Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Multitek Indonesia : Jurnal Ilmiah International Journal of Health Science
Nurul Baiah
Universitas Widya Husada Semarang
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Health Professions Public Health Social Sciences

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

Found 2 Documents
Search

 1 
PERANCANGAN SISTEM INFORMASI PENGARSIPAN DOKUMEN BERBASIS WEB DI BIDANG SUMBER DAYA KESEHATAN DINAS KESEHATAN KABUPATEN PATI Nurul Baiah; Sigit Sugiharto
MULTITEK INDONESIA Vol 20 No 1 (2026): July
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13443

Abstract

Advances in information technology have led government agencies to adopt digital changes in administrative management, including document archiving. Manual archive management is still widely used and causes various problems, such as difficulty in searching for documents, the risk of archive destruction, and a lack of work efficiency. The Health Resources Division of the Pati District Health Office has a high volume of documents, requiring an integrated, digital-based archiving system. This study aims to design and develop a Web-Based Document Archiving Information System to increase the effectiveness of archive management, speed up the document search process, and support orderly administration. The system was developed using the Waterfall method, which includes the stages of requirements, analysis, design, implementation, testing, and maintenance. The system was refined using the PHP programming language and a database as a centralized archive storage medium. The main features of the system include user authentication, document uploading by category, and document management in the form of viewing, downloading, renaming, and deleting documents. System testing was conducted using the Black Box method on 26 functional components, and the test results indicated that all system functions worked well as required. Thus the developed information system is capable of improving archive management capabilities and supporting the improvement of administrative service quality in the Health Resources Division of the Pati District Health Office.
Comparison of Support Vector Machine and Random Forest Classification Methods in Dengue Fever Sentiment Analysis Anisa Ranindia Arizky; Riska Fitria Ulandari; Grace Romauli Sihombing; Nurul Baiah; Frandianus Frandianus
International Journal Of Health Science Vol. 6 No. 2 (2026): July: International Journal of Health
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/ijhs.v6i2.7053

Abstract

Dengue fever is a prevalent disease in Indonesia, caused by the dengue virus and transmitted by Aedes aegypti and Aedes albopictus mosquitoes. This study aims to analyze sentiment in tweets related to dengue fever using two machine learning algorithms: Support Vector Machine (SVM) and Random Forest. The dataset consists of 1,000 manually labeled tweets, categorized into three sentiment classes: Positive, Negative, and Neutral. The results indicate that the SVM algorithm outperformed Random Forest in terms of performance metrics. Specifically, SVM achieved higher accuracy (77.07%) compared to Random Forest (73.65%). Additionally, SVM demonstrated superior precision, recall, and F1-score, making it a more effective model for sentiment analysis in this context. These findings suggest that SVM is a promising tool for analyzing public sentiment on social media platforms, particularly for monitoring health-related issues like dengue fever. The study highlights the potential of machine learning techniques in improving public health surveillance and response by analyzing social media data for real-time insights.
Co-Authors Anisa Ranindia Arizky Frandianus Frandianus Grace Romauli Sihombing Riska Fitria Ulandari Sigit Sugiharto