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Transaction on Informatics and Data Science
Published by Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Purwokerto
ISSN : -     EISSN : 30641772     DOI : https://doi.org/10.24090/tids
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Transactions on Informatics and Data Science (TIDS), with ISSN: 3064-1772 (online), is a scientific journal that publishes the latest research in the fields of informatics and data science, focusing on both theoretical advances and practical applications. Published by the Department of Informatics, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto, Purwokerto, this journal serves as a platform for researchers, academics, and practitioners to share new ideas and innovations in data science, artificial intelligence, natural language processing, cloud computing, and information technology applications across various domains. It promotes collaboration and deep knowledge exchange within the scientific community, bridging the gap between theory and practice in the rapidly evolving fields of informatics and data science. Aims Transaction on Informatics and Data Science aims to advance the frontiers of informatics and data science knowledge by publishing high-quality research that encompasses theoretical advancements and practical applications. The journal seeks to contribute significantly to the understanding and developing of innovative approaches, methodologies, and technologies in these domains. Scopes The scope of "Transaction on Informatics and Data Science" covers a wide range of topics related to informatics and data science, including but not limited to: - Data analysis and mining - Artificial intelligence and machine learning - Natural language processing and understanding - Cloud computing and big data technologies - Information retrieval and knowledge management - Data-driven decision-making and predictive modelling - Internet of Things (IoT) and data analytics - Cybersecurity and privacy in data science - Informatics and data science applications in various healthcare, finance, education, and other domains. The journal welcomes original research articles, reviews, case studies, and technical notes that contribute significantly to advancing knowledge and practice in informatics and data science. Submissions should demonstrate novelty, tightness, and relevance to the rapidly evolving landscape of information technology and data-driven decision-making processes.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
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Search results for , issue "vol. 3 no. 1 (2026)" : 5 Documents clear
“I Don’t Know Whom to Trust”: A Quantitative Study of Cybersecurity Awareness, Stress, and the Digital Divide Among Older Adults in India Priyanacy Gupta
Transactions on Informatics and Data Science Vol. 3 No. 1 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i1.13999

Abstract

This study investigates cybersecurity awareness and perceptions among older adults in Indore, India, focusing on psychological aspects, behavioural factors, and levels of knowledge regarding data protection. The rapid proliferation of Information Technology has not been matched by adequate digital literacy among seniors, leaving them vulnerable to online threats such as financial scams and identity theft. Using a quantitative approach, a structured questionnaire was administered to 115 elderly respondents selected through purposive sampling: 44% aged 50–60, 43% aged 60–70, and 13% aged 70+. 56% of respondents resided in rural areas. Data were analysed using SPSS, employing correlation, regression, and ANOVA. The findings reveal that a lack of technological knowledge significantly contributes to stress (B = 0.430, p < 0.001) and reduces quality of life (B = 0.418, p < 0.001). Behaviours such as trusting anonymous callers and sharing financial information were associated with low data privacy awareness. Correlation analysis indicated a strong negative association between increased knowledge of data protection and concerns about password security (r = –0.625, p < 0.001). The study concludes that significant knowledge gaps heighten older adults’ vulnerability to cyber threats, adversely affecting their psychological well-being and online behaviour. Future research should expand to more diverse and representative samples and evaluate the effectiveness of targeted cybersecurity training programmes. Policy implications include developing senior-friendly technology standards, community-based digital literacy initiatives, and stronger regulatory frameworks for data privacy to foster a safer digital environment for India’s ageing population
Leveraging L-BFGS Optimization and Activation Functions in Multi-Layer Perceptron for Small-Scale Credit Risk Assessment Dataset Ngọc Thiện Nguyễn
Transactions on Informatics and Data Science Vol. 3 No. 1 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i1.14945

Abstract

The urgent need for accurate credit risk models often clashes with the practical reality of operating with small, imbalanced datasets, where standard deep learning configurations can be inefficient. This research addresses the critical gap in optimizing models for such common yet understudied scenarios. We conduct a rigorous empirical study using a Multi-Layer Perceptron on a 381-instance mortgage dataset, systematically comparing the performance of a quasi-Newton optimizer (L-BFGS) against first-order methods (Adam, SGD) across four distinct activation functions. The methodology is grounded in the theory that L-BFGS is better suited for deterministic, full-batch optimization. The results show that while the Adam optimizer with a Logistic function achieved the highest F1-Score (0.8966), the L-BFGS optimizer produced a highly competitive F1-Score (0.8849). Critically, L-BFGS converged in just 55 iterations compared to Adam's 1556, validating its theoretical efficiency for this problem scale and revealing a significant performance-versus-cost trade-off. This study concludes that L-BFGS provides a superior practical solution for small-scale credit risk assessment, offering a near-optimal balance between high predictive accuracy and exceptional computational efficiency, especially in resource-constrained environments where rapid convergence is essential and model interpretability, robustness, and reproducibility are equally prioritized for deployment.
Comparison of AI Models for Predicting Shelf Life of Roasted Coffee Drink Sumit Goyal
Transactions on Informatics and Data Science Vol. 3 No. 1 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i1.15167

Abstract

This paper explores the application of artificial intelligence (AI), specifically artificial neural networks (ANN), to predict the shelf life of roasted coffee beverages. The research focuses on two advanced AI models: a competitive neural network based on self-organizing principles and a dynamic neural network with time-delayed backpropagation. Both models were rigorously tested and evaluated using several statistical benchmarks, including mean squared error (MSE), root mean square error (RMSE), R² (coefficient of determination), and E² (efficiency coefficient). The results reveal that the competitive neural network outperformed the time-delay backpropagation model. The competitive model achieved an exceptionally low mean squared error of 0.000905264 and a root mean square error of 0.030087601. Its R² value was 0.990947362, indicating a nearly perfect fit, and its E² efficiency coefficient was 0.998759409. In contrast, the time-delay backpropagation model produced a higher MSE of 0.005421264, RMSE of 0.07362923, R² of 0.945787365, and E² of 0.999948307. Using the superior competitive model, the predicted shelf life for roasted coffee drinks was 43 days. This estimate closely matches the observed shelf life of 45 days, demonstrating that ANNs, particularly competitive models, can reliably predict product freshness, potentially benefiting both manufacturers and consumers.
Prediction of Etawa Goat Productivity using Decision Tree Algorithm and Optimized with Particle Swarm Optimization Muhamad Nasim; Purwanto; Catur Supriyanto
Transactions on Informatics and Data Science Vol. 3 No. 1 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i1.15934

Abstract

The productivity of Etawa goats is influenced by various factors, including physical condition, environment, and care provided. This study proposes a Decision Tree (DT) algorithm optimized using Particle Swarm Optimization (PSO) to more accurately predict Etawa goat productivity. A case study was conducted at the "Bang Usman" farm in Pekalongan Regency, Central Java, Indonesia, using secondary data collected from January to December 2015. The dataset comprises 500 samples with 13 attributes, including goat age, body weight, body height, housing condition, feeding accuracy, feed type, reproductive cycle, daily milk production, ear type, horn shape, coat color, forehead shape, and mating calendar. Predicting productivity is a critical data-mining technique for measuring success in agricultural industries. The standard Decision Tree achieved an accuracy of 92.60%. By integrating PSO for attribute selection and parameter optimization, the PSO-DT model demonstrates measurable improvements: accuracy increased by 1.20%, precision by 3.04%, and recall by 0.68%. The model also identifies the eight most influential attributes to support farm decision-making. This research contributes a validated PSO-DT model that outperforms standard DT, offering practical value for livestock management and serving as a reference for future studies on optimization algorithms in agricultural productivity prediction in Indonesia.
Influence of Usability of Digital Population Identity Application on User Satisfaction at Dukcapil Sleman, Yogyakarta, Indonesia Prisccillia Titis Kristy Ningrum; Rahmat Rian Hidayat; Arum Marwati; Anas Azhimi Qalban
Transactions on Informatics and Data Science Vol. 3 No. 1 (2026)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v3i1.16222

Abstract

Sleman Regency recorded the highest activation rate of the Digital Population Identity (IKD) application in the Special Region of Yogyakarta at 13.07% as of February 2025. However, user complaints regarding system obstacles and complex activation processes persist on digital platforms. This study aims to determine how the usability dimensions of learnability, efficiency, memorability, and error handling influence user satisfaction at the Sleman Population and Civil Registration Agency (Dukcapil). Using a quantitative causal-associative approach, data were collected from 100 respondents through simple random sampling. A structured questionnaire of 32 items using a 5-point Likert Scale was distributed via Google Forms. Multiple linear regression analysis was employed to identify the specific influence of each usability dimension. Validity was assessed using the Pearson Product-Moment correlation, and reliability was confirmed using Cronbach's Alpha. The results indicate that usability dimensions significantly affect user satisfaction, with an F-value of 13.945 exceeding the F-table of 2.47, and a total contribution of 37% based on the coefficient of determination. Despite high satisfaction among digital natives, the study reveals critical areas for improvement in memorability and error aspects. Developers should prioritize optimizing server performance and simplifying the interface to minimize user errors and enhance inclusive satisfaction across all demographics.

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