Mandatory Task Research Articles

Peculiarities of the legal status of creditors and their representative bodies in cases of bankruptcy of limited liability companies

The article examines the legal status of creditors and their representative bodies in bankruptcy cases of limited liability companies. It is noted that one of the main tasks of the Code of Ukraine on bankruptcy procedures is to satisfy the demands of creditors. On the basis of judicial practice, it is determined that the mandatory task of bankruptcy proceedings is the fair satisfaction of the entire set of creditors’ claims in the order of priority established by law. Therefore, proceedings in bankruptcy cases are objectively formed on the basis of creditor competition. Therefore, the purpose of bankruptcy proceedings is to balance the realization of the rights and legitimate interests of the participants in the case. An urgent task is to develop and apply an effective mechanism for the interaction of creditors with other participants in bankruptcy proceedings, to establish at the legislative level and in the practical aspect the proper regulation of the legal status of creditors in order to provide them with effective means of protection against the dishonest behavior of other participants in bankruptcy procedures and to fairly satisfy them requirements It is proposed, in particular, to amend a number of provisions of the Code of Ukraine on Bankruptcy Procedures, in particular, to the second part of Article 90 of the Code of Ukraine on Bankruptcy Procedures and to put it in the wording determined by the author, to return the provisions of the second part of Article 11 of the Law of Ukraine «On Restoring the Debtor’s Solvency or Recognition of bankrupt», etc. Peculiarities of the legal status of secured, current, bankruptcy creditors in bankruptcy cases of limited liability companies are analyzed. Attention is drawn to the fact that with the adoption of the Code of Ukraine on bankruptcy procedures compared to the previous Law of Ukraine «On restoring the debtor’s solvency or declaring him bankrupt», the protection of the rights and legitimate interests of secured creditors has improved by improving the legal regulation of bankruptcy procedures. It was concluded that the creditors interact with each other by convening and holding creditors’ meetings and making a decision to create a representative body - the creditors’ committee, determine its quantitative composition and elect its members. At the same time, creditors of companies must take into account and take into account the rights and legitimate interests of each other, observing the principles of inadmissibility of abuse of procedural rights, competition and legality.

Creation of a Mathematical Model of a Stationary Rail Circuit in the Form of a Finite Discrete Automaton

Purpose. Ensuring the safety of train traffic is a mandatory task in the development of technical equipment of railway transport in Ukraine. To diagnose and verify the performance of such systems, simulation models of overhead devices, in particular, the rail circle, are used. The most commonly used models are in the form of differential equations and in operator form. Unfortunately, they are not fully suitable for solving this problem. In this regard, there is a need to create a mathematical model that is easier to integrate for checking both relay electrical interlocking and microprocessor-based interlocking systems. Methodology. To achieve this goal, the authors proposed to create a mathematical model in the form of a finite discrete automaton. This paper considers the creation of a model of a station rail circuit as a directed graph. During the creation of the model, the input and output values of the model and the states are determined. The tables of inputs and outputs of the automaton are constructed, sequential expressions for the abstract model of the automaton are created, and their minimization is performed. The states of the automaton are coded using trigger circuits. Findings. In the course of the research, a mathematical model of the rail circle in the form of a Moore model finite automaton was created, and its performance was tested in the Proteus software environment. The developed model allows to simulate the operation of a stationary rail circuit at the level of abstraction, which operates with binary signals. This makes it possible to simplify the coordination of the model with microprocessor-based centralization software. In general, it is now possible to more effectively check the performance of microprocessor-based interlocking systems at the design and commissioning stages. Originality. The developed mathematical model makes it possible to determine the response of the microprocessor-based centralization software to the behavior of the rail circuit in various, in particular atypical, operating modes, as well as to determine the response of the station electrical centralization system to individual failures and to the occurrence of several failures simultaneously. Practical value. The proposed mathematical model can be used both to check the operation of microprocessor-based centralization systems at the design and implementation stages and for relay centralization systems when developing diagnostic complexes for monitoring their performance.

Validasi Pengembangan Modul Ajar Virus Berbasis Problem Based Learning (PBL) Untuk Mata Pelajaran Biologi Fase E SMA/MA

In the Merdeka Curriculum, teachers are required to develop learning tools that include Learning Outcomes (CP), Learning Objective Flow (ATP), and teaching modules as a mandatory task for teachers. The implementation of the Independent Curriculum is still faced with a number of obstacles. Teachers experience difficulties in implementing learning based on the Independent Curriculum, mainly due to a lack of adequate socialization. Teachers need time to learn to adapt to new curriculum changes. One of the demands of the Independent Curriculum is the preparation of learning tools in the form of teaching modules. However, observation results show that teachers still do not have complete teaching modules, especially for phase E. This teaching module is a learning plan in the Independent Curriculum which contains worksheets for students and teaching materials. This research uses a type of development research with a 4D model, which consists of the Define, Design, Develop and Disseminate stages. However, due to time constraints, the Disseminate stage was not carried out in this study. The research subjects included two lecturers from the Biology department, FMIPA UNP and five Biology teachers. Based on the research results, the virus teaching module developed based on problem based learning is considered very valid with a validation value of 91%, and has a readability level with very readable criteria, with a readability value reaching 94%.

PV Power Forecasting in the Hexi Region of Gansu Province Based on AP Clustering and LSTNet

Accurate PV power forecasting is becoming a mandatory task to integrate the PV system into the power grid, schedule it, and ensure the safety of the power grid. In this paper, a novel model for PV power prediction using AP-LSTNet has been proposed. It consists of a combination of affinity propagation clustering and long-term and short-term time series network models. First, the affinity propagation algorithm is used to divide the regionally distributed photovoltaic station clusters into different seasons. The Pearson correlation coefficient is used to determine the strong correlation between meteorological factors of photovoltaic power, and the bilinear interpolation method is used to encrypt the meteorological data of the corresponding photovoltaic station cluster. Furthermore, LSTNet is used to mine the long-term and short-term temporal and spatial dependence of photovoltaic power, and meteorological factor series and linear components of auto-regression are superimposed to realize the simultaneous prediction of multiple photovoltaic stations in the group. Finally, PV power plants in five cities, Wuwei, Jinchang, Zhangye, Jiuquan, and Jiayuguan in the Hexi region of Gansu Province, China, will be selected to test the proposed model. The experimental comparison shows that the prediction model achieves high prediction accuracy and robustness.

Fed-mRMR: A lossless federated feature selection method

Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, we need to pre-select an optimal subset of features to reduce redundant computations. Federated learning is a machine learning technique based on training an algorithm over many decentralized edge devices holding local rather than global data on a centralized server. Application of this technique is extending to fields such as self-driving cars, medicine and health, and Industry 4.0, where data privacy is compulsory. Feature selection through federated learning is a complicated task since suboptimal features calculated by feature selection methods may be different in heterogeneous datasets from different nodes. In this paper, we propose a lossless federated version of the classic minimum redundancy maximum relevance (mRMR) feature selection algorithm, called federated mRMR (fed-mRMR), which, without losing any effectiveness of the original mRMR method, is applicable to federated learning approaches and capable of dealing with data that are not independent and identically distributed (non-IID data).Implementation can be found at: https://github.com/jorgehermo9/fed-mrmr

Optimal feature selection based on OCS for improved malware detection in IoT networks using an ensemble classifier

The increasing amount of IoT devices increases the size of network traffic data, causing an increase in the incidence of security breaches in IoT networks. Cybercriminals have developed malware to compromise the security of sensitive data, among other cyber threats. In the presence of inadequate and robust security mechanisms, sensitive data is prone to vulnerability. Hence, protecting data in the IoT environment is becoming a mandatory task. Various approaches have addressed malware detection using network data features. However, there is still room for improvement in developing superior techniques and utilizing more comprehensive datasets. This paper presents a novel lightweight ensemble voting classifier to detect malware traffic by deploying the best possible network data. The merits of the correlation coefficient and Opposition-Based Crow Search Algorithm (OCS) have been leveraged to compute the best possible features. Another advantage of this proposed experiment is its focus on a dataset tailored to malware traffic features. This focus enables highly accurate malware detection. After feature selection using OCS, the proposed malware classifier is trained and validated with both 5-fold and 10-fold cross-validation techniques. The tested results confirm that the presented malware classifier performs best using a minimal feature set, which is highly advantageous for IoT networks due to resource constraints.

Kompetensi Guru Dalam Perencanaan Pembelajaran Di UPT SPF SDN 107399 Bandar Khalipah

In the learning process, planning and organizing learning is a teacher’s mandatory task so that the expected learningg is in accordance with the learning that will occur. This research seeks to describe teacher’s abilities in preparing learning plans at SDN 107399 Bandar Khalipah. The approach used in this research is qualitative in the type of descriptive reasearch. The data for this reseacrh comes from sources who have been interviewed, while the data souces are class teachers at SDN 107399 Bandar Khalipah. The data collection process was carried out using checklist techniques, interviews and documentation. The interview technique used was a semi structured interview. In carring ut the documentation, the researcher reviewed 10 syllabi and lesson plans that had been prepared by the class teacher at SDN 107399 Bandar Khalipah. So that the interview data can be trusted, researchers use tools in the form of writing instruments and other electronic media. Based on data analysis, the findings of this research can be stated as follows. First, when we are going to prepare a learning plan,the teacher will use the syllabus as a guide in designing the learning implementation plan. The teacher must have 4 teacher competencies, namely the ability to develop and prepare learning implementation Plans that are in accordance with the curriculum, applies. The conclusion of this research is that the teacher at SDN 107399 Bandar Khalipah in planning learning can be seen from the way the teacher plans and prepares lesson plans that are inaccordance with the Merdeka Belajar curriculum and by showing competence in planning learning at school.

The development of an online serious game for oral diagnosis and treatment planning: evaluation of knowledge acquisition and retention

BackgroundWhile serious games seem to be supportive in healthcare education, none of them had been designed to develop competence in diagnosis and treatment planning of oral lesions. Therefore, this research aimed to develop an online simulation-based serious game for training diagnosis and treatment planning of oral lesions (SimOL) and to evaluate its educational impact in terms of knowledge improvement and retention.MethodsAs a mandatory task in an oral lesion course, all 28 students were required to participate in SimOL activities. Participants were instructed to complete a pre-knowledge assessment following a one-week washout period prior to the game activity. Subsequent to the game completion, they were tasked to complete a post-knowledge assessment I (Full score = 15) and satisfaction questionnaire. A post-knowledge assessment II was administered a week later to evaluate knowledge retention.ResultsThe findings demonstrated a significant increase in the assessment scores after interacting with the game (P < 0.001), where the pre- and immediate post-knowledge assessment scores were 8.00 (SD = 2.11) and 11.71 (SD = 2.39), respectively. The game also exhibited a positive impact on knowledge retention, as there was no significant difference between the scores of post-knowledge assessment I and II (P > 0.05). Additionally, students perceived the game as positively in all aspects, although the entertainment aspect achieved a slightly lower score of 3.70 (SD = 0.21), in comparison to the usefulness and ease of use with a score of 4.02 (SD = 0.11) and 4.02 (SD = 0.16), respectively.ConclusionSimOL demonstrated its potential as an effective learning tool for improving and retaining knowledge for diagnosis and treatment planning of oral lesions. The game was perceived positively by dental students in all aspects, however further improvements should prioritize the enhancement of entertaining components.

Results of the implementation of social assistance policies for ethnic minority migrant families in Lai Chau province

The Fifth Central Executive Committee Meeting of the Eleventh Tenure on Several Social Policy Issues for the Period 2012 – 2020 claimed that “For many years, our Party and State have always paid attention to constructing and organizing the implementation of social policies, considering them not only objective but also driving forces for sustainable development and stable political and social conditions”. The view point at the conference emphasized that “Ensuring social security is a regular and mandatory task of the Party, the State, the entire political system, and society as a whole.”, especially for the ethnic minorities migrating from poor and disadvantaged areas. The article discusses the results of implementing social assistance policies for ethnic minority migrant families in Lai Chau province. The content of the article reflects that Lai Chau has an undeveloped economy, with outdated customs still prevailing, and many ethnic minority households continue to face difficulties. The reasons for migration among ethnic minority people are primarily due to a lack of farming land and challenging living conditions, leading to migration with family members

A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion

Higher-level autonomous driving necessitates the best possible execution of important moves under all conditions. Most of the accidents in recent years caused by the AVs launched by leading automobile manufacturers are due to inadequate decision-making, which is a result of their poor perceivance of environmental information. In today’s technology-bound scenarios, versatile sensors are used by AVs to collect environmental information. Due to various technical and natural calamities, the environmental information acquired by the sensors may not be complete and clear, due to which the AVs may misinterpret the information in a different context, leading to inadequate decision-making, which may then lead to fatal accidents. To overcome this drawback, effective preprocessing of raw sensory data is a mandatory task. Pre-processing the sensory data involves two vital tasks, namely data cleaning and data fusion. Since the raw sensory data are complex and exhibit multimodal characteristics, more emphasis is given to data preprocessing. Since more innovative models have been proposed for data cleaning, this study focused on data fusion. In particular, this study proposed a generic data fusion engine, which classifies different formats of sensory data and fuses them accordingly to improve accuracy. This study proposed a generic framework to fuse the text, image, and audio data. In the first stage of this research, an innovative hybrid model was proposed to fuse multispectral image and video data. Simple and efficient models to extract the salient image features were also proposed. The hybrid image fusion model that was proposed did not yield satisfactory outcomes when combining 3D point cloud data, and its performance declined when evaluating large datasets. To address this issue, the study expanded by introducing an advanced generative adversarial network (GAN) to transform the hybrid image fusion model into a machine learning model capable of handling substantial datasets. Additionally, customized kernel functions were suggested to fuse 3D point cloud data effectively. The performance of the proposed models was assessed using standard metrics and datasets, comparing them with existing popular models. The results revealed that the proposed image fusion model outperformed the other models.

A paper-based device for glyphosate electrochemical detection in human urine: A case study to demonstrate how the properties of the paper can solve analytical issues

In the ever-growing demand for agricultural production, the use of pesticides and the consequential health risks is an issue that remains in the spotlight. The biomonitoring of pesticides in biological matrices is a mandatory task to point out the adverse effects on those people that are particularly exposed (i.e., occupational exposure) and to customize the use of pesticides for safer and more aware agricultural practices (i.e., precision agriculture). To overcome the bottleneck of costs and long sample treatments, we conceived a paper-based analytical device for the fast and smart detection of glyphosate in human urines, which is still the most widespread pesticide. Importantly, we demonstrate how to face the analytical interference given by uric acid to develop an electrochemical sensor for glyphosate detection using paper as a multifunctional material. To this purpose, a sample treatment was pointed out and integrated into a paper strip to decrease the level of uric acid in urines, finally delivering a ready-to-use device that combines lateral and vertical flow. The effective decrease of uric acid after the paper-integrated treatment is verified by direct oxidation in differential pulse voltammetry, whereas glyphosate detection can be carried out by enzyme inhibition assay in chronoamperometry. The system showed a limit of detection for glyphosate of 75 μg/L and a linear range of 100 - 700 μg/L. Additionally, the sustainability of the paper device was assessed and compared with reference chromatographic methods. Overall, this work provides an example of how to design green sensing solutions for addressing analytical challenges in line with the White Analytical Chemistry principles.

Blockchain‐based data deduplication using novel content‐defined chunking algorithm in cloud environment

AbstractThe cloud environment is inherently dynamic as users are added immensely in a short duration. It is indeed difficult to manage such user profiles and associated data. Meanwhile, the cloud data expand at a twofold‐to‐threefold rate on average, making storage space management and data integrity maintenance a mandatory task but also risky. The main approaches for addressing these data maintenance challenges in a cloud context are deduplication and data protection. In order to manage storage space, finding and removing identical copies of the same data from the cloud are possible, resulting in a reduction in the amount of storage space needed. Furthermore, duplicate copies are considerably reduced in cloud storage owing to data deduplication. Here, a decentralized ledger public blockchain network is introduced to protect the Integrity of data stored in cloud storage. This research proposes data deduplication using speedy content‐defined Chunking (SpeedyCDC) algorithm in the public blockchain. Many people and businesses outsource sensitive data to remote cloud servers because it considerably eliminates the hassle of managing software and infrastructure. However, the ownership and control rights of users data are nonetheless divided because it is outsourced to cloud storage providers (CSPs) and kept on a distant cloud. As a result, users have a great deal of difficulty in verifying the Integrity of sensitive data. Analysis using datasets from Geospatial Information Systems (GIS) revealed that the throughput increased by 5%–6% over that of the fastCDC technique, which offered Integrity since a blockchain network secured it.

Sustainability and mathematical modelling in 5th grade

This research investigates how pre-service teachers aim to bring awareness of environmental sustainability while learning mathematical modelling to their 5th-grade students. Theoretical perspectives from socio-critical modelling research can involve engaging students in societal issues, including focusing on action. Thematic coding of a mandatory task by a group of pre-service teachers, where they design, implement, and reflect on a modelling activity during practicum, is used to analyse how they combine environmental and mathematical aims. The findings show that the pre-service teachers emphasise the authenticity of tasks and data, engage students in actions in littering, reflect on students’ critique of assumptions, and focus on the meaning-making of environmental numbers using students’ everyday language. Implications to teacher education suggest that teachers can combine focusing on environmental sustainability through mathematics education. Further, teacher education can support pre-service teachers and students in actions through socio-critical modelling activities.

Validasi Pengembangan Modul Ajar Bioteknologi Berbasis Problem Based Learning

The Independent Curriculum requires teachers to compile teaching tools consisting of Learning Outcomes (CP), Learning Objectives Flow (ATP) and teaching modules as a mandatory task of a teacher. The teaching module is a form of learning implementation plan in the Independent Curriculum which is accompanied by student worksheets and teaching materials in it. This research uses a type of development research with a 4D model. The 4D model consists of four stages of Define, Design, Develop and Disseminate. Due to time constraints, this study did not carry out the disseminate stage. The subjects of this study consisted of lecturers from the Biology Department, FMIPA UNP, seven Biology teachers. Based on the research data, a problem-based learning biotechnology teaching module is obtained which is very valid with a validation value of 95.59%.

Image Deblurring using Deep Learning

Abstract: Image deblurring is a most considered problem in low-level computer vision with an objective to recover a high resolution image from a blurred input image. It is a mandatory task in image processing and has been studied for several years which is used in reconstruction of images which are blurred due to various reasons. Particularly in these years, deep learning approaches have shown great promises in various image restoration tasks, which includes image deblurring. Among these, DBSRCNN is a powerful deep learning approach that has been used for super-resolution and image deblurring. In this model developed we are going to implement De Blurring Super Resolution Convolutional Neural Network (DBSRCNN) for blurred reconstruction. The proposed method achieves superior results in both quantitative and qualitative evaluations, depicting its effectiveness in image deblurring tasks.

Hazard identification and analysis during the production of natural roasted ground coffee according to the principles of HACCP

The article identifies and analyzes dangerous factors that may arise during the production of natural roasted coffee, which are an important element for the implementation of permanent procedures of the food safety management system according to the principles of HACCP. The responsibility and, at the same time, the mandatory task of every management of the enterprise is to guarantee quality at all critical points of the technological process of manufacturing food products, minimizing the risks of dangerous factors as much as possible. Natural roasted coffee production technologies are constantly changing, so the process of ensuring the analysis of all technological stages for the identification of biological, chemical and physical dangerous factors is continuous. Coffee undergoes the most changes during roasting, because a number of chemical reactions occur, resulting in the formation of new compounds, including potentially dangerous ones. Producers of natural roasted coffee can autonomously model parameters and modes of roasting raw materials in order to best ensure the aromatic and taste properties of coffee. According to EU requirements from 2018. coffee producers must minimize acrylamide formation, monitor acrylamide levels through sampling and research, and keep records of research results and mitigation measures. The conducted study determined the degree of risks by type of hazards (chemical, microbiological and physical) in accordance with the course of the technological process of preparation, roasting of natural coffee, including packing and labeling of packaging. Guided by current safety regulations and taking into account the reliable results of scientific research, acceptable (permissible) levels of risk are indicated. Assessment of the level of risks from dangerous factors during the production of natural roasted ground coffee is presented in the form of an algorithm, which can serve as a basic procedure method for simplifying the approach to implementing the requirements of the food safety management system. Due to the identification and analysis of dangerous factors, manufacturers have the opportunity to more objectively determine critical control points and establish their management measures, which will subsequently ensure the production of roasted ground coffee in accordance with safety and quality standards.

MPF: A Robust Vehicle Localization Framework Based on Topological Map and Odometry

Robust and accurate localization is a mandatory task for autonomous driving. Global positioning method such as global positioning system (GPS) frequently faces signal blocking or city canyon effects in urban area. Dead reckoning sensors such as wheel/visual odometry (VO) or IMU suffer from cumulative drift and require frequent corrections from external sensors. We believe a topological map that is easily available nowadays can be a cheap and effective global sensor for this task. In this article, a flexible topological-map-based probabilistic filtering framework is proposed to tackle the cumulative error problem. Rather than matching the entire trajectory to the map, we prefer extracting sparse anchor points (APs) on the trajectory and producing correcting results for them during filtering. Considering the large uncertainty the trajectory observation contains, our framework is capable of adaptively correcting multiple positions jointly and updating their states only in situations with low uncertainty. This special mechanism explicitly avoids erroneous determination of the position at large uncertain situations such as dense road crossings or roads with small curvature. The experiments carried out on KITTI and our own datasets with both naive single-lane and more advanced multilane topological maps demonstrate that our method can greatly improve the localization accuracy and effectively constrain the cumulative error within a small bound.

Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model

Accurate PV power forecasting is becoming a mandatory task to integrate the PV plant into the electrical grid, scheduling and guaranteeing the safety of the power grid. In this paper, a novel model to forecast the PV power using LSTM-TCN has been proposed. It consists of a combination between Long Short Term Memory and Temporal Convolutional Network models. LSTM is used to extract the temporal features from input data, then combined with TCN to build the connection between features and outputs. The proposed model has been tested using a dataset that includes historical time series of measured PV power. The accuracy of this model is then compared to LSTM and TCN models in different seasons, time periods forecast, cloudy, clear, and intermittent days. For one step forecasting, the results show that our proposed model outperforms the LSTM and TCN model. It has carried out a reduction of 8.47%, 14.26% for the autumn season, 6.91%,15.18 for the winter season, 10.22%,14.26% for spring season and 14.26%, 14.23% for the summer season on the Mean Absolute Error compared with LSTM, TCN. For multistep forecasting, LSTM-TCN surpassed all compared models in different time periods forecast from 2 steps to 7 steps PV power forecasting.

Total variation‐based ultrasound image despeckling using method noise thresholding in non‐subsampled contourlet transform

AbstractMedical diagnostics rely heavily on ultrasound imaging since it is non‐invasive, inexpensive, and able to provide images in real time. However, the inherent existence of a kind of signal‐dependent noise in these images diminishes the use of ultrasound imaging systems. Speckle noise is inherently present in ultrasound images. Its inherent presence occurs during the image acquisition phase. It decreases the diagnostic usefulness of this imaging modality since it interferes with and reduces the image resolution and contrast. Speckle noise is multiplicative in nature. This multiplicative nature of speckle noise causes adverse effects on ultrasound imaging because it distorts the image quality and causes a loss of the patient's informative content in ultrasound imaging. This causes difficulty in tissue characterization. A lot of research has been done in this field to remove speckle noise while preserving medical information in the image. To handle this issue, research has been grouped into multiple domains. The two main domains include homomorphic and non‐homomorphic filtering. The homomorphic filtering uses a logarithmic transform that converts this multiplicative nature into an additive nature and uses any additive image restoration model to do a despeckling task. Non‐homomorphic filtering encompasses all methods that do not employ a logarithmic transform. Since the presence of speckle noise in ultrasound imaging is an inherent property. Therefore, the despeckling of medical ultrasound images is a mandatory task that cannot be avoided or ignored. This paper proposes a two‐step hybrid and homomorphic despeckling technique, where modified total variation method is applied as step one for speckle reduction purposes. Step two implements method of noise thresholding in the non‐subsampled contourlet transform (NSCT) domain using the bivariate shrinkage rule for edge preservation. The effectiveness and reliability of the proposed method are tested by comparing it with some of the latest methods based on qualitative and quantitative analyses. This paper will help the new researchers understand various problems and their solutions related to ultrasound image despeckling.

Pemahaman Konsep Matematika Melalui Pendidikan Progresif dengan Penggunaan Kalkulator Saintifik

The current education system must change according to the times. The existing conventional education system makes students far behind in keeping up with the times. Students are asked to remember knowledge from authentic sources such as reading textbooks, parents who rarely take part in educating their children, knowledge is only obtained from the results of teacher conversations in class, based on assignments given, very rare mastery of skills, and in the assessment, process are boxed according to the highest and lowest ratings. This conventional education system makes school a mandatory task and students must find ways to overcome it. The understanding of students' concepts is very lacking, especially the specter of mathematics which is seen as a difficult and long calculation. Progressive education is here to replace the current conventional education system and the use of scientific calculator digital technology media greatly facilitates students in understanding mathematical concepts which will be achieved from the indicators of concept understanding as follows: 1) restating the concepts that have been studied, (2) classifying objects -objects to achieve the formation of a concept, (3) identify the properties of a concept, (4) apply the concept logically, (5) provide correct and incorrect examples/contras of the concept being studied, (6) present the concept in the form of various kinds of mathematical representations such as tables, graphs, diagrams, pictures, sketches, or other mathematical models, (7) linking various concepts in mathematics and outside mathematics, (8) developing the requirements needed in understanding concepts.