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All Journal JURNAL ILMU EKONOMI & SOSIAL Journal of Informatics and Interactive Technology (JIITE)
Didi Prasetyo
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Angkatan Kerja dan Faktor yang Mempengaruhi Pengangguran Adriyanto Adriyanto; Didi Prasetyo; Rosmiyati Khodijah
EKONOMI & SOSIAL Vol 11 No 2 (2020): Jurnal Ilmu Ekonomi & Sosial
Publisher : Universitas Musamus,Merauke,Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35724/jies.v11i2.2965

Abstract

This writing is a study of the number of labor force and unemployment which aims to see the employment relationship and the factors that affect the unemployment rate. The method used is descriptive qualitative. The results showt that potentially, Indonesia has human resource capabilities to be developed and in other parties are faced problems of manpower fields. The development of the number of labor force is rapid but not followed by sufficient employment opportunities. Another obstacle is the supply of labor that is not in accordance with the needs or certain qualifications demanded by the labor market even though the demand is very high, which causes higher unemployment. The minimum wage also greatly affects the unemployment rate. The wages set by the government greatly affect the existing unemployment rate. Any increase in the wage rate will be followed by a fall in the workforce that follows, which means unemployment. It is hoped that the Government's commitment to increase the opening of new jobs that can absorb a large number of workers to reduce the unemployment rate which has implications for signs of the poverty rate.
Diabetes Detection Optimisation with Hyperparameter Tuning in Random Forest Algorithm Aji Septa, Adrian; Amar Al Farizi; Anas Nur Khafid; Didi Prasetyo; Nur Cholis Romadhon; Fandy Setyo Utomo
Journal of Informatics and Interactive Technology Vol. 1 No. 3 (2024): Desember
Publisher : ACSIT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63547/jiite.v1i3.42

Abstract

Diabetes is a common disease suffered by many people, one of which is Diabetes Mellitus. This disease is caused by disorders in the pancreas that affect the body's metabolism due to the lack of production of the hormone insulin, the use of technology that is associated with diabetes is one step to be able to classify diabetes. This study aims to develop a diabetes classification model using the Random Forest algorithm. The methods used include dataset selection from the Pima Indians Diabetes Database, data pre-processing by replacing missing values using the mean, and data balancing using the SMOTE technique. The model was then trained and evaluated using confusion matrix to measure accuracy, precision, recall, and F1-score. The results showed that the Random Forest algorithm with grid search hyperparameters produced good performance with 79% accuracy, 76% precision, 83% recall, and 80% F1-score. The conclusion of this research is that the Random Forest algorithm is effective in classifying diabetes data and shows improved performance compared to other algorithms such as Logistic Regression. This model can be used for more accurate early detection of diabetes, thus helping in early treatment and reducing the number of disabilities and deaths due to diabetes.
Co-Authors Adriyanto Adriyanto Aji Septa, Adrian Amar Al Farizi Anas Nur Khafid Fandy Setyo Utomo Nur Cholis Romadhon Rosmiyati Khodijah