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      <depositor_name>Rito Cipta Siggitta H</depositor_name>
      <email_address>technical@publicascientificsolution.com</email_address>
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        <full_title>International Journal of Social Health</full_title>
        <abbrev_title>ijsh</abbrev_title>
        <issn media_type="electronic">2984-7079</issn>
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      <journal_issue>
        <publication_date media_type="online">
          <month>11</month>
          <day>08</day>
          <year>2024</year>
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        <journal_volume>
          <volume>3</volume>
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        <issue>11</issue>
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          <doi>10.58860/ijsh.v3i11</doi>
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          <title>Analysis of the Use of K-Means Clustering Method in Brain Tumor MRI Segmentation</title>
        </titles>
        <contributors>
          <person_name contributor_role="author" sequence="first" language="en">
            <given_name>Hanifah Fitri</given_name>
            <surname>Maharani</surname>
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          <person_name contributor_role="author" sequence="additional" language="en">
            <given_name>Lina </given_name>
            <surname>Choridah</surname>
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          <person_name contributor_role="author" sequence="additional" language="en">
            <given_name>Darmini</given_name>
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            <given_name>Fatimah</given_name>
            <surname/>
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          <person_name contributor_role="author" sequence="additional" language="en">
            <given_name>Yeti </given_name>
            <surname>Kartikasari</surname>
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            <given_name>Gatot Murti </given_name>
            <surname>Wibowo</surname>
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          <jats:p>Accurate measurement of brain tumor volume in MRI examinations is critical for diagnosis and treatment planning. Traditionally, linear measurement is the gold standard, but it is prone to errors due to subjectivity and fatigue, and only provides a rough estimate of tumor volume. This research aims to compare brain tumor volume calculation on MRI images using linear measurement and k-means clustering method on post-contrast T1WI sequences to evaluate their accuracy and clinical consistency. Using a quasi-experimental design with post-test only and no control group, 32 MRI images of brain tumors were analyzed. Tumor volumes were calculated using both methods, and the results were statistically compared. The average tumor volume was 39,304.55 mm³ for the linear method and 35,374.69 mm³ for the k-means clustering method. Statistical analysis using the Wilcoxon test showed no significant difference between the two methods (p = 0.082; p &gt; 0.05). The results of this research suggest that although both methods produce comparable volume estimates, k-means clustering offers the advantage of reducing subjectivity, indicating its potential to improve consistency and reliability of measurements in clinical practice. The implication of this research is that the k-means clustering method may be a more reliable alternative in the measurement of brain tumor volume on MRI examinations, especially in reducing the subjectivity bias often present in linear measurement methods. This can help improve the accuracy and consistency of measurement results, which is crucial for more precise treatment planning and evaluation of patient therapy response.</jats:p>
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        <publication_date media_type="online">
          <month>11</month>
          <day>18</day>
          <year>2024</year>
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        <pages>
          <first_page>754</first_page>
          <last_page>765</last_page>
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          <doi>10.58860/ijsh.v3i11.264</doi>
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