cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Husni Teja Sukmana
Contact Email
husni@bright-journal.org
Phone
+62895422720524
Journal Mail Official
jads@bright-journal.org
Editorial Address
Gedung FST UIN Jakarta, Jl. Lkr. Kampus UIN, Cemp. Putih, Kec. Ciputat Tim., Kota Tangerang Selatan, Banten 15412
Location
Kota adm. jakarta pusat,
Dki jakarta
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
 26   27   28   29   30 
Designing Planograms for Retail Shelves: A Visual Merchandising Approach Using Apriori Algorithm and K-Means Clustering of Customer Preferences Elquthb, Jundi Nourfateha; Mansur, Agus
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.375

Abstract

The development of retail store businesses in Indonesia is widespread across various regions, positively impacting the increase in shopping activities among the community. Alongside the intense competition among retailers, fundamental issues are found regarding the shopping experience of customers who feel uncomfortable and dissatisfied with retail services. Customers are an essential aspect that needs attention, and their desires must be fulfilled to maintain the existence of a retail store. This study attempts to implement the concept of visual merchandising to enhance service quality through the planogram method aimed at improving the visual arrangement of sales shelfs. Regarding the layout of retail stores, shelf arrangement plays a significant role visually in influencing and attracting customer attention while shopping. In this study, two data mining techniques are used. The first method is association rule mining using the Apriori algorithm, which reveals the association rules formed between two or more product items, utilizing a total of 6,325 customer transaction records. The results indicate 12 rules formed based on product categories and 17 rules based on product sub-categories. The second technique is k-means clustering, which is used to identify differences in customer preferences in retail stores based on several variables regarding customers using 104 data customers. In practice, both the apriori and k-means algorithms face challenges, especially when handling large and complex data. Differences in preferences were found among the clusters, particularly regarding product arrangement on the sales shelf, such as grouping products by brand and price. A two-dimensional planogram design was developed to integrate the results from the previous stages. This design considers the availability and specifications of the shelves in the retail store. A planogram adjusted to customer purchasing patterns and preferences is expected to provide good service and positively impact store operations and stock management.
Opinion Mining in Text Short by Using Word Embedding and Deep Learning Orebi, Shaima Mahdi; Naser, Asmaa Mohsin
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.438

Abstract

Recently, with the increasing use of the Internet by people, millions use social media sites on a daily basis to express their opinions, suggestions and reactions about a new product or a specific topic. Through these views, the principle or topic of sentiment analysis. especially for text data (tweets), where classification techniques are used for the purpose of classifying these text tweets. Sentiment classification is a common and important in the field of natural language processing. Our study aims to utilize word embedding model. Word   embedding is used to convert text words into vectors for word representation, capturing the semantic and syntactic relationships between words. It contributes by presenting a comparison and analysis of word embedding model and deep learning techniques. In this research, we propose to analyze sentiments or opinions using word embedding Global Vectors for Word Representation (GLOVE) with Bidirectional LSTM neural networks and Long Short-Term Memory (LSTM). Where we relied on a deep learning model that combines the power of word representations in (GLOVE) and (LSTM)’s ability to understand linguistic context. This model showed good performance in sentiment classification, which indicates its effectiveness of combining the two models. Here we used tweet dataset regarding (Generative Pre-trainer Transformer), which is one of the tools of generative artificial intelligence, Dataset :(CHATGPT sentiment analysis) CHATGPT Tweets first month of launch. We analyzed the data or tweets about the opinions and sentiments of tweeters. The use of the word embedding model with short-term memory (BILSTM and LSTM) achieved good results about 89% and 90%. According to the performance metrics used (confusion matrix, accuracy, precision, recall, F1 score), compared with the results of the (WORD2VEC) model. These metrics are vital tools for evaluating sentiment analysis models and measuring the model's ability to correctly classify tweets into good, bad, or neutral sentiments.
Realtime and Spatial Data Analysis-based Monitoring System for Proboscis Monkey Habitat Health to Enhance Conservation Area Management Effectiveness Nurliani, Anni; Krisdianto, Krisdianto; Rezeki, Amalia; Munsyi, Munsyi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.334

Abstract

The conservation of proboscis monkeys (Nasalis larvatus), an endemic primate species of Borneo, faces significant threats due to habitat degradation and declining populations. This study aims to develop a real-time and spatial data analysis-based monitoring system to improve the management of conservation areas for the species’ natural habitats. Conducted in the wetland ecosystems of Curiak Island, South Kalimantan, the research integrates remote sensing, Geographic Information Systems (GIS), and Internet of Things (IoT) technologies to monitor key environmental parameters such as vegetation health, land surface temperature (LST), and others. Indices like the Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Environmental Critical Index (ECI) are utilized to assess habitat conditions. Initial results showed poor vegetation health, with an NDVI of 0.6085, high LST of 20.41°C, and considerable environmental stress, reflected by an ECI of 74. Restoration efforts, however, improved conditions, with the NDVI rising to 0.7288, LST decreasing to 20.75°C, and the ECI lowering to 53 in the restoration area, signaling recovery. Though the ECI still suggests moderate environmental stress, the trend is positive. IoT sensors provided continuous real-time data, including CO levels at 0.2 PPM, CO2 at 34,045 PPM, O2 at 20.4% Vol, temperatures ranging from 33.155°C to 33.185°C, humidity between 67.45% and 67.65%, and pH at 6.8. Data on dissolved oxygen, total dissolved solids (TDS), and turbidity were also collected, providing dynamic insights into environmental conditions. The integration of community-based approaches ensures sustainable conservation efforts through local participation. This comprehensive monitoring system supports both proboscis monkey conservation and broader ecological objectives like biodiversity preservation, climate change mitigation, and ecosystem service provision, emphasizing adaptive management in conservation strategies.
Evaluating the Impact of Sufficiency Economy Philosophy on Sustainable Innovation: A Data-Driven Analysis Rungruang, Thanachai; Tanitteerapan, Tanes; Jitgarun, Kalayanee; Sunthonkanokpong, Wisuit; Leekitchwatana, Punnee
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.524

Abstract

This study investigates the factors affecting sustainable innovation within Thailand's Provincial Electricity Authority, a nonprofit organization committed to sustainable energy solutions. With a focus on the Sufficiency Economy Philosophy as a developmental framework, the study examines how SEP principles of moderation, prudence, and resilience contribute to reducing greenhouse gas (GHG) emissions and achieving Sustainable Development Goals (SDGs). The research adopts a quantitative approach to analyze how SEP influences SI in PEA's operations alongside internal and external factors like disruptive leadership, digital transformation, and national sustainability initiatives. Through a series of correlation and regression analyses, the study identifies SEP as a critical component in fostering SI, with values of virtue, risk management, and informed decision-making emerging as influential elements. The findings indicate that integrating SEP's balanced approach to production and consumption facilitates organizational resilience, enabling the PEA to navigate internal and external shocks effectively. Furthermore, the results underscore the necessity of a holistic framework where internal initiatives align with broader cultural and ecological goals. The study highlights SEP's applicability beyond the energy sector, as seen in sustainable efforts in regions like Krabi and Koh Samui, which exemplify SEP-driven approaches toward low-carbon transitions. By leveraging SEP’s sufficiency principles, organizations can strengthen sustainable practices contributing to Thailand's environmental and social well-being. The research calls for further exploration into SEP’s role across sectors, positing that SEP could be a foundational pillar alongside economic, social, and environmental dimensions to drive sustainable innovation across diverse contexts.
Classification of Political Party Conflicts and Their Mediation Using Modified Recurrent Convolutional Neural Network Riyadi, Slamet; Suradi, Muhamad Arief Previasakti; Damarjati, Cahya; Chen, Hsing-Chung; Al-Hamdi, Ridho; Masyhur, Ahmad Musthafa
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.513

Abstract

The rapid proliferation of political information on the internet has exacerbated conflicts within political parties, including elite disputes, dualism, candidate controversies, and management issues, which can undermine political stability and public trust. To address these challenges, this study introduces the Modified Recurrent Convolutional Neural Network (M-RCNN), an enhanced RCNN model designed to improve classification accuracy and mitigate overfitting by incorporating additional layers and dropout mechanisms. The primary objective of this research is to provide an efficient and accurate framework for classifying political conflicts and mediation strategies, overcoming the limitations of traditional methods, particularly in handling imbalanced datasets and intricate data patterns. Using a dataset of 1,106 Indonesian news articles categorized into four conflict types—elite disputes, management, presidential, and legislative candidate conflicts—and four mediation strategies—leadership decisions, deliberation, legal channels, and none—the data underwent extensive preprocessing, tokenization, and an 80:20 training-testing split. The M-RCNN achieved a conflict classification accuracy of 98.0%, a precision of 99.0%, and a loss of 0.03, significantly outperforming baseline models, including CNN (85.0% accuracy), RNN with LSTM (88.0%), and standard RCNN (85.0%). For mediation strategy classification, the model demonstrated exceptional performance with an accuracy of 99.0%, a precision of 99.0%, and a loss of 0.01, highlighting its robustness and scalability. This study’s novelty lies in its ability to process imbalanced and complex datasets with unparalleled precision and efficiency, providing a practical framework for automated political conflict analysis and mediation. The findings underline the potential of the M-RCNN model to revolutionize political science applications by delivering reliable, fast, and accurate tools for analyzing and resolving political conflicts, thereby contributing to the advancement of artificial intelligence in promoting political stability and fostering public trust.
Utilizing Structural Equation Modelling to Evaluate Factors Affecting Investment Capital Attraction and Sustainable Development in Vietnam Dai, Do Thi Lan; Tam, Phan Thanh
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.428

Abstract

Vietnam and several nations globally are facing unparalleled effects of climate change, the progressive exhaustion of natural resources, and the global Covid-19 pandemic alongside non-traditional security threats. These tremendous challenges and occurrences underscore the significance of harmonious and balanced development among the three pillars of economics, society, and the environment. Sustainable development has emerged as an imperative necessity and an unavoidable trajectory in the evolution of human society. This study examines the principal aspects influencing investment capital attraction and sustainable development, consequently offering suggestions to enhance this process. The study employed two methodologies: qualitative research, executed via interviews, and concentrated on 15 economic expert group talks to modify the substance of observable factors to align with the business's features. Quantitative research was conducted on 800 representative managers from three Vietnamese provinces and one city to evaluate the model and research assumptions. The results indicate five elements influencing the attraction of investment capital, with a significance level of five percent, and the attraction of investment capital impacting sustainable development in Vietnam. This contribution enhances academic significance and is a reference for future studies on sustainable development in Vietnam. Five policy implications and contributions exist to advancing sustainable development in Vietnam, fostering innovation and enthusiasm. The novelty of this study is that sustainable development will establish Vietnam's working and living environment by concurrently advancing three dimensions: sustainable economic growth, a prosperous and equitable society, stable cultural diversity, a pristine environment, and preserved resources maintained sustainably. Consequently, a comprehensive framework of ethical principles for sustainable development encompasses the concepts of sustainability across the economic, social, and environmental dimensions.
The Cumulative Capacitated Vehicle Routing Problem with Time-dependent on Humanitarian Logistics for Disaster Management Hartama, Dedy; Wanayumini, Wanayumini; Damanik, Irfan Sudahri
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.481

Abstract

This study addresses the challenges of optimizing humanitarian logistics during disaster management by developing a Cumulative Capacitated Vehicle Routing Problem with Time-Dependent factors (CCVRP-TD) model. The primary objective is to enhance delivery efficiency by incorporating time-dependent variables such as fluctuating traffic and service durations into route planning. The research contributes a novel Mixed Integer Nonlinear Programming (MINLP) framework that dynamically adapts to real-world conditions like road closures and shifting priorities. Using the MINLP approach, the model was validated through numerical experiments involving four delivery vehicles serving six customers across five routes. Results demonstrated a significant improvement in routing efficiency, with a total cumulative travel distance of 110 km and adherence to specified delivery windows, such as 9:30 AM and 10:30 AM for Customer 1. Additionally, vehicle capacity constraints were effectively managed, with individual route lengths ranging from 20 to 35 km. These findings showcase the model’s ability to balance cost minimization, service reliability, and logistical adaptability. The novelty lies in the integration of time-dependent costs and service benefits into a multi-depot framework, enabling flexible yet precise route optimization under constrained conditions. This research provides a robust tool for enhancing disaster logistics and offers practical implications for improving the responsiveness and effectiveness of humanitarian aid delivery.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Transfer Learning Boosts Ensembles for Precise Sugarcane Leaf Disease Detection Das, Bappaditya; Das, Chandan; Raghuvanshi, C S
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.396

Abstract

The United Nations' Sustainable Development Goals (SDGs) are committed to ensuring that all individuals have access to sufficient, safe, and nutritious food by 2030, acknowledging that food security is a fundamental right of human survival.  However, the exponential growth of the world population raises concerns about the threat of global food insecurity by 2050. An increase in agricultural output is inevitable to meet the growing demand for food. Maximizing agricultural output requires safeguarding crops against disease due to the scarcity of arable land. In the modern age of technology-driven agriculture, the traditional approach of visually detecting agricultural diseases, employed by skilled farmers, is susceptible to inaccuracies and can be a time-consuming process. Transfer learning achieves exceptional accuracy on a noise-free image dataset by using pre-trained CNN models for early crop disease detection. However, their performance significantly deteriorates on datasets with images with complex natural backgrounds. This paper describes an ensemble of transfer learning-based binary classifiers to detect multiple sugarcane leaf diseases using a binary classification tree. Our model successfully classified five distinct sugarcane leaf diseases, achieving an impressive overall validation accuracy of 98.12%, macro-average precision of 97.75%, Recall of 97.93% and F1-score of 97.84%. Moreover, a methodological approach derived from the empirical observations of experienced agricultural experts led to a significant reduction in the computational complexity of our model, transitioning from exponential to linear search space framework.
User Interface Design of Interactive Video Based on Balinese Local Cultural Values Data in the Menek Daha Ceremony to Support Character Education Sudiarta, I Wayan; Divayana, Dewa Gede Hendra; Tegeh, I Made; Sudatha, I Gde Wawan
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.430

Abstract

The presence of interactive videos based on Balinese local cultural values data in the Menek Daha ceremony is very important to socialize knowledge about character education in learning process at the senior high school level in the independent learning policy. One of the benchmarks for the ease of developing an interactive video is the readiness of its user interface design. The reality found in the field is that there are still many interactive video creators who do not care about user interface design. The challenges are especially if the stages of making the user interface design are based on data/information related to Balinese local culture. Therefore, the purpose of this study was to show the user interface design of an interactive video based on Balinese local cultural values data (Menek Daha ceremony) in supporting character education in the independent learning policy. This was a research and development using the Borg and Gall development model which focuses on the design stages, initial testing of the design, and revisions. The subjects involved in testing the user interface design were 84 respondents. The test tool was a questionnaire consisting of 15 questions related to the user interface design of an interactive video based on Menek Daha ceremony data. Analysis of trial data was carried out by comparing the percentage of user interface design quality with quality standards referring to the five scale categorizations. The results of this research show that the quality of user interface design of interactive videos based on Menek Daha ceremony data is in the good quality category by a quality percentage of 81.83%. This research novelty is the emergence of an interactive video user interface design integrated with Balinese local cultural values data in the Menek Daha ceremony as a learning technology innovation in supporting character education in independent learning policy.

Page 28 of 52 | Total Record : 518

 26   27   28   29   30 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol 7, No 1: January 2026 Vol 6, No 4: December 2025 Vol 6, No 3: September 2025 Vol 6, No 2: MAY 2025 Vol 6, No 1: JANUARY 2025 Vol 5, No 4: DECEMBER 2024 Vol 5, No 3: SEPTEMBER 2024 Vol 5, No 2: MAY 2024 Vol 5, No 1: JANUARY 2024 Vol 4, No 4: DECEMBER 2023 Vol 4, No 3: SEPTEMBER 2023 Vol 4, No 2: MAY 2023 Vol 4, No 1: JANUARY 2023 Vol 3, No 4: DECEMBER 2022 Vol 3, No 3: SEPTEMBER 2022 Vol 3, No 2: MAY 2022 Vol 3, No 1: JANUARY 2022 Vol 2, No 4: DECEMBER 2021 Vol 2, No 3: SEPTEMBER 2021 Vol 2, No 2: MAY 2021 Vol 2, No 1: JANUARY 2021 Vol 1, No 2: DECEMBER 2020 Vol 1, No 1: SEPTEMBER 2020 More Issue