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All Journal Jurnal Teknologi Informasi dan Multimedia Edu Elektrika Journal
Husaini, Rahayun Amrullah
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Computer Science & IT Electrical & Electronics Engineering

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Klasifikasi Gizi Lansia Menggunakan Metode Naïve Bayes Classifier Kartarina, Kartarina; Hatina, Adelia Azzahrah; Rismayati, Ria; Rahmiati, Baiq Fitria; Fatimatuzzahra, Fatimatuzzahra; Husaini, Rahayun Amrullah
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 6 No 2 (2024): August
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i2.502

Abstract

Elderly people are a group that is vulnerable to experiencing various problems in terms of nutrition and health caused by changes in eating patterns. Nutritional status affects the independence of an elderly person, where good nutritional status means less dependence on other people and vice versa. It is necessary to treat malnutrition or malnutrition as early as possible, one of which is by having an elderly posyandu. Posyandu for the elderly as a community service provides services and assistance in special health for the elderly, by regularly recording, controlling and reviewing the medical records of the elderly in a document. The data processing method in this research uses the Naïve Bayes method, where the data used comes from the medical records of the elderly and then used as a reference as to whether the elderly have good nutrition or are malnourished and require further action. Medical record documents play an important role in posyandu services for the elderly, so that medical record documents should be digitally based and systematic in recommending the nutritional status of the elderly. The Naïve Bayes algorithm is an algorithm that can help in classifying data in diagnosis using criteria for the condition of elderly patients. Naïve Bayes also has precise accuracy when implemented in applications that have databases with large data and makes it easier for users to interpret the results. This is proven by this research which produces an accuracy value of 91% with the data used as a sample of 110 elderly patients. The system design aims to help users as posyandu cadres in knowing whether the condition of the elderly is good, whether the elderly are at risk of malnutrition and provide treatment that is appropriate to the condition of elderly patients as well as assisting the Posbindu PTM in transforming documents into computerized ones.
ANALISIS SENTIMEN KINERJA TENAGA MEDIS INDONESIA MENGGUNAKAN MODELING ROBERTA DAN METODE MACHINE LEARNING Husaini, Rahayun Amrullah; Priyanto, Dadang; Hendro Martono, Galih
Edu Elektrika Journal Vol. 13 No. 1 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/eduel.v13i1.22163

Abstract

The development of information technology has led to the emergence of various social media applications, such as X, which allow users to share information and opinions. However, social media can also function as a platform for the spread of hoaxes and hate speech. One of the challenges faced is determining whether user comments are negative, neutral, or positive through sentiment analysis. This study aims to compare the performance of various classification algorithms in sentiment analysis of medical personnel services in X, using the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) algorithms with RoBERTa model-based labeling. Data was collected through the tweet-harvest library and processed using the Python programming language. The results showed a significant increase in accuracy, with the model able to classify public opinion into positive, negative, or neutral categories. The SVM model achieved the highest accuracy of 91.8%, outperforming other models such as Random Forest and Naïve Bayes, and provided insight into sentiment towards government health services. These findings provide valuable insights for policymakers in improving the provision of health services and managing public perceptions of medical personnel.
Co-Authors Fatimatuzzahra Fatimatuzzahra Hatina, Adelia Azzahrah Hendro Martono, Galih Kartarina, Kartarina Rahmiati, Baiq Fitria Ria Rismayati