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All Journal OIKOS: Jurnal Kajian Pendidikan Ekonomi dan Ilmu Ekonomi Journal Of World Science
Supriatna, Nano
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Law, Crime, Criminology & Criminal Justice Social Sciences

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STRATEGI PENINGKATAN KINERJA PEGAWAI PADA PT GOTHRU MEDIA INDONESIA MENGGUNAKAN METODE SIX SIGMA Supriatna, Nano; Franciskus Antonius Alijoyo
OIKOS: Jurnal Kajian Pendidikan Ekonomi dan Ilmu Ekonomi Vol 9 No 2 (2025): OIKOS: Jurnal Kajian Pendidikan Ekonomi dan Ilmu Ekonomi
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan Universitas Pasundan

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Abstract

In an era of increasingly tight business competition, improving employee performance is crucial for the success of a company. One method that is being pursued to improve the quality and efficiency of business processes is the Six Sigma Method. The aim of this research is to explore and understand strategies for improving employee performance at PT Gothru Media Indonesia using the Six Sigma Method. This study used qualitative research methods. The data collection technique in this research is literature study. The data that has been collected is then analyzed in three stages, namely data reduction, data presentation and drawing conclusions. The research results show that the Six Sigma method can be used to improve employee performance at PT. Gothru Media Indonesia. Strategies that can be used include problem identification and definition, data measurement and analysis, solution development, solution implementation and monitoring and control. The application of the Six Sigma method needs to be carried out systematically and continuously to achieve optimal results.
Classification of Drug Usage Patterns and Identification of Diseases in the Provision of Drug Types Using the K-Nearest Neighbors Method Farizki, Rafi; Supriatna, Nano; Juliana, Christine
Journal of World Science Vol. 3 No. 11 (2024): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v3i11.600

Abstract

The increasing complexity of healthcare systems highlights the need for data-driven approaches to optimize drug usage patterns and improve disease management. This study employs the K-Nearest Neighbors (KNN) algorithm to analyze correlations between prescribed medications and associated diseases, utilizing a dataset comprising attributes such as patient demographics, drug types, dosages, and treatment frequencies. The results reveal significant trends, including the predominance of "Drug_D" due to its versatility across multiple conditions such as hypertension, diabetes, and cardiovascular diseases. The study also highlights the prevalence of chronic conditions like hypertension and respiratory disorders, underscoring the importance of preventive healthcare and resource allocation. Simplified dosage regimens, predominantly "Once_Daily," were found to enhance patient adherence, aligning with global best practices in chronic disease management. The analysis further emphasizes targeted prescribing practices, with specific drugs strongly correlated to particular diseases, such as "Drug_A" for hypertension and "Drug_B" for respiratory disorders. However, the broad usage of certain medications raises concerns about potential over-reliance, necessitating regular monitoring. These findings demonstrate the value of machine learning in improving healthcare decision-making, enhancing operational efficiency, and supporting evidence-based practices. Future research should expand the dataset to include genetic and lifestyle factors to further refine predictive accuracy and contribute to the advancement of personalized medicine. This study underscores the transformative potential of integrating data mining techniques into healthcare systems to achieve better patient outcomes and more effective resource management.
Co-Authors Farizki, Rafi Franciskus Antonius Alijoyo Juliana, Christine