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All Journal Infotech Journal SENAPAS
Yoga Hermawan
Universitas Majalengka
Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Pengembangan Sistem Informasi Lembaga Penelitian Pengabdian Kepada Masyarakat (LP2MI) Universitas Majalengka Bidang Pengajuan Permohonan HKI Yoga Hermawan; Tri Ferga Prasetyo
Seminar Nasional Penelitian dan Abdimas Vol 1 No 1 (2023): Juni
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/senapas.v1i1.7345

Abstract

Salah satu lembaga atau biro yang bertugas membantu dosen dalam mengurus hak kekayaan intelektual dosen di Universitas Majalengka adalah LP2MI. Pada Proses pengajuan permohonannya, pihak LP2MI masih menggunakan sistem manual dan belum memiliki sistem informasi tersendiri.Metode pengembangan sistem yang digunakan dalam penelitian ini adalah Metode RUP (Rational Unified Process). Pengembangan sistem ini dapat memberikan kecepatan dan kemudahan dalam pengolahan data dapat terlaksana sehingga diharapkan dapat membawa kemajuan dalam pelayanan permohonan dan pengelolaan Hak Kekayaan Intelektual (HKI) pada LP2MI Universitas Majalengka. Sistem Informasi Lembaga Penelitian dan Pengabdian Kepada Masyarakat dan Inovasi (LP2MI) Universitas Majalengka Bidang Pengajuan Permohonan HKI berhasil dikembangkan.
ANALYSIS FAVORITE GENERAL HOSPITALS IN WEST JAVA BASED ON INPATIENT VISITS USING K-MEANS SENTIMENT ANALYSIS Dadan Zaliluddin; Ii Sopiandi; Yoga Hermawan
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.10270

Abstract

This study examines the utilization of the K-Means algorithm for sentiment analysis in widely used hospital services utilizing the Python programming language. The main goal is to improve comprehension of patient satisfaction with the healthcare services provided at these hospitals. The data used for sentiment analysis was obtained via scraping patient evaluations from the web. The K-Means technique was utilized to classify the feelings into negative, neutral, and positive categories through the study of large-scale data. This investigation offers useful insights into the specific aspects that influence patients' opinions of healthcare services at their preferred hospitals. The study's findings provide valuable insights for hospital management to enhance the quality of healthcare services. Utilizing the K-Means algorithm in sentiment analysis facilitates the identification of prevalent trends and patterns that may not be discernible through manual techniques. Thus, this study integrates computational methodologies and sentiment analysis to offer a more holistic perspective on patient experiences at preferred hospitals.
Co-Authors Dadan Zaliluddin Ii Sopiandi, Ii Tri Ferga Prasetyo