bluuebunny/crossref_metadata_2025 huggingface dataset:
HUBUNGAN PENGETAHUAN IBU TENTANG GIZI DAN RIWAYAT ASI EKSLUSIF DENGAN RISIKO TERJADINYA STUNTING BALITA USIA 12 - 59 BULAN PADA MASA PANDEMI COVID-19 DI POSYANDU CEMPAKA BOGOR 2023
Authors: Sholihah Wierdha , Ambariani Ambariani , Pujiati Pujiati
Published: July 2024

Latar Belakang:  Stunting merupakan masalah global yang terjadi di banyak negara terutama Indonesia. Indonesia menghadapi tantangan yang kompleks akibat terjadinya stunting yang kemungkinan akan memburuk karena adanya pandemi COVID-19. Indonesia merupakan contoh negara dengan “tiga beban malnutrisi” salah satunya akibat dari kurangnya pengetahuan tentang gizi serta kesehatan sejak masa kehamilan hingga melahirkan juga berisiko besar menimbulkan stunting pada balita. Selain itu, ASI eksklusif juga merupakan penentu penting pada pertumbuhan dan perkembangan anak, sehingga ketidakberhasilan pemberian dalam riwayat ASI eksklusif juga menjadi penyebab risiko terjadinya stunting. Tujuan Penelitian: Untuk mengetahui hubungan pengetahuan ibu tentang gizi dan riwayat ASI ekslusif dengan risiko terjadinya stunting balita pada masa pandemi COVID-19 di Posyandu Cempaka Bogor. Metode Penelitian: Analitik kuantitatif dengan desain cross sectional. Sampel dalam penelitian ini berjumlah 52 responden dengan teknik purposive sampling. Uji statistik penelitian ini menggunakan uji chi-square. Hasil Penelitian: Menunjukkan bahwa terdapat hubungan pengetahuan ibu tentang status gizi dengan risiko terjadinya stunting balita usia 12-59 bulan pada masa pandemi COVID-19 dengan nilai P-value 0,022 dan terdapat hubungan riwayat ASI ekslusif dengan risiko terjadinya stunting balita pada masa pandemi COVID-19 dengan nilai P-value 0,027. Kesimpulan: Ada hubungan pengetahuan ibu tentang gizi dan riwayat ASI ekslusif dengan risiko terjadinya stunting balita usia 12-59 bulan pada masa pandemi COVID-19 di Posyandu Cempaka Bogor 2023.
YOLO-Based Personal Protective Equipment Monitoring System for Workplace Safety
Authors: Guney Emin , Altin Husna , Esra Asci Ayse , Bayilmis Oya Utuk , Bayilmis Cuneyt
Published: July 2024

Occupational health and safety are of paramount importance in industrial environments and various work fields. In this context, tracking personal protective equipment (PPE) is highly necessary. This article investigates the performance and application of deep learning-based object detection models to enhance the accuracy and speed of tracking personal protective equipment for ensuring occupational health and safety. These models detect personal protective equipment in images, enabling monitoring of their correct usage and intervention when necessary. The study aims to minimize damage resulting from accidents through the use of protective equipment and to prevent possible accidents. In our study, a dataset consisting of 2581 images, encompassing different workplace environments and workers, was prepared. This dataset was evaluated for performance using deep learning models. Popular deep-learning models such as YOLO-NAS, YOLOv8, and YOLOv9 were utilized in the comparisons. During the training of the models, the number of epochs was kept consistent for fair comparison. Upon examining the results, it is observed that the YOLO-NAS and YOLOv9 models generally exhibit similar and high performance.
Sistem Informasi Stok dan Penjualan Buku Berbasis Android di Penerbit Peneleh
Authors: Sila Sakti Ciptagusti , Efrat Najaf Abdul Rezha , Permatasari Reisa
Published: July 2024

Dalam era saat ini, perkembangan teknologi sangatlah cepat. Banyak bisnis yang mulai memperbarui proses bisnis mereka dengan memanfaatkan teknologi yang dapat memudahkan proses bisnis organisasi mereka. Penerbit Peneleh menjalankan kegiatan penjualan buku terbitan mereka. Penelitian ini membahas mengenai perancangan dan pembangunan Sistem Informasi Stok dan Penjualan Buku di Penerbit Peneleh yang dapat membantu organisasi memantau stok buku, mencatat penjualan, dan mengkompilasi informasi tentang harga cetak dan harga jual buku secara efisien. Metode pengembangan yang digunakan adalah model waterfall dengan pengumpulan data melalui wawancara dengan pihak organisasi. Hasil dari penelitian ini adalah aplikasi Android yang terintegrasi dengan barcode scanner dan Google Books API yang dapat membantu organisasi dalam menjalankan proses bisnisnya.
Application of Feature Engineering Techniques and Machine Learning Algorithms for Property Price Prediction
Authors: Sihombing Denny Jean Cross
Published: July 2024

This research applies feature engineering techniques and machine learning algorithms to predict property prices using a dataset from Kaggle. Three models were implemented: Linear Regression, Decision Tree, and Random Forest. The Random Forest model demonstrated the best performance with an average Mean Absolute Error (MAE) of 16472.76, Mean Squared Error (MSE) of 457407807.78, and R-squared (R²) of 0.83. Key features influencing property prices were identified through feature importance analysis, providing valuable insights for enhancing property appraisals and investment decisions.
Analysis of BCA Mobile Banking Application Complexity Factors on User Experience Using UTAUT
Authors: Octavia Debora , Tanaamah Andeka Rocky
Published: July 2024

In the present digital era, there has been a transformation in banking practices, moving away from traditional services towards those rooted in technology. Mobile Banking has become the primary means for users to conduct banking transactions. However, despite its prevalence, there are complexities in its usage that can affect user experience. This study proposes an analysis using the Technology Acceptance Model (TAM), specifically the Unified Theory of Acceptance and Use of Technology (UTAUT), to identify factors influencing the adoption of technology in BCA Mobile Banking. These factors include system complexity, lack of feature understanding, interface design issues in the application, and social barriers encountered during Mobile Banking usage. Through this study, it is anticipated that key factors affecting the adoption of Mobile Banking technology can be identified, enabling stakeholders to utilize this research as an assessment tool to enhance user experience and the efficiency of technology-based banking services.
LoRaWAN for Smart Street Lighting Solution in Pangandaran Regency
Authors: Enriko I Ketut Agung , Gustiyana Fikri Nizar , Kurnianingsih Kurnianingsih , Puspita Sari Erika Lety Istikhomah
Published: July 2024

Smart street lighting is a key application in smart cities, enabling the monitoring and control of street lamps through internet connectivity. LoRa/LoRaWAN, an IoT technology, offers advantages such as low power consumption, cost-effectiveness, and a wide area network. With its extensive coverage of up to 15 kilometers and easy deployment, LoRa has become a favored connectivity option for IoT use cases. This study explores the utilization of LoRaWAN in Pangandaran, a regency in the West Java province of Indonesia. Implementing LoRaWAN in this context has resulted in several benefits, including the ability to monitor and control street lighting in specific areas of Pangandaran and real-time recording of energy consumption. The primary objective of this research is to estimate the number of LoRaWAN gateways required to support smart street lighting in Pangandaran. Two methods are employed: coverage calculation using the free space loss approach and capacity calculation. The coverage calculation suggests a requirement of 34 gateways, whereas the capacity calculation indicates that only two gateways are needed. Based on these findings, it can be inferred that, theoretically, a maximum of 34 gateways would be necessary for smart street lighting in the Pangandaran area. However, further research, including driving tests, is recommended to validate these results for future implementation. This study provides insights into the practical application of LoRaWAN technology in smart street lighting, specifically in Pangandaran. The findings contribute to optimizing infrastructure and resource allocation, ultimately enhancing the efficiency and effectiveness of urban lighting systems.
Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods
Authors: Rangkuti Abdul Haris , Hasbi Athala Varyl , Haridhi Indallah Farrel , Tanuar Evawaty , Muliadi Kerta Johan
Published: July 2024

Several cities in Indonesia, such as Cirebon, Bandung, and Bogor, have several historical buildings that date back to the Dutch colonial period. Several Dutch colonial heritage buildings can be found in several areas. The existence of historical buildings also would attract foreign or local tourists who visit one of an area. We need a technology or model that would support the recognition and identification of buildings, including their characteristics. However, recognizing and identifying them is a problem in itself, so technology would be needed to help them. The technology or model that would be implemented in this research is the Convolutional Neural Network model, a derivative of Artificial Intelligent technology focused on image processing and pattern recognition. This process consists of several stages. The initial stage uses the Gaussian Blur, SuCK, and CLAHE methods which are useful for image sharpening and recognition. The second process is feature extraction of the image characteristics of buildings. The results of the image process will support the third process, namely the image retrieval process of buildings based on their characteristics. Based on these three main processes, they would facilitate and support local and foreign tourists to recognize historic buildings in the area. In this experiment, the Euclidean distance and Manhattan distance methods were used in the retrieval process. The highest accuracy in the retrieval process for the feature extraction process with the DenseNet 121 model with the initial process is Gaussian Blur of 88.96% and 88.46%, with the SuCK method of 88.3 and 87.8%, and with CLAHE of 87.7%, and 87.6%. We hope that this research can be continued to identify buildings with more complex characteristics and models.
Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data
Authors: Novitasari Dian C Rini , Fatmawati Fatmawati , Hendradi Rimuljo , Nariswari Rinda , Saputra Rizal Amegia
Published: July 2024

Diabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR.
Stock Price Movement Classification Using Ensembled Model of Long Short-Term Memory (LSTM) and Random Forest (RF)
Authors: Gunawan Albertus Emilio Kurniajaya , Wibowo Antoni
Published: July 2024

Stock investing is known worldwide as a passive income available for everyone. To increase the profit possibly gained, many researchers and investors brainstorm to gain a strategy with the most profit. Machine learning and deep learning are two of these approaches to predicting the stock's movement and deciding the strategy to gain as much as possible. To reach this goal, the researcher experiments with Random Forest (RF) and Long Short-Term Memory (LSTM) by trying them individually and merging them into an ensembled model. The researcher used RF to classify the results from LSTM models obtained throughout the Hyperparameter Optimization (HPO) process. This idea is implemented to lessen the time needed to train and optimize each LSTM model inside the ensembled model. Another anticipation done in this research to overcome the time needed to train the model is classifying the return for longer periods. The dataset used in this model is 45 stocks listed in LQ45 as of August 2021 This research results in showing that LSTM gives better results than RF model especially when using Bayesian Optimization as the HPO method, and that the ensembled model can return better precision in predicting stocks in comparison to the LSTM model itself. Future improvement can focus on the model structure, additional model types as the ensemble model estimator, improvement on the model efficiency, and datasets research to be used in predicting the stock movement prediction
Implementation of Big Data Information System Using Open-Source Metabase for Civil Registration and Vital Statistics Data Visualization in Surabaya
Authors: Budiarti Rizqi Putri Nourma , Sukaridhoto Sritrusta , Zuhdi Ubaidillah , Rasyid Alfandino , Sonhaji Agus Imam
Published: July 2024

Civil registration involves the mandatory and continuous documentation of important life events of a country's population under local legal requirements. In many countries, these documents are required to access government services such as education, healthcare, social services, formal employment, insurance benefits, and inheritance rights. Indonesia should prioritize building a comprehensive Civil Registration and Vital Statistics (CRVS) using a big data information system to ensure every individual has a legal identity, can access government services, and collect accurate and reliable statistics on vital events through geospatial maps. Surabaya, a city in Indonesia, still needs a comprehensive Civil Registration and Vital Statistics (CRVS) system. We produce many informative visualizations from the query and modeling processes in Metabase. Based on the PIECES framework, this application's importance level is 4.56 or 91.25%, meaning the application is important, and the satisfaction level is 4.29 or 85.76%, meaning the application is satisfied for the respondents. This research provides a brief overview of how Metabase works and how it can be used to generate visualizations of job-type data. It demonstrates the ease with which visualizations can be changed and customized. It had a good affordability point, making its implementation easier and more beneficial. It also emphasizes the importance of having a powerful tool like Metabase for data analysis and decision-making, especially for the dispendukcapil as a civil registration agency.
Optimizing Pigeon-Inspired Algorithm to Enhance Intrusion Detection System Performance Internet of Things Environments
Authors: Ratnawati Fajar , Siswanto Apri , Jaroji - , Effendy Akmar , Tedyyana Agus
Published: July 2024

Intrusion Detection Systems (IDS) are crucial in maintaining network security and safeguarding sensitive information against external and internal threats. This study proposes a novel approach by utilizing a Pigeon-Inspired Algorithm optimized with the Hyperbolic Tangent Function (Tanh) function to enhance the performance of IDS in threat detection specifically tailored for Internet of Things (IoT) environments. We aim to create a more robust solution for optimizing intrusion detection systems by integrating the efficient and effective Tanh function into the Pigeon-Inspired Algorithm. The proposed method is evaluated on three widely-used datasets in the field of IDS: NSL-KDD, CICIDS2017, and CSE-CIC-IDS2018. Experimental results demonstrate that integrating the Tanh function into the Pigeon-Inspired Algorithm significantly improves the performance of the intrusion detection system. Our method achieves higher accuracy, True Positive Rate (TPR), and F1-score while reducing the False Positive Rate (FPR) compared to traditional Pigeon-Inspired Algorithms and several other optimization algorithms. The Pigeon-Inspired Algorithm optimized with the Tanh function offers an efficient and effective solution for enhancing intrusion detection system performance, specifically in Internet of Things environments. This method holds great potential for application in diverse network environments, bolstering information security and safeguarding systems from evolving cybersecurity threats. By extending the applicability and effectiveness of the Pigeon-Inspired Algorithm optimized with the Tanh function, researchers can contribute to developing more comprehensive and robust security solutions, addressing the ever-evolving landscape of IoT-based cybersecurity threats.
Comparison of the Packet Wavelet Transform Method for Medical Image Compression
Authors: Atmaja I Made Ari Dwi Suta , Triadi Wilfridus Bambang , Astawa I Nyoman Gede Arya , Radhitya Made Leo
Published: July 2024

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.
Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification
Authors: Hidayat Tonny , Astuti Ika Asti , Yaqin Ainul , Tjilen Alexander Phuk , Arifianto Teguh
Published: July 2024

The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.
Improved Image Classification Task Using Enhanced Visual Geometry Group of Convolution Neural Networks
Authors: Zakaria Nurzarinah , Mohmad Hassim Yana Mazwin
Published: July 2024

Convolutional Neural Networks (CNNs) have become essential to solving image classification tasks. One of the most frequent models of CNNs for image classification is the Visual Geometry Group (VGG). The VGG architecture is made up of multiple layers of convolution and pooling processes followed by fully connected layers. Among the various VGG models, the VGG16 architecture has gained great attention due to its remarkable performance and simplicity. However, the VGG16 architecture is still prone to have many parameters contributing to its complexity. Moreover, the complexity of VGG16 may cause a longer execution time. The complexity of VGG16 architecture is also more highly prone to overfitting and may affect the classification accuracy. This study proposes an enhancement of VGG16 architecture to overcome such drawbacks. The enhancement involved the reduction of the convolution blocks, implementing batch normalization (B.N.) layers, and integrating global average pooling (GAP) layers with the addition of dense and dropout layers in the architecture. The experiment was carried out with six benchmark datasets for image classification tasks. The results from the experiment show that the network parameters are 79% less complex than the standard VGG16. The proposed model also yields better classification accuracy and shorter execution time. Reducing the parameters in the proposed improved VGG architecture allows for more efficient computation and memory usage. Overall, the proposed improved VGG architecture offers a promising solution to the challenges of long execution times and excessive memory usage in VGG16 architecture.
Node.js Performance Benchmarking and Analysis at Virtualbox, Docker, and Podman Environment Using Node-Bench Method
Authors: Pratama I Putu Agus Eka , Raharja I Made Sunia
Published: July 2024

As an asynchronous runtime environment (interpreter) for the development of scalable JavaScript-based network applications, it is necessary to know the performance of the web framework on Node.js in a virtualization-oriented development environment and a container-oriented development environment. This research aims to compare the performance of Node.js in several frameworks in VirtualBox, Docker, and Podman environments. The testing was carried out using some materials like a bench utility at Node Package Manager (NPM) involving the Adonis, Connect, Express, Fastify, Foxify, Hapi, Koa, Molecular, Plumier, Restify, and Sails frameworks, using Object Relational Mapping (ORM) and Raw Query Bookshelf, Knex, MySQL, MySQL2, and Sequelize at Ubuntu Linux operating system. The method research used in this research is the Node-Bench method with requests, latency, and throughput parameters. The testing results show that the best performance score is the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker and Podman), and the worst performance score is the Express framework with the Mysql2 library (Raw Query) in a virtualization-oriented development environment (VirtualBox). Based on the testing results, developers who use Node.js are more advised to use the Fastify framework with the Sequelize library (ORM) in a container-oriented development environment (Docker or Podman) to obtain better performance. For further research, the implementation and testing at container-oriented development can use cloud-based service (IaaS cloud or PaaS Cloud) for the read-only immutable environment, scalability, and security reasons.Keywordsâ€” Docker, Node-Bench method, Node.js, Podman, VirtualBox.
Comparison of Parametric and Nonparametric Forecasting Methods for Daily COVID-19 Cases in Malaysia
Authors: Agastya I Made Artha , Aminuddin Afrig
Published: July 2024

Numerous research studies are currently examining various measures to control the transmission of COVID-19. One essential task in this regard is predicting or forecasting the number of infected individuals. This predictive capability is crucial for governments to allocate resources effectively. However, the most effective approach to handling time series problems between the parametric and non-parametric methods is unclear. The parametric method utilizes a fixed number of parameters to calculate the value. On the other hand, the non-parametric method increases its parameters along with the number of observations. To address the issue, we conducted a study comparing parametric and non-parametric models for time series forecasting, specifically using Malaysia's daily confirmed COVID-19 cases from 18/3/2020 to 30/12/2020. Since there have been limited comparisons of these models in time series forecasting, we believe our study is beneficial. We considered various models, including persistence, autoregression, ARIMA, SARIMA, single, double, and triple exponential smoothing, multi-linear regression, support vector regression, artificial neural networks (ANN), K-nearest neighbor regression, decision trees regression, random forest regression, and Gaussian processes regression models. Our study revealed significant characteristics of these methods, and we found that exponential smoothing methods were the most effective in capturing the level and trend of the data compared to other methods. Additionally, ANN had the least forecasting error among the machine learning methods. In conclusion, non-parametric methods are not suitable for predicting daily cases of Covid-19 in Malaysia. Enhancing the parametric methods will be preferable in the future.
A Prediction Model of Power Consumption in Smart City Using Hybrid Deep Learning Algorithm
Authors: Noaman Salam Abdulkhaleq , Ahmed Ali Mohammed Saleh , Salman Aseel Dawod
Published: July 2024

A smart city utilizes vast data collected through electronic methods, such as sensors and cameras, to improve daily life by managing resources and providing services. Moving towards a smart grid is a step in realizing this concept. The proliferation of smart grids and the concomitant progress made in the development of measuring infrastructure have garnered considerable interest in short-term power consumption forecasting. In reality, predicting future power demands has shown to be a crucial factor in preventing energy waste and developing successful power management techniques. In addition, historical time series data on energy consumption may be considered necessary to derive all relevant knowledge and estimate future use. This research paper aims to construct and compare with original deep learning algorithms for forecasting power consumption over time. The proposed model, LSTM-GRU-PPCM, combines the Long -Short-Term -Memory (LSTM) and Gated- Recurrent- Unit (GRU) Prediction Power Consumption Model. Power consumption data will be utilized as the time series dataset, and predictions will be generated using the developed model. This research avoids consumption peaks by using the proposed LSTM-GRU-PPCM neural network to forecast future load demand. In order to conduct a thorough assessment of the method, a series of experiments were carried out using actual power consumption data from various cities in India. The experiment results show that the LSTM-GRU-PPCM model improves the original LSTM forecasting algorithms evaluated by Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for various time series. The proposed model achieved a minimum error prediction of MAE=0.004 and RMSE=0.032, which are excellent values compared to the original LSTM. Significant implications for power quality management and equipment maintenance may be expected from the LSTM-GRU-PPCM approach, as its forecasts will allow for proactive decision-making and lead to load shedding when power consumption exceeds the allowed level
The Implementation of the K-Medoid Clustering for Grouping Hearing Loss Function on Excessive Smartphone Use
Authors: Wahyudi Eri , Meidelfi Dwiny , - Nofrizal , Saam Zulfan , - Juandi
Published: July 2024

During the current pandemic, smartphones have become a means of learning for all students in Indonesia, including high school students. Students use smartphones to send assignments, learn via video calls, and conduct online exams. The prolonged use of smartphones, from the beginning of learning hours in the morning to study hours in the evening, has a terrible impact on the ear health of high school students in Padang. Excessive smartphone use caused a decrease in the student's hearing function. Therefore, this study aims to group the audiometry results of high school students in Padang who have a hearing loss function. The audiogram result is only performed as the result of a frequency test of the subject's hearing in both the left and right ear. Conventionally, an otolaryngologist concluded the final decision of hearing loss ability. This research proposed an automatic classification of audiometry results using machine learning methods. The K-Medoids clustering was selected to classify the audiometry data in this research. Of 210 audiometry data, 91 data is confirmed by an otolaryngologist as valid data. By using the K-Medoids clustering, 93 data is classified into Normal hearing, Mild Hearing loss, and Moderate Hearing loss. The proposed model successfully grouped the audiometry data into three categories. The confusion matrix is applied to measure the model performance, which has 28,3% accuracy, 64,3% precision, and 21,4% recall.
Teachers’ Acceptance and Readiness to Use Augmented Reality Book to Teach English Vocabulary in Kindergartens
Authors: Muryanti Elise , Pransiska Rismareni , Novrianti - , Arkanasya Ummayah Yasifa , Noer Azhari Azman Mohamed
Published: July 2024

English is currently introduced in several Kindergartens in West Sumatra, Indonesia. The focus of introducing English in Kindergartens is introducing simple instruction and vocabulary. The literature review and initial observations show that teaching materials and learning resources to introduce English are still limited. This study aims to explore kindergarten teachers' perspectives on storytelling and using Augmented reality books. This research used a mixing method. The participants in this subject were 57 Kindergarten teachers in West Sumatra, Indonesia. The data of this research was collected through questionnaires and interviews. First, the questionnaire was distributed to explore teachers' acceptance and readiness to use augmented reality storybooks in teaching English vocabulary in a classroom. To validate the data from the questionnaire and interview, they were triangulated. The result shows that most teachers do not have a background in English qualification. They believe that AR can be useful in teaching English vocabulary. Regarding readiness to implement the AR book, the teacher feels optimistic about using the AR stories in the classroom. The teachers believe that AR can motivate children to learn. The respondent teachers accept AR and believe that AR books can benefit children in learning English vocabulary. The ease of using AR and the benefits of AR for teaching vocabulary encourage teachers' readiness to utilize AR storybooks for teaching English vocabulary to Kindergartners. Based on the result of this research, the AR storybook is designed to teach English to Children in Indonesia Kindergarten.
Design and Development of Sound and Rhythm Perception Assessment Application for Students with Hearing Impairment
Authors: Damri - , Safaruddin - , Marlina - , Efendi Jon , Efrina Elsa
Published: July 2024

Technology use is becoming increasingly popular in life, including in educational aspects. Some widely used applications in education include metaverse, blended learning, game learning, cloud-based learning, mobile applications, and social media learning. Apps are generally in the form of software applications or programs designed to run on smartphones. In this study, we propose using applications in assessing children with hearing impairments at school. Design and Development of the Sound and Rhythm Perception Assessment Application uses the ADDIE development model of Analysis, Design, Development, Implementation, and Evaluation. The test subjects in this study were validation test subjects consisting of 3 experts to test the feasibility of the application. Data was collected through a questionnaire in the form of a tool tested for validity and reliability with a score of 90.1% for learning design, 88.9% for layout, and 94.7% for software. Validation was carried out through focus group discussions. The application was tested on four teachers who teach students with hearing impairments. The results of the main field experiment show that teachers can use the application to help them assess students with hearing loss. With availability, the accuracy of the Design and Development of the Sound and Rhythm Perception Assessment Application can be further improved by conducting training with more teachers who teach children with hearing impairments at school.
Drowsiness Detection System Through Eye and Mouth Analysis
Authors: Belle Lim Bey-Ee , Ng Kok Why , Ng Sew Lai
Published: July 2024

Traffic jams are one of the serious issues in many developed countries. After the pandemic, many employees were allowed to travel interstate to work. This contributes to more severe jams, especially in the capital and nearby states. Long-distance driving and congestion can easily make the drivers sleepy and thus lead to traffic accidents. This paper aims to study and analyze facial cues to detect early symptoms of drowsy driving. The proposed method employs a deep learning approach, utilizing ensemble CNNs and Dlib's 68 landmark face detectors to analyze the facial cues. The analyzed symptoms include the frequency of eyes opened or closed and yawning or no yawning. Three individual CNN models and an ensemble CNN structure are built for the classification of the eyes and mouth yawn. The model training and validation accuracy graph and training loss and validation loss graph are plotted to verify that the models have not been overfitted. The ensemble CNN models achieved an approximate accuracy of 97.4% from the eyes and 96.5% from the mouth. It outperforms the other pre-trained models. The proposed system can immediately alert the driver and send text drowsy messages and emails to the third party, ensuring timely intervention to prevent accidents. The proposed method can be integrated into vehicles and transportation systems to ensure driver's safety. It can also be applied to monitor the driving behavior of those who drive long distances
Processing Plant Diseases Using Transformer Model
Authors: Marcus Lye Hong Zheng , Ng Kok Why
Published: July 2024

Agriculture faces challenges in achieving high-yield production while minimizing the use of chemicals. The excessive use of chemicals in agriculture poses many problems. Accurate disease diagnosis is crucial for effective plant disease detection and treatment. Automatic identification of plant diseases using computer vision techniques offers new and efficient approaches compared to traditional methods. Transformers, a type of deep learning model, have shown great promise in computer vision, but as the technology is still new, many vision transformer models struggle to identify diseases by examining the entire leaf. This paper aims to utilize the vision transformer model in analyzing and identifying common diseases that hinder the growth and development of plants through the plant leave images. Besides, it aims to improve the model's stability by focusing more on the entire leaf than individual parts and generalizing better results on leaves not in the image center. Added features such as Shift Patch Tokenization, Locality Self Attention, and Positional Encoding help focus on the whole leaf. The final test accuracy obtained is 89.58%, with relatively slight variances in precision, accuracy, and F1 score across classes, as well as satisfactory model robustness towards changes in leaf orientation and position within the image. The model's effectiveness shows the vision transformer's potential for automated plant disease diagnosis, which can help farmers take timely measures to prevent losses and ensure food security.
IoT and Deep Learning Enabled Smart Solutions for Assisting Menstrual Health Management for Rural Women in India: A Review
Authors: Kesavan Revathi , Palanichamy Naveen , Thirumurugan Tamilselvi
Published: July 2024

A global medical issue, primarily raised in underdeveloped nations, is inappropriate Menstrual Hygiene Management (MHM) among teenage girls. Menstrual hygiene is a global concern because there are over 0.6 billion teenage females (about 8% of the population). The Asian and African continents are home to over 80% of these teenagers. Throughout, 355 million girls and women in India have periods. However, MHM causes discomfort and a lack of respect for millions of women all over the country. In alignment with today's technologies like cloud computing, artificial intelligence (AI), and Internet of Things (IoT), the MHM can be handled effectively. A quantitative survey was carried out among 184 random volunteers aged 18-22 to reveal the current status of MHM in India. The result of the survey confirmed that 72.8% of girls encountered stress during their period, 45% of them were unaware of hygiene products to be used while in the menstruation cycle, 65.2% of them used sanitary pads, and 57.6% of them received disrespectful treatments. This work aims to empower women with the MHM by facilitating knowledge on the menstrual cycle and guiding them about safe-to-use products and disposal strategies in home, work, or community places with the help of technological advancements. Further, introduce a simple friendhood discussion forum through an intelligent chatbot like "Sirona, " a chatbot built over Whatsapp that facilitates a complete ecosystem for MHM.
Arabic Character Recognition Using CNN LeNet-5
Authors: Satya Nugraha Gibran , Suta Wijaya I Gede Pasek , Bimantoro Fitri , Yudo Husodo Ario , Hamami Faqih
Published: July 2024

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.
Pyrolysis char from waste tyres its characteristics, upgrading and application
Authors: Bayero Amina Salihi , Yaro Nura Alhaji , Mohammed Musa Ibrahim , PK Singh Pramod K Singh , Muzakkari Bashir Abdu , Jibreel Umar Muhammad
Published: July 2024

The pyrolysis of waste tyres can recycle energy and produce reusable products (oil, char and gas). Although there are many reviews in the literature in regard to the pyrolysis characteristics of waste tyres, but this paper critically looked as pyrolysis char as one of the useful product. Its physical characteristics include pore diameter, pore volume, specific surface area, and composition. The common detection techniques of the physical characteristics include elemental analysis, proximate analysis, SEM, EDS, TGA, XRF, BET, and Raman spectroscopy. The chemical characteristics of tyre char mainly include calorific value, the surface functional groups (i.e phenols, alcohols, carboxylic acid and C-O/C-O-C chemical structures) which can be determined by FT-IR, XRD. The higher sulfur retention on the surface of tyre char is obtained at low temperature compared with that obtained at high temperature. Tyre char could also be directly used as a catalyst material to decrease the operational cost, and improve the quality of pyrolysis oil and gas. The modified tyre char with high specific surface area and lower ash content could be used as an activated carbon adsorbent material, catalyst and catalyst support, capacitor electrode to create higher commercial value, as an adsorbent, in batteries and so on. It is suggested that the recycling applications of tyre char should be developed, which can create a high level of potential economic prospects for the waste tyre pyrolysis industry.
Microwave assisted synthesis of NiMn2O4 as electrode material for supercapacitor applications
Authors: SHALOM PAULCY RANI P R , Sreekala Kumari Gopakumar Vaishnavi , Sarojam Sivakala , Aravind Anu Mini , Suryabai Xavier Thankappan
Published: July 2024

In this work, spinel NiMn2O4 was successfully synthesized through microwave assisted co-precipitation method and followed by calcination at 500°C. The crystal structure and the presence of functional groups in NiMn2O4 were characterized through X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR). The surface morphology was examined by field emission scanning electron microscopy (FE SEM). From the BET analysis surface area and average pore diameter of the mesoporous NMO nanoparticles are calculated to be 10.513 m2g-1and 8.55nm. The electrochemical performance of material as electrode material for supercapacitor applications was analysed by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The specific capacitance of the NMO electrode increased from 290.56 F/g to 751.57F/g with increase concentration from IM KOH to 6M KOH at scan rate of 5mV/s. These results indicate spinel NiMn2O4 as a promising candidate for high performance energy storage applications.
SUSTAINABLE SOLUTIONS: ANTIMICROBIAL ACTIVITY OF NEEM AND TULSI LEAF EXTRACTS AGAINST ENVIRONMENTAL MICROBIAL THREATS
Authors:   S. S. Jain Subodh P.G. College, Jaipur, Rajasthan, India., Arora Komal , Goyal Payal , Sharma Om
Published: July 2024

In the Indian context, the prevalence of antimicrobial resistance (AMR) is a pressing concern, with a reported 60,000 neonatal fatalities and an aggregate of 1,339,500 deaths either directly attributable to or associated with AMR. This epidemiological data underscores the imperative for the development of novel antimicrobial agents. The transition towards agents derived from organic sources is advocated to reduce reliance on synthetic chemicals and promoting eco-friendly practices. The qualities of natural herbs, including their antimicrobial, antioxidant, and other bioactive properties are increasingly recognized for their potential impact on environmental conditions. Emphasizing their role in reducing reliance on synthetic chemicals and promoting eco-friendly practices, the abstract highlights how these herbs contribute to biodiversity conservation, soil health improvement, and overall ecosystem resilience. By examining case studies and current research, the abstract underscores the importance of integrating natural herbs into environmental strategies and policies for achieving sustainable development goals. Empirical evidence thus derived within the scientific conditions attests to the antimicrobial prowess of extracts from Ocimum tenuiflorum (Tulsi), and Azadirachta indica (Neem). Investigations employing ethyl alcohol as a dissolving agent for these botanicals have been conducted to corroborate and elucidate their efficacious role in the synthesis of organic antimicrobial agents.
BRIDGING THE GAP: INDIA'S PROGRESS AND CHALLENGES IN ACHIEVING SDG 4: QUALITY EDUCATION FOR ALL
Authors:   Associate Professor, College of Vocational Studies, University of Delhi, New Delhi, India, Singh Bhupinder
Published: July 2024

Sustainable Development Goal 4 (SDG 4) aspires to ensure inclusive & equitable quality education for all. This study delves into India's progress towards this crucial objective. While advancements have been made in primary education enrolment and female literacy, significant hurdles remain. Millions of children lack fundamental literacy and numeracy skills, and overall learning outcomes fall short. The COVID-19 pandemic further exacerbated these inequalities, disproportionately impacting disadvantaged groups. India, a signatory to the SDGs, has implemented initiatives like the Right to Education Act (RTE) and digital learning platforms (DIKSHA, SWAYAM) to foster access and bridge geographical divides. However, substantial challenges persist. Socioeconomic disparities continue to hinder access for marginalized communities. Teacher shortages, inadequate training, and a lack of basic infrastructure in schools create further roadblocks in ensuring quality education. Achieving SDG 4 necessitates a multi-pronged approach in India. The curriculum and pedagogical approaches must be revamped to improve learning outcomes. Strategies to bridge the equity gap and address disparities based on gender, socioeconomic background, and location are vital. Investing in teacher training, addressing shortages, and providing essential school facilities are essential steps. Finally, promoting lifelong learning opportunities empowers individuals throughout their lives. Addressing these challenges with urgency is critical. By focusing on these priority areas, India can en route for achieving SDG 4 and ensure a future where quality education empowers all citizens.