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Juni Nurma Sari
Politeknik Caltex Riau
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Early Detection Of Alzheimer Disease In Elderly Web-Based Using Support Vector Machine Classification Method Juni Nurma Sari; Syaparudin BS; Kartina Diah KW; Puja Hanifah
International ABEC Vol. 2 (2022): Proceeding International Applied Business and Engineering Conference 2022
Publisher : International ABEC

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Abstract

Alzheimer's disease is characterized by dimentia diseases that usually begin with a decrease in memory. The number of people in around the world with dimentia diseases is estimated to reach 47.5 million and is increased to quadruple by 2050. The risk factors that make someone exposed Alzheimer's disease are aging, alcohol consumption, anterosclerosis, diabetes mellitus, down syndrome, genetics, hypertension, depression, and smoking. Aging is the biggest risk factor for Alzheimer's disease. People with age 65 years and over have a higher risk. Therefore, it is important to early detect Alzheimer's disease in order to start planning adequate care and medical needs. This study aims to create a web-based system for early detection of Alzheimer's disease in the elderly using support vector machine classification. Detection of Alzheimer's disease using the metric Mini Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) obtained through questionnaires to find out about cognitive function, thinking ability and ability to perform daily tasks. Classification is carried out using the Support Vector Machine (SVM) algorithm. Alzheimer's classification testing uses a confusion matrix with an accuracy value of 85%. For system testing carried out User Acceptance Test with general practitioner, the results were obtained that all the features and functions of the system had run as expected.
Perancangan Dan Pembuatan Video Profil Berbasis Produk Pada Prodi Teknik Informatika Politeknik Caltex Riau Menggunakan Teknik Motion Graphic Firman Danu Ilhamsyah; Juni Nurma Sari
ABEC Indonesia Vol. 10 (2022): 10th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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So far, very little information about the final project product of the PSTI (Informatics Engineering Study Program) Caltex Riau Polytechnic, this is evidenced by the results of a survey about the knowledge of the final project product that is produced is still unknown and from the survey conducted shows that 30% know and 70% still do not know, therefore a solution was made so that PSTI can be better known by making a video profile based on the final project product of the 17th generation of PSTI. In making this profile video using motion graphic techniques because by using this technique there are many elements that will be used. The results obtained from the making of PSTI profile videos using motion graphic techniques for the needs of PSTI Caltex Riau Polytechnic as a promotional media have been successfully created, this video helps PSTI to be known by many people through digital media in the form of profile videos, this is proven based on the tests conducted using the Pre-test and Post-test methods, the results obtained showed an increase in understanding of 27.11%, which means that the video media as a promotional medium is very effective as an effort to promote PSTI Caltex Riau Polytechnic.
Rancang Bangun Sistem Penjualan Furniture Berbasis Web Dengan Metode Prototype Gina Apriasiska; Juni Nurma Sari
ABEC Indonesia Vol. 10 (2022): 10th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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Edi Furniture is a business that is engaged in furniture and furniture and has been running for about 30 years. Many buildings such as schools, health centers, police houses, and other buildings have furniture and furniture made in Edi Furniture. Many contractors are already clients of Edi Furniture. However, payments and data processing are still done manually. That way we need a system that can process the furniture sales process online, can provide information from wood processing to completion, and process data obtained from the sales process in the form of order reports, payment reports, product reports, and Inventory reports. The solution to this problem is the construction of a system "Design and Build a Web-Based Furniture Sales System with the Prototype Method". This system can facilitate the product marketing process, can carry out the sales process easily and efficiently, and can perform data processing quickly and precisely. This system uses black box testing and usability testing. The development of this system was completed in 2 iterations and the system worked for approximately 4 months. Based on the results of black box testing, it can be concluded that the functionality of the system has been successful. Meanwhile, based on questionnaire testing on users, it was found that 81.11% of this system was satisfactory for users, and based on questionnaire testing on employees it was found that 88% of this system was satisfactory for employees.
Electrocardiogram signals classification using random forest method for web-based smart healthcare Juni Nurma Sari; Putri Madona; Hari Kusryanto; Muhammad Mahrus Zain; May Valzon
International Journal of Advances in Applied Sciences Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v12.i2.pp133-143

Abstract

Coronary heart is the highest cause of death in Indonesia reaching 26%. Therefore, to prevent the high mortality rate of coronary heart disease (CHD), early detection of CHD can be carried out. One way is to examine the electrocardiogram/electrocardiograph (ECG) recording. ECG hardware has been made in previous studies to record ECG signals. ECG research is an important study because it can detect cardiovascular disease. Cardiovascular diseases can be classified as arrhythmic diseases. Arrhythmia is a disorder that occurs in the heart rhythm. The method used to recognize and classify ECG signal patterns is the R-R interval (RRI) method. In this study, the ECG signal is classified as normal and abnormal. Abnormal means that a person has a heart rhythm disorder. The classification method used is random forest. The advantage of the random forest classifier is that it can handle noise and missing values and can handle large amounts of data. The accuracy of the ECG signal classification using the Random forest method is 96%. The contribution of this research is that early detection of heart rhythm disorders using an ECG can be monitored through the smart healthcare web.
Co-Authors Agus Urip Ari Wibowo Anggy Trisnadoli Ardianto Wibowo Boby Chandra Panjaitan Dadang Syarif Sihabudin Sahid Firman Danu Ilhamsyah Gebriani Amanda Putri Gina Apriasiska Hari Kusryanto Indah Lestari Kartina Diah K. W. Lukito Edi Nugroho Madona, Putri Maksum Rois Adin Saf Muhammad Arif F Ridha Muhammad Ihsan Zul Muhammad mahrus zain Ogssa Sukardi Paulus Insap Santosa Puja Hanifah Putra Nugraha Ridi Ferdiana Sellya Meizenty Surya, Ibnu Susiyanti Susiyanti Syaparudin BS Valzon, May Wawan Yunanto Yohana Dewi Lulu Widyasari