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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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Exploring Machine Learning to Support Software Managers' Insights on Software Developer Productivity Suwarno, Suwarno; Christian, Yefta; Yoputra, Keaton; Estrada, Yuki
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.661

Abstract

Software developer productivity is a complex issue with no single, universally accepted definition or measurement. Emerging technologies like machine learning offer a promising opportunity for more accurate productivity measurement. Semi-structured interviews were conducted to gain qualitative insights into software managers’ perception of developer productivity to identify issues and inform the development of applied machine learning solutions. It was discovered that digital distractions significantly hinder developer productivity and conventional methods to monitor developer activity were often inefficient. Therefore, machine learning models were developed to monitor developer activity by classifying screenshots captured during activity, along with the URL and text content scraped from accessed URLs. Train and test data were obtained from a cooperating software house, supplemented with online sources. For screenshot classification, transfer learning using EfficientNetV2B0 outperformed InceptionV3, Resnet50V2, and VGG16, reaching 99.6% accuracy. This was achieved without fine-tuning, which resulted in the fastest training and lowest resource consumption. For content classification, SVC hyperparameter-tuned using grid search outperformed six other classifiers, reaching 88.5% accuracy. The design concept for a web application that utilizes the developed models to help managers measure developer productivity was well-received by the managers interviewed.
Modeling Female Contraceptive Recommendation Using Hybrid Analytical Hierarchy Process and Profile Matching Zaidiah, Ati; Astriratma, Ria; Isnainiyah, Ika Nurlaili
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.613

Abstract

Multi-criteria decision-making (MCDM) methodologies have been extensively employed across various domains within healthcare. They can be utilized for disease prediagnosis, aiding clinical decision-making (e.g., surgery), conducting health technology assessments, as well as establishing healthcare priorities. This research presents the outcomes of a hybrid MCDM strategy, integrating the AHP and Profile Matching to facilitate clinical recommendations related to female contraception. Many cases in Indonesia show acceptors' inappropriateness in using available contraceptives, causing side effects resulting in negative effects. The challenges in the Keluarga Berencana or Planned Parenthood program in Indonesia are increasing, based on the decline in the number of new acceptors and the high unmet needs for contraception. Failure to meet the need for contraception has the potential to increase birth rates and maternal mortality rates, which requires serious attention and the development of appropriate strategies. Based on the problem, this study aims to create a decision support model in selecting suitable contraceptives for acceptors. The criteria used in this study consisted of age, medical history, weight (BMI), breastfeeding or not, history of childbirth, period of use, and income. The seven criteria are implemented in AHP with a consistency test result value of 2.2%. Based on the target value of contraceptives obtained from the results of Profile Matching, compatibility was determined with a sample of three acceptor profiles. The results that have been achieved indicate a sample recommendation model for acceptors of IUD-type contraception that can assist midwives or medical personnel in providing recommendations for selecting appropriate contraceptive methods. Future studies can integrate the results of recommendations for health service providers (e.g., hospitals, Public Health Center or Puskesmas) in procuring contraceptives.
Integration of Sentiment Analysis and RFM in Restaurant Customer Segmentation: A 7P-Based CRM Model with Clustering Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni; Rachmawati, Rina
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.633

Abstract

The increasing use of digital platforms like Tripadvisor has created opportunities to transform customer review data into strategic insights for Customer Relationship Management (CRM). This study proposes a novel CRM model by integrating the Recency, Frequency, Monetary (RFM) framework with the 7P marketing mix to segment restaurant customers more effectively. Using 3,716 Tripadvisor reviews, annotated based on 7P elements and clustered through unsupervised learning, three key customer segments were identified: acquisition, retention, and win-back. Evaluation metrics show strong clustering performance with a Silhouette Score of 0.73 and a Davies-Bouldin Score of 0.08. The acquisition cluster (Product) demonstrates the highest Frequency (37,664) and Monetary value (64.94), signifying high engagement and revenue potential. The retention cluster (Physical Evidence, Place, Process, Promotion, Traveler) shows stable interaction patterns with Recency values of 1261–1262 and moderate Frequency (378–2,079). The win-back cluster (Price, People) reflects lower Frequency (198–946) but equal Recency (1259), indicating recent but infrequent activity, which is ideal for reactivation strategies. By mapping customer reviews to 7P labels and analyzing them using RFM, the model uncovers specific behavioral patterns tied to service quality, pricing, and promotions. This integration allows restaurants to apply tailored strategies: offering loyalty rewards to high-frequency customers, promotional incentives for those with high Recency, and prioritizing high-monetary customers for exclusive programs. The novelty of this research lies in its combined use of sentiment-based review analysis and RFM–7P segmentation, offering a scalable, data-driven framework for enhancing customer satisfaction, loyalty, and long-term business growth in the restaurant industry.
Analyzing the Impact of Social Media Advertising on Consumer Interactions, Trust, and Purchase Intentions in the Cosmetics and Personal Care Sector Yee, Loo Shu; Hoo, Wong Chee; Wolor, Christian Wiradendi; Bakar, Syarifah Mastura Syed Abu; Nurkhin, Ahmad
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.669

Abstract

This study analyses the influence of social media advertisements on consumer purchasing intentions within Malaysia's cosmetics and personal care sector, highlighting the significance of interactivity, informativeness, and trust. Considering the growing prevalence of social media in marketing, comprehending how these elements affect consumer choices is crucial for enhancing digital advertising strategies.  This research utilises a quantitative approach to evaluate consumer behaviour. Data were gathered from 384 Malaysian consumers aged 18 and older who actively interact with cosmetic and personal care brands on social media using online survey. The research employed descriptive analysis, reliability and validity assessments, and hypothesis testing to investigate the relationships among principal variables.  Research demonstrates that informativeness and trust in social media advertisements substantially increase purchase intentions, with trust acting as a vital mediator. Although interactivity enhances trust, it does not directly affect purchase intention, indicating that its influence may be contingent upon contextual variables. These findings underscore the imperative for brands to deliver transparent, reliable, and informative content to enhance consumer trust and stimulate purchasing behaviour. The study's originality is rooted in its examination of the Malaysian cosmetics and personal care market, providing practical insights for marketers to enhance social media advertising strategies. The research underscores trust as a pivotal factor, offering essential insights for brands aiming to improve their digital engagement. This study enhances the existing literature on digital marketing in emerging markets, providing practical insights for businesses seeking to improve their social media efficacy in Malaysia.
Intelligent Solar Panel Monitoring Using Machine Learning and Cloud-Based Predictive Analytics Dhilipan, J.; Vijayalakshmi, N.; Shanmugam, D.B.; Maidin, Siti Sarah; Shing, Wong Ling
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.720

Abstract

The increasing global energy demand necessitates reliable and sustainable solutions, with solar photovoltaic (PV) technology emerging as a key carbon-neutral option. However, optimizing solar energy systems requires advanced monitoring and predictive analytics to enhance efficiency and ensure long-term performance. This study introduces an Internet of Things (IoT)-based solar energy monitoring system, integrating machine learning algorithms and cloud computing to enhance real-time performance assessment. The proposed system employs K-Means Clustering for condition classification, Support Vector Machine (SVM) for fault detection, Long Short-Term Memory (LSTM) for energy forecasting, Prophet for time-series predictions, and Isolation Forest for anomaly detection. The system was validated using a 125-watt photovoltaic module, monitoring temperature, solar radiation, voltage, and current. A Wi-Fi-enabled microcontroller collects data, which is processed through a cloud-based platform and visualized via the Blynk application. Experimental results demonstrate 94.2% energy prediction accuracy using LSTM, 89.7% fault classification accuracy with SVM, and 88.5% anomaly detection accuracy with Isolation Forest, confirming high reliability. The system's wireless tracking mechanism minimizes resource consumption, ensuring scalability and adaptability for commercial and industrial applications. The integration of IoT, machine learning, and cloud analytics provides a cost-effective and scalable approach for solar PV optimization. Future enhancements include deep learning models and reinforcement learning algorithms to improve energy forecasting, fault detection, and adaptive optimization, ensuring greater efficiency, resilience, and sustainability in solar energy management.
Assimilate Grid Search and ANOVA Algorithms into KNN to Enhance Network Intrusion Detection Systems Alsharaiah, Mohammad A.; Almaiah, Mohammed Amin; Shehab, Rami; Alkhdour, Tayseer; AlAli, Rommel; Alsmadi, Fares
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.604

Abstract

The recent progress of operational network intrusion detection systems (NIDS) has become increasingly essential. Herein, a fruitful attempt to introduce an innovative NIDS methodology that integrates the grid search optimization algorithm and ANOVA techniques with the K nearest neighbor (KNN) algorithm to analyze both spatial and temporal characteristics of data for network traffic. We employ the UNSW-NB15 benchmark dataset, which presents various patterns and a notable imbalance between the training and testing data, with 257674 samples. Therefore, the Synthetic Minority Oversampling Technique has been used since this method is effective in handling imbalanced datasets. Further, to handle the overfitting issue the K folds cross-validation method has been applied. The feature sets within the dataset are meticulously selected using ANOVA mechanisms. Subsequently, the KNN classifier is fine-tuned through hyperparameter tuning using the grid search algorithm. This tuning process includes adjusting the number of K neighbors and evaluating various distance metrics such as 'euclidean', 'manhattan', and 'minkowski'. Herein, all attack types in the dataset were labeled as either 1 for abnormal instances or 0 for normal instances. Our model excels in binary classification by harnessing the strengths of these integrated techniques. By conducting extensive experiments and benchmarking against cutting-edge machine learning and deep learning models, the effectiveness and advantages of our proposed approach are thoroughly demonstrated. Achieving an impressive performance of 99.1%. Also, several performance metrics have been applied to assess the proposed model's efficiency.
Classification of Batik Motifs Using Multi-Texton Co-Occurrence Descriptor and Binarized Statistical Image Features Maulana, Ahmad Rizki; Suprapto, Suprapto; Tyas, Dyah Aruming
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.622

Abstract

This study aims to enhance the classification accuracy of batik motifs through a novel integration of Multi-Texton Co-Occurrence Descriptor (MTCD) and Binarized Statistical Image Features (BSIF). The primary objective is to develop a robust feature extraction method that effectively captures both textural and statistical properties of batik images, specifically utilizing the Batik Nitik 960 dataset. Our methodology employs a combination of MTCD and BSIF, followed by Principal Component Analysis (PCA) for dimensionality reduction, optimizing the model's ability to learn from diverse characteristics inherent in batik motifs to augment the diversity and robustness of the training data, we enhanced the Batik Nitik 960 dataset by applying vertical flipping, in addition to existing rotations. We explored three feature fusion approaches: Combination 1, where features are combined before normalization and PCA, achieving an accuracy of 99.948%; Combination 2, where normalization occurs prior to feature combination, also achieving an accuracy of 99.948%; and Combination 3, which applies PCA separately to each feature before combination, resulting in an accuracy of 99.896%. Experimental results demonstrate a remarkable accuracy in classifying these motifs, with the combined MTCD-BSIF features significantly surpassing the individual performances of MTCD at 95.729% and BSIF at 99.531%. This substantial improvement addresses the limitations identified in previous research, which reported an accuracy of only 0.71 on the same dataset. Furthermore, we explore the impact of various feature fusion techniques on classification performance, providing insights into the effectiveness of our proposed methods. Our findings suggest that the combined MTCD-BSIF approach can serve as a benchmark for future studies aiming to enhance classification accuracy in similar domains, thereby contributing to advancements in automated classification systems and their applications across various fields.
Applying Structural Equation Modelling to Examine the Impact of Environmental Management Accounting on Financial Nguyen, Nga Thi Hang
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.728

Abstract

Environmental management accounting has garnered significant attention from various stakeholders. Enterprises that effectively implement environmental management accounting not only contribute to sustainable development but also enhance organizational performance. This study aims to examine the relationship between environmental management accounting, green innovation, and the financial performance of small and medium-sized enterprises in the context of a developing country like Vietnam. A quantitative research approach was employed to analyze data collected through a structured survey. The dataset comprises responses from 151 small and medium-sized enterprises, with financial managers and management accountants serving as key informants. Data analysis was conducted using the Smart Partial Least Squares software. The findings reveal that environmental management accounting has a direct positive impact on financial performance and an indirect impact through the mediation of green process innovation. While green product innovation exerts a direct impact on financial performance, environmental management accounting appears to have no significant influence on green product innovation. Consequently, green product innovation does not function as a mediating variable in the relationship between environmental management accounting and financial performance. The results underscore the greater significance of green process innovation over green product innovation in driving improvements in the financial performance of small and medium-sized enterprises. These results significantly contribute to the relatively unexplored theoretical relationship between environmental management accounting, green innovation, and the financial performance of small and medium-sized enterprises. Furthermore, the study provides a practical foundation for managers to boost their organizations’ financial performance by practicing environmental accounting and integrating green innovation into business operations.
Improving MCDM University Rankings through Statistical Validation Using Spearman’s Correlation and THE Benchmark Andryana, Septi; Mantoro, Teddy; Gunaryati, Aris; Raffliansyah, Alfarizky Esah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.796

Abstract

The evaluation of higher education institutions is a critical field for informing data-driven policy and institutional benchmarking. A key problem in this area is the lack of transparency and consistency in university rankings, particularly when using Multi-Criteria Decision-Making (MCDM) methods such as MABAC and MAIRCA, with limited research on how weighting techniques affect the reliability and alignment of these rankings with international standards like the Times Higher Education (THE) Rankings. This study proposes the use of MABAC and MAIRCA methods combined with two weighting techniques—Rank Order Centroid (ROC) and Rank Sum (RS)—to assess 20 top Indonesian universities based on five performance indicators: research quality, research environment, teaching, industry, and international outlook. Spearman’s rank correlation is used to compare the MCDM-generated rankings with THE Rankings 2025. The study contributes empirical evidence on the impact of weighting schemes on the consistency and reliability of university rankings and demonstrates that the MAIRCA-ROC method achieves the highest agreement with THE Rankings, with a correlation coefficient of 0.8135 and a p-value of 0.00001. These results validate the use of MCDM methods in higher education evaluation and emphasize the importance of selecting appropriate weighting techniques to develop transparent and robust ranking frameworks that support evidence-based policy decisions.
Mathematical Modeling of Water Quality Dynamics in Aquaculture: A Foundation for IoT Integration and Machine Learning-Driven Predictive Analytics Sarif, Muhammad Irfan; Efendi, Syahril; Sihombing, Poltak; Mawengkang, Herman
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.819

Abstract

Effective water quality management is paramount for sustainable aquaculture, yet conventional methods often fall short in providing timely and predictive insights. This paper details the development and analysis of a comprehensive suite of mathematical models designed to simulate key water quality dynamics in aquaculture systems. These models encompass critical biogeochemical processes, including the nitrogen cycle (ammonia, nitrite, nitrate, organic nitrogen), phosphorus cycle, Dissolved Oxygen (DO) balance, and Biochemical Oxygen Demand (BOD). Simulation results derived from these models illustrate the temporal evolution of these critical parameters, demonstrating their capability to capture complex interactions and provide a mechanistic understanding of the aquatic environment. This foundational modeling approach offers a robust tool for quantitative analysis and prediction of system responses under various conditions. The core contribution of this work is the articulation of these mathematical models, which serve as a crucial foundation for advanced, data-driven aquaculture management. To enhance their practical utility, we propose a conceptual framework for integrating these models with Internet of Things (IoT) sensor networks. Real-time data acquisition via IoT can be essential for model parameterization, continuous calibration, and validation against operational conditions. Furthermore, this paper discusses how outputs from these validated mechanistic models can serve as robust inputs for Machine Learning (ML) algorithms. This synergy enables the development of sophisticated predictive analytics for critical events, such as forecasting water quality deterioration, and supports optimized, proactive management strategies. This research lays the theoretical and methodological groundwork for developing more precise and resilient decision support systems in aquaculture. By emphasizing the synergistic potential of combining foundational mathematical modeling with data science techniques like IoT and ML, this work aims to contribute to transforming aquaculture into a more productive, sustainable, and environmentally responsible industry. Future efforts should focus on empirical validation and the practical implementation of the proposed integrated framework.

Page 39 of 52 | Total Record : 518

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