Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Manajemen Terapan (Penataran) Jurnal Teknologi Informasi Mura
Grace Fredriksz
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PENGARUH KOMPENSASI FINANSIAL DAN KOMPENSASI NONFINANSIAL TERHADAP KINERJA KARYAWAN Grace Fredriksz
Jurnal Penelitian Manajemen Terapan (PENATARAN) Vol. 2 No. 2 (2017)
Publisher : Program Studi Manajemen STIE Kesuma Negara Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (193.629 KB)

Abstract

Dalam Penelitian ini mempunyai tujuan untuk mengetahui; pertama, Pengaruh Kompensasi Finansial terhadap Kinerja Karyawan. Kedua, Kompensasi Nonfinansial terhadap Kinerja Karyawan, dan ketiga Pengaruh Kompensasi Finansial dan Kompensasi Nonfinansial secara simultan terhadap Kinerja Karyawan pada PT. Pelindo IV (Persero) Ambon.Responden yang digunakan dalam penelitian ini adalah karyawan PT. Pelindo IV (Persero) Ambon. Sampel yang digunakan sebanyak 40 karyawan, dengan metode pengumpulan data dilakukan dengan metode angket dan dokumentasi. Teknik analisa yang digunakan untuk Pengujian hipotesis pertama, kedua dan ketiga menggunakan regresi berganda.Hasil penelitian menunjukkan bahwa variabel Kompensasi Finansial (X1) dan Kompensasi Nonfinansial (X2) berpengaruh positif dan signifikan terhadap Kinerja Karyawan baik secara parsial maupun simultan. Kata Kunci: Kompensasi Finansial, Kompensasi Nonfinansial, Kinerja Karyawan
PERBANDINGAN KINERJA ALGORITMA SVM DAN NAIVE BAYES PADA KLASIFIKASI PRESTASI AKADEMIK SISWA: STUDI KASUS SMAS BPD TOBELO SELATAN Tiska Pattiasina; Grace Fredriksz; Join Rachel Luturmas; Andrie CH Salhuteru; Febiola Matuankotta; Laura S Nunumete; Jupriyanto Jupriyanto
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 1 (2026): Jurnal Teknologi Informasi Mura
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i1.2915

Abstract

Students’ academic achievement is an important indicator of the success of the educational process; however, its assessment is often subjective and not yet fully data-driven. Therefore, a systematic analytical approach is required to classify students’ academic achievement objectively and accurately. This study aims to compare the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying the academic achievement of grade III students at SMAS BPD Tobelo Selatan. A data mining approach using classification techniques was applied, involving 17 attributes as predictor variables and two target classes of academic achievement, namely Very Good and Good. Data processing and model evaluation were conducted using the WEKA software, with performance measured through accuracy, precision, recall, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results indicate that the SVM algorithm achieves the best performance in terms of accuracy, precision, and recall, each reaching 97.78%, while the Naive Bayes algorithm obtains the highest AUC-ROC value of 98.08%. These findings demonstrate that SVM is superior in prediction accuracy, whereas Naive Bayes shows excellent capability in class discrimination. This study is expected to support data-driven academic decision-making in school environments.
Co-Authors Andrie CH Salhuteru Febiola Matuankotta Join Rachel Luturmas Jupriyanto . Laura S Nunumete Tiska Pattiasina