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All Journal QISTINA: Jurnal Multidisiplin Indonesia Journal of Indonesia Vascular Access Jurnal Sistem Informasi dan Manajemen
Hasan, Afrizal
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Religion Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Analysis of Public Sentiment Regarding the Issue of Cancelling the Revision of the 2024 Regional Election Law with NLP Wardaniah, Sabina; Listia, Hijka; Wulandari, Siti; Ramadhani, Fanny; Dewi, Sri; Hasan, Afrizal
QISTINA: Jurnal Multidisiplin Indonesia Vol 3, No 2 (2024): December 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/qistina.v3i2.4166

Abstract

The latest changes regarding the age requirements for regional head candidates in the 2024 Election by the Supreme Court have given rise to various responses from the public. This decision has become an important issue that is widely discussed on social media and mass media, where this decision has caused various reactions, both positive, negative and neutral. Sentiment analysis is important to determine public opinion on this decision. This study aims to analyze public sentiment on the Issue of Cancellation of the Revision of the 2024 Pilkada Law. This study uses the Natural Language Processing (NLP) method with the Naive Bayes algorithm. The data used is text data in the form of public opinion on the Issue of Cancellation of the Revision of the 2024 Pilkada Law collected via Twitter. This data is then processed using NLP techniques such as cleaning, tokenization, normalization, filtering and stemming. After that, public opinion on the Issue of Cancellation of the Revision of the 2024 Pilkada Law is classified into positive, negative and neutral sentiment categories. The results of the study showed that the results of the model performance evaluation using data testing produced an accuracy of 92%, so it can be concluded that this model is good enough for sentiment analysis. Accuracy shows that the model makes correct predictions on 92% of the total data, which is dominated by the neutral class.
TRANSFORMASI DIGITAL RUMAH SAKIT MELALUI MEDICAL ARTIFICIAL INTELLIGENCY, HEALTHCARE SERVICE QUALITY, DAN PERCEIVED USEFULNESS: DAMPAKNYA TERHADAP PATIENT SATISFACTION DI RSUP DR. SITANALA Hasan, Afrizal; Hasanah, Neneng; Anggiani, Sarfilianty
JURSIMA Vol 12 No 3 (2025): Volume 12 Nomor 3 2025
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v12i3.1155

Abstract

Digital transformation in the healthcare sector is a strategic effort to improve efficiency, service quality, and patient satisfaction. The application of intelligent technology such as medical artificial intelligence, accompanied by improving the healthcare service quality and perceived usefulness of technology, is an important element in creating a superior service experience. This study aims to analyze “ Transformation of Hospital Through Medical Artificial Intelligency, Healthcare Service Quality, and Perceived Usefulness: Its Impact on Patient Satisfaction at RSUP Dr. Sitanala, Tangerang. A total of 234 respondents from inpatients in various healthcare units at RSUP. Dr. Sitanala, Tangerang have been collected from questionnaires directly and through the google forms application. Sampling in this study used purposive sampling. Data analysis used SmartPLS 3.0 as software. The findings of this study indicate that medical artificial intelligence, healthcare service quality, and perceived usefulness have a positive and significant effect on patient satisfaction.
Role of awake thoracoscopy in diagnosis before chest tube insertion in lung empyema: A Case Report Kartika, Ronald Winardi; Hasan, Afrizal; Dwiyanti, Hapsari; Yoshua, Edwin; Gisela, Esther; Supriyanto, Jaya
Journal of Indonesia Vascular Access Vol. 5 No. 1 (2025): Available online : 1 June 2025
Publisher : Indonesian Vascular Access Association (IVAA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51559/jinava.v5i1.76

Abstract

Background: Awake thoracoscopy diagnostic (ATD) is a valuable procedure performed before the insertion of a chest tube to visually identify lung and pleural diseases. This technique, which utilises thoracic spinal anaesthesia, allows patients to remain conscious during the procedure and eliminates the need for intensive care unit (ICU) admission post-operation. The purpose of this study is to demonstrate ATD's effectiveness in early identification and management of complex pleural infections, particularly in high-risk and resource-constrained settings Case Report: This case report describes a 56-year-old male who presented to the Emergency Department of RSUP Sitanala Tangerang with worsening intermittent dyspnea over three days, accompanied by low-grade fever, decreased appetite, weight loss, and a persistent cough. Diagnostic imaging revealed significant loculated pleural effusion, and thoracentesis yielded only 10 cc of purulent fluid, indicating a possible infectious process. A thoracic surgeon recommended awake thoracoscopy under thoracic spinal anaesthesia for direct evaluation and management. Intraoperative findings included thick pleural peel, necessitating thorough irrigation of the pleural cavity. A water-seal drainage (WSD) system was established for ongoing fluid management. Conclusion: Awake thoracoscopy diagnostic demonstrates significant advantages in the preoperative evaluation of patients with pleural diseases, particularly in cases involving active tuberculosis. Its minimally invasive nature and the benefits of awake procedures underscore its potential as a preferred diagnostic approach in thoracic surgery.
Co-Authors Dwiyanti, Hapsari Gisela, Esther Kartika, Ronald Winardi Listia, Hijka Neneng Hasanah Ramadhani, Fanny Sarfilianty Anggiani, Sarfilianty siti wulandari Sri Dewi Supriyanto, Jaya Wardaniah, Sabina Yoshua, Edwin