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This Author published in this journals
All Journal Journal of Economic, Bussines and Accounting (COSTING) Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Global Health research Target : Jurnal Manajemen Bisnis Jurnal Ekonomi Bisnis Dan Industri (EBI) BUSINESS AND ENTERPREUNERSHIP JOURNAL Jurnal Manajemen dan Bisnis (JMB) Journal of Accounting Taxing and Auditing (JATA) Business and Enterpreneurship Journal (BEJ) Jurnal Cendekia Ilmiah DIMENSIA (Diskursus Ilmu Manajemen STIESA)
Desmon Desmon
Universitas Mitra Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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The Influence of Motivation, Achievement, Work Experience and Competencies on the Performance of Mental Nurses Livana PH; Desmon Desmon; Yudhinanto Yudhinanto; Maria Septijantini Alie; Eka Travilta Oktaria
Indonesian Journal of Global Health Research Vol 6 No 6 (2024): Indonesian Journal of Global Health Research
Publisher : GLOBAL HEALTH SCIENCE GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/ijghr.v6i6.5995

Abstract

Performance in terms of quality and quantity achieved by an employee in carrying out his duties in accordance with the responsibilities given to him. Factors that can influence performance are motivation, competence, leadership, achievement, work environment and work experience. The aim of this research was to determine the influence of motivation, achievement, work experience and competency on the performance of nurses at RSJD Dr Amino Gondohutomo, Central Java Province. The method used in this research was quantitative descriptive, the number of samples used was 71 respondents from part of the existing population which had been obtained using the Slovin technique. The results of this research showed that the four variables of motivation, achievement, work experience and competence simultaneously influenced the performance of nurses at RSJD Dr Amino Gondohutomo, Central Java Province. The results of the motivation variable partially influence the performance of nurses at RSJD Dr Amino Gondohutomo, Central Java Province. The results of the achievement variable partially influence the performance of nurses at RSJD Dr Amino Gondohutomo, Central Java Province. The results of the work experience variable partially influence the performance of nurses at RSJD Dr Amino Gondohutomo, Central Java Province. The results of the competency variable partially influence the performance of nurses at RSJD Dr Amino Gondohutomo, Central Java Province. Suggestions are expected to use other independent variables, for example education, work discipline, wages, leadership, workload and so on.
Geospatial data processing and random forest-based intelligent system for regional investment readiness prediction Yudhinanto Cahyo Nugroho; Desmon Desmon; Hasbullah Hasbullah; Triyugo Winarko
Indonesian Journal of Electrical Engineering and Computer Science Vol 43, No 2: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v43.i2.pp651-661

Abstract

This paper presents an intelligent system that integrates geospatial data processing and a random forest (RF) classification model to categorize regional investment readiness (IR). Regional investment planning is often constrained by fragmented socio-economic data, unequal infrastructure distribution, and unquantified disaster risk, which reduce the accuracy of decision making. To address this problem, multidimensional data consisting of socio-economic indicators, infrastructure accessibility, and disaster risk factors were collected from a sample of 15 administrative regions in Lampung Province, Indonesia, and processed through data cleaning, normalization, and feature selection. An IR score was first computed for each region using a weighted composite formula, then discretized into readiness classes and used as the target label to train a RF classifier capable of modeling complex nonlinear relationships among the input features. Given the limited sample size, model performance was evaluated using leave-one-out cross-validation, and classification metrics—accuracy, precision, recall, and F1-score—were reported to assess predictive reliability. The results reveal spatial disparities in IR, where regions with higher human development and better infrastructure tend to exhibit greater investment potential, while areas exposed to higher disaster risk tend to show lower readiness levels. The prediction outputs are integrated into a web-based interactive dashboard that enables spatial visualization and exploration of IR patterns. Given the small and single province sample, the proposed system should be regarded as a preliminary decision-support tool for policymakers and investors to help identify priority regions, and further validation on larger, more geographically diverse datasets is recommended to strengthen generalizability.
Co-Authors Andi Surya Anwar, Amelia Armalia Reny Armalia Reny WA Aura Dewi dian herlambang Dinda Kianjung Herlozi Feerlie Moonthana Indhra1 Hairudin Hairudin Hasbullah Hasbullah Hasbulloh Hasbulloh Hero Desyantama Iin Anita Agustinar Kumalasari, Nety Livana PH M Renandi Ekatama M Renandi Ekatama Surya M. Renandi Ekatama M.Renandi Ekatama Surya Maria Septijantini Alie Masturoh Masturoh Mohammad Renandi Ekatama Muhammad Habib Munandar Muhammad Nasir Nur Salma Nuzulya Pahlawi Oktaria, Eka Travilta Pita Septiana Rizky Widagdo Athur Wigono Sri Mulyani Susi Indriani Susi Indriyani Triyugo Winarko Umar Bakti Yudhinanto Cahyo Nugroho Yudhinanto CN Yudhinanto Yudhinanto Yulistina Yulistina