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All Journal Jurnal Sains Manajemen JURNAL EKONOMI BISNIS DAN MANAJEMEN (EKO-BISMA) Journal of Computer Science Advancements
Dola Ramalinda
Universitas Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Vulnerabilities and Threats to AIS Security Systems Dola Ramalinda; Agung Raharja
Journal of Computer Science Advancements Vol. 2 No. 3 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i3.1055

Abstract

This research explores vulnerabilities and threats to Accounting Information Systems (AIS) and evaluates effective mitigation measures to address these issues. Using both qualitative and quantitative approaches, this research involved literature studies, surveys, expert interviews, and case analysis to identify and analyze the different types of vulnerabilities and threats faced by AIS. The results showed that human error, software flaws, and system integration complexity are the main sources of vulnerabilities in AIS. The most common threats are cyberattacks, insider threats, and Distributed Denial of Service (DDoS) attacks. The impact of these threats includes financial loss, reputational damage, and significant legal implications.Mitigation strategies identified include the development of robust security policies, the use of cutting-edge security technologies, and the conduct of regular audits and monitoring. The findings emphasize the importance of a layered approach to improving AIS security and reliability. This research provides an in-depth insight into how organizations can identify, evaluate and manage vulnerabilities and threats to their AIS, which is essential for maintaining the integrity, confidentiality and availability of accounting data in an increasingly complex digital age.
ANALISIS KEBUTUHAN SUMBER DAYA MANUSIA UNIT ASSEMBLING RAWAT JALAN MENGGUNAKAN METODE ANALISIS BEBAN KERJA KESEHATAN (ABK-KES) UNTUK MENINGKATKAN EFISIENSI KERJA DI RS X Dola Ramalinda
JURNAL EKONOMI BISNIS DAN MANAJEMEN (EKO-BISMA) Vol 4 No 1 (2025): JURNAL EKONOMI BISNIS DAN MANAJEMEN (EKO-BISMA)
Publisher : PUBLISHER ABISATYA DINAMIKA ISWARA PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58268/eb.v4i1.167

Abstract

Hospitals are required to have qualified health workers and in accordance with the amount of their workload in supporting the effectiveness of hospital service functions. So that there is no overload so that it can affect the quality and quantity of work. The purpose of this study is to determine the needs of health human resources in the type of assembling work and outpatient analysis at X Hospital. This study uses a descriptive qualitative research method using interviews and observations to determine available working hours, workload standards, and supporting task standards based on the health workload analysis method (ABK-Kes). The results of this study show that the Available Working Time of the assembling and outpatient analysis employees is 96,780 minutes/year with the results not in accordance with the effective working hours (JKE) which should be 72,000 minutes/year. The results of this study also prove that 4 competent health workers are needed. However, the number of assembling and outpatient analysis employees at the hospital only has 1 employee. The conclusion is that employees in the medical record unit of the assembling and outpatient analysis section still need additional health human resources employees so as not to experience an overload.
MODEL AI PREDIKSI BURNOUT BERBASIS WORKLOAD STAF KLINIS Dola Ramalinda; Yudi Darsono; Agung Rachmat Raharja
Jurnal Sains Manajemen Vol. 8 No. 2 (2026): Jurnal Sains Manajemen
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/af7yfj23

Abstract

Burnout pada tenaga kesehatan merupakan tantangan sistemik yang berdampak pada kualitas layanan dan keselamatan pasien. Beban kerja klinis, pola shift yang intens, serta tuntutan administratif dari penggunaan Electronic Health Record (EHR) menjadi faktor yang berpotensi meningkatkan stres kerja. Meskipun data operasional tersedia dalam Sistem Informasi Rumah Sakit (SIRS), pemanfaatannya untuk deteksi dini risiko burnout staf masih terbatas. Penelitian ini bertujuan mengembangkan dan mengevaluasi model supervised machine learning untuk memprediksi risiko burnout staf klinis berdasarkan data perilaku dan beban kerja dari SIRS dan EHR. Penelitian kuantitatif dengan desain prediktif melibatkan 120 staf klinis yang terdiri atas dokter dan perawat. Data primer tingkat burnout diperoleh melalui instrumen terstandar, sedangkan data sekunder mencakup aktivitas EHR, jumlah pasien, jam kerja, dan pola shift. Tiga algoritma dibandingkan, yaitu Logistic Regression, Random Forest, dan Gradient Boosting, menggunakan metrik akurasi dan Area Under the Curve (AUC). Hasil menunjukkan bahwa perawat dan staf dengan pola kerja shift memiliki proporsi risiko burnout sedang hingga tinggi yang lebih besar. Beban kerja, terutama volume pasien dan jam kerja, serta intensitas aktivitas EHR menjadi faktor penting yang berkaitan dengan risiko burnout. Gradient Boosting menghasilkan kinerja terbaik dengan akurasi 0,87 dan AUC 0,91. Model tersebut berpotensi digunakan sebagai instrumen deteksi dini untuk mendukung redistribusi beban kerja, optimalisasi jadwal shift, dan intervensi preventif berbasis data dalam manajemen SDM rumah sakit.
Co-Authors Agung Rachmat Raharja Agung Raharja Yudi Darsono