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All Journal MEDISAINS JITSI : Jurnal Ilmiah Teknologi Sistem Informasi
Ariyanto, Fajar
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Computer Science & IT Medicine & Pharmacology Nursing Public Health

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Psychosocial therapy model to reduce anxiety levels in hemodialysis patients Ariyanto, Fajar; Pratiwi, Arum; Hudiyawati, Dian
MEDISAINS Vol 22, No 3 (2024)
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/medisains.v22i3.23788

Abstract

Background: Chronic kidney disease (CKD) is a global health issue that significantly impacts the quality of life of patients undergoing hemodialysis. Anxiety is one of the common psychological challenges faced by these patients. While various studies have addressed psychological and social interventions, most have focused on only one aspect, such as family support or psychological therapy, without considering the biological, social, and spiritual needs simultaneously. No research has integrated all these dimensions into a holistic approach.Purpose: This study aims to develop and evaluate a holistic nursing management model that combines all of these aspects to reduce anxiety levels for hemodialysis patients.Methods: This study used a mixed-method approach with an action research design. It was conducted at the Klaten Islamic Hospital. The population in the study was patients undergoing hemodialysis; as many as 15 patients participated in this study. The holistic nursing module was tested in five sessions, each lasting 15-30 minutes. Anxiety levels were measured using the Hamilton Rating Scale for Anxiety (HRS-A) before and after the intervention.Results: The study's results showed that the average level of anxiety in patients before the intervention was 35.13±3.22; after the intervention, it decreased to 24.87±1.68 (p<0.001).Conclusion: The application of the holistic nursing management model effectively reduced anxiety levels in hemodialysis patients.
Komparasi Tingkat Akurasi Sentimen Algoritma K-Nearest Neighbor Dan Naïve Bayes Pemilihan Gubernur Jawa Tengah 2024 di Sosial Media X Ariyanto, Fajar; Saefurrohman
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 6 No 2 (2025)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.6.2.332

Abstract

This study highlights the importance of selecting appropriate algorithms for text data analysis and provides recommendations for future exploration of other machine learning and deep learning models to improve the accuracy of sentiment analysis. This research compares the accuracy level of the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms in sentiment analysis in the 2024 Central Java gubernatorial election using data from the social media platform X (formerly Twitter). The data consists of 1,337 posts classified as positive or negative sentiment. Data crawling was done using RapidMiner, and analysis was done via Python in Google Colab. The research results show that the KNN algorithm achieves the highest accuracy of 81%, while Naïve Bayes has a maximum accuracy of 79%. The KNN algorithm is superior in handling text data because of the dependent calculations between attributes, while Naïve Bayes which uses independent calculations has slightly lower performance. This research provides insight into the reaction of public sentiment towards the candidate for governor of Central Java, where the Andhika-Hendi pair received more positive sentiment than Lutfi-Yasin
Co-Authors Arum Pratiwi Dian Hudiyawati Saefurrohman