The Digital Game’s Impact on Student’s Interaction Related to Sociomathematical Norms: A Systematic Literature Review

The use of digital games as learning mathematics media is ubiquitous. It comes in a variety of approaches, designs, and purposes. But, its impact on students’ interactivity leads to a classroom social construct called sociomathematical norms is not revealed yet. Beside, technology use in education and traditional game use separately could raise students’ sociomathematical norms. Although, sociomathematical norms are known as specific interactions among students and teachers which form mathematical concepts. This systematic literature review study, based on the PRISMA statement, conducted to collect critical information about the use of digital games' impact on students' sociomathematical norms. Articles which published between 2016 and 2020 in mathematics education field are screened. The findings show that the use of digital games raises students’ sociomathematical norms if it is followed by an open ended learning approach and sharing feature. Otherwise, the norms cannot be detected explicitly. Future research related to this finding is also recommended

A Concept of V2G Battery Charging Station as the Implementation of IoT and Cyber Physical Network System

The integration of the internet of things (IoT) and cyber physical network into the battery charging station system is critical to the success and long-term viability of the vehicle to grid (V2G) trend for future automobiles in terms of environmental and energy sustainability. The goal of this article is to create a V2G battery charging station concept using the internet of things (IoT) and a cyber physical network system. The V2G charging station concept was developed with the idea that every charging electric vehicle (EV) can communicate and coordinate with the charging station's control center, which includes a cyber physical system that addresses privacy and security concerns. The communication protocol must also be considered by the charging station. The preliminary test has been taken into consideration. Normal hours (for case one), peak hours (for case two), and valley hours (for case three), respectively, were created as charging circumstances for EVs at charging stations. Simulations were run for each of the three case scenarios. Each EV's battery state of charge (SoC) is provided a 50 percent initial charge and user-defined SoC restrictions. The MATLAB/SIMULINK platform was used to run the case simulations. The grid frequency, charging station output power, and the EV's battery SoC were all observed during the 24-hour simulation. As a result, the developed V2G charging station concept can regulate its input and output power depending on the battery status of the EVs inside the charging station, as well as provide frequency regulation service to the grid while meeting the energy demand of EV customers

Aceh's Historic Tourist Attractions: An Augmented Reality-Based Prototype of a Virtual Tour Application

Indonesia has attractive tourist destinations for tourism such as beautiful interior areas and historical places. The purpose of this research is to design and build a virtual tour application for Aceh tourism objects using augmented reality. One of the problems that occur in tourist objects is that foreign tourists do not have an idea about the tourist objects they want to visit. The technology used in this study and previous research is Augmented Reality, but previous research only displays 3D tourist objects, while in this study, augmented reality technology is incorporated into the design of the 4 Aceh tourist attractions by showing a 3-dimensional illustration of the object as a whole. for the outside of the building and displays a virtual tour image in the form of a video to illustrate the inside of the tourist attraction building on the Android mobile platform. Based on the results of distance and angle testing, the best (ideal) distance that produces clear and bright marker detection is found at a distance between 25 to 45 cm, while the best angle is between an angle with a slope of 0° to 60°. Measurements of distances and angles are carried out using threads, bows and measuring tapes. The 3D object is successfully displayed by pointing the camera at the marker to be detected.

A Concept of V2G Battery Charging Station as the Implementation of IoT and Cyber Physical Network System

The integration of the internet of things (IoT) and cyber physical network into the battery charging station system is critical to the success and long-term viability of the vehicle to grid (V2G) trend for future automobiles in terms of environmental and energy sustainability. The goal of this article is to create a V2G battery charging station concept using the internet of things (IoT) and a cyber physical network system. The V2G charging station concept was developed with the idea that every charging electric vehicle (EV) can communicate and coordinate with the charging station's control center, which includes a cyber physical system that addresses privacy and security concerns. The communication protocol must also be considered by the charging station. The preliminary test has been taken into consideration. Normal hours (for case one), peak hours (for case two), and valley hours (for case three), respectively, were created as charging circumstances for EVs at charging stations. Simulations were run for each of the three case scenarios. Each EV's battery state of charge (SoC) is provided a 50 percent initial charge and user-defined SoC restrictions. The MATLAB/SIMULINK platform was used to run the case simulations. The grid frequency, charging station output power, and the EV's battery SoC were all observed during the 24- hour simulation. As a result, the developed V2G charging station concept can regulate its input and output power depending on the battery status of the EVs inside the charging station, as well as provide frequency regulation service to the grid while meeting the energy demand of EV customers

Perbandingan Algoritma C4.5 dan Adaptive Boosting dalam Klasifikasi Penyakit Alzheimer

Penyakit alzheimer adalah penyakit yang menyerang sistem saraf di dalam otak. Penyakit ini dapat menyebabkan terganggunya aktivitas sehari-hari, ingatan yang tidak terorganisir, dan berkurangnya daya ingat. Deteksi dini penyakit alzheimer dapat memanfaatkan pendekatan matematis menggunakan data mining. Data mining memiliki model-model klasifikasi yang dapat digunakan untuk mendeteksi dini penyakit alzheimer. Beberapa algoritma yang dapat digunakan untuk klasifikasi diantaranya adalah C4.5 dan Adaptive Boosting (AdaBoost) yang diterapkan pada penelitian ini untuk mengklasifikasikan penyakit alzheimer. Perbandingan kedua algoritma ini bertujuan untuk memperoleh algoritma mana yang paling tepat dalam klasifikasi penyakit alzheimer. Untuk menguji kedua algoritma ini digunakan dua teknik pengujian yaitu percentage split dan k-fold cross validation. Pada percentage split dipilih ukuran split sebesar 80% untuk data latih dan 20% sebagai data uji dan k-fold cross validation dipilih nilai k sebesar 10. Hasil penerapan dari kedua algoritma diperoleh bahwa untuk k-fold cross validation bekerja lebih baik dibandingkan dengan percentage split. Hal ini dikarenakan k-fold cross validation meningkatkan persentase nilai presisi, recall, dan akurasi dari masing-masing algoritma. Untuk kinerja masing-masing algortima, AdaBoost dalam penggunaanya bekerja lebih baik dibandingkan dengan C4.5 dengan nilai presisi, recall dan akurasi secara berturut-turut, yaitu 91.5%, 91% dan 91.15%. Dari hasil yang diperoleh dapat disimpulkan bahwa algoritma AdaBoost dengan teknik k-fold cross validation memiliki performa yang paling baik dalam melakukan klasifikasi penyakit alzheimer dibandingkan algoritma dan teknik pengujian lainnya

Penerapan ELPSA Framework Berbantuan Game pada Materi Eksponensial

The Application of the ELPSA Framework Assisted by Game in Teaching Exponential. There is still a tendency for students to avoid mathematical assignments is a challenge for educators. Therefore, to present interesting and quality mathematics learning, one of them can be through the ELPSA framework. ELPSA framework is a mathematical learning approach that contains five components, namely experience, language, pictorial, symbolic, and application. The game was chosen to present training as a part of symbolic component that attract students. This study aims to determine the mastery of student learning through the application of the ELPSA framework assisted by games on exponential material, student responses to the learning activity, and the dynamics of students' emotional activity in game utilization. This research use mixed method with convergent parallel mixed method design. The instruments used were learning outcomes tests, student response questionnaires, and observation sheets of student emotional activity. The study population was all students of MTsN 1 Aceh Tengah, and the sample was 17 students of class VIII-1. Based on the results of data processing using the t test, it can be concluded that the value of students reaches completeness. In addition, students respond well to learning and the use of games. Observation of the emotional activity of students during play shows the seriousness is dominating the emotional activity that arises, followed by pleasure, and curiosity. Whereas signs of boredom and disappointment do not arise. This study presents alternative mathematical learning designs to achieve learning goals, as well as attracting students

ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION

Soil is a solid-particle that covers the earth's surface. Soils can be classified based their color. The color can be an indication of soil properties and soil conditions. Soil image classification requires high accuracy and caution. CNN works well on image classification, but CNN requires a large amount of data. Augmentation is one technique to overcome data needs like rotation and improving contrast. Rotation is the movement of rotating the image position randomly to various degrees. Gamma Correction is a method to improve image by decreasing or increasing the contrast. The rotation and Gamma Correction on augmentation can increase the amount of training data from 156 to 2500 soil images data. The classification of soil data is not referred to soil taxonomy system such as Entisols and Histosols but it used arbitrary simple classification based on color.  Unfortunately, the weakness of the CNN is vanishing and exploded gradients. Another Deep learning that can overcome vanishing and exploded gradients is dense blocks. This study proposes a combination of Augmentation and CNN-Dense block where in the augmentation a combination of rotation and Gamma-correction techniques is used and Soil image classification based on color is used by the CNN-Dense block. The combination method is able to give excellent results, where all performances accuracy, precisions, recall and F1-Score are above 90%. The combination of rotation and Gamma Correction on augmentation and CNN is a robust method to use in soil image classification based on color

Contrast enhancement for improved blood vessels retinal segmentation using top-hat transformation and otsu thresholding

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels

The Comparison of ReliefF and C.45 for Feature Selection on Heart Disease Classification Using Backpropagation

One of the datasets used to predict heart disease is UCI dataset. unfortunately, the dataset contains missing data. the missing data dramatically affects the performance of the backpropagation classification method. One of the techniques used to handle missing data is feature selection. This study compares the ReliefF and the C4.5 algorithm in feature selection to handle missing data. The results of these algorithms are applied to the classification of heart disease using the Backpropagation. The results will be measured based on accuracy, precision, and recall. The performance results of the ReliefF and Backpropagation are an accuracy of 82.653%, a precision of 82.7%, and a recall of 82.7%. The performance results of the C4.5 and backpropagation are an accuracy of 80.61%, a precision of 80.4%, and a recall of 80.6%. Based on the results it can be concluded that the ReliefF gives better performance results on backpropagation than the performance results of the C4.5. Although, the results of C4.5 are below ReliefF but the results are quite satisfactory because of the accuracy, precision and recall results obtained above 80%. This shows that ReliefF and C4.5 can select features that affect the UCI heart disease patient dataset

Combination Contrast Stretching and Adaptive Thresholding for Retinal Blood Vessel Image

To diagnose diabetic retinopathy is to segment the blood vessels of the retinal, but the retinal images in the DRIVE and STARE datasets have varying contrast, so the enhancement is needed to obtain a stable image contrast. In this study, image enhancement was performed using the Contrast Stretching and continued with segmentation using the Adaptive Thresholding on retinal images. The image that has been extracted with green channels will be enhanced with Contras Stretching and segmented with Adaptive Thresholding to produce a binary image of retinal blood vessels. The purpose of this study was to combine image enhancement techniques and segmentation methods to obtain valid and accurate retinal blood vessels. The test results on DRIVE were 95.68 for accuracy, 65.05% for sensitivity, and 98.56% for specificity. The test results of Adam Hoover’s ground truth on STARE were 96.13% for, 65.90% for sensitivity, and 98.48% for specificity. The test results for Valentina Kouznetsova’s ground truth on the STARE were 93.89% for accuracy, 52.15% for sensitivity, and 99.02% for specificity. The conclusion obtained is that the processing results on the DRIVE and STARE datasets are very good with respect to their accuracy and specificity values. This method still needs to be developed to be able to detect thin blood vessels with the aim of being able to improve and increase the sensitivity value obtained