Article Per Year (5 Year)
p-Index From 2020 - 2025
1.475
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) KLIK: Kajian Ilmiah Informatika dan Komputer
Lola Oktavia
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Sistem Komputer dan Informatika (JSON)

 1 
Penerapan Data Mining untuk Menentukan Penyebab Kematian di Indonesia Menggunakan Metode Clustering K-Means Lili Rahmawati; Alwis Nazir; Fadhilah Syafria; Elvia Budianita; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5912

Abstract

Death in medical science is studied in a scientific discipline called tanatology. death is not only experienced by elderly people, but also can be experienced by young people, teenagers, or even babies. Death can be caused by various factors, namely, due to illness, old age, accidents, and so on. Based on information provided by the World Health Organization (WHO), there are five highest causes of death including ischemic heart disease, Alzheimer's, stroke, respiratory disorders, neonatal conditions. In this study, k-means is used to group causes of death in Indonesia based on the number of deaths that occur to determine the cases of death that have the most impact on the high mortality rate in Indonesia. Knowing what these death cases are will provide early preparation in anticipating the causes of death in Indonesia. The purpose of this study was to classify mortality rates based on the number of causes of death which were included in the low, medium, and high clusters by applying the K-Means method. In this study the authors used the K-Means clustering algorithm to classify death rates in data on causes of death in Indonesia from 2017-2021. The results of this study formed 3 clusters which were evaluated using the Davies Bouldin Index (DBI) in Rapidminer with a value of 0.259. Clustering results from a total of 21 cases obtained high, medium and low clusters. This cluster grouping was obtained according to the number of deaths per case, namely the first cluster (C0) was low with 17 cases, the second cluster (C1) was moderate with 3 cases and the third cluster (C2) was high with 1 case.
Sistem Pakar Diagnosa Gangguan Stress Pasca Trauma Menggunakan Metode Certainty Factor Marliana Safitri; Fitri Insani; Novi Yanti; Lola Oktavia
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6309

Abstract

Mental health disorder or commonly called Mental Health Disorder is a disturbing psychological behavior and is followed by traumatic events such as shock shell, war fatigue, accidents, victims of sexual violence, and the covid pandemic. Cases of post traumatic stress disorder data from Indonesian Psychiatric Association amounted to 80% of 182 examiners experiencing symptoms of post-traumatic stress due to exposure to covid, 46% experienced severe symptoms, 33% moderate, 2% mild and others did not show symptom. This study aims to diagnose post traumatic stress disorder using the assurance factor method with 35 symptom data and 3 levels of post traumatic stress disorder as a knowledge base. The certainty factor is a circulation management method and a decision-making strategy using the confidence factor in the system. Based on the research results of the expert system for diagnosing post traumatic stress disorder, the test results obtained an accuracy of 80%. The results of the accuracy of this expert system indicate that the expert system can potentially be used to diagnose post traumatic stress disorder.
Pemodelan Klasifikasi Untuk Menentukan Penyakit Diabetes dengan Faktor Penyebab Menggunakan Decision Tree C4.5 Pada Wanita Nining Nur Habibah; Alwis Nazir; Iwan Iskandar; Fadhilah Syafria; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6202

Abstract

Diabetes is closely related to the pancreas, where the pancreas produces the natural hormone insulin, but its function is problematic which causes an increase in blood sugar levels in the body. Rising blood pressure can affect organ function in damaging the function of organs in a person's body such as the kidneys, heart and brain. Where makes a person have a history of diabetes. Diabetes that attacks adults can be prevented through exercise and a regular and healthy diet. According to the International Diabetes Federation (IDF) organization, it is estimated that at least 19.5 million Indonesian people between the ages of 20 and 79 will suffer from diabetes in 2021. China is in first place with diabetes with 140.9 million people. India is next in line with the number of people with diabetes of 74.2 million people. Therefore, early diagnosis is very important because it aims to reduce diabetes and diabetes complications in the future. It is necessary to collect data on patients with diabetes who are expected to be able to do prevention. Therefore applying classification techniques with data mining with the C4.5 algorithm. Where the classification can achieve better accuracy. Algorithm C4.5 is generally used in determining the nodes of a decision tree. Based on the test results, the accuracy is 76.67 percent, the precision is 72 percent, and the recall is 41.67 percent.
Co-Authors Alwis Nazir Eka Suryani Indra Septiawati Elin Haerani Elvia Budianita Fadhilah Syafria Febby Kurniawan Fitra Kurnia Fitri Insani Heri Susanto Ihda Syurfi Iwan Iskandar Jasril Jasril Lili Rahmawati Marliana Safitri Muhammad Affandes Nining Nur Habibah Novi Yanti Novriyanto Novriyanto Nurainun Nurainun Raja Sultan Firsky Reski Mai Candra Sola Huddin Tiara Ramayanti Yusra Yusra