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Husni Teja Sukmana
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INDONESIA
Journal of Applied Data Sciences
Published by Bright Publisher
ISSN : -     EISSN : 27236471     DOI : doi.org/10.47738/jads
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes applied to collect, treat and analyze data will help to render scientific research results reproducible and thus more accountable. The datasets itself should also be accessible to other researchers, so that research publications, dataset descriptions, and the actual datasets can be linked. The journal Data provides a forum to publish methodical papers on processes applied to data collection, treatment and analysis, as well as for data descriptors publishing descriptions of a linked dataset.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 518 Documents
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The Success Factors of E-Philanthropy are Determined Based on Perceived Trust, Perceived Usefulness, Subjective Norms, Enjoyment and Religiosity: A Case Study on a Charity Site Sukmana, Husni Teja; Nanang, Herlino; Agustin, Fenty Eka Muzayyana; Aristoi, Zidny Fiqha; Azizah, Khansa
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.310

Abstract

The rapid development of information technology and social media has significantly influenced people's behaviors and preferences in various activities, including philanthropy. Traditionally, philanthropic activities necessitated direct interpersonal interactions. However, the advent of ephilanthropy has enabled more practical and accessible ways to engage in charitable activities anytime and anywhere using electronic technology. This study examines the perceived role of e-philanthropy users in Indonesia and their intention to make actual donations through crowdfunding for humanitarian purposes. The research integrates the Technology Acceptance Model (TAM) and the IS success model, supplemented by additional variables like trust, usefulness, subjective norms, and religiosity. Data were collected from 231 respondents across Indonesia using online questionnaires and analyzed using the PLS-SEM method. The findings indicate significant relationships between perceived quality and trust (t-value = 7.156, path coefficient = 0.681), trust and perceived usefulness (t-value = 31.724, path coefficient = 0.886), and religiosity and intention to use (t-value = 3.206, path coefficient = 0.360). However, perceived enjoyment (t-value = 1.100, path coefficient = 0.140), subjective norms (t-value = 1.448, path coefficient = 0.162), and perceived trust (t-value = 1.023, path coefficient = 0.128) did not significantly influence the intention to use e-philanthropy platforms. These insights can inform strategies to enhance user participation and trust in e-philanthropy initiatives in Indonesia.
Utilizing Support Vector Machine and Dimensionality Reduction to Identify Student Learning Styles within the Felder-Silverman Model Hananto, Andhika Rafi; Musdholifah, Aina; Wardoyo, Retantyo
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.337

Abstract

This research explores the impact of questionnaire structure on the accuracy of learning style classification, focusing on the optimization of the Felder-Silverman Learning Style Model (FSLSM) using advanced machine learning techniques. By employing Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, the study identifies and retains the most informative variables from the original 44-question FSLSM instrument. These refined features are then processed through a Support Vector Machine (SVM) algorithm to evaluate classification performance across various core-to-secondary item ratios. Results indicate that the most optimal configuration—produced through the combined PCA-t-SNE reduction—achieved a peak accuracy of 89.54%, surpassing other configurations and highlighting the effectiveness of selective question modeling. This approach not only enhances prediction accuracy but also introduces a more efficient and streamlined FSLSM formula, reducing redundancy without compromising diagnostic precision. The study contributes to educational data mining by presenting a data-driven strategy for learning style assessment and offers practical implications for the development of adaptive, personalized learning systems grounded in statistically validated models.
Exploring Visitor Sentiments: A Study of Nusantara Temple Reviews on TripAdvisor Using Machine Learning Hariyono, Hariyono; Wibawa, Aji Prasetya; Noviani, Erina Fika; Lauretta, Giovanny Cyntia; Citra, Hana Rachma; Utama, Agung Bella Putra; Dwiyanto, Felix Andika
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.208

Abstract

This study examines the mood of tourist evaluations for the Nusantara Temples, such as Borobudur, Prambanan, Ijo, Plaosan, and Mendut Temples, on TripAdvisor using Stochastic Gradient Descent (SGD), Logistic Regression (LR), and Support Vector Machine (SVM) classification techniques. The study examines the viewpoints and encounters of tourists from different nations on Indonesia's cultural legacy through English-language evaluations. The evaluation findings show that LR achieves the highest performance in sentiment classification, with an accuracy rate of 91.66%. The research offers valuable insights but has limits in portraying local visitors and relies heavily on the English language. Future studies might focus on doing sentiment analysis on more historical tourism sites in Indonesia, integrating multilingual data, and experimenting with novel categorization methods. This study significantly enhances our understanding of how technology and social media impact tourists' impressions of cultural heritage in the digital age via strengthening analytical methodologies and investigating alternative destinations.
Implementation of Scale-Invariant Feature Transform Convolutional Neural Network for Detecting Distracted Driver Fhadilla, Nahdatul; Sulandari, Winita; Susanto, Irwan; Slamet, Isnandar; Sugiyanto, Sugiyanto; Subanti, Sri; Zukhronah, Etik; Pardede, Hilman Ferdinandus; Kadar, Jimmy Abdel
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.222

Abstract

A distraction while driving a vehicle may result in fatal consequences, namely accidents that may leave road users seriously injured or even dead. In order to mitigate this risk, it is imperative to establish a distracted driver detection system that is both precise and real-time. This research focuses on the application of artificial intelligence, with a particular emphasis on deep learning, which is achieved through the utilization of the Convolutional Neural Network (CNN) model. In order to enhance the detection of inattentive drivers and produce a more precise model, a scaleinvariant feature transform (SIFT)-CNN combination is proposed. The activities of the driver while operating a vehicle are categorized into ten categories in this study. One of these categories is considered a normal condition, while the remaining nine are classified as inattentive behaviors. This study implemented Adam optimization with 64 batches, a learning rate of 0.001, and epochs of 20, 25, 50, and 100. The proposed CNNSIFT model is capable of achieving superior performance in comparison to the solitary CNN model, as evidenced by the experimental results. The CNN-SIFT model has achieved 99% accuracy and a 0.05 loss when the hyperparameter configuration is optimized for 50 epochs. The analysis indicates that the accuracy of the features obtained from CNN-SIFT can be improved by approximately 1% compared with CNN to classify the type of driver distraction behavior. The model's reliability was further enhanced by its evaluation on test data, which resulted in high accuracy, precision, recall, and F1-score values. The model's ability to accurately identify driver behavior with a high degree of reliability is demonstrated by these results, which are a positive contribution to the improvement of road safety.
Deep Wiener Deconvolution Denoising Sparse Autoencoder Model for Pre-processing High-resolution Satellite Images Kiruthika, S.; Priscilla, G. Maria; Vijendran, Anna Saro; Batumalay, M.; Xu, Zhengrui
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.357

Abstract

The detection of geospatial objects in surveillance applications faces significant challenges due to the misclassification of object boundaries in noisy and blurry satellite images, which complicates the detection model's computational complexity, uncertainty, and bias. To address these issues and improve object detection accuracy, this paper introduces the Deep Wiener Deconvolution Denoising Sparse Autoencoder (DWDDSAE) model, a novel hybrid approach that integrates deep learning with Wiener deconvolution and Denoising Sparse Autoencoder (DSAE) techniques. The DWDDSAE model enhances image quality by extracting deep features and mitigating adversarial noise, ultimately leading to improved detection outcomes. Evaluations conducted on the NWPU VHR-10 and DOTA datasets demonstrate the effectiveness of the DWDDSAE model, achieving notable performance metrics: 96.32% accuracy, 86.88 edge similarity, 75.47 BRISQUE, 28.05 IQI, 38.08 PSNR (dB), 0.883 SSIM, 98.25 MSE, and 0.099 RMSE. The proposed model outperforms existing methods, offering superior noise and blur removal capabilities and contributing to Sustainable Development Goals (SDGs) such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). This research highlights the model's potential for inclusive innovation in object detection applications, showcasing its contributions and novel approach to addressing existing limitations.
Applied Regression Modelling to Propose Recommendations for the Development of Banking Services: A Case Study of Commercial Banks in Vietnam Linh, Phan Thi
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.200

Abstract

The trend of global economic integration has created conditions for the strong development of our country's economy during the integration process. More and more foreign banks have been participating in our country's financial market, creating increasingly fierce competitive pressure in the commercial banking system. Despite many efforts, domestic banks still face difficulties in surviving and developing. Facing so many competitive challenges and the entry of foreign banks requires banks to have a clear strategy to maintain and increase market share. Many banks have made great strides in service quality, improving management levels, and applying applications such as Internet banking, ebank, Fintech, etc. Providing customers, the best quality of service has become more critical than ever if banks want to survive and develop in the current period. Therefore, the article aims to discover the essential factors contributing to developing commercial banking services in Vietnam. In addition, policy implications for banking service development are proposed. Based on the goal, the author surveyed 500 customers using the banking services, applied the multiple linear regression method, and processed data using SPSS 20.0. The key findings showed seven factors affecting banking service development with a significance of 0.05. The contributions of this study have focused on analyzing and identifying factors and the level of influence of factors on banking service development. Based on the research results, the author proposed some recommendations to help commercial bank leaders develop banking services in the coming time. The research novelty discusses the proposed seven policy recommendations, which include (1) tangibles, (2) responsiveness, (3) competence, (4) empathy, (5) reliability, (6) management capacity, and (7) technological capacity. Finally, the results are also scientific evidence and are very important for managers and policymakers for Vietnamese commercial banks to apply to contribute to developing the banking services to the higher demand of customers.
Applied Regression Modelling to Recommend Green Business Development in Vietnam Nga, Lu Phi; Huy, Nguyen Quoc; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.294

Abstract

The green businesses engaged in environmentally friendly business activities, minimizing negative impacted on the ecosystem and used resources effectively. Green businesses contributed to the country's sustainable development goals and brought practical benefits to the businesses themselves. Therefore, the article aimed to identify the key factors affecting green business development and proposed policy implications for green business development. Based on the study goal, the authors surveyed 400 managers of green enterprises in Vietnam and applied simulation modelling based on the quantitative research method. The findings explored five critical factors affecting green business development: (1) financial incentives, (2) corporate strategy and culture, (3) technological innovation, (4) mechanisms and policies, and (5) regulatory environment. The novelty of this study is that it increased the level of reputation and trust of customers, partners, and the community. Green businesses built a positive brand image to affirm their social responsibility and pioneering role in environmental protection. Green businesses could also attract and retain talent when employees feel proud and attached to the business's mission. A significant benefit of green businesses was their contribution to protecting the living environment for humans and animals. The article’s contributions helped to reduce greenhouse gas emissions, air, water, and soil pollution, reduce waste, and increase energy and resource efficiency. Green business development can also support nature conservation activities, habitat restoration, and biodiversity maintenance. The article's contributions proposed recommendations to management agencies in developing and implementing mechanisms and policies to support and promoted businesses to build in a green direction.
Machine Learning Algorithm Optimization using Stacking Technique for Graduation Prediction Herianto, Herianto; Kurniawan, Bambang; Hartomi, Zupri Henra; Irawan, Yuda; Anam, M Khairul
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.316

Abstract

Graduating on time is crucial for academic success, impacting time, costs, and education quality. Hang Tuah University Pekanbaru (UHTP) is currently struggling to meet its goal of achieving a 75% on-time graduation rate. This study introduces an innovative approach using machine learning techniques, particularly ensemble learning with Stacking Machine Learning Optuna SMOTE (SMLOS), to address this issue. Our primary objective is to enhance data classification accuracy to predict student graduation timelines effectively. We employ algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (C4.5), Random Forest (RF), and Naive Bayes (NB). These were combined with meta-models, including Logistic Regression (LR), Adaboost, XGBoost, LR+Adaboost, and LR+XGBoost, to create a robust prediction model. To address class imbalance, we applied the Synthetic Minority Over-sampling Technique (SMOTE) and utilized Optuna for hyperparameter tuning. The findings reveal that SMLOS with the Adaboost meta-model achieved the highest accuracy of 95.50%, surpassing previous models' performances, which averaged around 85%. This contribution demonstrates the effectiveness of using SMOTE for class imbalance and Optuna for hyperparameter optimization. Integrating this model into UHTP's academic information system facilitates real-time monitoring and analysis of student data, offering a novel solution for promoting a Smart Campus through more accurate student performance predictions. This technique is not only beneficial for predicting student graduation but can also be applied to various machine learning tasks to improve data classification accuracy and stability.
Enhancing Federated Learning Performance through Adaptive Client Optimization with Hyperparameter Tuning Putra, Made Adi Paramartha; Utama, I Komang Ram Pramartha; Utami, Nengah Widya; Putra, I Gede Juliana Eka
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.251

Abstract

The effectiveness of Industrial Internet of Things (IIoT) systems requires a robust fault detection mechanism, a task effectively accomplished by leveraging Artificial Intelligence (AI). However, the current centralized learning approach proves inadequate. In response to this limitation, Federated Learning (FL) enables decentralized training, ensuring the protection of individual data. The traditional FL settings are not sufficient to provide an effective learning process, which needs to be refined. This paper introduces an Adaptive Distributed Client Training (ADCT) mechanism designed to optimize performance for each FL participant, thereby establishing an efficient and resilient system. The proposed ADCT utilizes two parameters, namely the accuracy threshold and grid search step, to find the optimal hyperparameter for each client in a specific number of federation rounds. The evaluation results, conducted using the MNIST and FMNIST datasets in non-IID settings, indicate that the proposed ADCT enhances the F1-score by up to 37.13% compared to state-of-the-art methods.
Lower Limb Prosthetics using Optimized Deep Learning Model – a Pathway Towards SDG Good Health and Well Being S, Prakash; M, Jeyasudha; S, Priya; Batumalay, M
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.239

Abstract

This research article aimed at revolutionizing prosthetic leg technologies to enhance accessibility, affordability, and environmental sustainability. With a focus on addressing the diverse needs of amputees globally, the program integrates principles of eco-design, community engagement, technological innovation, and policy advocacy to foster inclusive and resilient societies which leads to the attainment of Sustainable Development Goal (SDG) Good Health and Well Being. Lower Limb Prosthetics of Activity Recognition is an innovative field combining prosthetic technology and activity recognition systems. The challenge of activity recognition in lower limb prosthetics to optimize the performance and responsiveness of mock limbs. In this work, the problem is overcome by using the Optimized deep learning technique, which improves activity recognition in lower limb prosthetics. The proposed methodology consists of (1) Pre-processing (2) Feature extraction (3) Feature classification. The collected images are pre-processed via improved wavelet demonizing and Empirical mode decomposition. From pre-processed data, the features are extracted using an improved sliding window method. The obtained extracted features are moved on to the Feature classification process. The classification process is done by the Optimized Long short- term memory. They are designed to better capture dependencies and patterns in sequential data, which makes them highly effective for tasks involving time series, natural language processing and other sequential data problems. Optimization can be done by proper data preprocessing and tuning the data from data extraction. The weight of the LSTM model is optimized to improve the performance of this model by the improved Black Window Optimization Algorithm. The main contributions of the paper are to obtain the best classification accuracy, an optimized LSTM model is introduced in this paper, and the weight of the LSTM model is enhanced by the improved Black Window Optimization algorithm. It improves the performance of the proposed system.

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