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All Journal Journal of Applied Engineering and Technological Science (JAETS) Journal of Governance and Local Politics (JGLP)
Ampauleng Ampauleng
STIEM Bongaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences

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Pengaruh Etos Kerja dan Kepuasan Kerja Terhadap Kinerja Pegawai Pada Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Daerah Kota Makassar Ampauleng Ampauleng
Jurnal Pemerintahan dan Politik Lokal Vol 5 No 1 (2023): JGLP, MEI 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/jglp.v5i1.811

Abstract

Peneltian ini bertujuan untuk mengetahui apakah pengaruh etos kerja dan kepuasan kerja berpengaruh terhadap kinerja pegawai Pada Badan Kepegawaian Dan Pengembangan Sumber Daya Manusia Daerah (BKPSDMD) Kota Makassar. Pengumpulan data menggunakan Teknik sample jenuh. Populasinya yaitu seluruh pegawai ASN BKPSDMD Kota Makassar yang berjumlah 66 orang. Hasil kuesioner telah diuji validitas dan reliabilitas, juga telah diuji asumsi klasik berupa uji normalitas, heteroskedastisitas, dan multikolinearitas. Metode analisis menggunakan Teknik analisis deskriptif dan analisis regresi linear berganda. Hasil penelitian menunjukkan bahwa hipotesis yang diajukan diterima karena menunjukkan hasil hipotesis yang positif dan signifikan. Ini berarti bahwa faktor etos kerja dan kepuasan kerja berpengaruh positif dan signifikan terhadap kinerja.
Development of an Optimized Ensemble Least Squares Model for Identifying Potential Deposit Customers Firman Aziz; Mutia Maulida; Jafar Jafar; Nurafni Shahnyb; Norma Nasir; Ampauleng Ampauleng
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5974

Abstract

The banking sector faces significant challenges in effectively promoting its products and services. While direct marketing has proven to be a potent tool for customer acquisition, it often leads to customer dissatisfaction, thereby tarnishing the bank's reputation. Leveraging Business Intelligence (BI) technology offers a strategic advantage by enabling the classification and analysis of customer data, particularly for time deposit customers. This study presents the development and optimization of an Ensemble Least Squares (ELS) algorithm to enhance the classification of potential deposit customers. The proposed Ensemble Least Squares Support Vector Machine (ELS-SVM) algorithm demonstrated superior performance compared to traditional SVM and LS-SVM methods. Notably, the ELS-SVM achieved an average performance improvement of 10.04% over standard Support Vector Machine (SVM) techniques.
Co-Authors Firman Aziz Jafar Jafar Mutia Maulida Nasir, Norma Nurafni Shahnyb