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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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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.
Determining Important Features for Dengue Diagnosis using Feature Selection Methods Bria, Yulianti Paula; Nani, Paskalis Andrianus; Siki, Yovinia Carmeneja Hoar; Mamulak, Natalia Magdalena Rafu; Meolbatak, Emiliana Metan; Guntur, Robertus Dole
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.v6i1.445

Abstract

This research aims to determine the important features including symptoms and risk factors for dengue diagnosis. This study’s dataset was obtained from medical records collected from two hospitals in Indonesia from patients with dengue and nondengue diseases. Four feature selection methods including feature importance, recursive feature elimination, correlation matrix and KBest were leveraged to determine significant features. Feature importance employed a tree-based classifier to derive the importance scores of the features. Recursive feature elimination employed a machine learning classifier to choose the most important features from the given dataset. Correlation matrix was employed to select the best features because it has the ability to use the correlation between each feature with the target. Univariate feature selection – Kbest has the ability to choose the best features based on univariate statistical tests. Important features were also gathered from fifteen Indonesian medical doctors to confirm the results. We used six machine learning techniques for dengue prediction. The random forest classifier yields the highest accuracy for the best combination of features with the accuracy of 0.93 (LR: 0.90 (0.04), KNN: 0.89 (0.04), XGBoost: 0.91 (0.03), RF: 0.93 (0.04), NB: 0.88 (0.09), SVM: 0.89 (0.04)) and precision of 0.90 (LR: 0.86 (0.22), KNN: 0.67 (0.14), XGBoost: 0.77 (0.13), RF: 0.90 (0.13), NB: 0.66 (0.20), SVM: 0.66 (0.18)). This study shows the significant features for dengue diagnosis including fever, fever duration, headache, muscle and joint pain, nausea, vomiting, abdominal pain, shivering, malaise, loss of appetite, shortness of breath, rash, bleeding nose, bitter mouth, temperature and age. This knowledge is pivotal to educate society to seek medical advice when dengue symptoms appear to avoid severe conditions. Arthralgia/joint pain and myalgia/muscle pain are the most significant features for the dengue prediction. This knowledge is important for medical doctors as a starting point for clinical dengue diagnosis.
Applied Structure Equation Model for Policy Suggestions to Develop the Digital Economy in Vietnam Hien, Lam Thanh; Tam, Phan Thanh
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.v6i1.525

Abstract

In the current context, promoting innovation in digital economic growth models associated with economic restructuring is a prerequisite for sustainable development. Therefore, the article aims to explore the critical factors influencing the digital economy and proposes policy recommendations for developing the digital economy. The study applies quantitative research methods mainly through actual data surveying of economic experts to evaluate factors affecting the digital economy based on the structural equation model to measure the digital economy in five big cities of Vietnam, including Hanoi, Hai Phong City, Da Nang City, Ho Chi Minh City, and Can Tho City. The data collection strategy involves direct interviews via a structured questionnaire, with a sample size of 800 economic experts, and analysis using SPSS version 20.0 and Amos software. The study's novelty identifies eight critical factors influencing the digital economy at a significance level of 0.01 and eight accepted hypotheses, including (1) Information technology and digital infrastructure, (2) Digital transformation capacity in businesses, (3) Government policies and laws, (4) Human resources, (5) Digital consumer needs and behavior, (6) E-commerce and financial technology, (7) International economic integration, and (8) Market. The findings highlight the significant influence of information technology and telecommunications infrastructure on Vietnam's digital economy. Finally, the authors proposed policy recommendations to enhance the digital economy; moreover, the digital economy is a way of doing business that relies on digital technology and data as its main inputs, operates mainly in a digital environment, and employs information and communication technologies to boost labor productivity, create new business models, and optimize economic structures. This model can be used by agencies, researchers, experts, and economic managers.
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.
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset Anam, M. Khairul; Munawir, Munawir; Efrizoni, Lusiana; Fadillah, Nurul; Agustin, Wirta; Syahputra, Irwanda; Lestari, Tri Putri; Firdaus, Muhammad Bambang; Lathifah, Lathifah; Sari, Atalya Kurnia
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.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
A Framework for Diabetes Detection Using Machine Learning and Data Preprocessing Abu-Shareha, Ahmad Adel; Qutaishat, Haneen; Al-Khayat, Asma
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.v5i4.363

Abstract

People with diabetes are at an increased risk of developing other complications, such as heart disease and nerve damage. Therefore, diabetes prediction is crucial to reduce the severe consequences of this disease. This study proposed a comprehensive framework for diabetes prediction to maximize the information from available diabetes datasets, which include historical records, laboratory tests, and demographic data. The proposed framework implements a data imputation technique for filling in missing values and adopts feature selection methods to remove less important features for better diabetes classification. An oversampling technique and a parameter tuning approach were used to increase the samples and fine-tune the parameters for training the machine learning algorithms. Various machine learning algorithms, including Neural Networks, Logistic Regression, Support Vector Machines, and Random Forest, were used for the prediction. These algorithms were evaluated using both train-test split and cross-validation techniques. The experiments were conducted on the Pima Indian Diabetes dataset using various evaluation metrics, including accuracy, precision, recall, and F-measure. The results showed that the Random Forest algorithm, particularly when fine-tuned with Grid Search Cross Validation, outperformed other algorithms, achieving an impressive accuracy of 0.99. This demonstrates the robustness and effectiveness of the proposed framework, which outperformed the accuracy of state-of-the-art approaches.
Factors Influencing User Satisfaction with Mobile Applications for Promoting Thai Community Products Pislae-ngam, Kattakamon; Inmor, Sureerut; Pukrongta, Nisit
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.v5i4.383

Abstract

This study investigates key factors affecting user satisfaction with mobile applications like Shopee, Lazada, and TikTok Shop, focusing on promoting community products in Pathum Thani Province, Thailand. As mobile applications gain significance for marketing local goods, the research aims to explore how various features influence satisfaction and trust among users. The study collected data from 400 local entrepreneurs between January and March 2024, all experienced in using mobile apps to sell products. A confirmatory factor analysis (CFA) was conducted to examine five critical factors: requirements, accessibility, accuracy, security, and trust. The findings indicate that accuracy (β = 0.75) and accessibility (β = 0.71) significantly impact user satisfaction, emphasizing the importance of precise content and ease of use. Additionally, security (β = 0.76) and trust (β = 0.72) play crucial roles in maintaining user confidence in app transactions. All model indicators were validated at the 0.01 significance level, indicating a good fit for the hypothesized relationships between factors. The study’s novelty lies in highlighting specific app features that enhance user experiences in promoting local products. By focusing on the essential aspects of mobile app functionality, this research provides valuable insights to developers and local businesses for creating effective platforms, ultimately supporting sustainable economic growth.
Palm Oil Industry Dynamics: Assessing P/B Ratios of Indonesian Palm Oil Companies through Palm Tree Profile, Average FFB Yield, and Palm Oil Extraction Rate Hadi, Nixxen Dimitri; Handoko, Bambang Leo; Lindawati, Ang Swat Lin; Sarjono, Haryadi
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.v6i1.483

Abstract

The primary objective of this paper is to explore the impact of palm tree profile (age), Fresh Fruit Bunch (FFB) yield, and Oil Extraction Rate (OER) on the Price-to-book (P/B) ratio of Indonesian palm oil companies listed on the Indonesia Stock Exchange (IDX) with return on equity (ROE) as a mediating variable. This study is important because it explores the variables that affect business valuations in a vital industry that employs more than 16 million people and generates huge export earnings, significantly supporting the Indonesian economy. Multiple linear regression is used in a quantitative analysis of secondary data gathered from the financial statements and annual reports of 15 palm oil enterprises from 2013 to 2023. The results show a strong positive relationship between OER and FFB yield with the P/B ratio, indicating that increased operational productivity and efficiency raise firm values. Specifically, the regression analysis revealed that each percentage point increase in OER is associated with a 0.1546 increase in the P/B ratio (p 0.001), and each unit increase in FFB yield contributes to a 0.1013 increase in the P/B ratio (p 0.001). On the other hand, the P/B ratio is negatively impacted by palm tree age, suggesting that older palms are less productive, with a coefficient of -0.1035 (p 0.001). The relationship between productivity ratio and valuation was also shown to be influenced by Return on Equity (ROE), which was identified as a mediating variable. The findings suggest that enhancing internal factors, such as plantation management and mill efficiency, can improve company valuation. It is recommended for future research to use larger sample sizes and longer observation periods to confirm these findings and explore additional variables.
Stochastic Queuing System Model Design Based on Stakeholder Aspirations Widodo, Imam Djati; Parkhan, Ali; Qurtubi, Qurtubi
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.v5i4.314

Abstract

A good queuing system will provide satisfaction and trust for consumers and operational cost efficiency for service providers. This study aims to obtain the optimal number of service facilities by considering the aspirations of stakeholders, namely customers and service providers. Using aspiration theory, this research contributes to obtaining a dynamic solution to the number of service facilities with reference to service operating costs that can be determined with certainty and waiting costs that vary based on customer profiles. The study began by designing sampling for arrival time and service time data based on simple random sampling. The probability distributions of arrival time and service time are determined based on the data collection results of the sampling design. Based on the queuing profile and distribution of the two data, a suitable queuing model is built. Poisson distribution-based multi-channel queue model is constructed ((M/M/c):(GD/∞/∞)), and an optimization analysis is carried out on the number of service facilities provided by considering the aspirations of the two stakeholders. The results showed that based on stakeholder aspirations, optimal conditions were achieved at the number of servers c = 2 if the waiting cost (C2): IDR 0/hour≤ C2 ≤ IDR 11,076/hour, and the number of servers c = 3 if the waiting cost (C2): IDR 11,076/hour ≤ C2 ≤ IDR 120,690/hour.  Given that there are two conditional alternatives, the company can decide subjectively to take preventive and adaptive actions proactively according to the customer's appreciation of the waiting time in the company. Flexibility in opening service facilities will require the availability of workers and facilities to be provided. Multi-skilled workers will significantly help the flexibility of the system being built. Future research certainly needs to conduct a more in-depth study related to monthly fluctuations in arrival and service times within that period.

Page 29 of 52 | Total Record : 518

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