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All Journal Elkom: Jurnal Elektronika dan Komputer Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Khatulistiwa Informatika Jurnal Informatika Universitas Pamulang Antivirus : Jurnal Ilmiah Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) Computer Science (CO-SCIENCE) Jurnal Teknik Informatika Journal of Artificial Intelligence and Digital Business Akademika
Diantika, Sri
Universitas Bina Sarana Informatika
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Journal : Jurnal Informatika Universitas Pamulang

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Perbandingan Algoritma Klasifikasi K-Nearest Neighbor, Random Forest dan Gradient Boosting untuk Memprediksi Ketertarikan Nasabah pada Polis Asuransi Kendaraan Diantika, Sri; Subekti, Agus; Nalatissifa, Hiya; Lase, Mareanus
Jurnal Informatika Universitas Pamulang Vol 6, No 3 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i3.9419

Abstract

An insurance policy provides coverage for compensation for specified loss, damage, illness, or death in exchange for premium payments. Likewise for vehicle insurance, every year the customer needs to pay a premium to the insurance company so that if an accident occurs that is not profitable for the vehicle, the insurance company provides compensation to the customer. The purpose of this research is to classify the health insurance cross-sell prediction dataset so that certain patterns or relationships can be found between the data to become valuable information and build a model to predict whether policyholders (customers) from the previous year will also be interested in insurance. Vehicles provided by the company. The researcher uses the K-nearest neighbor classification algorithm, Random Forest, and gradient boosting classifier as well as Python data mining tools. After doing the research, it was found that the K-nearest neighbor classification algorithm produces a higher accuracy of 91%, when compared to the Random Forest algorithm which is 87% and the boosting classifier algorithm is 88% in classifying customer interest in taking a vehicle insurance policy.
Perbandingan Kinerja Algoritma Klasifikasi Naive Bayes, Support Vector Machine (SVM), dan Random Forest untuk Prediksi Ketidakhadiran di Tempat Kerja Hiya Nalatissifa; Windu Gata; Sri Diantika; Khoirun Nisa
Jurnal Informatika Universitas Pamulang Vol 5, No 4 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v5i4.7575

Abstract

Absence is a problem for the company. Absenteeism is defined as a task that is assigned to an individual, but the individual cannot complete the task when he is not present. Absence from work is influenced by many factors, including mismatched working hours, job demand and other factors such as serious accidents / illness, low morale, poor working conditions, boredom, lack of supervision, personal problems, insufficient nutrition, transportation problems, stress, workload, and dissatisfaction. The purpose of this study is to predict absenteeism at work based on the Absenteeism at work dataset obtained from the UCI Machine Learning repository site using the Weka 3.8 application and the Naïve Bayes algorithm, Support Vector Machine (SVM), and Random Forest. In the results of the study, the Random Forest algorithm obtained the highest accuracy, precision, and recall values compared to the Naïve Bayes and SVM algorithms, which resulted in an accuracy value of 99.38%, 99.42% precision and a recall of 99.39%.
Co-Authors AA Sudharmawan, AA Agus Subekti Ahmad Fauzi Ahmad Fauzi Akrom, Akrom Aprilia Kartini, Kasih Dafa, Muhammad Fauzi Ahmad Muda Gata, Windu Hiya Nalatissifa Hiya Nalatissifa Khoirun Nisa Mareanus Lase Mawadatul Maulidah Nurlaelatul Maulidah Nuzuliarini Nuris Rifandi, Julian Riki Supriyadi Salsabila, Nina Nur Silvia, Hana Windu Gata