Analisa Tingkat Kepuasan Pelanggan Terhadap Aplikasi Ojek Online Dengan Menerapkan Dashboard Business Intelligence

NAUFALSYAH, ENRICO ESTIAWAN (2023) Analisa Tingkat Kepuasan Pelanggan Terhadap Aplikasi Ojek Online Dengan Menerapkan Dashboard Business Intelligence. S1 thesis, Universitas Mercu Buana Bekasi.

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Abstract

Hipertensi dikenal secara luas sebagai penyakit kardiovaskular. Diperkirakan telah menyebabkan 4,5% dari beban penyakit secara global, dan prevelensinya hampir sama di Negara berkembang maupun Negara maju. Menurut JNC (Joint National Committee), batas normal tekanan darah adalah 120-140 mmHg sistolik dan 80 – 90 mmHg diastolik. Faktor-faktor yang mempengaruhi terjadinya hipertensi dibagi dalam dua kelompok besar yaitu faktor yang tidak dapat dikendalikan seperti jenis kelamin, umur, genetik, ras dan faktor yang dapat dikendalikan seperti pola makan, kebiasaan olahraga, konsumsi garam, kopi, alkohol dan stres akan datang. Penelitian di lakukan dengan menerapkan algoritma Support vector Machine (SVM) dengan Hasil akurasi terbaik menggunakan pembagian data dengan aturan 70% data training dan 30% data testing dengan total 250 data dengan 81,71% hasil presisi sebesar 81,71% dan hasil recall pada 100,00%. Dan hasil algoritma decision tree sebesar 72,00% akurasi dengan hasil presisi 80,30% dan hasil recall 86,89% dengan nilai tersebut untuk perbandingan algoritma yang dilakukan denga hasil terbaik ada pada algotima Suport Vector Machine (SVM) Kata Kunci: Hipertensi, Klasifikasi, Support Vector Machine (SVM), Decision Tree Hypertension is widely known as a cardiovascular disease. It is estimated to have caused 4.5% of the global burden of disease, and the prevalence is almost the same inboth developing and developed countries. According to the JNC (Joint National Committee), the normal limit for blood pressure is 120-140 mmHg systolic and 80- 90 mmHg diastolic. Factors that influence the occurrence of hypertension are divided intotwo major groups, namely factors that cannot be controlled such as gender, age, genetics, race and factors that can be controlled such as diet, exercise habits, consumption of salt, coffee, alcohol and future stress. The research was carried out byapplying the Support vector Machine (SVM) algorithm with the best accuracy results using data division with the rule of 70% data training and 30% data testing with a totalof 250 data with 81.71% precision results of 81.71% and recall results on 100.00%. And the results of the decision tree algorithm are 72.00% accuracy with 80.30% precision results and 86.89% recall results with these values for comparison of algorithms that are carried out with the best results in the Support Vector Machine (SVM) algorithm Keywords : Hypertension, Classification, Support Vector Machine (SVM), Decision Tree

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