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All Journal JEMSI (Jurnal Ekonomi, Manajemen, dan Akuntansi) INTERNAL (Information System Journal)
Indra Rustiawan
Universitas Putra Indonesia
Computer Science & IT Economics, Econometrics & Finance Education Other

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The Effect of Compensation and Organizational Commitment on Work Satisfaction in Transportation Company Yayuk Suprihatini; Indra Rustiawan; Muhamad Irpan Nurhab; Taryana Taryana; Arjang
JEMSI (Jurnal Ekonomi, Manajemen, dan Akuntansi) Vol. 9 No. 2 (2023): April 2023
Publisher : Sekretariat Pusat Lembaga Komunitas Informasi Teknologi Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jemsi.v9i2.1044

Abstract

The purpose of this study is to ascertain how organizational commitment and pay affect employee job satisfaction. The dissemination of questionnaires and the usage of library data were the approaches used in this study's quantitative methodology. Using the saturated sample method, 50 employees made up the study's sample. Using SPSS 22 for data analysis, multiple linear regression is the method employed. The data quality test, the conventional assumption test, the hypothesis test, and the coefficient of determination are the data analysis techniques that are employed (R2). The findings of this study suggest that organizational commitment and salary have a considerable impact on employee job satisfaction, either partially or simultaneously. Employee work satisfaction is most strongly influenced by organizational commitment.
Deteksi Wajah Kehadiran Mahasiswa Saat Perkuliahan Daring Menggunakan Metode Klasifikasi Nearest Neighboarhood Emil Herdiana; Indra Rustiawan; Zatinniqotaini Zatinniqotaini; Nova Indarayana Yusman
INTERNAL (Information System Journal) Vol. 4 No. 2 (2021)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v4i2.257

Abstract

Recording student attendance  during lectures with an online system [on the network] is very necessary to assist both lecturers and the academic department in recording each student's attendance. Therefore the author will make an approach method based on face detection [face recognition] with the K-Nearest Neighbor algorithm or often called the K-NN algorithm, which is a supervised learning algorithm where the results of the new instance are classified based on the majority of the k-nearest neighbors. . The purpose of this algorithm is to classify new objects based on attributes and samples of student attendance/attendance. The k-Nearest Neighbor algorithm uses the Neighborhood Classification which will be used as the predictive value of the new instance so that it will get a value that will approximate the student's facial resemblance.
Co-Authors arjang Emil Herdiana Muhamad Irpan Nurhab Nova Indarayana Yusman Yayuk Suprihatini Zatinniqotaini Zatinniqotaini