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All Journal Variance : Journal of Statistics and Its Applications JIMEB: Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis RESWARA: Jurnal Riset Ilmu Teknik
Pratama, Rizky Adi
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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CLUSTERING OF DISTRICTS IN CENTRAL JAVA ACCORDING TO PEOPLE'S WELFARE INDICATORS USING WARD'S METHOD Purwanto, Dannu; Pratama, Rizky Adi; Lein, Raymond Bolly; Prastyo, Ikwan; Haris, M. Al
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page73-82

Abstract

One of the main goals of development activities carried out by every country was to improve people's welfare. Community welfare was a situation where citizens could fulfill and adequately fulfill their material and spiritual needs. The poverty rate of Central Java Province was recorded: out of a total population of 37.03 million people, around 3,831.44 thousand people were poor. The population density of Central Java Province reaches 1,120 people per km2, the third largest number of poor people in Indonesia. This study aimed to group regencies/cities in Central Java based on the characteristics of the community welfare indicators. The indicators used in this study were the Open Unemployment Rate (UR), Labor Force Participation Rate (LFPR), Poverty, Human Development Index (HDI), and District Minimum Wage (DMW). The method used in this research was Ward's Agglomerative Hierarchical Clustering. The final results concluded that the best number of clusters formed was 6 clusters. The first cluster consists of 13 Regencies/Cities, the second cluster consists of 8 Regencies/Cities, the third cluster consists of 3 Regencies/Cities, the fourth cluster consists of 1 Regency/City, the fifth cluster consists of 5 Regencies/Cities, the sixth cluster consisting of 5 Regencies/Cities.
Implementation of Internet of Things for Predictive Maintenance in Manufacturing Industry Pratama, Rizky Adi; Collins, James Andrew
RESWARA: Jurnal Riset Ilmu Teknik Vol. 3 No. 3 (2025): RESWARA: Jurnal Riset Ilmu Teknik, July 2025
Publisher : Lembaga Penelitian dan Pendidikan (LPP) Kalibra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/reswara.v3i3.450

Abstract

The rapid advancement of Industry 4.0 has accelerated the adoption of Internet of Things (IoT) technologies in manufacturing systems, particularly in predictive maintenance applications. Traditional maintenance strategies, such as corrective and preventive maintenance, often result in unplanned downtime, increased operational costs, and inefficient resource utilization. This study aims to analyze and synthesize recent scientific literature on the implementation of IoT-based predictive maintenance in the manufacturing industry, focusing on system architecture, data acquisition, analytics techniques, and operational impacts. A qualitative systematic literature review method was employed, analyzing peer-reviewed journal articles, conference proceedings, and book chapters published between 2020 and 2025. The findings indicate that IoT-enabled predictive maintenance significantly improves equipment reliability, reduces downtime by up to 50%, lowers maintenance costs, and enhances production efficiency. The integration of machine learning, edge computing, and digital twin technologies further strengthens real-time decision-making and failure prediction accuracy. This study contributes by providing a comprehensive and structured understanding of IoT-driven predictive maintenance implementations and identifying research gaps related to scalability, data interoperability, and cybersecurity. The results serve as a reference for both researchers and practitioners seeking to adopt predictive maintenance solutions in smart manufacturing environments.
Transformational Leadership in Enhancing the Performance of Generation Z Employees Pratama, Rizky Adi; Lestari, Nadya Puspita
Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis Vol. 5 No. 1 (2026): JANUARI| JIMEB : Jurnal Ilmiah Manajemen, Ekonomi, Bisnis
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3e8ckb84

Abstract

The motivation behind this research is the growing influence of Generation Z in the labor force, which requires leadership styles that are more flexible, inspiring, and focused on individual development. Among these, transformational leadership is the most relevant, as it fosters internal motivation and employee participation, leading to better performance. The purpose of this research is to examine the impact of transformational leadership on the performance of Generation Z workers. A quantitative method with an explanatory research design was used in a survey of 128 Generation Z employees selected through purposive sampling. The data were gathered using a Likert-scale questionnaire and processed using simple linear regression. The results reveal that transformational leadership not only positively but also significantly impacts the performance of Gen Z employees and accounts for a considerable portion of the variance in work quality, work quantity, punctuality, and responsibility. The innovation of this research lies in combining a generational view with non-monetary performance measurement in management accounting. The theoretical contributions to leadership studies and practical recommendations for companies on developing effective, long-lasting leadership strategies are the twofold benefits of this study.
Co-Authors Collins, James Andrew Dannu Purwanto Lein, Raymond Bolly Lestari, Nadya Puspita M. Al Haris Prastyo, Ikwan