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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 3 Documents
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Search results for , issue "Vol. 2 No. 2 (2025)" : 3 Documents clear
Positional Accuracy Measurement: Prototype Geographic Information System for Banyumas Regency Tourism Priyono, Putra Aditya; Qalban, Anas Azhimi; Salma, Azora Sania
Transactions on Informatics and Data Science Vol. 2 No. 2 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i2.13874

Abstract

Banyumas Regency has high tourism potential, both in terms of nature, culinary, and culture. This study aims to assess the accuracy of geographic positioning (longitude and latitude) in a web-based Geographic Information System (GIS) to ensure the accurate and reliable presentation of tourist destination location data. Accurate spatial data is essential for planning, navigation, promotion, and improving visitor experiences through enhanced digital mapping and integrated tourism development strategies. The method used is prototyping, which allows the initial system development to be tested and refined based on the results of accuracy measurements. The system was developed using the Google Maps API and the PHP programming language and integrated with a GPS device to obtain real-time coordinates. Testing was conducted by comparing the field coordinates with digital map data to identify positional deviations. Analysis was carried out spatially using the Positional Accuracy Tolerance approach to improve the accuracy of the maps visualized in the Tourism Geographic Information System. The results of the study indicate the need for recalibration at several location points to improve the system's precision. This GIS is expected to provide valid location information for tourists in planning their tourism trips.
Predicting SPAD Values in Sri Lankan Paddy Rice Fields using UAV-based Vegetation Indices Fonseka, Ishani; Hewagamage, K.P.; Rathnayake, Upul; Bandara, R.M.U. S; Halloluwa, Thilina
Transactions on Informatics and Data Science Vol. 2 No. 2 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i2.14980

Abstract

In Sri Lanka, the overuse of fertilizers by farmers leads to financial losses and environmental concerns. Due to the high cost and limited availability of SPAD equipment, UAV-based imagery presents a practical and cost-effective alternative for monitoring nitrogen levels in crops. This study evaluates the use of UAV-based multispectral imagery to assess the nitrogen status of rice crops and compares various vegetation indices with SPAD readings. Focusing on the BG300 rice variety. several vegetation indices were compared with SPAD readings using a Linear Regression (LR) model. Among them, NDVI showed the strongest correlation (R = 0.81106), confirming its reliability as an indicator of nitrogen status. This approach offers a cost-effective solution for Sri Lanka’s rice farming sector, aiding farmers in making more informed decisions to reduce fertilizer use and mitigate environmental impacts
Mitigating the Cost Amplification of Platform Defaults in Lean Cloud Data Deployments Through Configuration Hygiene Diaz, Manuel O.
Transactions on Informatics and Data Science Vol. 2 No. 2 (2025)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v2i2.14737

Abstract

Cloud data platforms offer flexibility, but their default configurations are implicitly optimized for enterprise-scale environments, leading to hidden, disproportionate costs for small-scale deployments. This paper introduces the concept of a silent tax, where platform defaults, such as minimum billing durations and over-provisioned column types, disproportionately inflate costs for small business organisations with modest compute budgets, exemplified by a 10 IPU per month scenario. These defaults act as regressive cost drivers, penalizing users unable to amortize inefficiencies. We employ an analytical cost-modelling and Monte Carlo simulation approach, using Snowflake and Informatica Intelligent Data Management Cloud (IDMC) as reference platforms, and validate it through a paired, parametric Monte Carlo experiment using truncated normal and lognormal distributions. Our findings indicate that fixed-cost components embedded in these defaults can consume approximately 65% of a minimal budget. Key mechanisms include 60-second warehouse billing minimums, schema-driven memory over-allocation, and CI/CD pipeline reconfiguration costs. The study demonstrates that cost waste in minimal deployments is predominantly structural. An optimization pathway focusing on configuration hygiene—including explicit data types, aggressive warehouse tuning, and schema-as-code—can reclaim over 80% of this overhead, aligning with FinOps guidance for small estates. This research addresses a gap in cloud cost governance, empowering small teams to leverage cost intelligence for sustainability, and suggests transparent disclosure of default cost impacts by providers.

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