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Journal : Bulletin of Multi-Disciplinary Science and Applied Technology

Diagnosa Hipertensi Dengan Menggunakan Metode Certainty Factor Ahmad Kurniawan Prahadi; Yani Maulita; Magdalena Simanjuntak
Bulletin of Multi-Disciplinary Science and Applied Technology Vol 1 No 6 (2022): Oktober 2022
Publisher : Forum Kerja Sama Penddikan Tinggi (FKPT)

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Abstract

Hypertension does not occur suddenly, but through a process that lasts quite a long time. Hypertension is an increase in blood pressure in the arteries that is systemic in nature or lasts continuously for a long period of time. Uncontrolled high blood pressure for a certain period will cause permanent high pressure called hypertension. Studies conducted by health institutions in the UK stated that in general hypertension is experienced by men and women aged 48.5 years. Although there are young people who suffer from hypertension, the percentage is relatively small. With this fact, hypertension is included in the group not a congenital disease. In diagnosing hypertension, of course, it can not only be done by measuring blood pressure, but requires further examination by a doctor. However, to see a doctor the patient must queue with another patient to be examined. This will certainly make the patient bored and will even cause blood pressure to increase even more due to waiting too long to be treated. To deal with the problems mentioned above, it is necessary to build an expert system that can diagnose hypertension quickly and get a treatment solution like a doctor using the Certainty Factor method. The certainty factor is a derivation and development of the conditioned probability theory (Bayes theorem). The certainty factor is obtained from the operation of reducing the value of belief (measure of belief) by the value of distrust (measure of disbelief). The main purpose of using the certainty factor is to process the uncertainty of facts and phenomena by avoiding the need for large data and calculations.
Data Mining Pengelompokan Pengajuan Kredit Pensiun Pada Bank Sumut Menggunakan Metode Clustering (Studi Kasus : PT. Bank Sumut Cabang Binjai) Alma Alawiyah Gultom; Budi Serasi Ginting; Magdalena Simanjuntak
Bulletin of Multi-Disciplinary Science and Applied Technology Vol 1 No 6 (2022): Oktober 2022
Publisher : Forum Kerja Sama Penddikan Tinggi (FKPT)

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Abstract

PT. Bank SUMUT Binjai branch is one of the financial institutions that provides several products offered to retirees such as retirement savings and pension loans. Most of the information is only seen as archives that are not used and can be destroyed at any time. This is a wrong view, because with proper and clever handling, these data can be processed and manipulated by data mining, so that later they can be used to produce useful information in making a decision. Data mining can help companies explore new knowledge by processing existing data with clustering methods and using the K-Means algorithm. From the credit application data, several criteria/variables will be taken, including the basic salary variables, allowances and loan guarantees (collateral) used. The data is processed with the Matlab program to produce a cluster center and the relationship between variables is obtained by the group with the highest value. The use of data mining techniques is expected to provide knowledge that was previously hidden in the data warehouse so that it becomes valuable information. The calculation that has been done is that the number of members of group 1 is 141 data, the number of members of group 2 is 90 data and the number of members of group 3 is 148 data. With the results centroid 1: 2.5319, 1, 1.5319, centroid 2: 5.2222, 3.5556, 1.9889 and centroid 3: 4,3286,1.8295, 2.1818.