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All Journal PREPOTIF : Jurnal Kesehatan Masyarakat Media Publikasi Promosi Kesehatan Indonesia (MPPKI)
Kario, Asrit Jessica
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Public Health

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Prediction of Anemia Using Machine Learning Algorithms: Scoping Review Kario, Asrit Jessica; Rico Kurniawan
Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Vol. 7 No. 11: NOVEMBER 2024 - Media Publikasi Promosi Kesehatan Indonesia (MPPKI)
Publisher : Fakultas Kesehatan Masyarakat, Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/mppki.v7i11.6289

Abstract

Introduction: One of the major public health problems is anemia, especially affecting newborn and infant children, adolescent girls, young women, pregnant women, and postpartum women. The cause of anemia is the reduced supply of red blood cells in the human body or the damage or weakening of the structure of red blood cells. One of the preferences of utilizing machine learning is the prediction of results. Objective: The purpose of this study is to compare effective algorithms, related to the origin or source of the data set, data set size, metric evaluation and accuracy and produce predictors in predicting anemia using machine learning. Method: This research uses a scoping review method on 4 databases, namely Scopus, EBSCO, PubMed, and IEEE Xplore from 2019 - 2024 with keywords anemia, algorithms, machine learning, and prediction. The results of screening articles on the Scopus, EBSCO, PubMed, and IEEE Xplore databases obtained 384 articles which were then selected through several stages and obtained 9 articles. Result: The review found that the highest algorithm performance in anemia prediction, namely Penalized Regression (LASSO regression) accuracy above 64%, XGboost accuracy 100% and execution time 0.2404 seconds, Catboost accuracy 97.6%, Random Forest accuracy 95.49% and 72%, J48 algorithm accuracy of 97.7%, Logistic Regression accuracy 66% and AUC 69%, and SVM linear AUC 79.9%. Conclusion: Machine learning can assist in the development of anemia prediction models by exploring large amounts of data and producing precise and fast predictors. The predictors obtained are determined by the selection of algorithms in the study.
EVALUATION OF MERCURY (HG) CONCENTRATION IN CILEMAHABANG RIVER, BEKASI REGENCY Sembiring, Eva Kasih; Fitria, Laila; Kusnoputranto, Haryoto; Kario, Asrit Jessica
PREPOTIF : JURNAL KESEHATAN MASYARAKAT Vol. 9 No. 1 (2025): APRIL 2025
Publisher : Universitas Pahlawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/prepotif.v9i1.43419

Abstract

Penelitian ini mengevaluasi konsentrasi merkuri dalam air sungai Cilemahabang, Kabupaten Bekasi yang diduga telah mengalami pencemaran. Pengambilan sampel dilakukan di tiga titik berbeda, yaitu hulu, tengah, dan hilir sungai. Sampel dianalisis dengan menggunakan metode ICP-MS (Inductively Coupled Plasma Mass Spectrometry) untuk mengukur konsentrasi merkuri (Hg). Data yang diperoleh dibandingkan dengan nilai batas aman air sungai yang berlaku menurut PP No. 22 Tahun 2021. Analisis sampel dilakukan di Laboratorium PT Bumi Ventila Indonesia. Waktu penelitian mulai dari survei pendahuluan, pengambilan sampel, uji laboratorium dan analisis data dilakukan pada bulan Oktober 2024 hingga Maret 2025. Hasil penelitian menunjukkan bahwa konsentrasi merkuri (Hg) pada air sungai Cilemahabang dengan rata-rata 0,003100 mg/l telah melebihi nilai batas aman yang ditetapkan pada PP No. 22 Tahun 2021 sebesar 0,001 mg/l dengan konsentrasi merkuri (Hg) semakin meningkat pada bagian tengah dan hilir sungai. Penelitian ini menemukan pencemaran merkuri yang signifikan pada bagian tengah dan hilir sungai, sehingga perlu adanya pengawasan yang lebih ketat terhadap implementasi kebijakan dan peraturan yang ada dalam mengurangi potensi pencemaran air sungai dan perlu dilakukan penelitian lebih lanjut mengenai penilaian risiko kesehatan lingkungan untuk merancang strategi mitigasi yang efektif.
Co-Authors Haryoto Kusnoputranto Laila Fitria Rico Kurniawan Sembiring, Eva Kasih