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All Journal Jurnal Tekno Insentif Journal of Applied Computer Science and Technology (JACOST)
Roni Habibi
Universitas Logistik dan Bisnis Internasional
Computer Science & IT

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Pendekatan Supervised Learning untuk Diagnosa Kehamilan Fahira Fahira; Zian Asti Dwiyanti; Roni Habibi
Jurnal Tekno Insentif Vol 17 No 2 (2023): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v17i2.1102

Abstract

Abstrak Dalam studi ini dilakukan evaluasi performa dari beberapa algoritma machine learning dalam mendiagnosis kehamilan. Tujuan dari studi ini adalah untuk menemukan algoritma yang paling efektif antara decision tree dengan random forest dalam mendiagnosis kehamilan. Studi ini juga bertujuan untuk memberikan pengetahuan yang lebih baik mengenai bagaimana teknik pembelajaran mesin dapat digunakan dalam proses diagnosa kehamilan dan memberikan informasi yang berguna bagi para dokter dan peneliti dalam membuat keputusan yang tepat dalam mendiagnosis kehamilan. Dari hasil evaluasi dapat disimpulkan bahwa model Random Forest dengan menggunakan dataset yang seimbang dan metode Gini memiliki akurasi terbaik sebesar 81%. Hal ini menunjukkan bahwa menggunakan dataset yang seimbang dapat meningkatkan performa dalam mendiagnosis kehamilan. Algoritma Random Forest merupakan metode yang sering digunakan dalam proses pengklasifikasian karena kinerjanya yang baik. Algoritma ini bekerja dengan membuat pohon keputusan yang digunakan untuk membuat prediksi. Pada studi ini, kami menggunakan algoritma Random Forest dengan metode Gini untuk melakukan prediksi kehamilan. Abstract This study evaluates the performance of several machine learning algorithms in diagnosing pregnancy. The purpose of this study is to find the most effective algorithm between decision trees and random forests in diagnosing pregnancy. This study also aims to provide better knowledge about how machine learning techniques can be used in the process of diagnosing pregnancy and provide useful information for doctors and researchers in making the right decisions in diagnosing pregnancy. From the evaluation results it can be concluded that the Random Forest model using a balanced dataset and the Gini method has the best accuracy of 81%. This shows that using a balanced dataset can improve performance in diagnosing pregnancy. Random Forest Algorithm is a method that is often used in the classification process because of its good performance. This algorithm works by creating a decision tree that is used to make predictions. In this study, we used the Random Forest algorithm with the Gini method to predict pregnancy.
SLR Systematic Literature Review: Metode Penilaian Kinerja Karyawan Menggunakan Human Performance Technology Roni Habibi; Artha Glory Romey Manurung
Journal of Applied Computer Science and Technology Vol 4 No 2 (2023): Desember 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i2.511

Abstract

Performance appraisal is an activity conducted by groups or individuals in a company with the aim of evaluating employee performance based on specific indicators. The purpose of this evaluation is to determine the level of employee performance, provide fair job opportunities, and enhance employee motivation and performance. The objective of this research is to review the literature that employs the Systematic Literature Review (SLR) method, while considering several aspects such as previous research on employee performance appraisal and human performance technology. Initially, the search was limited to 450 journals. Then, a review and further screening were conducted, resulting in 161 remaining journals using inclusion and exclusion criteria, yielding 17 journals that met the requirements. This research was conducted over several years to assess employee performance. Therefore, this systematic literature review is carried out to identify the methods used for employee performance appraisal and mentions that optimization algorithms and fuzzy comprehensive methods are used as evaluation methods to assess performance appraisal methods.
Co-Authors Artha Glory Romey Manurung Fahira Fahira Zian Asti Dwiyanti