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Winda Nia Purba
Universitas Prima Indonesia
Computer Science & IT

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IMPLEMENTASI DATA MINING DENGAN METODE POHON KEPUTUSAN ALGORITMA ID3 UNTUK MEMPREDIKSI PENJUALAN PADA CV. MITRA BAJA CEMERLANG Winda Nia Purba; Demak Situmorang; Yulia Alfani; Delima Hutabarat; Fransiskus William Anggiono
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 2 No 1 (2019)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.941 KB) | DOI: 10.37600/tekinkom.v2i1.96

Abstract

This study aims to determine the inventory control carried out by CV. Pematangsiantar Shining Steel Partners. The data used in this study are secondary data sourced from company records for the last 3 months. The data analysis method used in this study is the Decision Mining Data Tree method with ID3 algorithm. Through the application of data mining CV. Cemerlang Steel Partners will know more about the items that are of interest to consumers and items that are not in demand, so there will be no more stockpiles that accumulate in the warehouse. The results of this study found that the Root Node in determining which items are in demand and not in demand is the Size attribute. Rule obtained as many as 14 rules, which consists of 7 rules that are worth yes and 7 rules that have no value.
PENERAPAN DATA MINING UNTUK PENGELOLAAN DATA REKAM MEDIS MENGGUNAKAN METODE K-MEANS CLUSTERING PADA RUMAH SAKIT ROYAL PRIMA MEDAN Winda Nia Purba; Gamaliel Armando Sembiring; Mawar Theresia Turnip; Andreas Saputra; Ben Jua Ivand Manihuruk
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.857

Abstract

In this digital era, medical record data in hospitals has grown to be very large and complex. This medical record data includes information about the patient, diagnosis, treatment, and other medical history. Efficient and effective management of medical record data is essential to improve the quality of health services, appropriate decision-making, and medical research. This study uses data mining techniques with the K-Means Clustering method to cluster patient medical record data. Cluster 1 consists of 1827 people suffering from Emergency, Orthopedics, Obgyn, Internal Medicine, Pulmonary, NICU/PISU, Heart Disease, Perinatology, Neonatal and Growth and Development, Obstetrics Oncology, as well as male and female 9227 and 8990 respectively. Cluster 4 consists of 417 people who suffer from Urology, ENT, General, Neurology, Rheumatology diseases, and the male gender is 195 people and the female gender is 112 people. by using data mining, researchers can find new information about how royal prima medan hospital manages various types of care. Researchers hope to be a reference for hospitals, to be able to socialize and prevent sources of disease based on gender and treatment.
ANALISIS PEMBERIAN INSENTIF TENAGA MEDIS MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING Dwi Cahya Prana Ginting; Jonggi Samuel Parluhutan Sihombing; Nia Natalia Aritonang; Ribka Patricia Sinaga; Winda Nia Purba
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.858

Abstract

Intensive funds are very important for health workers in caring for Covid-19 patients. Researchers conducted research using a dataset from a list of names of health workers at the puskesmas who were proposed to get intensive handling of Covid-19 in the city of Medan. One of the stages for preprocessing the data set is carried out using the application of the linear regression method. The researcher uses several k means clustering algorithms so that from this process the results can be obtained for anyone who deserves intensive handling of the Covid-19 pandemic. The algorithms used include Decision Tree C4.5, K-Nearest Neighbor, Naive Bayes, C4.5 Algorithm, K-Means clustering, Online Analytical Processing. The researcher conducted a test using a data mining tool, namely with RapidMiner version 9.0 using the K-means Clustering Algorithm method, data results from RapidMiner that have been connected to the K-Means Clustering method and obtained predictive results from data obtained from health workers 2019-2022. In this study using a dataset from a list of names of health workers at the puskesmas who were proposed to get incentives for handling the Covid-19 disease pandemic in Medan City. The data was obtained from the results of the list of names of health workers at the puskesmas from 2019-2022. The dataset preprocessing stage is carried out using the application of the Linear Regression Method. Based on the results of Cluster officers, the total number of data is 279, there are 5 clusters, which consist of Cluster 0, Cluster 1, Cluster 2, Cluster 3 results. There are 6 officers who get incentives of Rp. 3,000,000, 44 officers get incentives of Rp. 4,000,000 and 229 officers who received Rp. 5,000,000. The results of this analysis obtained Cluster 0: 93 items, Cluster 1: 83 items, Cluster 2: 91 items, Cluster 3: 2 items, Cluster 4: 10 items and a total number of times 279.
PENGGUNAAN ALGORITMA K – MEANS CLUSTERING UNTUK MENENTUKAN PENILAIAN KEDISIPLINAN KARYAWAN RUMAH SAKIT ROYAL PRIMA Winda Nia Purba; Michael Kosasih; Donny Kallamas; Michael Wijaya
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.856

Abstract

Hospital is a place that has community service facilities engaged in the health sector, in the hospital provides outpatient and inpatient care. A hospital has several services such as health services for the elderly, health services for children and health services for adults. Before a hospital implements all these services for the community, all employees of the hospital need to do a training process so that the management of the hospital can assess the level of discipline of all employees of the hospital. Apart from the training process, the department of a hospital company will conduct an employee assessment, which the goal is to find out the level of discipline of the hospital employees. The research methodology used in this study is descriptive. a research method used to discuss a problem by researching, processing data, analyzing and describing with regular discussion. In this descriptive method research, the author uses three aspects that are the criteria for discipline of Royal Prima Hospital Employees, namely Discipline, Absence and Appreciation. The data collected by the author is through observation techniques and sampling techniques.
IMPLEMENTASI DATA MINING CLUSTERING DALAM MENGUKUR KEPUASAN TERHADAP PELAYANAN PERPUSTAKAAN DI UNIVERSITAS PRIMA INDONESIA Winda Nia Purba; Rinaldi Syahputra; Fine Reza Nainggolan; Gabriel Immanuel Manullang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1213

Abstract

This research aims to measure student satisfaction with Prima Indonesia University library services using data mining methods. Clustering techniques are used to group student satisfaction data based on various attributes such as service quality, resource availability, facility comfort, and interaction with library staff. Data was collected through questionnaires distributed to students. The clustering results revealed significant patterns in student satisfaction, which were analyzed to identify key factors influencing satisfaction levels. These results provide library managers with valuable insights for optimizing services and increasing user satisfaction. The application of clustering data mining has proven effective in helping libraries understand student needs and preferences and plan service improvements more accurately and efficiently.
Co-Authors Andreas Saputra Ben Jua Ivand Manihuruk Delima Hutabarat Demak Situmorang Donny Kallamas Dwi Cahya Prana Ginting Fine Reza Nainggolan Fransiskus William Anggiono Gabriel Immanuel Manullang Gamaliel Armando Sembiring Jonggi Samuel Parluhutan Sihombing Mawar Theresia Turnip Michael Kosasih Michael Wijaya Nia Natalia Aritonang Ribka Patricia Sinaga Rinaldi Syahputra Yulia Alfani