Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Jurnal Kesehatan Saintika Meditory Jurnal Software Engineering and Computational Intelligence
Siska Lestari
Unknown Affiliation
Computer Science & IT Education Health Professions Medicine & Pharmacology Nursing Public Health

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
HUBUNGAN MOTIVASI KERJA DENGAN KETERAMPILAN MENGELOLA KELAS GURU PAUD DI KELURAHAN SIMPANG BARU KECAMATAN TAMPAN PEKANBARU Siska Lestari; Wilson '; Ria Novianti
Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Vol 3, No 1 (2016): Wisuda Februari 2016
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Absract: This study has to purpose the correlation of work motivation and classroom management skills of early childhood teacher in Simpang Baru Village Tampan Sub-District Pekanbaru. The population in this study were all early childhood teachers in Simpang Baru Village District Tampan Sub-District Pekanbaruas many as 74 teachers consisting of 12 early childhood Institution. The research sample using simple random sampling technique , namely taking a random sample , in it can besamples in this study were as many as 42 teachers who have received training in early childhood institutions in Simpang Baru Village District Tampan Sub-District Pekanbaru.Data collection techniques used are questionnaires and observatio.Data were analyzed using descriptive analysis and correlation analysis product moment that have previously been tested assumptions. The results showed that there was a significant relationship between work motivation and classroom management skills. It is seen from r amounted 0,412.Keywords : Relationship, work motivation, classroom management skill
THE RELATIONSHIP BETWEEN SMOKING AND QUALITY OF LIFE IN BRONCHIAL ASTHMA PATIENTS Marlin Sutrisna; Siska Lestari; Elsi Rahmadani
Jurnal Kesehatan Saintika Meditory Vol 4, No 1 (2021): Mei 2021
Publisher : STIKES Syedza Saintika Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30633/jsm.v4i1.1800

Abstract

ABSTRACT  Bronchial asthma represents 1.8% of the total global disease. It is estimated that 346,000 deaths worldwide each year are due to bronchial asthma. The purpose of this study was to determine the relationship between smoking and the quality of life of bronchial asthma patients. The method used is descriptive with a cross sectional approach. The population in this study were all bronchial asthma patients at the Puller Health Center in Mukomuko Regency in 2020. The sampling technique used a purposive sampling technique, a total sample of 46 people. This study uses the chi-square test. The results of the univariate test analysis showed that most of the respondents (58.6%) with bronchial asthma smoked and most of the respondents (58.7%) with bronchial asthma had a poor quality of life. While the bivariate analysis test found that there was a relationship between smoking and quality of life in bronchial asthma patients. It is suggested to the puskesmas to be able to improve education in bronchial asthma patients to provide counseling to patients regarding several factors that cause a decrease in the patient's quality of life. Keywords : Bronchial Asthma ; Quality of Life ; Smoke
IMPLEMENTASI DEEP LEARNING UNTUK ANALISIS SENTIMEN PADA DATA X DALAM PREDIKSI TREN KOREAN STYLE Siska Lestari; Hermanto; Dian Hafidh Zulfikar
Jurnal Software Engineering and Computational Intelligence Vol 3 No 02 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i02.6137

Abstract

This study implements a deep learning model based on Convolutional Neural Network (CNN) for sentiment analysis of Indonesian-language tweets related to the Korean Style trend. A dataset of 7,187 tweets was collected via web crawling using keywords such as “Korean Style”, “K-pop”, and “Korean fashion”. The preprocessing pipeline included case folding, removal of special characters and emojis, stopword elimination, tokenization, and lemmatization. Word2Vec and FastText embeddings were employed for text representation. The CNN model classified tweets into three sentiment categories: positive, negative, and neutral. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. Results showed 71.26% validation accuracy with the highest F1-score of 0.81 for the neutral class, while negative sentiment classification remained weak due to class imbalance. Word2Vec outperformed FastText in stability. This research contributes to sentiment analysis in Indonesian social media using deep learning and provides insights into public opinion on Korean cultural trends
Co-Authors Dian Hafidh Zulfikar Elsi Rahmadani Hermanto Ria Novianti Sutrisna, Marlin Wilson '