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All Journal Manuju : Malahayati Nursing Journal Jurnal Manajemen, Akuntansi dan Logistik (JUMATI)
Achmad Naufal
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Economics, Econometrics & Finance Health Professions Nursing Public Health

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Pengaruh Psikologi Positif dan Kepemimpinan Terhadap Motivasi Kerja Karyawan Feti Fatonah; Achmad Naufal; Eldo Tri Rahmadani; Faris Muhammad Fahrezi; Muhamad Adimukti Prasojo
Jurnal Manajemen, Akuntansi dan Logistik (JUMATI) Vol. 2 No. 1 (2024): Jurnal Manajemen, Akuntansi dan Logistik (JUMATI)
Publisher : Cipta Kind Publisher

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Purpose – The aim of this research is to determine (1) the influence of positive psychology on employee work motivation; (2) the influence of leadership on employee work motivation. In this study, a literature review method is employed for research. Design/methodology/approach – In this research, the systematic literature review (SLR) method is employed, which is utilized to search for national and international literature data with reputable sources in SINTA and SCOPUS. Findings – The results of this compilation and analysis are used to draw conclusions regarding the influence of psychology and leadership on employee work motivation. Research limitations/implications – The conclusions drawn from this research indicate that based on the above analysis: (1) Positive psychology encourages employees to achieve targets, overcome challenges, and adapt to changing work environment situations. (2) To enhance employee performance, companies are expected to identify and develop employees' psychological capital through the provision and improvement of various resources that support the achievement of employee goals, such as competence, autonomy, feedback, mental resilience, social support, and guidance from superiors. (3) Leadership style is one of the factors that can influence motivation. (4) Leadership style is an approach used by a leader to influence the performance of subordinates. Practical implications – The conclusion of implementing a leadership style is to provide inspiration, enthusiasm, and motivation to employees in carrying out tasks assigned by the organization.
Pioneering Prostate Cancer Diagnosis: Evaluating AI Against Traditional Methods - A Systematic Literature Review Zaki Farhan Virawan; Achmad Naufal
Malahayati Nursing Journal Vol 6, No 10 (2024): Volume 6 Nomor 10 (2024)
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/mnj.v6i10.17042

Abstract

ABSTRACT Prostate cancer is one of the most common forms of men's cancer in the world and the understanding of its epidemiology is crucial to effective prevention, early detection and management strategies. This systematic review highlights the potential of artificial intelligence-based diagnostic methods to revolutionize the early diagnosis of prostate cancer. A comprehensive literature search was carried out using databases such as PubMed, Google Scholar and Scopus. After removing duplicates, titles and abstracts were examined in 279 studies and the following full texts were examined in 128 studies. We have ten studies to review in total. Therefore, we chose to review data for all 10 identified studies that applied AI techniques to detect ca prostate. The Paige Prostate Alpha AI system significantly enhances pathologists' diagnostic capabilities, leading to more accurate and effective prostate cancer detection. These limitations indicate that further research is required to address these issues and validate the results in a more diverse and controlled environment. AI-based diagnostic methods have shown significant promise in enhancing the early detection of prostate cancer. As technology continues to advance, integrating AI with traditional diagnostic approaches could lead to more effective, efficient, and accurate prostate cancer screening and diagnosis. Future studies should focus on large-scale clinical trials and real-world applications to validate these findings and facilitate the adoption of AI in clinical settings. Keywords: Prostate Cancer, Artificial Intelligent, Deep Machine Learning, MRI
Co-Authors Eldo Tri Rahmadani Faris Muhammad Fahrezi Feti Fatonah Muhamad Adimukti Prasojo Zaki Farhan Virawan