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.