Article Per Year (5 Year)
p-Index From 2019 - 2024
0.61
P-Index
This Author published in this journals
All Journal Faktor Exacta
Irwansyah Saputra
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
SISTEM BERBASIS CLOUD COMPUTING UNTUK IDENTIFIKASI RESEP DOKTER “BARSEP” Irwansyah Saputra; ANDI SARYOKO; GANDA WIJAYA; MEILYNDA TRISIANA; ASEP MULYANA; DANDI YUSBIAL BAYANI; DHARMA WINATA; VILSAFA KHOIRUNNISAK
Faktor Exacta Vol 13, No 4 (2020)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v13i4.7569

Abstract

A doctor's prescription is a doctor's written request to the pharmacist to prepare and give medicine to the patient. Prescriptions are made according to the needs of the patient after the doctor has examined and diagnosed the patient. However, doctor’s writing on a prescription that considered unclear can cause errors when compounding / preparing the drug and using prescribed drugs. In fact, the cure rate and life expectancy of patients is directly proportional to the administration of the right medicine. This study aims to prevent errors in the process of identification of prescription drugs by pharmacists. The technology used is cloud computing with the PHP 7.1.3 programming language, Laravel framework, and database storage using MySQL. BarSep application works by adding QR Code on recipe paper. The QR Code contains patient examination information including patient data, prescription drugs, and diagnoses, so that when the pharmacist scans the QR Code, the system will display all patient information that has been inputted by the doctor at the time of the examination. The results obtained from the implementation of the BarSep application at the Rapha Farma Pharmacy is BarSep applications effective for tackling errors in reading doctor's prescriptions that can save patients from medication errors. A doctor's prescription is a doctor's written request to the pharmacist to prepare and give medicine to the patient. Prescriptions are made according to the needs of the patient after the doctor has examined and diagnosed the patient. However, doctor’s writing on a prescription that considered unclear can cause errors when compounding / preparing the drug and using prescribed drugs. In fact, the cure rate and life expectancy of patients is directly proportional to the administration of the right medicine. This study aims to prevent errors in the process of identification of prescription drugs by pharmacists. The technology used is cloud computing with the PHP 7.1.3 programming language, Laravel framework, and database storage using MySQL. BarSep application works by adding QR Code on recipe paper. The QR Code contains patient examination information including patient data, prescription drugs, and diagnoses, so that when the pharmacist scans the QR Code, the system will display all patient information that has been inputted by the doctor at the time of the examination. The results obtained from the implementation of the BarSep application at the Rapha Farma Pharmacy is BarSep applications effective for tackling errors in reading doctor's prescriptions that can save patients from medication errors.                                                 
Analisis Sentimen Pengguna Marketplace Bukalapak dan Tokopedia di Twitter Menggunakan Machine Learning Irwansyah Saputra; RAHMAD SINGGIH AJI PAMBUDI; HANAFI EKO DARONO; FACHRI AMSURY; MUHAMMAD RIZKI FAHDIA; BENNI RAMADHAN; ANGGIE ARDIANSYAH
Faktor Exacta Vol 13, No 4 (2020)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v13i4.7074

Abstract

      A collection of tweets from Twitter users about Marketplace Bukalapak and Tokopedia can be used as a sentiment analysis. The data obtained is processed using data mining techniques, in which there is a process of mining the text, tokenize, transformation, classification, stem, etc. Then calculated into three different algorithms to be compared, the algorithm used is the Decision Tree, K-NN, and Naïve Bayes Classifier with the aim of finding the best accuracy. Rapidminer application is also used to facilitate writers in processing data. The highest results from this study are Decision Tree algorithm with 82% accuracy, 81.95% precision and 86% recall.
Text Mining of PeduliLindungi Application Reviews on Google Play Store Irwansyah Saputra; Taufik Djatna; Riki Ruli A. Siregar; Dinar Ajeng Kristiyanti; Hasbi Rahma Yani; Andri Agung Riyadi
Faktor Exacta Vol 15, No 2 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i2.10629

Abstract

Aplikasi PeduliLindungi merupakan aplikasi buatan pemerintah indonesia  untuk melakukan pelacakan dan penghentian  penyebaran Covid-19. Ulasan terkait aplikasi tersebut tidak seluruhnya baik, hal ini dibuktikan dengan beragamnya peringkat bintang yang diberikan pengguna sehingga terjadinya kesulitan dalam melihat sentimen positif atau negatif terkait aplikasi tersebut. Penelitian ini bertujuan untuk mengklasifikasi ulasan mengenai aplikasi PeduliLindungi kepada dua kelas, yakni sentimen positif dan sentimen negatif. Algoritma klasifikasi yang digunakan adalah klasifikasi Naive Bayes Classifier (NBC). Hasil Menunjukkan Accuracy  85%, Precision 77,7%, Recall 98%, dan F1-Score 86,7%.
Co-Authors Andi Saryoko Andri Agung Riyadi ANGGIE ARDIANSYAH ASEP MULYANA BENNI RAMADHAN DANDI YUSBIAL BAYANI DHARMA WINATA Dinar Ajeng Kristiyanti FACHRI AMSURY Ganda Wijaya HANAFI EKO DARONO Hasbi Rahma Yani Hermawan Prasetya dan Taufik Djatna MEILYNDA TRISIANA MUHAMMAD RIZKI FAHDIA RAHMAD SINGGIH AJI PAMBUDI Riki Ruli A. Siregar VILSAFA KHOIRUNNISAK