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All Journal Bulletin of Information Technology (BIT) Horizon: Indonesian Journal of Multidisciplinary
Dini Rizqi Dwikunti Siregar
Universitas Syiah Kuala
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Social Sciences Other

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Penerapan Metode Multi-Objective Optimization On The Basis Of Ratio Analysis Dalam Sistem Pendukung Keputusan Penilaian Kinerja Pegawai Klinik Kecantikan Dewi Yohana br Ginting; Surizar Rahmi Danur; Dito Putro Utomo; Eka Feby Ronauli Lubis; Sri Novida Sari; Dini Rizqi Dwikunti Siregar
Bulletin of Information Technology (BIT) Vol 5 No 3: September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i3.1408

Abstract

The research conducted is to conduct an employee performance assessment process at a Beauty Clinic. The employee performance assessment process at a Beauty Clinic is carried out to provide awards to qualified employees at the Beauty Clinic. This study applies a Decision Support System (DSS) which is used as a system that can process employee performance assessments at the Beauty Clinic. In the employee performance assessment process at the Beauty Clinic, there are 6 criteria, namely Service Orientation, Integrity, Responsibility, Initiative, Discipline and Attendance. Thus, a system is needed that can make decisions, namely DSS by implementing the MOORA method to obtain preference values ​​from employee performance assessments at the Beauty Clinic, there is the best alternative in alternative A5 with a preference value of 0.37472
Evaluasi Algoritma Random Forest dan KNN dalam Memprediksi Risiko Diabetes Berdasarkan Fitur Klinis Siti Jamilah Br Tarigan; Alyiza Dwi Ningtyas; Arif Hamied Nababan; Devanta Abraham Tarigan; Dini Rizqi Dwikunti Siregar
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.6400

Abstract

This study aims to demonstrate the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in predicting diabetes risk based on numerical clinical data. The study used a dataset of 757 samples with eight clinical features, namely the number of pregnancies, glucose levels, blood pressure, skin thickness, insulin, body mass index (BMI), familial diabetes predisposition function, and age. The data was divided into 80% training data and 20% testing data, with data scale adjustments to support the classification process. The evaluation results showed that Random Forest produced better performance with an accuracy of 73.7% and an F1-Score of 0.623, compared to KNN with an accuracy of 72.4% and an F1-Score of 0.604. Comparison of classification results showed that Random Forest was able to provide more consistent predictions in distinguishing groups at risk of diabetes from healthy groups. The contribution of this study is to provide an empirical evaluation of the description of two classification algorithms commonly used on numerical clinical data and show that Random Forest is more suitable for the development of a decision support system for diabetes risk prediction. This research can be the basis for the development of more accurate prediction models through the use of broader datasets and other machine learning methods.
Co-Authors Alyiza Dwi Ningtyas Arif Hamied Nababan Devanta Abraham Tarigan Dewi Yohana br Ginting Dito Putro Utomo Eka Feby Ronauli Lubis Siti Jamilah Br Tarigan Sri Novida Sari Surizar Rahmi Danur