bluuebunny/crossref_metadata_2025 huggingface dataset:
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: January 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.
Mel Frequency Cepstral Coefficients (MFCC) Method and Multiple Adaline Neural Network Model for Speaker Identification
Authors: Sasongko Sudi Mariyanto Al , Tsaury Shofian , Ariessaputra Suthami , Ch Syafaruddin
Published: January 2024

Speech recognition technology makes human contact with the computer more accessible. There are two phases in the speaker recognition process: capturing or extracting voice features and identifying the speaker's voice pattern based on the voice characteristics of each speaker. Speakers consist of men and women. Their voices are recorded and stored in a computer database. Mel Frequency Cepstrum Coefficients (MFCC) are used at the voice extraction stage with a characteristic coefficient of 13. MFCC is based on variations in the response of the human ear's critical range to frequencies (linear and logarithmic). The sound frame is converted to Mel frequency and processed with several triangular filters to get the cepstrum coefficient. Meanwhile, at the speech pattern recognition stage, the speaker uses an artificial neural network (ANN) Madaline model (many Adaline/ which is the plural form of Adaline) to compare the test sound characteristics. The training voice's features have been inputted as training data. The Madaline Neural Network training is BFGS Quasi-Newton Backpropagation with a goal parameter of 0,0001. The results obtained from the study prove that the Madaline model of artificial neural networks is not recommended for identification research. The results showed that the database's speech recognition rate reached 61% for ten tests. The test outside the database was rejected by only 14%, and 84% refused testing outside the database with different words from the training data. The results of this model can be used as a reference for creating an Android-based real-time system.
Improvement of Starling Image Classification with Gabor and Wavelet Based on Artificial Neural Network
Authors: Rahman Aviv Yuniar , Istiadi Istiadi , Hananto April Lia , Fauzi Ahmad
Published: January 2024

Indonesia is a country that has a diversity of animal species with the top 10 predicate in the world. The population of animal species, including starlings, is very widely known in the country. Starlings currently in Indonesia are diverse, ranging from standard to rare in Indonesia. This starling has its characteristics based on the type, color, sound, etc. In the first problem, the first accuracy performance when using the GLCM texture feature with Artificial Neural Network is 68%. Furthermore, the second problem is the accuracy performance of typing using the GLCM texture feature with a Decision Tree of 50%. This research aims to improve the starling classification system accuracy using Gabor and Wavelet texture features with artificial Neural Networks. Based on testing in the classification of starlings using the GLCM, Gabor, and Wavelet features, the highest degree of precision can, therefore, be concluded to be at the GLCM and Wavelet feature levels. The GLCM and Wavelet level accuracy results reached 83% at a rate of learning 0.9. In the experiments that have been done, the GLCM and Wavelet levels can increase accuracy using Artificial Neural Networks. In the classification process, the type of starlings also shows that the computational time in testing is much faster in producing accuracy values. In addition, the accurate accuracy while testing the starling category also increases.
Performance Improvement of Deep Convolutional Networks for Aerial Imagery Segmentation of Natural Disaster-Affected Areas
Authors: Nugraha Deny Wiria , Ilham Amil Ahmad , Achmad Andani , Arief Ardiaty
Published: January 2024

This study proposes a framework for improving performance and exploring the application of Deep Convolutional Networks (DCN) using the best parameters and criteria to accurately produce aerial imagery semantic segmentation of natural disaster-affected areas. This study utilizes two models: U-Net and Pyramid Scene Parsing Network (PSPNet). Extensive study results show that the Grid Search algorithm can improve the performance of the two models used, whereas previous research has not used the Grid Search algorithm to improve performance in aerial imagery segmentation of natural disaster-affected areas. The Grid Search algorithm performs parameter tuning on DCN, data augmentation criteria tuning, and dataset criteria tuning for pre-training. The most optimal DCN model is shown by PSPNet (152) (bpc), using the best parameters and criteria, with a mean Intersection over Union (mIoU) of 83.34%, a significant mIoU increase of 43.09% compared to using only the default parameters and criteria (baselines). The validation results using the k-fold cross-validation method on the most optimal DCN model produced an average accuracy of 99.04%. PSPNet(152) (bpc) can detect and identify various objects with irregular shapes and sizes, can detect and identify various important objects affected by natural disasters such as flooded buildings and roads, and can detect and identify objects with small shapes such as vehicles and pools, which are the most challenging task for semantic segmentation network models. This study also shows that increasing the network layers in the PSPNet-(18, 34, 50, 101, 152) model, which uses the best parameters and criteria, improves the model's performance. The results of this study indicate the need to utilize a special dataset from aerial imagery originating from the Unmanned Aerial Vehicle (UAV) during the pre-training stage for transfer learning to improve DCN performance for further research.
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: January 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.
Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naive Bayes Algorithms
Authors: Gunawan Wawan , Wiradiputra Raychal Ababil , Puspita Sari Anggi , Prayama Deddy , Rikardo Nainggolan Esron
Published: January 2024

The competition between native and cross-platform app development makes application development simpler, safer, and more scalable. However, developers must have sufficient fundamentals, and the industry must conduct good research to shorten development time and minimize expenses. In order to solve these problems, this study made a prediction that discusses the technology that has a chance to survive in the industry so as not to be left behind in technology. Using Naïve Bayes and C 4.5 algorithms into a dataset with nine programming languages related to mobile app development. Results obtained in This research show Dart as a programming language that supports cross-platform frameworks and Kotlin as a programming language that supports native app frameworks is a technology that would have the opportunity in the future with an accuracy level above 90% with Naïve Bayes and C 4.5 algorithms. These results are obtained by testing an algorithm model using MAPE, consistent dataset sharing, and careful data processing. This research Can help entry-level developers learn and deepen the fundamentals of technology and can add knowledge to the industry in choosing a technology.
Storychart: A Character Interaction Chart for Visualizing the Activities Flow
Authors: Abidin Zainal , Munir Rinaldi , Akbar Saiful , Mandala Rila , Widyantoro Dwi H.
Published: January 2024

Event-predicate-based storyline extraction results in a chronologically ordered activity journal. The extraction results contain complex human activities, so the activity journal requires a visualization model to describe actor interactions. This paper proposes a chart to visualize the activities' flow to describe the characters' interactions in an activity journal. This chart is called a storychart. Storycharts have an actor channel that can accept single entities or teams. The actor channel allows changing the type from single to a team or vice versa and moving members to other teams. The activity channel serves as a connector to accommodate interactions between actors. The activity channel provides a visual space for the elements of what, where, and when. Event predicates are the core of what. Therefore, the storychart visualizes the event predicate using glyphs to attract the reader’s attention. The main contribution of this paper is to introduce a team channel that can visualize the identity of team members and an activity channel that can visualize the details of events. We invited participants to discover the reader’s perception of the ease of team recognition and the integrity of the meaning of the narrative visualized by the storychart. Participants involved in the evaluation were filtered by literacy score. Evaluation of storychart reading showed that readers could easily distinguish teams from single actors, and storycharts could convey the story in the activity journal with little reduction in meaning.
A Systematic Literature Review of Design Thinking Approach for User Interface Design
Authors: Zamakhsyari Fardan , Fatwanto Agung
Published: January 2024

The user interface is an influential element in software applications. A well-designed user interface will potentially increase the usability of software applications. Therefore, user interface designers should deliberate when considering which approach and method to implement for designing user interfaces. Design thinking is currently a widely followed approach in user interface design practices. Hence, this study aimed to explore research trends and current practices of design thinking approach for user interface design. This study employed a systematic literature review following the Kitchenham method. This study found 39 articles deemed relevant to the design thinking approach. In general, our study found five common stages broadly mentioned in the articles, i.e., empathize, define, ideate, prototype, and test. The most widely practiced method during those five stages is interview, user persona, brainstorming, user interface, and usability testing. However, there is no consensus on what kind of stage(s) and which method(s) should be employed when following the design thinking approach for user interface design. Therefore, it will depend on the designer's decision in choosing which stage(s) and their related method(s) will be employed.
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: January 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
A Layered Architecture and Taxonomy for Blockchain-empowered Reputation-based Reward Systems
Authors: Yadav Jitendra Singh , Yadav Narendra Singh , Sharma Akhilesh Kumar
Published: January 2024

Blockchain based rating and review systems have changed the operational structure of the traditional market by introducing characteristics like immutability, security, anonymity etc. to liberate users from potential malicious acts of sellers such as altering and hiding ratings or reviews, collusion with users or service providers. The lack of standardization for developing decentralized applications does not depict flow of information and cataloguing of specific functions and roles for a particular set of tasks. The development of decentralized applications for e-commerce systems is in its immature age of progress and has lack of interoperable sharing of data and workflows for new innate systems. Thus, it is significant to catalogue blockchain-based rating and review systems by identifying key parameters to generate a taxonomy and develop a conceptual layered framework for identifying core components and their interaction. This manuscript presents a substantial analysis of existing blockchain-empowered reputation-based reward systems. It uses an iterative approach following observed to rational and rational to observed for taxonomy development. The analysis results identify 11 key parameters for categorizing systems and propose a 4 layered architecture to signify IPFS, P2P network, Blockchain and DApps. The proposed model identifies underlying subsystems, their services, and their interaction. The new taxonomy identifies natural roadmaps in system development process. This study is key because it allows developers to design new reputation-based reward framework in different dimensions by following an open workflow with a common understanding of underlying core entities.
A Review of Neural Network Approach on Engineering Drawing Recognition and Future Directions
Authors: Mohd Yazed Muhammad Syukri , Ahmad Shaubari Ezak Fadzrin , Yap Moi Hoon
Published: January 2024

Engineering Drawing (ED) digitization is a crucial aspect of modern industrial processes, enabling efficient data management and facilitating automation. However, the accurate detection and recognition of ED elements pose significant challenges. This paper presents a comprehensive review of existing research on ED element detection and recognition, focusing on the role of neural networks in improving the analysis process. The study evaluates the performance of the YOLOv7 model in detecting ED elements through rigorous experimentation. The results indicate promising precision and recall rates of up to 87.6% and 74.4%, respectively, with a mean average precision (mAP) of 61.1% at IoU threshold 0.5. Despite these advancements, achieving 100% accuracy remains elusive due to factors such as symbol and text overlapping, limited dataset sizes, and variations in ED formats. Overcoming these challenges is vital to ensuring the reliability and practical applicability of ED digitization solutions. By comparing the YOLOv7 results with previous research, the study underscores the efficacy of neural network-based approaches in handling ED element detection tasks. However, further investigation is necessary to address the challenges above effectively. Future research directions include exploring ensemble methods to improve detection accuracy, fine-tuning model parameters to enhance performance, and incorporating domain adaptation techniques to adapt models to specific ED formats and domains. To enhance the real-world viability of ED digitization solutions, this work highlights the importance of conducting testing on diverse datasets representing different industries and applications. Additionally, fostering collaborations between academia and industry will enable the development of tailored solutions that meet specific industrial needs. Overall, this research contributes to understanding the challenges in ED digitization and paves the way for future advancements in this critical field.
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: January 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.
Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model
Authors: Hartanto Anggit Dwi , Nur Kholik Yanuar , Pristyanto Yoga
Published: January 2024

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties.
Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification
Authors: Hidayat Tonny , Astuti Ika Asti , Yaqin Ainul , Tjilen Alexander Phuk , Arifianto Teguh
Published: January 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.
Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control
Authors: Anandika Arrya , Ferdian Rian , Eriyandha Alivia , Suwandi Rifki , Hafidz Muhammad
Published: January 2024

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages
Improved Image Classification Task Using Enhanced Visual Geometry Group of Convolution Neural Networks
Authors: Zakaria Nurzarinah , Mohmad Hassim Yana Mazwin
Published: January 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: January 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: January 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.
The Extension of the UTAUT2 Model: A Case Study of Indonesian SMEs Acceptance of Social Commerce
Authors: Arista Artika , Tjahjanto Tjahjanto , Ernawati Iin , Purabaya Rudhy Ho , Abdullah Engku Fadzli Hasan Syed
Published: January 2024

An entirely updated e-commerce platform referred to as Social Commerce was developed in response to the rise in social media use. Social commerce integrates interactions between buyers and sellers made possible by social media platforms and Web 2.0 technology. It is frequently seen as a subfield of e-commerce. Social commerce has been successfully introduced in developing countries. Many businesses around the world are small and medium enterprises (SMEs). For instance, SMEs in Indonesia can contribute up to 60.34% of the country's GDP and have a substantial labor pool. Using social commerce as an e-commerce platform can significantly improve the operational efficiency of small and medium-sized enterprises (SMEs). However, little empirical research has specifically examined how SMEs embrace social commerce. Given the high level of concern, further research is required. Therefore, the current study experimentally examined how local SMEs in rural Indonesia introduced social commerce. The study was modeled using the Unified Theory of Technology Acceptance and Use (UTAUT) 2 model and several previous studies. SmartPLS 4 software was used to model and evaluate data using partial least squares structural equation modeling (PLS-SEM). The findings of the 114 samples showed that relative advantage, social support, facilitation conditions, and the government's support of social commerce influenced behavioral intention to use social commerce. Behavioral intention to use social commerce influences the actual use of social commerce. The findings of this study can help local governments and policymakers develop social trade promotion regulations to help potential SMEs and entrepreneurs gain long-term business support.
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: January 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
Literature Reviews of RBV and KBV Theories Reimagined - A Technological Approach Using Text Analysis and Power BI Visualization
Authors: Arief Ikhwan , Hasan Alizar , Putri Nilda Tri , Rahman Hafiz
Published: January 2024

Over the years, the Resource-Based View (RBV) and Knowledge-Based View (KBV) have solidified their roles as pivotal paradigms in strategic management literature. With an emphasis on Small and Medium Enterprises (SMEs), this study uses text analysis and Microsoft Power BI to explore these concepts innovatively. The study implements a systematic literature review, extracting data from Scopus, Web of Science, and DOAJ databases to assemble a comprehensive literature corpus. The methodology incorporates text analysis to draw out key themes, relationships, and trends, and these are subsequently visualized using Power BI to create an engaging, interactive representation of data. Components like word clouds, co-occurrence networks, and trend lines are generated, while Power BI's dynamic filtering and drill-down functionalities facilitate thorough data investigation. The results display significant overlap between RBV and KBV, denoting possible integration junctures for these theories within the domain of strategic management. Additionally, the study underscores the relevance of these insights for SMEs, emphasizing the part played by unique resources, encompassing knowledge assets, in catalyzing innovation and fostering a competitive edge. The study concludes by recognizing the significant theoretical and practical implications of integrating text analysis and Power BI in conducting literature reviews. This methodology bolsters our understanding of RBV and KBV, offering small and medium-sized enterprises a beneficial instrument to traverse these intricate theories. The study suggests that future research could broaden the application of this methodological approach to encompass other strategic management theories.
Project-Based Learning Model Development Using Flipped Classroom for Drawing Learning in College
Authors: Mayar Farida , Putra Febri Wandha , Monia Fenny Ayu , Kosassy Siti Osa , Fadli Rima Pratiwi , Arinalhaq Ririen
Published: January 2024

The learning process was initially carried out online during the pandemic then after the post-pandemic, learning activities began to be carried out face-to-face. This raises a new problem, because lecturers who teach are starting to be required to use various media in the learning process in the new normal era. But in reality, the lecturers still use old methods such as lectures or task-based learning. Therefore, the research objective is to develop a new model that can be used in the learning process in higher education. This type of research is development research using the ADDIE approach. The instrument used was a student learning motivation questionnaire and a student learning satisfaction questionnaire following the lesson. Another instrument used is a Likert model scale with three alternative answers. The operational procedure taken in this research and development goes through three stages, namely: (1) preliminary study, (2) preparation of conceptual models (3) validity test and (4) practicality test. The results of the validity test show that the model book and manual are in the very valid category from the aspects of design, language and content. Furthermore, the practicality test results show that model books and guidebooks for project-based learning models using flipped classrooms for drawing lectures in tertiary institutions are practically used by lecturers. The implications of this research can help lecturers in designing practical learning.
Comparative Analysis of VGG-16 and ResNet-50 for Occluded Ear Recognition
Authors: Tey Hua-Chian , Chong Lee Ying , Chong Siew-Chin
Published: January 2024

Occluded ear recognition is a challenging task in biometric systems due to the presence of occlusions that can hinder accurate identification. There is still a research gap in enhancing the robustness of deep learning to handle severities of occlusions with different datasets. This research focuses on developing a robust occluded ear recognition system by implementing fine-tuning techniques on three popular pre-trained deep learning models, Residual Neural Network (ResNet-50), Visual Geometry Group (VGG-16), and EfficientNet. The system is evaluated on two manually occluded ear datasets, which are the AMI ear dataset and the IITD ear dataset. The experiment results showed the fine-tuned ResNet-50 model performs better than the fine-tuned VGG-16 model. The results indicate that the model's ability to accurately predict the classes or labels decreases as more data is occluded. Higher occlusion rates lead to a loss of important information, making it more challenging for the model to distinguish between different patterns and make accurate predictions. According to the findings, the amount of occlusion influenced the identification accuracy and worsened as the occlusion became larger. In the future, ear recognition systems will likely continue to improve in accuracy and be adopted by a wider range of organizations and industries. They may also be integrated with other biometric technologies and used for personalization purposes. However, ethical considerations related to the use of ear recognition systems will also need to be addressed.
Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall
Authors: Huda Ilham , Achmad Suhendra Agus , Arif Bijaksana Moch
Published: January 2024

The classification of churn is driven by the potential risks e-commerce companies face, such as losing customers who discontinue their service usage or churn. Marketing specialists have shifted their efforts from acquiring new customers to retaining existing ones in order to mitigate customer churn. Predictive models are created using data mining techniques to identify customer churn patterns. This study proposes a data mining model aimed at predicting customer behavior, with the processed results utilized as suggestions for improvements and company strategies in customer retention through segmentation and classification. Segmentation and classification involve several variables: Session, Interaction with Application, Actions taken during the interaction, purchasing, claim, and discount. This study employs a clustering technique based on the Recency, Frequency, and Monetary (RFM) model, which considers factors such as the time since the last visit, the number of visits, and the total amount spent by the customer. The classification algorithm model was evaluated by comparing three classification algorithms: decision tree and Support Vector Machine (SVM). The decision tree algorithm had the highest accuracy, achieving an impressive 87% accuracy rate in customer classification. Factors influencing customer churn include purchasing behavior, session activity, claim feature utilization, adding products to cart, and discounts. Improving stock management is crucial to prevent stock shortages, likely to cause churn. Additional measures like sending emails/notifications and offering vouchers/loyalty points can be implemented for customers who added products to their carts but didn't complete the purchase, with a focus on popular products.
Multi-Temporal Factors to Analyze Indonesian Government Policies regarding Restrictions on Community Activities during COVID-19 Pandemic
Authors: Fachri Pane Syafrial , Adiwijaya Adiwijaya , Dwi Sulistiyo Mahmud , Akbar Gozali Alfian
Published: January 2024

Concerning the implementation of the government policy regarding the Restriction of Community Activities (PPKM) during the COVID-19 pandemic era, there are still discrepancies in the economic sector and population mobility. This issue emerges due to irrelevant data and information in one region of Indonesia. The data differences should be carefully solved when implementing the PPKM policy. Besides, the PPKM must also pay attention to some specific factors related to the real conditions of a region, such as the data on the epidemiology of COVID-19, economic situations, and population mobility. These three are called Multi Factors. Then, based on the data, COVID-19 has a specific spreading period that cannot be repeated and thus is called temporal. Therefore, using the Multi-Temporal Factors approach to identify their correlation with the PPKM policy by applying Machine Learning, such as the Multiple Linear Regression model and Dynamic Factors, is essential. This research aims to analyze the characteristics and correlations of the COVID-19 pandemic data and the effectiveness of the government's policy on community activities (PPKM) based on the data quality. The results show that the accuracy of the multiple linear regression models is 84%. The Dynamic Factor shows that the five most important factors are idr_close, positive, retail_recreation, station, and healing. Based on the ANOVA test, all independent variables significantly influence the dependent one. The linear multiple regression models do not display any symptoms of heteroscedasticity. Thus, based on the data quality, the implementation of PPKM by the government has a practical impact.
Analyzing the Impact of Project-Based Learning on Student Entrepreneurship Readiness: A Structural Equation Modeling and Statistical Analysis in Higher Education
Authors: Yulastri Asmar , Ganefri Ganefri , Ferdian Feri , Elfizon Elfizon
Published: January 2024

The study aimed to examine the influence of entrepreneurial passion, entrepreneurial literacy, role model inspiration, and self-efficacy on entrepreneurship readiness among higher education students and the role of the project-based learning model implementation as a moderator variable. The population in the study were students in higher education in Indonesia who had taken entrepreneurship courses. Data from 313 valid respondents were analyzed against the research model using the Partial Least Squares Structural Equation Modelling. The findings revealed that entrepreneurial passion, entrepreneurial literacy, and role model inspiration were found to positively influence self-efficacy as well as entrepreneurship readiness among students in higher education. Unpredictably, the moderator project-based learning models’ implementation was shown to have an insignificant effect on the influence of entrepreneurial passion, entrepreneurial literacy, and role model inspiration toward entrepreneurship readiness among students in higher education. The findings of this study provide several important theoretical and practical implications for entrepreneurship readiness among students in higher education.  higher education in Indonesia who had taken entrepreneurship courses. Data collected from 313 valid respondents were analyzed against the research model using the Partial Least Squares Structural Equation Modelling. The findings revealed that entrepreneurial passion, entrepreneurial literacy, and role model inspiration were found to positively influence self-efficacy as well as entrepreneurship readiness among students in higher education. Unpredictably, the moderator project-based learning models’ implementation was shown to have an insignificant effect on the influence of entrepreneurial passion, entrepreneurial literacy, and role model inspiration toward entrepreneurship readiness among students in higher education. The findings of this study provide several important theoretical and practical implications for entrepreneurship readiness among students in higher education.