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All Journal IJIIS: International Journal of Informatics and Information Systems Indonesian Journal of Forestry Research
Ayyasy, Yahya
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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GROWTH AND WOOD TRAITS EVALUATION OF 15-YEAR-OLD TENGKAWANG (Shorea spp.) TREE STANDS IN GUNUNG WALAT UNIVERSITY FOREST, WEST JAVA, INDONESIA Fifi Gus Dwiyanti; Rosdayanti, Henti; Yulita, Kusumadewi Sri; Rachmat, Henti Hendalastuti; Ayyasy, Yahya; Muharam, Karima Fauziah; Rahman, Mohamad Miftah; Adzkia, Ulfa; Siregar, Iskandar Zulkarnaen
Indonesian Journal of Forestry Research Vol. 11 No. 2 (2024): Indonesian Journal of Forestry Research
Publisher : Association of Indonesian Forestry and Environment Researchers and Technicians

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59465/ijfr.2024.11.2.243-258

Abstract

Gunung Walat University Forest (GWUF) in Sukabumi Regency, Indonesia, plays a crucial role in providing various ecosystem services. Five important Shorea trees, i.e., S. stenoptera, S. mecisopteryx, S. pinanga, S. palembanica, and Shorea leprosula have been planted in GWUF as an effort for its conservation and object of research. An evaluation of the adaptability and suitability of these species to the GWUF ecosystem, as well as their wood characteristics, needs to be carried out regularly. Therefore, the study aimed to examine the growth performances and physical wood properties of five Shorea species, i.e., Shorea stenoptera, S. mecisopteryx, S. pinanga, S. palembanica, and S. leprosula at the age of 15-year-old planted in GWUF. The results indicated that S. leprosula exhibited the best growth performance in terms of average diameter (19.64 cm), volume (0.27 m3), slenderness (126.58), and wood density (0.94 g/cm3), and S. stenoptera showed the best performance in average height (23.35 m). While the poor performance was shown by S. palembanica in terms of average diameter (6.73 cm), height (11.15 m), volume (0.02 m3), wood density (0.87 g/cm3), and specific gravity (0.45), and S. stenoptera in terms of average slenderness (202.73). In addition, significant differences in tree height, diameter, volume, wood density, specific gravity, and moisture content were found in S. palembanica compared with other species. The relationship between the growth and physical wood properties parameters varied between species. The study revealed that planting the five Shorea species in GWUF is suitable for increasing vegetation cover and conserving the species.
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 Adzkia, Ulfa Fifi Gus Dwiyanti Henti Rosdayanti, Henti ISKANDAR ZULKARNAEN SIREGAR KUSUMADEWI SRI YULITA Maidin, Siti Sarah Muharam, Karima Fauziah Rachmat, Henti Hendalastuti Rahman, Mohamad Miftah Yi, Ding