The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease Article Swipe
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32627/internal.v5i2.595
Heart disease in Indonesia, especially in the productive age, there is always an increase in the number of cases. The main cause of the increase in the number of heart patients is an unhealthy lifestyle and diet. The increase in patients with heart disease also has an impact on decreasing the standard of living. With this in mind, there is a need for research related to comparing classification methods on heart disease datasets. The dataset obtained is not balanced so that an oversampling technique is needed. The oversampling technique used is SMOTE. This research method uses Support Vector Machine (SVM) and Logistic Regression (LR). In order for this research method to be applied successfully, the data acquisition, data pre-processing and data transformation techniques are used to ensure accurate results. The model evaluation technique used is K-Fold Cross Validation. Based on the results of the analysis, it showed that the data partition using k-fold cross validation without oversampling gets the same accuracy value but the precision value is quite low. Conversely, if using the SMOTE technique, the accuracy value is as good as the precision value. The results of the SVM accuracy value get a value of 91.69%. LR is 91.76%. While the results of the SVM precision value of 57.81% and LR 54.82%. If using the SVM oversampling technique, the score is 75.79% and the LR is 75.84%. Meanwhile, the precision value obtained in SVM is 75.74%. At LR by 74.77%.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32627/internal.v5i2.595
- https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418
- OA Status
- diamond
- Cited By
- 6
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393174894
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4393174894Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32627/internal.v5i2.595Digital Object Identifier
- Title
-
The Performance Comparison of Classification Algorithm in Order to Detecting Heart DiseaseWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-31Full publication date if available
- Authors
-
Chepy Sonjaya, Anis Fitri Nur Masruriyah, Dwi Sulistya Kusumaningrum, Adi Rizky PratamaList of authors in order
- Landing page
-
https://doi.org/10.32627/internal.v5i2.595Publisher landing page
- PDF URL
-
https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Pattern recognition (psychology), Medicine, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4393174894 |
|---|---|
| doi | https://doi.org/10.32627/internal.v5i2.595 |
| ids.doi | https://doi.org/10.32627/internal.v5i2.595 |
| ids.openalex | https://openalex.org/W4393174894 |
| fwci | 1.91574724 |
| type | article |
| title | The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease |
| biblio.issue | 2 |
| biblio.volume | 5 |
| biblio.last_page | 175 |
| biblio.first_page | 166 |
| topics[0].id | https://openalex.org/T11396 |
| topics[0].field.id | https://openalex.org/fields/36 |
| topics[0].field.display_name | Health Professions |
| topics[0].score | 0.6401000022888184 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3605 |
| topics[0].subfield.display_name | Health Information Management |
| topics[0].display_name | Artificial Intelligence in Healthcare |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.48320138454437256 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.41740065813064575 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.41186514496803284 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C71924100 |
| concepts[3].level | 0 |
| concepts[3].score | 0.3430848717689514 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[3].display_name | Medicine |
| concepts[4].id | https://openalex.org/C11413529 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3275628685951233 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[4].display_name | Algorithm |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.48320138454437256 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.41740065813064575 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.41186514496803284 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/medicine |
| keywords[3].score | 0.3430848717689514 |
| keywords[3].display_name | Medicine |
| keywords[4].id | https://openalex.org/keywords/algorithm |
| keywords[4].score | 0.3275628685951233 |
| keywords[4].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.32627/internal.v5i2.595 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210175409 |
| locations[0].source.issn | 2621-9433, 2656-0259 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2621-9433 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | INTERNAL (Information System Journal) |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | INTERNAL (Information System Journal) |
| locations[0].landing_page_url | https://doi.org/10.32627/internal.v5i2.595 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5094245584 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Chepy Sonjaya |
| authorships[0].countries | ID |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I177671886 |
| authorships[0].affiliations[0].raw_affiliation_string | Universitas Buana Perjuangan Karawang |
| authorships[0].institutions[0].id | https://openalex.org/I177671886 |
| authorships[0].institutions[0].ror | https://ror.org/00qjgk605 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I177671886 |
| authorships[0].institutions[0].country_code | ID |
| authorships[0].institutions[0].display_name | Mercu Buana University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chepy Sonjaya |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Universitas Buana Perjuangan Karawang |
| authorships[1].author.id | https://openalex.org/A5026803316 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2449-4426 |
| authorships[1].author.display_name | Anis Fitri Nur Masruriyah |
| authorships[1].countries | ID |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I177671886 |
| authorships[1].affiliations[0].raw_affiliation_string | Universitas Buana Perjuangan Karawang |
| authorships[1].institutions[0].id | https://openalex.org/I177671886 |
| authorships[1].institutions[0].ror | https://ror.org/00qjgk605 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I177671886 |
| authorships[1].institutions[0].country_code | ID |
| authorships[1].institutions[0].display_name | Mercu Buana University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Anis Fitri Nur Masruriyah |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Universitas Buana Perjuangan Karawang |
| authorships[2].author.id | https://openalex.org/A5068504563 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Dwi Sulistya Kusumaningrum |
| authorships[2].countries | ID |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I177671886 |
| authorships[2].affiliations[0].raw_affiliation_string | Universitas Buana Perjuangan Karawang |
| authorships[2].institutions[0].id | https://openalex.org/I177671886 |
| authorships[2].institutions[0].ror | https://ror.org/00qjgk605 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I177671886 |
| authorships[2].institutions[0].country_code | ID |
| authorships[2].institutions[0].display_name | Mercu Buana University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dwi Sulistya Kusumaningrum |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Universitas Buana Perjuangan Karawang |
| authorships[3].author.id | https://openalex.org/A5085056184 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Adi Rizky Pratama |
| authorships[3].countries | ID |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I177671886 |
| authorships[3].affiliations[0].raw_affiliation_string | Universitas Buana Perjuangan Karawang |
| authorships[3].institutions[0].id | https://openalex.org/I177671886 |
| authorships[3].institutions[0].ror | https://ror.org/00qjgk605 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I177671886 |
| authorships[3].institutions[0].country_code | ID |
| authorships[3].institutions[0].display_name | Mercu Buana University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Adi Rizky Pratama |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Universitas Buana Perjuangan Karawang |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | The Performance Comparison of Classification Algorithm in Order to Detecting Heart Disease |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11396 |
| primary_topic.field.id | https://openalex.org/fields/36 |
| primary_topic.field.display_name | Health Professions |
| primary_topic.score | 0.6401000022888184 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3605 |
| primary_topic.subfield.display_name | Health Information Management |
| primary_topic.display_name | Artificial Intelligence in Healthcare |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W3031052312, https://openalex.org/W4389568370, https://openalex.org/W3032375762, https://openalex.org/W1995515455, https://openalex.org/W2080531066, https://openalex.org/W3108674512, https://openalex.org/W1506200166, https://openalex.org/W2033914206, https://openalex.org/W2042327336 |
| cited_by_count | 6 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.32627/internal.v5i2.595 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210175409 |
| best_oa_location.source.issn | 2621-9433, 2656-0259 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2621-9433 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | INTERNAL (Information System Journal) |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | INTERNAL (Information System Journal) |
| best_oa_location.landing_page_url | https://doi.org/10.32627/internal.v5i2.595 |
| primary_location.id | doi:10.32627/internal.v5i2.595 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210175409 |
| primary_location.source.issn | 2621-9433, 2656-0259 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2621-9433 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | INTERNAL (Information System Journal) |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | https://jurnal.masoemuniversity.ac.id/index.php/internal/article/download/595/418 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | INTERNAL (Information System Journal) |
| primary_location.landing_page_url | https://doi.org/10.32627/internal.v5i2.595 |
| publication_date | 2022-12-31 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W3013583789, https://openalex.org/W4210242902, https://openalex.org/W3018746868, https://openalex.org/W2944880943, https://openalex.org/W3121216533, https://openalex.org/W4210630286, https://openalex.org/W3210536505, https://openalex.org/W4280650773, https://openalex.org/W4205121480, https://openalex.org/W4285342190, https://openalex.org/W3044247003, https://openalex.org/W4301132610 |
| referenced_works_count | 12 |
| abstract_inverted_index.a | 60, 193 |
| abstract_inverted_index.At | 237 |
| abstract_inverted_index.If | 213 |
| abstract_inverted_index.In | 104 |
| abstract_inverted_index.LR | 197, 211, 225, 238 |
| abstract_inverted_index.an | 12, 32, 46, 81 |
| abstract_inverted_index.as | 179, 181 |
| abstract_inverted_index.be | 111 |
| abstract_inverted_index.by | 239 |
| abstract_inverted_index.if | 170 |
| abstract_inverted_index.in | 2, 5, 14, 25, 39, 56, 233 |
| abstract_inverted_index.is | 10, 31, 59, 76, 84, 90, 134, 166, 178, 198, 221, 226, 235 |
| abstract_inverted_index.it | 145 |
| abstract_inverted_index.of | 17, 22, 28, 52, 142, 187, 195, 203, 208 |
| abstract_inverted_index.on | 48, 69, 139 |
| abstract_inverted_index.so | 79 |
| abstract_inverted_index.to | 65, 110, 125 |
| abstract_inverted_index.SVM | 189, 205, 216, 234 |
| abstract_inverted_index.The | 19, 37, 73, 86, 129, 185 |
| abstract_inverted_index.and | 35, 100, 119, 210, 223 |
| abstract_inverted_index.are | 123 |
| abstract_inverted_index.but | 162 |
| abstract_inverted_index.for | 62, 106 |
| abstract_inverted_index.get | 192 |
| abstract_inverted_index.has | 45 |
| abstract_inverted_index.not | 77 |
| abstract_inverted_index.the | 6, 15, 23, 26, 50, 114, 140, 143, 148, 158, 163, 172, 175, 182, 188, 201, 204, 215, 219, 224, 229 |
| abstract_inverted_index.This | 92 |
| abstract_inverted_index.With | 54 |
| abstract_inverted_index.age, | 8 |
| abstract_inverted_index.also | 44 |
| abstract_inverted_index.data | 115, 117, 120, 149 |
| abstract_inverted_index.gets | 157 |
| abstract_inverted_index.good | 180 |
| abstract_inverted_index.low. | 168 |
| abstract_inverted_index.main | 20 |
| abstract_inverted_index.need | 61 |
| abstract_inverted_index.same | 159 |
| abstract_inverted_index.that | 80, 147 |
| abstract_inverted_index.this | 55, 107 |
| abstract_inverted_index.used | 89, 124, 133 |
| abstract_inverted_index.uses | 95 |
| abstract_inverted_index.with | 41 |
| abstract_inverted_index.(LR). | 103 |
| abstract_inverted_index.(SVM) | 99 |
| abstract_inverted_index.Based | 138 |
| abstract_inverted_index.Cross | 136 |
| abstract_inverted_index.Heart | 0 |
| abstract_inverted_index.SMOTE | 173 |
| abstract_inverted_index.While | 200 |
| abstract_inverted_index.cause | 21 |
| abstract_inverted_index.cross | 153 |
| abstract_inverted_index.diet. | 36 |
| abstract_inverted_index.heart | 29, 42, 70 |
| abstract_inverted_index.mind, | 57 |
| abstract_inverted_index.model | 130 |
| abstract_inverted_index.order | 105 |
| abstract_inverted_index.quite | 167 |
| abstract_inverted_index.score | 220 |
| abstract_inverted_index.there | 9, 58 |
| abstract_inverted_index.using | 151, 171, 214 |
| abstract_inverted_index.value | 161, 165, 177, 191, 194, 207, 231 |
| abstract_inverted_index.57.81% | 209 |
| abstract_inverted_index.75.79% | 222 |
| abstract_inverted_index.K-Fold | 135 |
| abstract_inverted_index.SMOTE. | 91 |
| abstract_inverted_index.Vector | 97 |
| abstract_inverted_index.always | 11 |
| abstract_inverted_index.cases. | 18 |
| abstract_inverted_index.ensure | 126 |
| abstract_inverted_index.impact | 47 |
| abstract_inverted_index.k-fold | 152 |
| abstract_inverted_index.method | 94, 109 |
| abstract_inverted_index.number | 16, 27 |
| abstract_inverted_index.showed | 146 |
| abstract_inverted_index.value. | 184 |
| abstract_inverted_index.54.82%. | 212 |
| abstract_inverted_index.74.77%. | 240 |
| abstract_inverted_index.75.74%. | 236 |
| abstract_inverted_index.75.84%. | 227 |
| abstract_inverted_index.91.69%. | 196 |
| abstract_inverted_index.91.76%. | 199 |
| abstract_inverted_index.Machine | 98 |
| abstract_inverted_index.Support | 96 |
| abstract_inverted_index.applied | 112 |
| abstract_inverted_index.dataset | 74 |
| abstract_inverted_index.disease | 1, 43, 71 |
| abstract_inverted_index.living. | 53 |
| abstract_inverted_index.methods | 68 |
| abstract_inverted_index.needed. | 85 |
| abstract_inverted_index.related | 64 |
| abstract_inverted_index.results | 141, 186, 202 |
| abstract_inverted_index.without | 155 |
| abstract_inverted_index.Logistic | 101 |
| abstract_inverted_index.accuracy | 160, 176, 190 |
| abstract_inverted_index.accurate | 127 |
| abstract_inverted_index.balanced | 78 |
| abstract_inverted_index.increase | 13, 24, 38 |
| abstract_inverted_index.obtained | 75, 232 |
| abstract_inverted_index.patients | 30, 40 |
| abstract_inverted_index.research | 63, 93, 108 |
| abstract_inverted_index.results. | 128 |
| abstract_inverted_index.standard | 51 |
| abstract_inverted_index.analysis, | 144 |
| abstract_inverted_index.comparing | 66 |
| abstract_inverted_index.datasets. | 72 |
| abstract_inverted_index.lifestyle | 34 |
| abstract_inverted_index.partition | 150 |
| abstract_inverted_index.precision | 164, 183, 206, 230 |
| abstract_inverted_index.technique | 83, 88, 132 |
| abstract_inverted_index.unhealthy | 33 |
| abstract_inverted_index.Indonesia, | 3 |
| abstract_inverted_index.Meanwhile, | 228 |
| abstract_inverted_index.Regression | 102 |
| abstract_inverted_index.decreasing | 49 |
| abstract_inverted_index.especially | 4 |
| abstract_inverted_index.evaluation | 131 |
| abstract_inverted_index.productive | 7 |
| abstract_inverted_index.technique, | 174, 218 |
| abstract_inverted_index.techniques | 122 |
| abstract_inverted_index.validation | 154 |
| abstract_inverted_index.Conversely, | 169 |
| abstract_inverted_index.Validation. | 137 |
| abstract_inverted_index.acquisition, | 116 |
| abstract_inverted_index.oversampling | 82, 87, 156, 217 |
| abstract_inverted_index.successfully, | 113 |
| abstract_inverted_index.classification | 67 |
| abstract_inverted_index.pre-processing | 118 |
| abstract_inverted_index.transformation | 121 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.88559253 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |