Data Processing Approach Research Articles

Perceived Greenwash and Green Trust of Danone-Aqua as B Corporation Certified Company in Indonesia

Amidst customers' growing awareness regarding their consumption's influence on environmental sustainability in consumption processes, many businesses must take necessary actions to maintain consumer trust that they embrace the same environmentally responsible values as customers. Greenwashing, which involves making misleading statements about a company's pro-environmental stance or failing to meet a commitment to lessen negative environmental impacts, has significantly eroded consumer trust. This study investigated the influence of perceived greenwashing on environmental trust and the mediating effect of perceived green risk and perceived green value. This study's sample consisted of 200 respondents selected using a method of purposive sampling based on the criterion of Generation Z members with knowledge of greenwashing methods and the B Corporation accreditation awarded to Danone-Aqua Indonesia. The data processing approach employs the SEM-PLS technique with the SmartPLS 3.0 application. The study results indicate that perceived greenwash has no significant effect on green trust, but the effect becomes significant when perceived green risk and value are present as mediating variables. Therefore, in order to maintain consumer trust in the company's performance in preserving the environment, Danone-AQUA is encouraged to make continuous efforts to increase customer perceived value and minimize the perceived risks that consumers experienced, including maintaining the B Corporation Accredition value excellent, amidst circulating greenwashing concerns.

Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R

In this paper, we describe two related scripting methods of cartographic data processing and visualization that provide 2D and 3D mapping of Japan with different algorithm complexity. The first algorithm utilizes Generic Mapping Toolset (GMT), which is known as an advanced console-based program for spatial data processing. The modules of GMT combine the functionality of scripting with the aspects of geoinformatics, which is especially effective for the rapid analysis of large geospatial datasets, multi-format data processing, and mapping in 2D and 3D modes. The second algorithm presents the use of the R programming language for cartographic visualization and spatial analysis. This R method utilizes the packages ‘tmap’, ‘raster’, ‘maps’, and ‘mapdata’ to model the morphometric elements of the Japanese archipelago, such as slope, aspect, hillshade and elevation. The general purpose graphical package ‘ggplot2’ of R was used for mapping the prefectures of Japan. The two scripting approaches demonstrated an established correspondence between the programming languages and cartography determined with the use of scripts for data processing. They outperform several well-known and state-of-the-art GIS methods for mapping due to their high automation of data processing. Cartography has largely reflected recent advances in data science, the rapid development of scripting languages, and transfer in the approaches of data processing. This extends to the shift from the traditional GIS to programming languages. As a response to these new challenges, we demonstrated in this paper the advantages of using scripts in mapping, which consist of repeatability and the flexible applicability of scripts in similar works.

Comparing Statistics and Machine Learning to Detect Insincere Grip Force Testing Using Manugraphy.

Background Currently, there are no tests that have been proven to be capable of rating an individual's grip force measurement as sincere or insincere. However, different parameters have been found to vary in grip force testing for maximal versus submaximal effort. A novel data analysis and processing approach might be key to improving these measurements. This study explores the use of a machine learning (ML) algorithm as a means to more accurately determine the sincerity or insincerity of grip force testing. The ML algorithm compares the hand's load distribution pattern with the information generated using conventional statistical methods. Methodology This study uses manugraphy data collected as part of a previous investigation that analyzed load distribution patterns of the right and left hands of 54 healthy subjects. The subjects underwent grip force testing using maximal or submaximal effort, and the percentage contributions of each of the seven defined anatomical areas of the hand were calculated with respect to the total load applied. The predictions based on the load distribution and its use for rating individual grip force measurements as sincere or insincere were compared with the results of conventional statistical methods (thresholds for a bi-manual area-to-area comparison) and an ML algorithm. Results Based on an area-to-area comparison, our method achieved a sensitivity of 54% and a specificity of 78% to detect insincere effort. A predictive ML model developed using these data was capable of recognizing submaximal effort based on the hand's load distribution pattern, determining a sensitivity of 94% and a specificity of 99%. Conclusions Compared to conventional methods, the use of an ML algorithm considerably improved the validity of manugraphy results in discerning the sincerity or insincerity of grip effort.

Pengaruh Kreativitas dan Pendidikan Kewirausahaan Terhadap Minat Berwirausaha Melalui Motivasi Berwirausaha Pada Santri dan Santriwati Pondok Pesantren Ad-Daraaen Makassar

Entrepreneurship is a creative ability, interest, and motivation. keen to see opportunities and always open to any input and positive changes that can keep the business growing. Business should have value and be useful. The purpose of this research is to explore creativity, interest, and motivation in entrepreneurship in Islamic boarding schools, as well as to support Islamic spiritual education in entrepreneurship education. This research is a quantitative research using a data processing approach for Islamic boarding schools in exploring the entrepreneurial process through education at Islamic boarding schools. In this study, data collection used a questionnaire filling technique by sending it to Islamic boarding schools, observing and studying documents. The results of the study show that entrepreneurship is internalized in Islamic boarding schools through religious and entrepreneurial values based on Worship (worship to Allah SWT) and Khidmah (piety to humanity), where all business and economic activities carried out aim to worship Allah SWT. and for the benefit of society.

An Efficient IIoT-Based Smart Sensor Node for Predictive Maintenance of Induction Motors

Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step of signal processing is performed on the node at the edge, reducing the burden on a centralized cloud for processing data from multiple sensors. The proposed architecture utilizes the lightweight Message Queue Telemetry Transport (MQTT) communication protocol for seamless data transmission from the node to the local and main brokers. The broker’s bridging allows for data backup in case of connection loss. The proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation, ensuring its performance and accuracy in real-world industrial environments. The data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying faults. The proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through real-time monitoring and early fault detection, ultimately leading to minimized unscheduled downtime and cost savings.

AN INDENTATION METHOD FOR ADHESIVE STRENGTH EVALUATION OF POLYMER COATINGS

An indentation method was developed for adhesive strength evaluation of a polymer coating using the example of an epoxy composition with titanium alkoxide deposited on the surface of low-carbon steel. The method is based on the numerical implementation of the fracture mechanics approach in experimental data processing. It was found that the penetration of a Rockwell indenter perpendicular to the coating surface causes annular delamination buckling of the coating around the indentation, which is due to adhesive bond breaking as a result of radial shear when the coating material is forced out from under the indenter. A controlled parameter in finite element modeling was the experimentally determined width of the coating delamination zone at a fixed indentation depth at which delamination occurred. The adhesive contact conditions were set using the bilinear law of the cohesive zone model, which describes the relationship between the adhesive shear stress and adhesive bond stretching during shear of interacting surfaces in the contact plane. The limiting specific surface energy of adhesive failure was used as a quantitative evaluation criterion for adhesive strength. The optimal value of the limiting specific surface energy of adhesive coating failure was determined for the cohesive zone model parameters that provide the best convergence of the numerical and experimental data.

Infrared Spectroscopy for Structure Analysis of Protein Inclusion Bodies.

Infrared (IR) spectroscopy is a widely used technique for evaluation of protein secondary structure. In this chapter, we focus on the application of this analytical technique for analysis of inclusion bodies. After a general introduction to protein analysis by IR spectroscopy, different approaches for spectra acquisition, data processing, and secondary structure evaluation are presented.

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection.

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.

An experimental and theoretical approach to assessing the structure of fine-grained modified concretes

Introduction. The development of approaches to assessing structural characteristics of multicomponent modified concretes using non-destructive testing methods at all stages of hydration is a relevant means focused on ensuring optimal rheological and strength characteristics of concretes.
 Materials and methods. Modified fine-grained concrete was analyzed as the material under study, since its composition meets the requirements of additive technologies applied in the construction industry. Modification was implemented by introducing additives and mechanically activating the initial cement-sand composition. The strength of resulting specimens was controlled as of the onset of curing. Structure formation was studied using a computed tomography scanner.
 Results. A tomography data processing approach was developed to evaluate the modified concrete’s structure. Key aspects of the stage-by-stage processing of specimens’ images were identified and a solution was obtained for determining the threshold values of correspondence between image shades having different density structures. In the course of compa­ring the results of the strength and structure study, the value of the percentage ratio of inclusions was obtained for various densities during the curing process.
 Conclusions. The proposed data acquisition approach contributes to development of further research in this area; it can become a stage in developing a new concrete structure analysis method. The study of specimens at early curing stages in combination with non-destructive control methods contributes to (1) development of rapid assessment techniques, applied to the quality of materials, and (2) an efficiency increase in the process of development.

Development of anomaly detector for motor bearing condition monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder

Anomaly detection in motor bearings is a critical task for preventing downtime and ensuring efficient operation. This paper proposes a novel approach for anomaly detection using Fast Fourier Transform (FFT) and Long Short-Term Memory (LSTM)-Autoencoder (AE). A data processing approach based on FFT was developed to pre-process the raw sensor data. This helped to reduce noise and improve the Signal-to-Noise Ratio (SNR). Additionally, an anomaly detection model based on LSTM-Autoencoder was developed and trained on the pre-processed data. The proposed approach was able to detect anomalies at a low threshold and achieved a high accuracy score.

Verification and estimation of uncertainties of Tobias Mayer's 18th century astronomical observations

Perceiving the uncertainty of the measurement has been changing over the past centuries, reflecting the advancement in the experimental techniques, the urge for reliable and reproducible measurement methodology, and development of mathematical data processing and evaluating algorithms. From the historical perspective, the concepts of considering the measurement uncertainty were firstly introduced with geographic and cartographic measurements. In this context, the works of Tobias Mayer on lunar landscape measurements are widely highlighted which, at that time, presented innovative approaches in data processing with the method of averages and pioneeringly addressed the issue of measurement error. In this study, we analyze in details the Mayer’s set of 27 non-linear equations with 3 unknown parameters and discuss the effect of Mayer’s linearization and subsequent mathematical procedures on the accuracy of the parameter values in contrast with the results from rigorous treatment of non-linear regression model involving the least-square method. In particular, we compare the values of the unknown parameters and their uncertainties in several variants in the linearized and non-linearized model, providing monitoring of a small deviation of the Mayer’s linearization. The results, presented here, show that despite the conceptual and computational simplification of the Mayer’s method, such an approach to data processing can be exploited, with an acceptable level of accuracy, in several practical situations even today.

All-fiber frequency shifted self-mixing interferometry for slow flow measurements

In this study, an all-fiber structure self-mixing sensor using a distributed feedback Bragg (DFB) fiber laser as the light source is proposed to measure flow velocity. In pursuit of slower flow velocity, the external cavity frequency-shifting optical technique is introduced into the optical sensing system to effectively avoid complex noises existing in the low-frequency region. Furthermore, the additional bonus is that the identification of the velocity direction can be obtained by the asymmetric shape near the feedback frequency shift on the spectrum. At the same time, a suitable solution is developed for extraction of the characteristic feature parameters from the asymmetrical spectrum of the flow velocity signal. It was demonstrated the actual conformity of the measurement results by means of this new approach of data processing. According to the practical experimental measurements, the maximum measurable Doppler shift concerning the slow fluid can be obtained as low as 15.37 Hz. This indicates that the designed all-fiber laser self-mixing flow velocity meter can be prospectively extended to slow fluid measurement associated with the related sensing and medical applications.

Development of infrastructure for anomalies detectionin big data

The work describes the conducted analysis of models, methods, and technologies for detecting anomalies in data. It concludes that, based on the analysis, the solution to the problem of anomaly detection in data should be considered as a complex technology, which consists of the formation and application of mathematical models in combination with the research of data processing approaches. The article analyses the current state of big data stream processing technologies and reflects the peculiarities of the most commonly used and advanced of them, e.g. Apache Hadoop, Apache Spark, Apache Cassandra, Apache Kafka, Apache Storm, and Apache Beam. On top of these, it pays attention to the infrastructure, in which the created software models can be deployed and used, taking into account the high-load real-time nature of the data. The article proposes to form an infrastructure for anomaly detection in data as an applied example of big data processing cloud infrastructure. The paper demonstrates the developed infrastructure model for anomaly detection in real-time stream data, which is based on an expert method of forming requirements for a software component, choosing an algorithm for detecting anomalies, selecting tools, and improving the algorithm. The highlighted anomaly detection tools allow us to create a secure real-time anomaly detection solution using Dataflow, BigQuery ML, and Cloud DLP. The paper presents the applied implementation of anomaly detection in real-time using GCP and Apache Beam -data stream analysis of software logs in the information system and detection of fraudulent ones among them, which will help improve the cyber security of the system. In the end, the work demonstrates possible improvements to the basic model that could help to speed it up

To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities.

High-throughput profiling of microbial functional traits involved in various biogeochemical cycling pathways using shotgun metagenomic sequencing has been routinely applied in microbial ecology and environmental science. Multiple bioinformatics data processing approaches are available, including assembly-based (single-sample assembly and multi-sample assembly) and read-based (merged reads and raw data). However, it remains not clear how these different approaches may differ in data analyses and affect result interpretation. In this study, using two typical shotgun metagenome datasets recovered from geographically distant coastal sediments, the performance of different data processing approaches was comparatively investigated from both technical and biological/ecological perspectives. Microbially mediated biogeochemical cycling pathways, including nitrogen cycling, sulfur cycling and B12 biosynthesis, were analyzed. As a result, multi-sample assembly provided the most amount of usable information for targeted functional traits, at a high cost of computational resources and running time. Single-sample assembly and read-based analysis were comparable in obtaining usable information, but the former was much more time- and resource-consuming. Critically, different approaches introduced much stronger variations in microbial profiles than biological differences. However, community-level differences between the two sampling sites could be consistently observed despite the approaches being used. In choosing an appropriate approach, researchers shall balance the trade-offs between multiple factors, including the scientific question, the amount of usable information, computational resources and time cost. This study is expected to provide valuable technical insights and guidelines for the various approaches used for metagenomic data analysis.

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.

THE EFFECT OF INNOVATION ON F&B MMSMEs IN MALANG CITY

The purpose of this study was to examine the effect of marketing innovation, product innovation, and cafe atmosphere on purchasing decisions in F&B MSMEs. The sampling technique used was purposive sampling. The research uses qualitative methods with a descriptive approach and quantitative methods in data processing for hypothesis testing. Hypothesis testing uses a structural regression approach with Smart-PLS 3.0 software using the t-test at a significance level of 5%. The choice of software is because this study uses path analysis with a sample size of 75 F&B MSMEs consumers in Malang City

Whole-brain dynamical modelling for classification of Parkinson's disease.

Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.

The Effect of Socialization Regarding Digital Literacy to Respond to Challenges in The Era of Disruption to The Knowledge of Students of Junior High School 12 Bandung

This This study aims to find out how the impact of socialization on digital literacy to answer challenges in the era of disruption to knowledge and application in everyday life regarding digital literacy for students at Junior High School 12 Bandung. The situation in this era of disruption is increasing the use of social media as online media. Children and parents use social media a lot. Young people as the millennial generation or can also be called digital natives are the largest users in the use of social media today. The spread of social media networks makes it easier for inaccurate news to come from anywhere. It is hoped this research can add insight into appropriate digital literacy. The subjects of this study were students of Junior High School 12 Bandung as a young age group with an age range of 13-17 years who were active users of social media. Respondents amounted to 15 students. Respondents consisted of 5 students in grade 7, 1 student in grade 8, and 9 students in grade 9. This study used a quantitative descriptive method. The technique used for data collection is using a questionnaire. There are three stages in data collection, namely (i) distributing the pre-test questionnaire; (ii) providing socialization actions using powerpoint; and (iii) distribution of post-test questionnaires. The data processing approach used is a quantitative approach. The results showed there was an increase between the average pre-test and post-test values, namely an increase from the pre-test average value of 73.33 to 96, 44 on the post-test score / after receiving socialization treatment. The calculation of N-Gain is carried out to determine the effectiveness of the socialization carried out. The results show the average N-Gain value is 85% (N-Gain > 40%). It can be concluded the socialization of digital literacy using powerpoint media is effective for students of Junior High School 12 Bandung. Effective results due to lack of insight and interest in literacy in Junior High School 12 Bandung students are low and after the socialization treatment is given, it adds insight to students at JUNIOR HIGH SCHOOL 12 Bandung because the post-test scores of students increase significantly. It can be concluded the socialization of digital literacy using powerpoint media is effective for students of Junior High School 12 Bandung. Effective results due to lack of insight and interest in literacy in Junior High School 12 Bandung students are low and after the socialization treatment is given, it adds insight to students at Junior High School 12 Bandung because the post-test scores of students increase significantly. It can be concluded the socialization of digital literacy using powerpoint media is effective for students of Junior High School 12 Bandung. Effective results due to lack of insight and interest in literacy in Junior High School 12 Bandung students are low and after the socialization treatment is given, it adds insight to students at Junior High School 12 Bandung because the post-test scores of students increase significantly.

The Effect of Socialization Regarding Digital Literacy to Respond to Challenges in The Era of Disruption to The Knowledge of Students of Junior High School 12 Bandung

This This study aims to find out how the impact of socialization on digital literacy to answer challenges in the era of disruption to knowledge and application in everyday life regarding digital literacy for students at Junior High School 12 Bandung. The situation in this era of disruption is increasing the use of social media as online media. Children and parents use social media a lot. Young people as the millennial generation or can also be called digital natives are the largest users in the use of social media today. The spread of social media networks makes it easier for inaccurate news to come from anywhere. It is hoped this research can add insight into appropriate digital literacy. The subjects of this study were students of Junior High School 12 Bandung as a young age group with an age range of 13-17 years who were active users of social media. Respondents amounted to 15 students. Respondents consisted of 5 students in grade 7, 1 student in grade 8, and 9 students in grade 9. This study used a quantitative descriptive method. The technique used for data collection is using a questionnaire. There are three stages in data collection, namely (i) distributing the pre-test questionnaire; (ii) providing socialization actions using powerpoint; and (iii) distribution of post-test questionnaires. The data processing approach used is a quantitative approach. The results showed there was an increase between the average pre-test and post-test values, namely an increase from the pre-test average value of 73.33 to 96, 44 on the post-test score / after receiving socialization treatment. The calculation of N-Gain is carried out to determine the effectiveness of the socialization carried out. The results show the average N-Gain value is 85% (N-Gain > 40%). It can be concluded the socialization of digital literacy using powerpoint media is effective for students of Junior High School 12 Bandung. Effective results due to lack of insight and interest in literacy in Junior High School 12 Bandung students are low and after the socialization treatment is given, it adds insight to students at JUNIOR HIGH SCHOOL 12 Bandung because the post-test scores of students increase significantly. It can be concluded the socialization of digital literacy using powerpoint media is effective for students of Junior High School 12 Bandung. Effective results due to lack of insight and interest in literacy in Junior High School 12 Bandung students are low and after the socialization treatment is given, it adds insight to students at Junior High School 12 Bandung because the post-test scores of students increase significantly. It can be concluded the socialization of digital literacy using powerpoint media is effective for students of Junior High School 12 Bandung. Effective results due to lack of insight and interest in literacy in Junior High School 12 Bandung students are low and after the socialization treatment is given, it adds insight to students at Junior High School 12 Bandung because the post-test scores of students increase significantly.