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All Journal Jurnal JSIKA Journal of Technology and Informatics (JoTI) Jurnal Sistem Informasi Universitas Dinamika
Lukman Arif Sanjani
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mechanical Engineering Other

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Rancang Bangun Sistem Informasi Penggajian Pegawai Dan Remunerasi Jasa Medis Pada Rumah Sakit Bedah Surabaya Sanjani, Lukman Arif; Hartati, Sulis Janu; Sudarmaningtyas, Pantjawati
Jurnal JSIKA Vol 3, No 1 (2014)
Publisher : Jurnal JSIKA

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Abstract: it often heard some grumble from employee when the paid time of salary late a few hours than the normal paid time. It can imagined how if the paid time of salary late not only in a few hours but more than that, a few days. If that condition really happen it can harm employees’ motivation where motivation here are needed to give optimal service from employees to customers, in this case, patients and doctors. It same here with doctors who need on time payment and accurate total of medical remuneration because some doctor have their own medical treatment records. It only harm the hospital if a doctor feel disappointed because of some problem on the payment of medical remuneration that make that doctor want to leave that make hospital will loss reliable medical personel. So far that case is rarely happened, because of Human Resource and Development(HRD) and Financial Department always take an overtime job on a few days to make salary and medical remuneration paid on time, but they do an inefficient thing because they still do it manually. Based on that case, an application created to make their job can be processed automatically. This application connected to fingerprint system database and  HMIS database to get the data that needed and processed the data to automatically generate salary and count medical remuneration. The evaluation results of the application got 3.52 from range 1 until 4 counted using likert scale method from 10 respondent that it means the application in the good range. The application can count salary and medical remuneration accurate and faster than the way they do before. Usually HRD spent 22 minutes to count salary of one employee, but using this application, employee only need 0.1806 seconds to do it. With the application performance, it means no need for HRD and Finance Department to take overtime job again to processed it and this means that it can lower the costs for Rumah Sakit Bedah Surabaya.
Rancang Bangun Sistem Informasi Penggajian Pegawai Dan Remunerasi Jasa Medis Pada Rumah Sakit Bedah Surabaya Sanjani, Lukman Arif; Hartati, Sulis Janu; Sudarmaningtyas, Pantjawati
Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA) Vol 3, No 1 (2014)
Publisher : Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA)

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Abstract

Rancang Bangun Sistem Informasi Penggajian Pegawai Dan Remunerasi Jasa Medis Pada Rumah Sakit Bedah Surabaya Sanjani, Lukman Arif; Hartati, Sulis Janu; Sudarmaningtyas, Pantjawati
Jurnal Sistem Informasi dan Komputerisasi Akuntansi (JSIKA) Vol 3, No 1 (2014)
Publisher : Jurnal Sistem Informasi Universitas Dinamika (JSIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (217.767 KB)

Abstract

Exploring the Application of Machine Learning for Automatic Inbound Email Classification in CRM System at XYZ Company Lukman Arif Sanjani; Bimo Mandala Putra, Raden; Laili Yuhana, Umi
Journal of Technology and Informatics (JoTI) Vol. 6 No. 1 (2024): Vol. 6 No.1 (2024)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v6i1.715

Abstract

Customer service has become increasingly crucial in today's business landscape, necessitating companies to provide fast, responsive, and personalized assistance to their clientele. However, amidst the challenges posed by surges in email volume, manual categorization and response strategies often lead to performance declines. To address this, we propose a system leveraging Machine Learning techniques for automated email classification. Our evaluation reveals promising results, with SVM achieving the highest accuracy of 96.59%, followed by XGB (96.02%) and RF (95.27%). These models exhibit commendable precision, recall, F1 scores, and Matthews Correlation Coefficient (MCC), showcasing their effectiveness in improving customer service efficiency and responsiveness. This integration of technology not only enhances operational efficiency but also fosters harmonious customer relationships, ultimately leading to increased loyalty and profitability for companies.
Co-Authors Bimo Mandala Putra, Raden Laili Yuhana, Umi Pantjawati Sudarmaningtyas Sulis Janu Hartati