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 53 Documents
 2   3   4   5   6 
Search results for , issue "Vol 5, No 4: DECEMBER 2024" : 53 Documents clear
Environment Sentiment Analysis of Bali Coffee Shop Visitors Using Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 2 (GPT2) Model Yuniari, Ni Putu Widya; Iswari, Ni Made Satvika; Kumara, I Made Surya
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.v5i4.302

Abstract

Bali is one of the provinces with the most abundant natural and cultural wealth in Indonesia. One commodity that supports it is coffee. Bali Coffee is not only a gastronomic identity, but also a cultural identity which makes it have added value to be developed into various business lines. One business derivative that is quite promising is a coffee shop. However, these favorable conditions also need to be maintained to ensure good quality reaches consumers. One thing that can do is analyze reviews from customers. One of the most popular methods is Sentiment Analysis. This technique allows business to analyze customer reviews on social media. It can be a feedback to maintaining and improving quality and good relationships with customers. This research aims to create a machine learning model to analyze customer reviews at several coffee shops in Bali which are divided into three labels, namely: positive, negative and neutral. The methods used are: scraping, cleaning, stopword removal, embedding, undersampling, and modeling. The algorithms used are Bidirectional Encoder Representation from Transformer (BERT) and Generative Pre-trained Transformers (GPT). The performance metrics used in this research are precision, recall, accuracy and loss. This research succeeded in creating a sentiment analysis model for coffee shop customers in Bali. The BERT model obtained an accuracy value of 78% without undersampling with a loss in the 10th iteration of 0.27. Meanwhile, the BERT model with undersampling obtained an accuracy value of 32.85% with a loss in the 10th iteration of 0.16. The GPT2 model without undersampling gets an accuracy of 78% with a loss in the 10th iteration of 0.25. Meanwhile, the GPT model with undersampling obtained an accuracy value of 32.85% with a loss in the 10th iteration of 0.15.
An Adaptive Cuckoo Search Algorithm with Deep Learning for Addressing Cyber Security Problem Jeyaboopathiraja, J.; Mariajohn, Princess; Maidin, Siti Sarah; Sun, Jing
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.v5i4.366

Abstract

IoT (Internet of Things) offers continued services to organizations by connecting systems, application and services using the medium of internet. They also leave themselves open to threats including virus attacks and software thefts where the risks of losing crucial information are high. These threats harm both the business’ finances and reputation. This work offers a combined Deep Learning strategy using Artificial Neural Networks that can assist in detecting illegal software and malware tainted files. The proposed cyber security architecture uses data mining techniques to forecast cyber-attacks and prepare Internet of Things for suitable countermeasures. This framework uses two phases namely detections and predictions. This paper proposes Adaptive Cuckoo Search Optimization-based Algorithms for cloud network routes. Adaptive Cuckoo Search Algorithm are a bio-inspired protocol based on cuckoo birds’ characteristics. Artificial Neural Networks classify assaults on cloud environments. The major goal of this work is to separate malicious servers from legitimate servers that are impacted by Denial of Service and Distributed Denial of Service assaults and thus safeguard server data and ensuring they are sent to legitimate servers. The outcome from this research proposed scheme shows better performances for protecting systems from cyber-attacks in terms of values for accuracy, Precision, Recall and F1-Measure when compared to existing algorithms.
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.
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.
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.
Performance Evaluation of Fuzzy Logic System for Dendrobium Identification Based on Leaf Morphology Putra, Arie Setya; Syarif, Admi; Mahfut, Mahfut; Sulistiyanti, Sri Ratna; Hasibuan, Muhammad Said
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.v5i4.224

Abstract

Dendrobium is the second-largest family of flowering plants in the world. There are several classes of Dendrobium, which usually identify by its, including leaves and flowers. Due to the similarity of its characteristics, identifying orchid types is complicated and usually can only be done by an expert. Moreover, those characteristics are typically non-deterministic; examining the orchid species is very challenging. This research aims to develop a novel fuzzy-based system to identify the species of orchid based on unprecise existing leaf characteristics. We used the main characteristics of Dendrobium leaves, including shape, length, width, and tips of the leaves. Based on the information from the expert, we develop the membership for each class of Dendrobium. By adopting this knowledge, we develop the system by using compatible programming with this case, and Borland Delphi as complex application development. The experiment is done by using 200 real datasets from the Liwa Botanical Gardens, West Lampung Regency, Lampung Province, Indonesia. The results are compared with those given by a Dendrobium expert. A confusion matrix is a valuable evaluation tool for measuring the performance of classification models. From the above results, we can determine the confusion matrix and calculate the TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative). The confusion matrix given from the experiments is shown in Table 6. This indicates that the system can provide the same results as experts recommended. It is shown that the system can identify orchid types with an accuracy value of 94,6 %.  Thus, this system will be beneficial for automatically determining the orchid genus.
Development of a Theoretical Model for the Breathability of Textile Fabrics Rajabov, Ilqar
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.v5i4.379

Abstract

The objective of this study is to develop a theoretical model for predicting the breathability of textile fabrics, with a particular focus on their structural properties and airflow dynamics. Textile fabrics are employed in a multitude of fields, including fashion, medicine, and industry. Consequently, an understanding of their breathability is of paramount importance for a plethora of applications. The research identifies the key factors influencing breathability, including material density, thickness, porosity, and fibre geometry. The study primarily examines factors such as material density, thickness, porosity, and fibre geometry, with additional consideration of potential influences on breathability, including fibre type and fabric finishing treatments. This approach provides a more comprehensive understanding of the factors affecting breathability. The model incorporates the concept of a porous “ideal stone” system and applies the Poiseuille formula for capillary flow to describe the movement of air through textiles. The Poiseuille formula is relevant in that it is capable of representing airflow through a system of parallel capillaries, thereby accounting for the laminar flow that is observed in textile materials. The porous “ideal stone’ system serves to model the internal structure of the fabric, thereby facilitating a detailed understanding of the patterns of airflow and pressure variation across a range of textiles. The findings indicate that a loop model, which accounts for the cross-sectional shape of fibres at the thread level, provides a more accurate representation of airflow behaviour. Testing of elastic knitwear samples in standardized conditions showed loop spacing of approximately 1.58 mm, contrasting with theoretical calculations that suggested 2.14 mm gaps between loops. All tests were conducted at 20°C ± 2°C with 65% ± 4% humidity. The outcomes of this study have practical applications in optimizing textile design, allowing for better recommendations on fabric selection based on specific breathability requirements.
Biophysical Model of Mount Babaris for Predicting Carbon Potential using Remote Sensing Jauhari, Ahmad; Syauqiah, Isna; Taati, La; 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.v5i4.321

Abstract

The biophysical model of Mount Babaris aims to predict carbon potential using remote sensing technology to address high levels of greenhouse gases, particularly CO2. This study combines satellite data with field measurements to create a validated model analyzing Forest Canopy Height (FCH), Normalized Difference Vegetation Index (NDVI), Vegetation Density (VD), and Land Surface Temperature (LST). A multiple regression analysis shows a strong correlation between these parameters and VD, with an R² value of 0.8673, indicating that 86.73% of the variation in vegetation density can be explained by these variables. Field validation, including drone photographs, crown and stem base density measurements, and plant size, ensures the accuracy of the satellite-derived data. The model uses the equation VD = 123.295486 x NDVI - 0.413961 x LST - 0.410253 x FCH - 3.173195, validated through field data. For processing field measurements, the equation LBDstemCor = 0.007645 x LBDcrown + 0.034348 x VD - 1.575439, with an R² value of 0.9564, further demonstrates its accuracy. To estimate carbon potential in kilograms per pixel (CPP), the equation CPP = LBDstemCor x FCHcor x 0.7 x 680 x 1.34 x 0.47 was used. The predicted carbon potential for Mount Babaris (1,576 ha) ranges from 607,767.55 to 607,829.54 tons, reflecting the model's precision in estimating carbon storage. This model plays a crucial role in monitoring and predicting carbon potential, supporting environmental management and climate change mitigation efforts. By integrating GIS and remote sensing, the model offers a scalable, replicable methodology adaptable to other regions with similar characteristics. It enhances the accuracy of carbon stock estimations and provides essential data for developing strategies to increase carbon sequestration, contributing to global climate change mitigation. The combination of satellite data, field measurements, and statistical analysis makes this model an invaluable tool for effective ecosystem conservation and restoration.

Page 4 of 6 | Total Record : 53

 2   3   4   5   6 

Filter by Year

2024 2024


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