Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Fitri Anggraini
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of the SMART Algorithm in Determining Patient Satisfaction Levels with Outpatient Services Patar Simbolon; Muhammad Zarlis; Sundari Retno Andani; Fitri Anggraini
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 1 (2023): March
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i1.159

Abstract

This study aims to implement the SMART algorithm in determining the level of patient satisfaction with outpatient services at Vita Insani Hospital Pematangsiantar. This study uses four evaluation criteria, namely speed of service, friendliness of staff, clarity of information, and comfort of the room. There are nine alternatives evaluated, namely registration, polyclinic, doctor, cashier, laboratory, radiology, pharmacy, emergency room, and security guard. This study uses the SMART method (Simple Multi-Attribute Rating Technique) in determining the level of patient satisfaction with outpatient services. Calculations are performed either manually or computerized. The results showed that the two calculation methods yielded the same results, namely alternative A9 (Security Guard) was selected as an alternative that needed to improve its services in improving outpatient services at Vita Insani Hospital. By using the SMART algorithm, it is hoped that the hospital can identify service areas that need to be improved to increase patient satisfaction in outpatient services. This research provides valuable information for hospital management in making strategic decisions to improve service quality and meet patient expectations.
Prediksi Impor Hasil Pertanian (Sayuran) Menurut Negara Asal Utama Menggunakan Algoritma Levenberg-Marquardt Achmad Daengs GS; Irma Hakim; Fitri Anggraini
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.347

Abstract

Vegetables are very important as a source of nutrition for society and provide various vitamins, minerals, fiber and antioxidants needed to maintain human health. This research aims to predict imported production of vegetable crops by main country of origin in 2024 using the Levenberg-Marquardt algorithm. This algorithm was chosen because of its ability to integrate historical data and current information, as well as considering risk factors that impact vegetable import production. Historical data regarding imported production of vegetable crops from the main countries of origin used covers the period 2014 to 2022. The data is processed from customs documents of the Directorate General of Customs and Excise (PEB and PIB) and quoted from the Indonesian Statistics Publication. The vegetable import production prediction model was evaluated using accuracy level metrics. The research results are expected to provide reliable and accurate predictions for vegetable import production based on the main country of origin in 2024. The accuracy resulting from this research is 91% and the MSE level is 0.10042088158. Vegetable import production according to the main country of origin based on predicted results is expected to decline when compared with the previous year (2014-2022). These predictions provide a more detailed understanding to stakeholders such as farmers, producers, government and industry players in planning production, managing resources, allocating budgets and making effective decisions. This research also has the potential to contribute to the development of knowledge, especially in the development of more advanced and efficient prediction methods. This applies both to imported vegetable production and to other agricultural sectors, by utilizing the Levenberg-Marquardt algorithm.
Co-Authors GS , Achmad Daengs Irma Hakim Muhammad Zarlis Patar Simbolon Sundari Retno Andani