Arterial Spin Marking, now an important fMRI predictor in diagnosing dementia disorders, has not yet been thoroughly studied. For the first time in this research, ASL images from magnetic resonance structural images have been prepared successfully. Theoretically, ASL objects’ production from functional magnetic resonance imaging will be indicated by a new, highly unstable, discrimination-based paradigm fitted with new resNet post carried out a broad variety of tests. Useful statistical evaluation of this newly released model to synthesize ASL pictures close to the actual ones acquired during the actual scanning of ASL photographs from the current model shows excellent performance while undergoing extreme regional and voxel-based partial volume correction checks which are necessary for ASL pictures.
An Integrated and Sustainable Model for E-Waste Management for Pune City Households
Authors: Bhat Viraja , Patil Yogesh
Published: July 2021

Worldwide use of electronic devices has led to a serious socio-economic-technical challenge for managing them at their end-of-life stage. Managing EOL electronic devices or e-waste using traditional methods has been potentially hazardous to the environment and health. United nation’ sustainable development goals also have linked e-waste to three of its SDGs and support the sustainable consumption and quantification of e-waste. Policy interventions, consumer awareness, and active environmentalist roles have forced developed nations to engage in scientific ways of e-waste treatment. The high cost of treatment and increasing volume has led to a scenario where developing nations, including India, have been more attractive and easy targets for dumping e-waste. Out of 1.7 million tons of e-waste generated in India, 8% of e-waste reaches the landfill as useless material, 1.5% of which are formal recycling, and the informal sector handles more than 90%. Ineffective policy implementation and lack of stakeholder participation pose a challenge in improving the scenario of e-waste management. Although, there is many stakeholders have been brought into the sphere of policy, household shave not been considered major generators, with about 16% of the total e-waste. Growing urbanization increased dependency on electronic product usage due to the pandemic; we need to channel the household e-waste to the formal treatment units with a systematic approach. Pune is one of the proposed smart cities and has been in the news due to the solid waste treatment challenges due to the treatment sites and infrastructure. With municipal solid waste, e-waste collection and treatment at the regular treatment facilities, not equipped to handle e-waste leads to untreated e-waste parts in whole or in traces. Lack of awareness and non-segregation at source, inadequate collection channels has impacted the environment, requiring a focused and workable systematic framework for household generated e-waste disposal. Access to disposal at the doorstep develops an integral behavior that can be altered by providing proper doorstep MSW disposal infrastructure as an extension of the existing municipal solid waste segregation model. This paper proposes a theoretical, sustainable, and integrated model built with the current MSW model foundation aimed at household e-waste management, providing collection and inventory services for Pune residents.
MeDrone- A Smart Drone to Distribute Drugs to Avoid Human Intervention and Social Distancing to Defeat COVID-19 Pandemic for Indian Hospital
Authors: Maheswari R , Ganesan R , Venusamy Kanagaraj
Published: July 2021

Many healthcare centres generally have a centralized unit-dose drug distribution system (CUDD). The in-patient’s drugs are warehoused in a centralized location such as hospital pharmacy. The required or prescribed drugs available in the pharmacy will be distributed to the patient by either the nurse or attendant. The attendant often collects the medicine from the pharmacy. This process is quite time-consuming and in this COVID‘19 pandemic should maintain social distancing by the attendant in the pharmacy. In a field where time is of great importance and can save lives and also, a human intervention which in turn need to maintain social distancing, we can use MeDrones that will dispense medicines to the designated patient(s) location which will avoid human intervention and social distancing to defeat COVID-19 pandemic which is the need of the hour. The drone will be designed to deliver payload (tablets/saline) within/across the hospital premises. Apart from delivering medicines to the respective patients, the drone will be equipped to decide the optimal path to reach patients and prioritize attending to critical patients based on the hospital’s central database. The drones can be customized to return the unused medicines from the patient’s location to the pharmacy. Lightweight secured drugs container kit with authentication enabled will be designed for carrying the medicines to distribute to the respective destination inside the healthcare centre. The drone regulations result from increasing demand in UAV that must be devised to permit adaptation extensively as a logistics solution, to escalate efficacy in the healthcare domain.
An Efficient Algorithm for Increasing Modularity in IoT Based Automation Systems
Authors: Kumar Sumit , Singh Abhaya Pal , Ghosh Aritra
Published: July 2021

IoT-based platforms allow for an integrated approach to digital systems. With the increase in complexity of devices and processes in industry and commerce, automation presents itself as a technological panacea in the form of IoT (Internet of Devices) based applications. This paper presents an adaptive, efficient, and affordable system architecture through which modularity in IoT-based automation can be realized. The proposed technique involves an IoT-based platform to coordinate, control also calibrate microcontrollers connected to various sensors and devices. Using a web page or application as an interface, the system can be configured to operate diversified workloads via distributive control and remote monitoring. The flexible nature of the system allows for a modular approach to parameter design and system integration while allowing dynamic task allocation. Data available through feedback can be used to further streamline and orient tasks in real-time. This allows a single platform to multitask using an adaptive algorithm.
Towards Development of Performance Metrics for Benchmarking SLAM Algorithms
Authors: Bhargava Mudit , Mehta Rushad , Adhikari Chandan Das , Sivanathan K
Published: July 2021

The true autonomy of mobile robots cannot be achieved without Simultaneous Localization and Mapping (SLAM). With this capability, mobile robot could concurrently build a map of the environment and locate itself with respect to the map. Although there are several variants of SLAM algorithms contributed by researchers so far, only a very few works were aimed at comparing their performances with appropriate metrics and providing detailed directions and insights to the user on selection criteria and indicative use cases. In this work, we presented a comparative study of three popular SLAM algorithms and provide some significant quantitative performance measures of the same by using our novel | R | and | S | performance metrics as well as conventional metrics. The comparative study was carried out in ROS (Robot Operating System) using Turtlebot3 robot model on three SLAM packages viz G-mapping, Karto SLAM, and Frontier Exploration SLAM. Furthermore, the results show that the proposed metrics are very efficient and compact in comparing and quantifying the performance of SLAM algorithms.
COVID-19 Death Risk Assessment in Iran using Artificial Neural Network
Authors: Adebayo Ifeoluwapo R , Supriyanto Eko , Taheri Sahar
Published: July 2021

Since the pandemic spread of COVID-19, it has posed a unique public health concern worldwide due to its increased death rate all around the world. The pandemic disease is caused by the SARS-CoV-2, which is the main cause of Middle East Respiratory Syndrome (MERS) and severe acute respiratory syndrome (SARS). Risk assessment is a vital action toward disease risk reduction as it increases the understanding of the risk factors associated with the disease and allows existing data to decide on adequate preventive and mitigation measures. Machine learning techniques have gained strength since 2000, as it has crucial role in data analysis and is really helpful to develop standard mortality models. This study aims to find the best model for data analysis using the Artificial Neural Network (ANN) and other risk factors, which contribute to the high mortality and morbidity associated with COVID-19 in Iran, to predict the risk of death for the people with different situation. A systematic review and meta-analysis were examined by using patient risk factor data from studies done by researchers to estimate COVID-19 death risk. Risk factors for the disease were extracted from an existing study. Using ANN, the best risk prediction for the disease is calculated. Assessment of a different number of hidden neurons with a different training function using the Bayesian Regularization algorithm, the best training function for the ANN model with 5 hidden neurons is found to have the most satisfying results. The coefficient of determination (R) and Root Mean Square Error (RMSE) was 9.99999e-1 and 4.54201e-19 respectively.
Awareness of Reuse Reduce Recycle & Dispose of E-Waste in Chennai
Authors: Ganesan R , Ramesh B , Teja Charan
Published: July 2021

In this survey, I came to know that the development of area or city leads to the growth of E-waste, but the percentage of the public who are aware of E-waste is not growing up to the mark. The technical solutions, segregation process, preventive methods, skilled labors working on E-waste, modern technology adopted for disposal are very less in growing cities when compare to the metropolitan cities. I came to know about the urgency of creating awareness to every person using electronic gadgets about E-waste and its ill-effects on the human race. Segregation of metals and components while disposing of the E-waste also causes ill effects for the workers involved in that, as facilities like gloves, caps, masks are not available in poor countries. Dumping of E-waste into seas or in barren islands leads to water pollution, affecting aquatic and marine life. As most of Chennai is a coastal area, many small and medium industries and household E-wastes are releasing into the seas easily. The survey conducted in Chennai city reveals the immediate reaction to the proper disposal of E-waste.
An Experimental Study on Polymer Impregnated Concrete with Fly Ash
Authors: Jaganathan Krithika , Ramesh Kumar G B
Published: July 2021

Fly ash can be used in research paper to partly substitute cement for three separate types of concrete, M20, M40, and M60. Until the curing process is finished, the samples are made to undergo partial polymer impregnation and dried for 8 hours. For performance criteria, compressive strength, flexural strength and break structural stress, the concrete samples are then tested. The values obtained are compared with 0 percent of fly ash concrete cast specimens. Also, referred to as a nominal mix in this paper. Fly ash is chosen to perform this experiment because it is an eco-friendly material. It is a waste product that contributes to sustainability and incorporation of it in concrete is possible due to its pozzolanic characteristic. For the impregnation process, the polymers used are polyester and styrene. The experimental is conducted to evaluate the effect of fly ash onto cement along with partial polymer impregnation and their effects on strength properties. The research is also an initiative to contribute to sustainability. After the completion of the experiment, it can be observed that the use of fly ash as a partial replacement of cement does not negatively affect the strength parameters, alternatively, in the long run, it can be used to obtain strength higher than the nominal mix. The impregnation of polymer does have a significant impact on the concrete samples in terms of strength quality. The use of cement in concrete accounts for about 40% carbon dioxide emission, causing a greenhouse effect. On the other hand, the partial substitution of fly ash reduces the amount of cement needed in the construction process without compromising the performance of concrete. Even, the treatment of fly ash takes care of that. Due to the clear existence of fly ash, this form of selective replacement of cement in concrete can be used extensively.
Normalised Difference Vegetation Index Based Drought Monitoring Using Remote Sensing and GIS
Authors: Sreekanthand S Vidhya Lakshmi B
Published: July 2021

The objective of this research is to analyse vegetation’s reactions to precipitation. Three Pinups sp. combinations and water tension conditions (pine) forests and NDVI’s assessment of the capacity to identify drought and water stress in this sort of ecosystem. For the purposes of this analysis, a 30-year time series of NDVI indices, derived from SPOT-Vegetation data, is built for all three study sites and compared with field measured precipitation data for the same time span. Results indicate a good relationship between the two indices suggesting a stronger capacity for investigating the vegetation water status. In general, fluctuations in high seasonal precipitation tend to have a strong influence on both NDVI ratios, while a smoother spread of precipitation results in a poorer relationship with the two indices.
Smart Liquid Mixing System
Authors: Gauni Sabitha , Vamsi Vuddagiri Krishna
Published: July 2021

Smart drink appliances with a wide area of different control specifications have become a popular research area. Most of these systems are expensive and takes a lot of time to replace the whole system. The proposed method below will help us upgrade the existing appliances that make liquid mixing possible and a feature where we can control it with the help of voice. This system will help the present technologies upgrade them for automation, which will be easy since this is done using simple methods while having an ease of access. The system runs on an android smart phone, connected with the help of Bluetooth to raspberry pi 3 nodes. The application analyses the commands given through voice and sends the data to the Raspberry Pi. Based on the motors connected to it, the application searches for keywords in the command and takes the required control action.
Ocean of Things: Marine Environment Monitoring using Discriminatory Model
Authors: Menaka D , Gauni Sabitha
Published: July 2021

It is generating complex Marine Environment monitoring models for the ocean-based on data from Discriminant Analysis. The Ocean of Things (OoT) is adopted in this paper to help interface multiple sensors to predict climate conditions for Aquatic life monitoring. This study, therefore, contemplates five years of climate observations in two oceans. An effort is made to study the temperature, salinity, current of water, how it varies with relation to the ocean, and the reasons for differences in the location parameters. The findings are based on Canonical correlation, Lambda of Wilks, and characterization in general. It is found that the generated model is useful for weather forecasting and aquatic life monitoring.
Systematic Evaluation and Analysis of Bituminous Road Pavement Failure
Authors: Shaikh Salim G , Wadekar Abhijeet P
Published: July 2021

Presence of good quality roads and better connectivity to all habitants is counted one of the tool to see the development and development index of any country. In the past 30-40 years, roads were constructed rapidly; hence road length has increased to a greater extent in India. This paper will provide a systematic way of investigation and collection of field data about different types of bituminous pavement failure and their nature. This sample research, quantitative data is collected for the10 km road stretch of Mumbai-Goa NH-66. This study site is a typical representation of the condition of bituminous pavements in India and most of the parts of the world. However, their nature and magnitude change depends upon their meteorological, geographical parameters, type of road, traffic load etc. The research methodology is framed in four phases. Data is collected by trained engineers and staff working at the field level responsible for the construction and maintenance of bituminous road under the guidance of an experienced highway engineer. Detailed analysis of bituminous pavement distress is carried out systematically. It is also interpreted and represented by the statistical tools to understand it more broadly. The seriousness about type of distress and its effect on the overall failure of asphalt pavement is worked out. Results/findings worked out under this research paper is effective and concentrated the eyes of the researcher towards the root causes of asphalt pavement failure. This will definitely help the present and new researchers to further study and work on these root causes to find out the best possible solutions.
Risk assessment for earthquakes, debris flow and flooding in the ah 03 de octubre, in chosica, district of luringancho, Lima
Authors: Castillo Edwar Miguel Pagaza
Published: July 2021

In the district of Lurigancho, in spite of being a district that presents many natural hazards, there are no risk evaluations in all its sectors of high and multiple exposure, which would lead to the impossibility of proposing control measures for such risks in these sectors, which would result in future damages and losses. The present study allows to deepen the knowledge of the risks associated with the AH 03 de Octubre (flood, debris flow and earthquake), so it will allow to generate semi-quantitative data on its vulnerability; and from the risk assessment to support the labor of decision makers at the local or regional government level. The methodology considered was based on the structure of the manual for the evaluation of risks originated by natural phenomena of the National Center for the Estimation, Prevention and Reduction of Disaster Risks (CENEPRED). The risk levels obtained for the hazards present were generally high, with some slight variations in the percentage of blocks, which shows that the AH 03 de Octubre has a highly vulnerable population.
Fuzzy Interference System based Link Failure prediction in MANET
Authors: Durai Senthil Kumar Swami , Duraisamy Balaganesh , Thirukrishna J T
Published: July 2021

Mobile Ad-Hoc Network (MANET) contains wireless mobile nodes establishing a short-term network lacking the assistance of individual structure. Owing to the nodes mobility, such mobile nodes are auto-configuring also auto-organizing. In MANETs, approaches are essential to discover definite routes among the sender to receiver. One important concern for routing in MANET is how to choose a quality link route that can last longer because mobility may reason links to break regularly. To solve this issue, in this approach, a Fuzzy Interference System based Link quality prediction (FLQP) in MANET is proposed. In this approach, select the intermediate CH using Fuzzy Interference System (FIS) is predicting the sensor link quality by Received Signal Strength (RSS), Communication range (CR), Remaining Energy (RE), packet transmission rate (PTR) parameters. This FIS can be utilized to extend a metric for path selection in terms of link quality that can improve the network performance as to be exposed by the simulation results.
Synthesis and characterization of Schiff’s base-based chemosensor and its detection potential of Fluoride ions
Authors: Kurniawan C , Wati R R , Prasetya A T , Priatmoko S , Kasmui K , Bahron H
Published: July 2021

The development of selective and sensitive chemical sensors to overcome fluoride ions is needed, especially for applications in chemical and biological processes. One of the chemical compounds that can act as sensors is the Schiff base compound group. This study aims to synthesis a Schiff base based on salicylaldehyde and as a fluoride ion sensor. The one-pot synthesis approach was carried out in room terms by mixing salicylaldehyde with hydrazine hydrate or phenylenediamine with a molar ratio of 2: 1 in ethanol solvent under stirring for 24 hours. The bright yellow crystal was obtained with a yield of 80% for salicylaldazine (SB). The compounds in the dimethyl sulfoxide (DMSO) solvent tend to be colorless, but in the presence of F-ions, the solution turns orange. The results indicate an interaction between SB1 with F-ions in solution.
The effect of the REACT CBA strategy on the material of reaction rate on student achievement at SMA Kristen 1 Tomohon
Authors: Papia M , Rombang W A R , Karundeng M
Published: July 2021

The purpose of this study was to determine whether there was an effect of the REACT CBA strategy on the material reaction rate on student achievement at SMA Kristen 1 Tomohon. The REACT CBA strategy was applied in the experimental class with 33 student’s in number, and conventinal learning in the control class with 33 student’s. Studen achievement scores are obtained from the pretest and posttest scores. The results showed that there was no significant difference in the pretest scores of the experimental and control class, namely U = 543,5; p = 0,990 (p > 0,05). While for the posttest scores there is a significant difference between the experimental and control classes where U = 151,0; p = 0,000 (p = < 0,05). So that the conclusion is that H0 is rejected and H1 is accepted, it means that there is an effect of the material reaction rate on student achievement.
The influence of the learning approach of Science Technology Engineering and Mathematics (STEM) on attitudes and learning outcomes in class XI students of SMA Negeri 5 Halmahera Utara for material reaction rate
Authors: Manis A S , Tani D , Rombang W A R
Published: July 2021

The purpose of the study was to determine the effect of the Science Technology Engineering and mathematics (STEM) learning approach on attitudes and learning outcomes on the reaction rate material. This research was conducted at SMA Negeri 5 North Halmahera in the 2019/2020 school year. The research method used is descriptive research design with a pre-test and post-test control design. 30 students of Class XI MIA 1 were randomly selected to be assigned as research sample. Data on student attitudes towards learning chemistry were obtained through a questionnaire of initial test and final test of 20 items and data learning outcomes were obtained through the pre-test and post-test of 15 objective items. The results of the study obtained an average score of student attitudes towards chemistry after using the STEM learning approach of 93, 73 higher than before using the STEM approach 49.73 while the average score of learning outcomes after using the STEM learning approach was 78.33 higher than learning outcomes before using the STEM approach of 37.83. Based on the research findings, this show that there is an effect of the STEM learning approach on students’ attitudes and learning outcomes on the reaction rate material.
Convolutional neural network for classification of skin cancer based on image data using google colab
Authors: Kharisudin I , Hidayati A , Agoestanto A , Mashuri M
Published: July 2021

Climate change causes the world’s weather to become hotter and has an impact on human health. The direct impact that can be seen is the increase in skin cancer cases due to rising temperatures. This study aims to perform digital image data classification modeling by implementing the Convolutional Neural Network (CNN) method in skin cancer cases using Google Colab software. Research on deep learning applications for identifying and classification image data has been carried out in many recent articles. We used secondary skin cancer image data obtained by a dermoscopy consisting of malignant and benign skin cancer. From 3297, there are 1,800 images of benign skin cancer and 1,497 images of malignant skin cancer. For modeling purposes, it was divided into 2967 training data and 330 testing data. The training process uses variations of the epoch and learning rate to determine the best results. The accuracy value obtained is 99.60% and the validation accuracy value is 92.12%. These results were obtained using 100 epochs and a learning rate of 0.00001. Based on the prediction results using a confusion matrix for testing data, the accuracy value is 90%.
Increased accuracy of prediction hepatitis disease using the application of principal component analysis on a support vector machine
Authors: Alamsyah A , Fadila T
Published: July 2021

Data mining has been widely used to diagnose diseases from medical data. Classification is a data mining technique that can be used to predict disease. In previous studies, a support vector machine was widely used to obtain high accuracy in predicting hepatitis. In this study, the principal component analysis was applied to the support vector machine. A principal component analysis is used to extract features and reduce the number of features or attributes. Principal component analysis can reduce data dimensions without removing important information from the dataset. The extracted and reduced data are then used to classify the support vector machine. Classification performance measurement is done by using a confusion matrix. Hepatitis prediction accuracy achieved was 93.55%. This result is better than the support vector machine classification results without the application of principal component analysis.
Analysis of student’s expectation on science virtual class models during the covid-19 pandemic era in science education study program
Authors: Taufiq M , Dewi N R , Savitri E N , Parmin P , Darmawan M S
Published: July 2021

The period of the COVID-19 pandemic has forced all activities in educational institutions to maintain distance and all material delivery through unusual media and methods. To get around this unfavourable situation, online methods are one of the most effective options to overcome it. This research aims to analyse the most expected virtual class model on science learning based on student’s perspective. This research is a case study conducted through a survey method using an online questionnaire using google form. The subjects of this research were all students of basic science courses in the Science Education Study Program Faculty of Mathematics and Natural Sciences Universitas Negeri Semarang on odd semester of 2020. The results of this research indicate that the types of virtual classes implemented in basic science courses consist of LMS, LCMS, SLNs, combination of LMS-SLNs and LCMS-SLNs. The virtual classroom model implemented in science learning is in accordance with the objectives that include connectivity, flexibility, interaction, collaboration, development opportunities and motivation. The most expected virtual classroom model in science learning during the Covid-19 pandemic based on student perspectives is a combination of LMS-SLNs of 66.23%, LMS of 23.38%, LCMS-SLNs of 6.49%, and LCMS of 3.90%. The conclusion of this research indicates that the combination Virtual Class model of LMS (Learning Management System) and SLNs (Social Learning Networks) is the most expected according to student perspectives.
Analysis of student error in solving story problems in linear program materials
Authors: Kaeng F F , Mangobi J U L , Regar V E
Published: July 2021

The purpose of this research was to determine the percentage of student errors in solving story problems on the linear program material. The errors noted were misunderstandings of questions, errors in mathematical modeling, errors in calculating / solving, and errors in drawing conclusions. The research was conducted on class XI MIPA students of SMA Negeri 1 Langowan. The results showed that all students made mistakes solving story problems with the material of the linear program. There were 38 errors (23.89%) at the question understanding stage. There were 5 errors (3.14%) at the mathematical modeling stage. There are 90 errors (56.60%) at the computation / completion stage. There were 24 errors (15.09%) at the conclusion stage. There were 2 other errors (1.25%) made by students.
Identification of students’ conceptual understanding on electrolyte and non-electrolyte solution material using a three tier multiple choice test
Authors: Noho S S , Sihaloho M , Musa W J A
Published: July 2021

This study aims to identify the percentage of students’ conceptual understanding using the Three-Tier Multiple-Choice test on electrolyte and non-electrolyte solutions. The research method used is descriptive quantitative. The instrument used was a Three-Tier Multiple-Choice test. The research subjects were class X IPA MAN 1 Gorontalo City with a total sample of 20 students. The data were processed based on the students’ answer patterns which were grouped into categories of understanding the concept, not understanding the concept and misconceptions (misconceptions 1,2, and 3). The results showed that the category of understanding the concept of students was in the low category with a percentage of 28%, did not understand the concept in the high category with a percentage of 54%, and misconceptions were in the low category with a percentage of 18%. Of the 18% students who experienced misconception, the percentage was 8% misconception 1, misconception 2 was 2%, and misconception 3 was 8%.
Distance weight of GWR-Kriging model for stunting cases in East Java
Authors: Ardianti D , Pramoedyo H , Nurjannah N
Published: July 2021

The chosen of distance weights is needed to form an accurate Geographically Weighting Regression model. There are 3 types of distance weights namely Gaussian kernel, Bisquare kernel and Tricube kernel. The weighting in GWR describes the closeness relation between locations. For data that has spatial heterogeneity, GWR models are more suitable models than OLS models. This study was conducted to obtain the best distance weighting based on minimum cross-validation method. Using secondary data from the Health Department in East Java with 34 districts for observation, the dependent variable is stunting and five independent variables that influence stunting cases. Based on the result, GWR models with fixed gaussian models produces a better accuracy in higher R
2 values compared to OLS models. The predicted map of the spread stunting cases conducted by interpolation GWR Kriging using exponential semivariogram.
Development of PIMCA learning model on magnetic field
Authors: Patol R , Tulandi D A , Umboh I , Poluakan C , Komansilan A , Tumimomor F
Published: July 2021

Research on physics learning on magnetic field has been carried with the PIMCA Learning Model (Presentation, Idea Mapping, Conceptualization, Formative Assessment introduced by Cosmas Poluakan). The research objective was to develop a PIMCA learning model based on Multiple Representation-Semiotic Resources. This research and development with reference to the 10 steps of developing the Dick and Carey model. This research was conducted in the Physics Education department of Manado State University, with 30 student teachers candidate as respondents. The research procedure starts from the development step by doing the validity of the expert test and then doing trials in small groups and large groups with pre-test and post-test. The results showed that the validity of the expert test got an average value of 3. 28 so that the learning model was in the very good category and was worthy to be disseminated. In the small group trial results, the pre-test mean score was 0.2 and the post-test mean score was 2.2. Whereas for the large group the results of the pre-test and post-test trials were 0.7 and 2.5. In the student response, 46.6% chose very good, 40% chose were good, and 13.4% chose were quite good. The results of this research indicate that the PIMCA learning model is suitable for use as a learning model that can help students understand the concept of magnetic fields. 6% chose very good, 40% chose were good, and 13.4% chose were quite good. The results of this research indicate that the PIMCA learning model is suitable for use as a learning model that can help students understand the concept of magnetic fields. 6% chose very good, 40% chose were good, and 13.4% chose were quite good. The results of this research indicate that the PIMCA learning model is suitable for use as a learning model that can help students understand the concept of magnetic fields.
PIMCA learning models to improve student learning outcomes in optic magnifying glass tool
Authors: Hartati H , Mondolang A H , Silangen P M , Tulandi D A , Marianus M , Poluakan C
Published: July 2021

The research was carried out on the students of physics education Faculty mathematics and science Manado state university with a total of 28 students as respondents. The purpose of this study was to determine the increase in student learning outcomes on the autik lup material using the PIMCA learning model The research instrument used was a test. The research process started with the pre-test, continued with the implementation of physics learning according to the 4 steps of the PIMCA model and ended with a post-test. The results showed that the mean value before the test was 27,86 and the mean value after the test was 75 The results showed that the MR-SR-based PIMCA learning model can improve student learning outcomes to properly understand concepts of physics, especially in the subject of magnifying glass tool.
Implementation of the PIMCA model to learning convex mirror
Authors: Reskin S , Silangen P M , Tumangkeng J V , Poluakan C , Londa T K , Mondolang A H
Published: July 2021

Learning in the 4.0 era requires various learning models, one of which is the MR-SR based PIMCA learning model (Presentation, Idea Mapping, Conceptualization, Assessment Formative) which presents the same concept in several formats. This study aims to determine the increase in student learning outcomes before and after implementing the MR-SR based PIMCA learning model introduced and developed by Cosmas Poluakan. The research method used of One group pre-test post-test design. This research of conducted in the Department of Physics, State University of Manado, with 29 student respondents. The research process begins with problem formulation, management, analysis of data, and conclusion. The results of the data analysis showed that the average score before the test of 20,69 and the average score after the test is 72,41. With a maximum value of 75, a gain of 0.9 is obtained. This means that there is an increase in learning outcomes in the high category because g > 0,7. The results of this study indicate that the MR-SR based PIMCA learning model can improve student learning outcomes in mastery of physics concepts.
Students learning difficulties in understanding the Lorentz force
Authors: Rettob R J , Poluakan C , Tulandi D A , Mongan S W , Polii J
Published: July 2021

Research has been carried out using the PIMCA learning model introduced by Cosmas Poluakan. The aims of this study is to describe the learning difficulties of the third semester students of Physics Education study program, in understanding the Lorentz force concept through learning using PIMCA learning model based on MR-SR (multiple representations - semiotic source). The research method is a descriptive analysis method, with the procedure beginning with giving a pre-test, then following the 4 stages of PIMCA learning. The results showed that the average of pre-test results was 7% and the average of post-test results was 37%. These results indicate that even though there is an increase in the average of mastery the Lorentz force concept, generally students still have difficulty in writing down the complete vector magnitude, determining the relationship of force, electric current and magnetic field in a three-dimensional coordinate system and also need to be given more time so that students more freely explore the concept of Lorentz force. The results of this study recommend that in order to understand the Lorentz force concept, the use of the PIMCA model needs to be accompanied by giving more practice questions, and doing virtual practicum.
Development of two variable linear equation system learning development with a problem-based learning model
Authors: Lainata R P , Damai I W , Pesik A
Published: July 2021

This study aims to produce a two-variable linear equation system learning tool using the Problem Based Learning model for students of Santa Rosa De Lima Tondano Catholic Middle School. This research method is a descriptive method. At the research, development stage, the device development model used in this study was a modified 4-D model. The learning tools developed are the Learning Implementation Plan, Student Activity Sheets, and Evaluation of Learning Outcomes. Based on the results of the descriptive analysis, it was found that the development of the Problem Based Learning model of learning produced good learning tools because it met valid criteria based on expert opinion, the ability of teachers to carry out learning was also good, Students were active during the learning process, students gave positive responses to learning, and evaluation of results. Learning meets the criteria of valid, sensitive, and reliable. Based on the descriptive analysis, it can be concluded: (1) The development research carried out at Santa Rosa De Lima Tondano Catholic Junior High School with the research subjects being students of class VIII in the 2019/2020 school year has produced learning tools that are valid, practical, and effective after being tested and through the process. Content validation. The tools developed are the Learning Implementation Plan, Student Worksheets, Evaluation of Learning Outcomes with the Problem Based Learning model on the material of the two-variable linear equation system, (2) Validated learning devices produce data with valid criteria, (3) In terms of practicality, the tool, learning is in the very practical category according to student responses or responses, and (4) Effective learning tools in terms of the Teacher Readiness Level which are in the very good category and the percentage of classical completeness is in the very good classification.