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All Journal BERKALA SAINSTEK Paradigma JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmu-Ilmu Kesehatan Jurnal ABDINUS : Jurnal Pengabdian Nusantara Community Empowerment EXPLORER Jurnal Ilmu-Ilmu Kesehatan Sekolah Tinggi Ilmu Kesehatan Garuda Putih Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal of Hospital Administration and Management (JHAM) Jurnal Manajemen Sistem Informasi Nuansa Informatika ABDIMAS CSRID ARUMAS International Journal of Computer Technology and Science Systematic Literature Review Journal JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of New Trends in Sciences KREATIF: Jurnal Pengabdian Masyarakat Nusantara Global Science: Journal of Information Technology and Computer Science
Rudolf Sinaga
Universitas Dinamika Bangsa
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : arumas

 1 
Perbandingan Sistem Informasi Edukasi dan Dukungan Pasien Yang Tepat Untuk Perawatan Pasien Home Care Renny Afriany; Samsinar Samsinar; Rudolf Sinaga
ARUMAS Vol 1 No 1 (2024): Jurnal Penelitian Administrasi Rumah Sakit
Publisher : STIKES Garuda Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In home care patient care, patient education and support information systems play an important role in providing appropriate information and support to patients and their families. This information system can be accessed by patients and their families anytime and anywhere, thereby increasing the effectiveness of care. There are various patient education and support information systems available on the market. Each system has its own advantages and disadvantages. Therefore, it is important to compare these systems to determine the most appropriate system for treating home care patients. From these results and discussion, it can be concluded that each platform has its own advantages and disadvantages. The best choice depends on the Home Care patient's individual preferences and specific needs. The level of ease of access, service response, and availability of complete medical information are the main factors in choosing a suitable platform to be used as a home care referral platform.
Data Mining untuk Evaluasi Kualitas Layanan Persalinan: Studi Komparatif RapidMiner dan SPSS Irwandi, Irwandi; Samsinar, Samsinar; Sinaga, Rudolf
ARUMAS Vol 2 No 1 (2025): Jurnal Administrasi Rumah Sakit
Publisher : STIKES Garuda Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52741/ars.v2i1.102

Abstract

Improving the quality of maternity services is a strategic priority in the health system. With the development of technology, data mining has become an effective approach to evaluate the quality of big data-based services. This study aims to compare the performance of two data mining tools—RapidMiner and SPSS—in analyzing labor service data to assess the effectiveness, efficiency, and ease of interpretation of the analysis results. A quantitative approach was used as a method with a comparative design of 500 childbirth data from hospitals. Data were analyzed using RapidMiner with Decision Tree and K-Means algorithms, as well as SPSS with logistic regression and correlation tests. The indicators assessed include prediction accuracy, processing time, and ease of use. The results of the analysis showed that RapidMiner achieved a prediction accuracy of 85.4% and was able to cluster with a silhouette coefficient of 0.65. The processing time is about 12 minutes. SPSS shows an accuracy of 81.2% with a faster processing time of 8 minutes. Significant factors found include the mother's age, complications, and type of delivery. RapidMiner excels in predictive analysis and big data processing, while SPSS is more efficient for conventional statistical analysis. A combination of the two is recommended to obtain more comprehensive service evaluation results. The integration of data mining in health information systems needs to be strengthened to support data-based policies in improving the quality of maternity services.
Prediction of Hospital Administration Study Program Students' Graduation Using Decision Tree C4.5: Prediction of Hospital Administration Study Program Students' Graduation Using Decision Tree C4.5 Samsinar, Samsinar; Sinaga, Rudolf; Afriany, Renny
ARUMAS Vol 2 No 2 (2025): Jurnal Administrasi Rumah Sakit
Publisher : STIKES Garuda Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52741/ars.v2i2.109

Abstract

The timely graduation success of students is a crucial indicator in evaluating the quality of higher education institutions. This study aims to predict the graduation rate of undergraduate students in the Hospital Administration Program at STIKES Garuda Putih Jambi using the Decision Tree C4.5 algorithm. The data utilized includes the Cumulative Grade Point Average (CGPA) of 35 students over the first four semesters. The dataset was processed using RapidMiner to generate a prediction model with CGPA as the main variable. The model evaluation indicated an accuracy level of 73.33%. This classification model successfully categorized student graduation outcomes into three groups: satisfactory, very satisfactory, and with distinction. The findings of this study are expected to provide insights for better academic decision-making, as well as enhance the quality of evaluation and learning processes in higher education institutions.
Co-Authors Adha Taufik, Anvika Afriany, Renny Errissya Rasywir Frangk Frangky Frangky Frangky Frangky Frangky Frangky Frangky Frangky Taan Frangky, Frangky Ika Widyastuti Irwandi Irwandi M. Yogi Riyantama Isjoni N, Renny Afriany Nurhadi Nurhadi Priyo Wibowo Raihansyah, M. Renny Afriany Renny Afriany Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Samsinar Uswatun Kasanah Yovi Pratama Zhenya, Zhenya Zeldha Elyzvania