Archives

ACSIE (International Journal of Application Computer Science and Informatic Engineering), Volume 7 Number 2 (November 2025) presents a multidisciplinary collection of applied research contributed by scholars from the Institut Bisnis dan Teknologi Indonesia (INSTIKI), Okayama University (Japan), Politeknik Pertanian Negeri Samarinda, and Universitas Musamus Merauke. This issue emphasizes the application of information systems, decision support methods, financial technology, and intelligent interaction systems to address practical challenges in business, tourism, and digital innovation.

The published studies include the implementation of a Break Even Point (BEP)-based financial information system and a financial ratio-based information system design for organizational financial analysis at CV Bali Indigo. Additional contributions explore the development of an accrual-based accounting information system using the incremental budgeting method, the application of the Simple Additive Weighting (SAW) method in a decision support system for tourism destination selection, and a camera-based hand gesture recognition system for game control, demonstrating the integration of computational intelligence with interactive technologies.

This issue reflects ACSIE’s commitment to publishing practical, data-driven, and solution-oriented research that bridges information systems, computational methods, and real-world applications. Through collaboration between Indonesian and international institutions, this volume strengthens the journal’s role in advancing applied computer science and informatics engineering research across academic, industrial, and technological domains.

ACSIE (International Journal of Application Computer Science and Informatic Engineering), Volume 7 Number 1 (May 2025) presents a diverse collection of applied computer science and data-driven research contributed by scholars from the Institut Bisnis dan Teknologi Indonesia (INSTIKI), Universitas Kristen Duta Wacana (UKDW) Yogyakarta, and Okayama University (Japan). This issue highlights contemporary applications of machine learning, forecasting models, decision support systems, and data mining techniques in business, agriculture, finance, and consumer analytics.

The published articles include a machine learning-based hotel booking cancellation prediction model using XGBoost, an AdaBoost regression approach for agricultural yield modeling using climatic and pesticide variables, and a TOPSIS-based decision support system for strategic business location selection. Additional studies explore Apriori-based market basket analysis for understanding grocery consumer purchasing behavior, as well as ARIMA-based forecasting of the LQ45 stock index, demonstrating the application of computational intelligence in economic and financial decision-making.

This issue reflects ACSIE’s commitment to disseminating interdisciplinary, practical, and solution-oriented research that bridges computational methods with real-world applications. By bringing together contributions from Indonesian and international institutions, this volume strengthens the journal’s role in advancing applied computer science and informatics engineering research across academic and professional domains.

ACSIE (International Journal of Application Computer Science and Informatic Engineering), Volume 6 Number 2 (November 2024) presents a collection of applied research in accounting information systems, environmental analytics, and machine learning contributed by researchers from the Institut Bisnis dan Teknologi Indonesia (INSTIKI), Okayama University (Japan), Politeknik Pertanian Negeri Samarinda, Universitas Musamus Merauke, and Sekolah Tinggi Bisnis Runata. This issue emphasizes the integration of computational intelligence and information systems to address challenges in financial management and sustainable agriculture.

The published studies include the development of an accrual-based accounting information system integrated with an internal control system, aimed at improving financial transparency and organizational governance. In the domain of predictive analytics, this issue features a Random Forest-based model for urban green energy score prediction utilizing solar radiation, wind speed, and geographic variables. Additional contributions explore LightGBM-based approaches for agricultural yield forecasting, incorporating environmental and pesticide factors, as well as efficient crop yield prediction models using soil and climatic indicators. Furthermore, an AdaBoost regression model is presented for crop yield estimation under multivariate environmental conditions, demonstrating the effectiveness of ensemble learning techniques in agricultural analytics.

This issue reflects ACSIE’s commitment to publishing interdisciplinary, data-driven, and solution-oriented research that bridges computational methods with real-world applications. Through collaboration between national and international institutions, this volume strengthens the journal’s role in advancing applied computer science and informatics engineering across diverse sectors, including finance, environment, and agriculture.

ACSIE (International Journal of Application Computer Science and Informatic Engineering), Volume 6 Number 1 (May 2024) presents a collection of applied research in financial information systems, machine learning, and time-series forecasting contributed by researchers from the Institut Bisnis dan Teknologi Indonesia (INSTIKI), Okayama University (Japan), Politeknik Pertanian Negeri Samarinda, Universitas Kristen Duta Wacana (UKDW) Yogyakarta, and Sekolah Tinggi Bisnis Runata. This issue highlights the integration of advanced computational techniques with real-world applications in finance, agriculture, and digital asset analysis.

The published studies include the implementation of a web-based accrual accounting information system using the variable costing method, aimed at improving financial reporting accuracy and efficiency. In the field of predictive analytics, this issue features Ethereum price forecasting using Bidirectional GRU neural networks optimized with SGD, as well as Bitcoin price prediction using LSTM networks with the Adam optimizer, and Solana price prediction using BiLSTM models, demonstrating the growing role of deep learning in cryptocurrency market analysis. Additionally, a LightGBM-based model for crop green energy score prediction is presented, integrating climate and geographic variables to support sustainable agricultural decision-making.

This issue reflects ACSIE’s commitment to publishing interdisciplinary, data-driven, and solution-oriented research that bridges computational intelligence with practical applications. By bringing together contributions from national and international institutions, this volume strengthens the journal’s role in advancing applied computer science and informatics engineering across diverse domains.

ACSIE (International Journal of Application Computer Science and Informatic Engineering), Volume 5 Number 1 (May 2023) presents a focused collection of research contributions in accounting information systems and environmental data analytics, authored by researchers from Okayama University (Japan) and the National Taiwan University of Science and Technology (Taiwan Tech). This issue highlights the integration of advanced computational techniques with financial systems and sustainability-oriented analytics.

The articles in this issue explore the development of accrual-based accounting information systems, including implementations using cash budgeting approaches, audit trail methods, and cost control mechanisms, aiming to enhance financial transparency, accountability, and organizational efficiency. In addition, the issue features studies on urban green energy score prediction, utilizing machine learning techniques such as AdaBoost Regression and XGBoost, based on environmental, climatic, and geographic variables.

This volume reflects ACSIE’s commitment to advancing applied computer science through international collaboration, particularly in bridging financial information systems with intelligent data-driven models. The contributions demonstrate the growing role of machine learning in supporting sustainable urban development and improving decision-making processes across financial and environmental domains.

Available Online since November 30, 2019

Available Online since May 31 2019