Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance
Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-...
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Fakultas Ilmu Komputer UMI
2025-04-01
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| Series: | Ilkom Jurnal Ilmiah |
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| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/2363 |
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| author | Mochamad Wahyudi Imeldi Sintagel br Sianipar Lise Pujiastuti Solikhun Solikhun Deny Kurniawan |
| author_facet | Mochamad Wahyudi Imeldi Sintagel br Sianipar Lise Pujiastuti Solikhun Solikhun Deny Kurniawan |
| author_sort | Mochamad Wahyudi |
| collection | DOAJ |
| description | Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-Medoids method with a quantum computing approach to predict AIDS based on clinical and demographic data. K-Medoids is chosen to group large amounts of data using a clustering technique that determines the center point (medoid) of each cluster, minimizing the overall distance between data in a cluster. The Manhattan distance is used because it is easier to process data. The quantum computing approach is used to overcome the limitations of classical computing when processing large-scale medical data. This study shows that the application of quantum algorithms to the K-Medoids method allows for faster and more accurate predictions in the diagnosis of AIDS. The tests carried out showed that the prediction accuracy of classical and quantum methods was comparable, namely 85%. The results support the great potential of quantum computing to improve the efficiency of medical predictions. The research involves converting data into quantum format, processing it with the K-Medoids algorithm, and evaluating its performance based on metrics such as intercluster distance and computation time. The research will also identify patterns and risk factor for the spread of AIDS that can be used to develop more effective health interventions. The conclusion of the research is that integrating the K-Medoids techniques can only increase the speed of data processing but also provide competitive accuracy compared to traditional techniques. This research opens up new possibilities in medical data analysis, especially when managing large and complex data sets. The bottom line is that these findings can help make better medical decisions and strategically support AIDS prevention and treatment efforts. |
| format | Article |
| id | doaj-art-84b1b803e707430385f38ef685e3bf02 |
| institution | Kabale University |
| issn | 2087-1716 2548-7779 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj-art-84b1b803e707430385f38ef685e3bf022025-08-20T03:33:15ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792025-04-01171445310.33096/ilkom.v17i1.2363.44-53743Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan DistanceMochamad Wahyudi0Imeldi Sintagel br Sianipar1Lise Pujiastuti2Solikhun Solikhun3Deny Kurniawan4Universitas Bina Sarana InformatikaSTIKOM Tunas BangsaSTMIK Antar BangsaSTIKOM Tunas BangsaUniversitas Bina Sarana InformatikaAcquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) is one of the deadliest infectious diseases in the world. Understanding its spread and epidemiological characteristics is crucial for developing and preventing more effective treatments. This study uses the K-Medoids method with a quantum computing approach to predict AIDS based on clinical and demographic data. K-Medoids is chosen to group large amounts of data using a clustering technique that determines the center point (medoid) of each cluster, minimizing the overall distance between data in a cluster. The Manhattan distance is used because it is easier to process data. The quantum computing approach is used to overcome the limitations of classical computing when processing large-scale medical data. This study shows that the application of quantum algorithms to the K-Medoids method allows for faster and more accurate predictions in the diagnosis of AIDS. The tests carried out showed that the prediction accuracy of classical and quantum methods was comparable, namely 85%. The results support the great potential of quantum computing to improve the efficiency of medical predictions. The research involves converting data into quantum format, processing it with the K-Medoids algorithm, and evaluating its performance based on metrics such as intercluster distance and computation time. The research will also identify patterns and risk factor for the spread of AIDS that can be used to develop more effective health interventions. The conclusion of the research is that integrating the K-Medoids techniques can only increase the speed of data processing but also provide competitive accuracy compared to traditional techniques. This research opens up new possibilities in medical data analysis, especially when managing large and complex data sets. The bottom line is that these findings can help make better medical decisions and strategically support AIDS prevention and treatment efforts.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/2363clusteringdata miningk-medoidsmanhattan distancequantum computing. |
| spellingShingle | Mochamad Wahyudi Imeldi Sintagel br Sianipar Lise Pujiastuti Solikhun Solikhun Deny Kurniawan Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance Ilkom Jurnal Ilmiah clustering data mining k-medoids manhattan distance quantum computing. |
| title | Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance |
| title_full | Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance |
| title_fullStr | Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance |
| title_full_unstemmed | Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance |
| title_short | Quantum Computing Approach in K-Medoids Method for AIDS Disease Prediction Using Manhattan Distance |
| title_sort | quantum computing approach in k medoids method for aids disease prediction using manhattan distance |
| topic | clustering data mining k-medoids manhattan distance quantum computing. |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/2363 |
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