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All Journal Jurnal Penelitian Manajemen Terapan (Penataran) Jurnal Teknologi Informasi Mura
Join Rachel Luturmas
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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PENGARUH BUDAYA KERJA DAN KEPUASAN KERJA TERHADAP PRODUKTIVITAS KERJA KARYAWAN AJB BUMIPUTERA 1912 CABANG AMBON Join Rachel Luturmas
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 (273.285 KB)

Abstract

Menciptakan kondisi dan lingkungan kerja yang memuaskan dan membuat karyawan merasa nyaman dalam bekerja adalah salah satu tantangan dalam mengelola sumber daya manusia yang berkaitan dengan kebutuhan para karyawan. Penelitian ini bertujuan untuk mengetahui : (1) pengaruh budaya kerja terhadap produktivitas kerja, dan (2) pengaruh kepuasan kerja terhadap produktivitas kerja.Responden yang digunakan dalam penelitian ini adalah karyawan AJB BUMIPUTERA 1912 Cabang Ambon. Jumlah populasi sebesar 30 karyawan dan sampel yang digunakan adalah seluruh karyawan AJB BUMIPUTERA 1912 Cabang Ambon sebanyak 30 karyawan dengan menggunakan teknik sensus. Pengumpulan data dilakukan dengan metode angket dan dokumentasi. Teknik analisa yang digunakan untuk Pengujian hipotesis pertama, kedua dan ketiga menggunakan regresi berganda.Berdasarkan hasil analisis dari penelitian yang dilakukan, Budaya Kerja dan kepuasan kerja berpengaruh positif dan signifikan terhadap Produktivitas Kerja secara simultan maupun parsial. Kata kunci: Budaya Kerja, Kepuasan Kerja, Produktivitas Kerja
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 Grace Fredriksz Jupriyanto . Laura S Nunumete Tiska Pattiasina