Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Jurnal Informasi dan Komunikasi Administrasi (JIKAP)
Cicilia Dyah Sulistyaningrum I.
Universitas Sebelas Maret
Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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

Found 2 Documents
Search

 1 
PENGARUH PRESTASI KERJA DAN LOYALITAS KARYAWAN TERHADAP PROMOSI JABATAN PADA PT.DJITOE INDONESIAN TOBACCO KOTA SURAKARTA Zaenab Paska Triani; Cicilia Dyah Sulistyaningrum I.; Susantiningrum - -
JIKAP (Jurnal Informasi dan Komunikasi Administrasi Perkantoran) Vol 3, No 3 (2019): Agustus
Publisher : Program Studi Pendidikan Administrasi perkantoran FKIP UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jikap.v3i3.35369

Abstract

The research is aimed to identify whetever or not: (1) there’s  positive and  significant effect of work performance to be job promotion; (2 )there’s positive and significant effect of employees loyalty to be job promotion; (3) there’s positive and significant effect between work performance and employee loyalty together toward job  promotion. This study used quantitative approach. The population of this research was 229 people and the total sample of this study were 70 people taken by proportional random sampling. The method of collecting data this research was used observation, questionnaires, and analysis document. The technique of analizying the data used multiple regression analysis by using SPSS version 20. The results of this research showed that:(1) there was positive and significant influence between the variables of work performance to job promotion (tcount=3.503, sign<0,05), (2 )there was positive and significant influence between employees loyalty to job promotion (tcount=3.785, sign<0,05), (3) there was positive and significant influence between work performance and employee loyalty together toward job promotion (Fcount=17.172, sign<0,05). The multiple linear regression equation is Ŷ = 16,881 + 0,475 X1 + 0,490 X2.
The Enterprise School Readiness Prediction System (ESRPS) Uses Machine Learning to Assess Children's Readiness for Entering Elementary School Muhammad Choerul Umam; Cicilia Dyah Sulistyaningrum I.; Dydik Kurniawan; Priyono Tri Febrianto
Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Vol 10, No 4 (2024): December
Publisher : Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jk.v10i4.13488

Abstract

This study aims to develop and evaluate the Enterprise School Readiness Prediction System (ESRPS) to predict children's readiness for elementary school using machine learning algorithms.  This research employs the Research and Development (R&D) method using Borg and Gall’s model and Instruments include questionnaires, programming tools, performance evaluation metrics, and web/database development tools to ensure the system's validity, reliability, and practical applicability.The research analyzes data from 300 students in various Indonesian cities, focusing on attributes like age, gender, and parental education. The system implements four algorithms: Decision Tree, Random Forest, Naive Bayes, and SVM. Data preprocessing, model training, and hyperparameter tuning were conducted, followed by evaluation using metrics like accuracy and precision. A web-based application was developed for user interaction and deployment. The result showed that the Decision Tree and Naive Bayes algorithms achieved the highest accuracy at 55%, followed by SVM at 50%, and Random Forest at 45%. This suggests that simpler models may be more suitable for the dataset's characteristics. The system also demonstrated the feasibility of practical deployment for educational use. The study concludes that ESRPS effectively uses machine learning to assess school readiness, highlighting the value of data preprocessing and model tuning in enhancing accuracy. Despite moderate accuracy levels, the study confirms the system's potential for aiding educators and parents in supporting children's transition to school.
Co-Authors Kurniawan, Dydik Muhammad Choerul Umam Priyono Tri Febrianto Susantiningrum - - Zaenab Paska Triani