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All Journal Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Healthy Tadulako Journal (Jurnal Kesehatan Tadulako)
Hakim, Raden Bagus Fajriya
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Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Energy Engineering Health Professions Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Transportation

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Automatic Detection of Acne Types Using the YOLOv5 Method Pinasty, Salsabila; Hakim, Raden Bagus Fajriya
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 1 (2025): March 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i1.35617

Abstract

Acne is very common due to several factors such as hormones, hygiene, and environmental exposure. This research aims to develop an automatic detection system for facial skin problems using the You Only Look Once v5 (YOLOv5) algorithm, focusing on the problem of acne types on acne-prone faces, and this research is the latest research that has never been done before. The research methodology was carried out by taking datasets directly on acne faces, with a sample of 1230 images. The research process includes data collection, labeling using the Roboflow platform, dividing the dataset into training, testing, and validation data, and implementing the YOLOv5 algorithm using Google Colab. The research stages include data input, object labeling, dataset configuration, YOLOv5 preparation, modeling, model testing, hyperparameter tuning, and model performance evaluation. The results of this study resulted in an accuracy rate seen based on the mapped value of 87.6%, so this can be considered that the model is considered good in detecting the type of acne on facial skin problems in accordance with testing on data, and this model can be implemented to automatically detect facial skin problems, especially on faces with acne, in the future.
APPLICATION OF RANDOM FOREST ALGORITHM ON WATCH PRICE PREDICTION SYSTEM USING FRAMEWORK FLASK Dalimunthe, Dzakiyyatul Kirom; Hakim, Raden Bagus Fajriya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.74 KB) | DOI: 10.30598/barekengvol17iss1pp0171-0184

Abstract

In the modern era like today, watches not only function as timepieces, but have become a fashion trend for the community, especially teenagers. The increasing market demand for watches opens up opportunities for counterfeit watch sellers to sell their products by claiming that the watches they sell are genuine watches by offering relatively cheaper prices compared to genuine watches. This is very detrimental to consumers and also the watch industry. To minimize fraud committed by fake watch sellers, it is necessary to know the price of the original watch in advance, before buying the desired watch. Therefore, the purpose of this study is to predict the price of watches using the Random Forest method and will be developed into a web system using the Framework Flask. The results of the study using 3337 trees obtained an accuracy rate of 84,98% with a MAPE of 15,02%. The most influential variable on the price of watches is the material variable with the level of importance obtained at 0,359. After getting the best model, the model is then developed into a web system using the help of the Framework Flask and Heroku which can later be accessed online.
Spatial Pattern Analysis and Determinants of Stunting Prevalence in Central Sulawesi, Indonesia: Using Linear Regression, Local Moran’s I, and Random Forest Approaches Arifuddin, Adhar; Fauzan, Achmad; Hakim, Raden Bagus Fajriya; Nur, A Fahira
Healthy Tadulako Journal (Jurnal Kesehatan Tadulako) Vol. 11 No. 3 (2025)
Publisher : Faculty of Medicine, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/htj.v11i3.1863

Abstract

Background: Stunting remains a significant public health issue in Indonesia, particularly in Central Sulawesi, where socio-economic and environmental factors contribute to its prevalence. Understanding these determinants is crucial for effective intervention strategies. Objective: This study aims to analyze the spatial distribution and predictors of stunting prevalence in Central Sulawesi, focusing on socio-economic and environmental factors. Methods: An observational design was employed, utilizing secondary data from the Central Sulawesi Provincial Health Department. Spatial analysis, including Moran’s I and Local Moran’s I, assessed spatial autocorrelation and identified outliers. Regression analysis and Random Forest modeling examined predictors of stunting prevalence. Results: The study found significant spatial clustering in stunting prevalence. Key socio-economic factors identified were maternal education and household income, with poverty being the most influential predictor. Random Forest analysis highlighted sanitation and access to health facilities as important, although access to clean water did not show a significant effect. Conclusion: The findings provide valuable insights into the socio-economic determinants of stunting and emphasize the need for targeted, comprehensive intervention strategies focusing on improving maternal education and addressing poverty, along with enhancing healthcare access in Central Sulawesi
Co-Authors Achmad Fauzan, Achmad Adhar Arifuddin Dalimunthe, Dzakiyyatul Kirom Pinasty, Salsabila