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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Emerging Statistics and Data Science Journal
Pinasty, Salsabila
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Computer Science & IT Mathematics

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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.
Studi Klasterisasi Usaha Pertanian Perorangan di Kabupaten Bantul Tahun 2023 dengan Pendekatan Hirarki: Studi Klasterisasi Usaha Pertanian Perorangan di Kabupaten Bantul Tahun 2023 dengan Pendekatan Hirarki Nafri, Tania Chelsia; Pinasty, Salsabila; Kartika Dini, Sekti; Nurcahayani, Helida
Emerging Statistics and Data Science Journal Vol. 3 No. 1 (2025): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol3.iss.1.art3

Abstract

Penelitian ini bertujuan untuk menganalisis struktur dan pengelompokkan usaha pertanian perorangan di Kabupaten Bantul menggunakan pendekatan hirarki dengan metode Ward. Data yang digunakan merupakan data sekunder dari hasil pencacahan lengkap Sensus Pertanian 2023. Metode Ward dipilih karena kemampuannya dalam mengelompokkan data berdasarkan kesamaan karakteristik dan meminimalkan jumlah kuadrat dalam setiap klaster yang terbentuk. Hasil penelitian menunjukkan bahwa terdapat tiga klaster utama usaha pertanian di Kabupaten Bantul. Klaster pertama didominasi oleh usaha pertanian dengan intensitas rendah di hampir semua sektor kecuali peternakan dan perikanan. Klaster kedua menunjukkan intensitas usaha pertanian yang sedang dengan sektor perikanan sebagai sektor unggulan. Klaster ketiga memiliki intensitas usaha pertanian yang tinggi di hampir semua sektor kecuali perikanan. Hasil analisis ini diharapkan dapat memberikan wawasan yang lebih mendalam mengenai dinamika usaha pertanian di Kabupaten Bantul dan mendukung pengambilan kebijakan yang lebih tepat guna oleh pemerintah daerah.
Co-Authors Hakim, Raden Bagus Fajriya Kartika Dini, Sekti Nafri, Tania Chelsia Nurcahayani, Helida