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Universitas Halu Oleo
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PERILAKU JOB CRAFTING PEGAWAI DALAM MENINGKATKAN PELAYANAN PUBLIK DI KECAMATAN Syamsul Alam; La Ode Mustafa; Gunawan; La Ode Muhammmad Elwan
Journal Publicuho Vol. 7 No. 2 (2024): May - July - Journal Publicuho
Publisher : Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35817/publicuho.v7i2.401

Abstract

The important role of employees in interactions between government and society in sub-districts attracts attention, especially regarding discretionary behavior such as job crafting which is considered to improve public services. The aim of this research is to determine the proportion of employees in the sub-district who are actively involved in job crafting behavior in improving public services, and to identify variations in the level of involvement among employees in the three dimensions of job crafting. This research used quantitative methods and involved all 39 employees from a sub-district government in Kendari City. Data was obtained through the use of a questionnaire with a Likert scale and distributed directly manually, and then analyzed using descriptive statistical analysis methods. The data analysis technique used was descriptive statistical analysis. The research results show that the majority of employees in this sub-district are active in job crafting, achieving around 75.9% ideal scores. However, variations in the level of involvement are seen among the dimensions of job crafting, namely seeking resources, seeking challenges, and reducing demands on the job. With a high standard deviation, it shows significant variation in the level of job crafting.
IMPLEMENTASI MODEL TRANSFER LEARNING PADA KLASIFIKASI KESEHATAN TERUMBU KARANG BERBASIS CITRA DIGITAL Azeslim Azeslim; Andi Tenriawaru; Gunawan
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 1 (2025): Juni 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i1.110

Abstract

Coral reefs are marine ecosystems that are highly vulnerable to damage and require regular monitoring of their health conditions. However, the manual classification process of coral reef health tends to be time-consuming. Therefore, this research aims to develop an application that implements a transfer learning model for classifying coral reef health based on digital images. This study utilizes three pretrained model architectures: DenseNet121, MobileNetV2, and EfficientNet-B0. Each model is trained and evaluated to measure its performance in classifying coral reef images. The best-performing model, DenseNet121, is then integrated into a mobile application for real-time classification. The evaluation results show that DenseNet121 achieved the highest accuracy compared to MobileNetV2 and EfficientNet-B0. The training data accuracy of DenseNet121 reached 98.80%, and the testing data accuracy was 98.25%.
Co-Authors Andi Tenriawaru Azeslim Azeslim La Ode Muhammmad Elwan La Ode Mustafa Syamsul Alam