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 38 Documents
 1   2   3   4 
Search results for , issue "Vol 5, No 2: MAY 2024" : 38 Documents clear
An Unsupervised Learning and EDA Approach for Specialized High School Admissions Paramita, Adi Suryaputra; Ramadhan, Arief
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.v5i2.178

Abstract

This research investigates disparities in access and representation within specialized high school admissions processes, focusing on public middle schools in New York City. Leveraging a dataset by a non-profit organization dedicated to increasing diversity in specialized high school admissions, the study employs exploratory data analysis and unsupervised learning techniques to identify schools with high levels of underrepresentation and academic potential. The analysis reveals significant disparities in access to specialized high schools, with certain demographic groups and schools facing barriers to entry. Through k-means clustering, schools are categorized based on their academic performance and demographic composition, enabling targeted intervention strategies to address disparities in access and representation. The research proposes general use towards education, including on-campus interventions, awareness campaigns, and regional information sessions, aimed at fostering equitable access to specialized high school programs. This study contributes to the broader discourse on educational equity and offers valuable insights for policymakers, educators, and researchers seeking to promote diversity and inclusion within educational systems.
Design of Green City with Lower Carbon based on Vegetation in Banjarbaru using Sentinel-2 Nirwana, Hanifah Dwi; Saidy, Akhmad Rizalli; Hatta, Gusti Muhammad; Nugroho, Agung
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.v5i2.218

Abstract

In addressing the pervasive issue of Urban Heat Islands (UHI) and the related carbon sequestration challenges in urban settings, this study utilizes Sentinel-2 imagery to propose a vegetative blueprint for the design green city with lower carbon in Banjarbaru. This research intricately links the role of increased vegetation cover in mitigating UHI effects and enhancing carbon absorption in urban environments. By employing a combination of Geographic Information Systems (GIS), field data, and real-time data via Wireless Sensor Networks (WSN), the study highlights the significant cooling and environmental benefits of strategically increasing green spaces in urban areas. Moreover, the study identifies specific zones within Banjarbaru that are optimal for the strategic placement of vegetation to maximize thermal comfort and carbon storage. This focus on localized green infrastructure development not only provides a pathway to more sustainable urban living conditions but also serves as a model for other cities facing similar ecological and climatic challenges. The integrated approach adopted here emphasizes continuous monitoring and dynamic adjustments in urban planning, ensuring long-term sustainability and resilience against the ongoing threats posed by climate change and urban expansion.
Coverless Text Information Hiding Based on Built-in Features of Arabic Scripts Rashid, Sabaa Hamid; Nasrawi, Dhamyaa A
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.v5i2.243

Abstract

Text steganography is crucial in information security due to the limited redundancy in text. The Arabic language features offer a new method for data concealment. In this paper, the researchers propose a new coverless text information hidingmethod based on built-in features of Arabic scripts. The first word of each row in the dataset is tested based on eight features to get one byte containing 1 or 0. That is a result of the presence or absence of the following features: mahmoze, diacritics, isolated, two sharp edges, vowels, dotted, looping, and high frequency.Then, each byte is converted to a decimal number (ASCII code) to implement a dynamic mappingprotocol with the most frequent letter.In the hiding process, each character in the secret message is converted to ASCII code and successfully matched in the dataset. Thus, after matching, the candidate text is sent to the receiver. In contrast, the pre-agreed dynamic mappingprotocol was implemented in a receiver to extract secret messages. Three Arabic datasets are used in this paper (SANAD (Single-Label Arabic News Articles Dataset) includes 45500 articles, Arabic Poem Comprehensive Dataset (APCD) contains 1,831,770 poetic verses in total, Arabic Poetry Dataset contains more than 58000 poems). The suggested approach withstands existing detecting methods because of no modification or generation. Moreover, there is an enhancement in hiding capacity, which can conceal a (character /word). Hence, all the messages are embedded successfully using dynamic mapping.
Performance Improvement of Covid-19 Cough Detection Based on Deep Learning with Segmentation Methods Suyanto, Suyanto; Zanjabila, Zanjabila; Atmaja, Bagus Tris; Asmoro, Wiratno Argo
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.v5i2.205

Abstract

COVID-19 is an emergency problem that is being widely discussed in the world, one of which is the deep learning-based COVID-19 detection method which has been developed based on images of the patient's chest or cough. In this research, we propose a way to improve the performance of deep learning-based COVID-19 cough detection by using a segmentation method to produce several audio files containing one cough signal from one audio file containing several cough sound signals. In addition, we enabled two automatic cough segmentation methods, namely a Hysteresis Comparator based on the power spectrum and an RMS threshold based on the RMS energy value. The results obtained show that using the segmentation method for cough sounds can improve the model's performance in detecting COVID-19 coughs by 4% to 8%. The segmentation process can also remove noise between cough sound signals and provide a standard input model in the form of one cough signal. In addition, the segmentation results show information related to the characteristics of COVID-19 cough. The evaluation results show that the hysteresis comparator method has better results with an unweighted accuracy (UA) value of 83.19% compared to the RMS threshold method with a UA value of 79.06%.
Information Technology Readiness and Acceptance Model for Social Media Adoption in Blended Learning: A Case Study in Higher Education Institutions in West Java, Indonesia Yusuf, Fahmi; Rahman, Titik Khawa Abdul; Subiyakto, Aang
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.v5i2.195

Abstract

Technological developments, including the internet, and learning opportunities are increasing. This also encourages the development of learning strategies and models. The blended learning model is applied in almost all universities in Indonesia and the world. With so many universities in Indonesia, implementing blended learning is a challenging thing because it requires a lot of technological preparation and human resources. This research aims to identify factors, develop a model, and evaluate the model to see the readiness and acceptance of technology for adopting social media in blended learning among private higher education institutes students in Indonesia. The population of this research is students from private higher education institutes in West Java, Indonesia, who are directly involved in using blended learning and social media. This quantitative research used a research instrument with five-Likert’s scale. The research population was 663,307, with a sample of 384 students spread across West Java. The contribution of this research is to make a significant contribution to the theoretical framework by expanding and refining existing concepts, providing a more comprehensive understanding of the readiness and acceptance factors for the adoption of social media in blended learning so that it has the potential to provide information to learning planners at private higher education institutes in West Java, Indonesia to help make the right decisions and optimize blended learning planning using social media technology. These findings statistically explain that 19 of 31 the hypotheses are the accepted ones. Moreover, nine of 12 variables influenced the readiness and acceptance of social media technology in blended learning based on the student perception among the private higher education institutions. They were the technological literacy factor, perceived validity, perceived trust, and technology readiness factors, namely optimism and Innovativeness, and technology acceptance factors, namely perceived effectiveness, perceived easy to use, intention to use and usage behaviour.
Insulation Coordination System 150kV Substation and Transmission Line against Lightning Surge Interference in a Nickel Smelting Plant Samad, Busyairi; Manjang, Salama; Kitta, Ikhlas; Utamidewi, Dianti; Amri, Arham
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.v5i2.248

Abstract

Related with the increasing demand for electrical energy at nickel smelting plant, a highly reliable electric power system is needed to be able to supply important loads such as electric furnaces and auxiliary equipment. The electric power system delivers electrical power to consumers through substations and transmission lines. The distribution of electrical power through high voltage overhead lines (SUTT) sometimes goes through areas with a high enough lightning strike potential that it can cause sudden blackouts due to direct strikes and back flashovers. Therefore, it is necessary to insulation coordination of the substation and transmission line to avoid damage to electrical equipment. This research aims to determine the magnitude of the voltage due to lightning strikes on GSW and Conductor by varying the location of the lightning protection system on 150 kV overhead line which is useful for obtaining isolation coordination systems on transmission lines and substations in the nickel smelting industry. which is useful for obtaining isolation coordination systems on transmission lines and substations in the nickel smelting plant. This research was carried out by selecting lightning strikes in the current strike of 100 kA on transmission line with simulation use device ATP draw software. This research showed that the installation of lightning protection equipment on high-voltage overhead lines and transmission towers resulted in a significant voltage drop due to lightning strikes lowering under BIL existing insulators.
Analysis Of Product Recommendation Models at Each Fixed Broadband Sales Location Using K-Means, DBSCAN, Hierarchical Clustering, SVM, RF, and ANN Trianasari, Nurvita; Permadi, Thifan Anjar
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.v5i2.210

Abstract

The telecommunications industry proliferates in the digitalization era, especially Fixed Broadband services. Fast and stable internet access is essential, especially at sales locations with appropriate products. This research aims to develop an optimal product recommendation model for each sales location, using machine learning with a mixed method approach, with a combination method of clustering and classification, where the clustering method is used for the geographic segmentation stage. Then, the results of each cluster from the geographic segmentation are used as input for the classification method, which is a stage called sales forecasting. Next, the performance analysis measured the accuracy level of each combination of models. The best model combines clustering and classification models, which, on average, across all clusters, gives the best accuracy value. The data used in this research is GIS-based POI data and sales history data, which is internal data from a telecommunications company in Indonesia. From the tests carried out in this research, the best model combination is the K-Means and the Random Forest models, with an accuracy value of 82.08%. Meanwhile, the lowest performance resulted from a combination of the K-Means and ANN models with an accuracy value of 79.50%. With an average combination model performance above 80%, this research shows that using mixed methods with clustering and classification can provide valuable insights in subsequent research, especially in the context of the telecommunications industry, especially in fixed broadband services.
Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions Baroud, Shadia Yahya; Yahaya, Nor Adnan; Elzamly, Abdelrafe M.
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.v5i2.196

Abstract

Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.

Page 4 of 4 | Total Record : 38

 1   2   3   4 

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