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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal for Applied Information Management Jurnal Abdi Masyarakat Indonesia
Yi, Ding
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Public Health Social Sciences

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Transforming Agriculture: An Insight into Decision Support Systems in Precision Farming Yi, Ding; Jun, Luo; Haodic, Gao; Xing, Zhang; Lie, Ye; Maidin, Siti Sarah; Ishak, Wan Hussain Wan; Wider, Walton
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.274

Abstract

Precision agriculture seamlessly incorporates advanced technologies and data analysis to improve farming efficiency and sustainability through immediate resource allocation. Therefore, this study aims to synthesize research findings related to agriculture, Decision Support Systems, and precision agriculture through a systematic literature review conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search was performed on the Scopus database, specifically focusing on publications published in English between the years 2017 and 2023. Out of 126 periodicals, a rigorous process was used to determine which publications met the specific criteria for inclusion and exclusion. As a result, only 8 relevant studies were chosen. The review emphasizes the substantial capacity of Decision Support Systems in precision agriculture, demonstrating that DSS has the capability to enhance crop yields by 15% and decrease water consumption by 20%. Through the utilization of big data, machine learning, and advanced technologies, Decision Support Systems has the potential to transform the agricultural industry by enhancing productivity, optimizing resource allocation, and enabling early identification of pests and diseases. The utilization of real-time data from Decision Support Systems empowers farmers to make well-informed choices, effectively managing production while upholding environmental sustainability. This, in turn, plays a crucial role in ensuring the economic viability of farms and enhancing global food security. However, addressing challenges like data privacy concerns, enhancing user-friendly interfaces, establishing robust data administration infrastructure, and providing adequate training and support for end-users is imperative for the successful implementation of data-driven Decision Support Systems in precision agriculture.
Predicting the Popularity Level of Roblox Games Using Gameplay and Metadata Features with Machine Learning Models Yi, Ding; Jun, Luo; Govindaraju, S
International Journal for Applied Information Management Vol. 5 No. 1 (2025): Regular Issue: April 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v5i1.97

Abstract

The online gaming platform Roblox has become a significant player in the gaming industry, providing a space for user-generated content. Predicting the popularity of Roblox games can help developers design better games and optimize user engagement. This study explores the use of machine learning models to predict the popularity of games on Roblox using gameplay features and metadata. A dataset of 9,734 games was collected, including variables such as likes, visits, game age, and active players. Three machine learning models, Decision Tree, Random Forest, and Gradient Boosting were employed to predict the number of favorites, which serves as a proxy for game popularity. Among the models tested, Gradient Boosting outperformed the others, achieving the highest R-squared score (0.85) and the lowest Root Mean Squared Error (11,470). Key features such as likes, game age, and visits were identified as the most influential in predicting game popularity. Based on these findings, this study recommends that developers focus on features that increase player engagement, such as regular updates and optimizing game exposure. Additionally, incorporating additional data sources, such as user reviews, and exploring explainability methods like SHAP can further improve model accuracy and transparency. This research contributes valuable insights into how machine learning can support decision-making in the development and optimization of Roblox games.
Penguatan Keterampilan Menulis Ilmiah Dosen Universitas Amikom Purwokerto pada Bidang Data Science untuk Publikasi Internasional Hariguna, Taqwa; Sarmini, Sarmini; Wahid, Arif Mu'amar; Pratama, Satrya Fajri; Yi, Ding
Jurnal Abdi Masyarakat Indonesia Vol 5 No 5 (2025): JAMSI - September 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2082

Abstract

Kemampuan menulis ilmiah merupakan keterampilan esensial bagi dosen di bidang data science untuk meningkatkan produktivitas dan kualitas publikasi di jurnal internasional bereputasi.. Permasalahan utama yang dihadapi oleh dosen di Fakultas Ilmu Komputer Universitas Amikom Purwokerto adalah kurangnya keterampilan dalam menulis artikel ilmiah yang memenuhi standar jurnal internasional, yang berdampak pada terbatasnya publikasi mereka. Sebagai solusi, kegiatan pengabdian ini menyelenggarakan pelatihan dan pendampingan penulisan artikel ilmiah dengan tujuan untuk meningkatkan kapasitas dosen dalam menulis secara sistematis dan sesuai standar jurnal internasional. Workshop interaktif yang diadakan diikuti oleh 25 dosen dari empat program studi, dengan sesi teori, praktik langsung, dan peer review. Hasil evaluasi menunjukkan peningkatan signifikan pada kemampuan peserta: aspek struktur penulisan meningkat dari 60% menjadi 80%, bahasa ilmiah dari 58% menjadi 82%, serta pemahaman standar jurnal dari 52% menjadi 76%. Selain itu, 8 peserta berhasil menghasilkan draf artikel yang siap disubmit ke jurnal internasional. Kegiatan ini juga berhasil mendorong terbentuknya komunitas penulis ilmiah yang menjadi langkah awal dalam membangun budaya akademik kolaboratif secara berkelanjutan di Fakultas Ilmu Komputer.
A Multiple Linear Regression Approach to Predicting AI Professionals’ Salaries from Location and Skill Data Maidin, Siti Sarah; Yi, Ding; Ayyasy, Yahya
International Journal of Informatics and Information Systems Vol 7, No 3: September 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i3.213

Abstract

The rapid growth of Artificial Intelligence (AI) industries worldwide has increased the demand for skilled professionals and highlighted the need to understand salary determinants in this sector. This study aims to analyze the factors influencing the compensation of AI professionals globally, with a particular focus on the effects of company location, experience level, and required technical skills. Using a dataset of 15,000 AI job postings collected from multiple countries, a Multiple Linear Regression (MLR) model was developed to identify predictive relationships between independent variables—location, experience, and skills—and the dependent variable, annual salary in U.S. dollars. Data preprocessing included one-hot encoding for categorical variables, standardization of numerical attributes, and vectorization of text-based skill descriptions. Model evaluation produced strong predictive results, with an R² of 0.82, a Mean Absolute Error (MAE) of 18,677 USD, and a Root Mean Squared Error (RMSE) of 25,704 USD. Statistical tests confirmed that company location and experience level significantly affected salary outcomes (p 0.05), while technical skills contributed only marginally. These findings suggest that structural factors such as geography and seniority play a more decisive role in determining AI salaries than specific technical competencies. The study concludes that MLR offers a transparent and interpretable analytical framework for exploring salary disparities in the global AI workforce. The results provide practical implications for organizations designing fair compensation policies, professionals assessing market value, and educators aligning training programs with evolving industry demands.
Co-Authors Ayyasy, Yahya Govindaraju, S Haodic, Gao Ishak, Wan Hussain Wan Lie, Ye Luo Jun Maidin, Siti Sarah Pratama, Satrya Fajri Sarmini Sarmini Taqwa Hariguna Wahid, Arif Mu'amar Wider, Walton Zhang Xing