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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika)
Ester Arisawati
Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri (STMIK Nusa Mandiri)
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Penerapan K-Nearest Neighbor Berbasis Genetic Algorithm Untuk Penentuan Pemberian Kredit Ester Arisawati
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (614.67 KB) | DOI: 10.30645/j-sakti.v1i1.24

Abstract

Consumer financing is financing activities for the procurement of goods based on the needs of consumers with payment in installments. While the Financing Company is a business entity specifically set up to conduct leasing, factoring, consumer finance, or business credit card. The finance company will approve the proposed consumer credit after a credit analysis of the feasibility of providing consumer financing, if approved and not disetujui.Dalam analysis process for consumers, there are some that are not accurate, therefore consumers can not afford to pay in a timely manner resulting in bad debts , To solve the problem we need a model that is able to classify and predict consumer data is problematic and not problematic. In this research, testing ie k-Nearest Neighbor and k-Nearest Neighbor optimized genetic algorithm is applied to the data consumer that gets better the consumer credit financing is problematic or not. From the test results by measuring the performance of the three algorithms using Cross Validation testing methods, Confusion Matrix and ROC curves, it is known that the k-Nearest Neighbor algorithm optimized Genetic Algorithm has the AUC value and highest accuracy.
Analisa Kemanfaatan Dan Kemudahan Terhadap Penerimaan Sistem OPAC Menggunakan Metode TAM Citra Kharismaya; Linda Sari Dewi; Ester Arisawati; Frisma Handayanna
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v1i1.27

Abstract

This study describes the reception OPAC Davis 1989 TAM variables namely perceived usefulness (perceived benefit), perceived ease of use (ease perceived) and accepted (acceptance) OPAC. In this study, data were collected through a questionnaire using Likert scale to 100 users si¬stem den response OPAC (Online Public Access Catalog). Sampling technique used is purposive sampling to determine the level of acceptance of the system OPAC (Online Public Access Catalog). Quantitative analysis includes the validity, reliability. In the classic assumption test, used test for normality, multicollinearity and heterokedastisitas with F and t hypothesis testing. Multiple linear regression analysis is used to determine the effect of the independent variables with the dependent variable. The results showed that perceived usefulness and perceived ease have a significant effect on the acceptance by the user system (R Square) amounted to 40.8%.
Penerapan K-Nearest Neighbor Berbasis Genetic Algorithm Untuk Penentuan Pemberian Kredit Ester Arisawati
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (614.67 KB) | DOI: 10.30645/j-sakti.v1i1.24

Abstract

Consumer financing is financing activities for the procurement of goods based on the needs of consumers with payment in installments. While the Financing Company is a business entity specifically set up to conduct leasing, factoring, consumer finance, or business credit card. The finance company will approve the proposed consumer credit after a credit analysis of the feasibility of providing consumer financing, if approved and not disetujui.Dalam analysis process for consumers, there are some that are not accurate, therefore consumers can not afford to pay in a timely manner resulting in bad debts , To solve the problem we need a model that is able to classify and predict consumer data is problematic and not problematic. In this research, testing ie k-Nearest Neighbor and k-Nearest Neighbor optimized genetic algorithm is applied to the data consumer that gets better the consumer credit financing is problematic or not. From the test results by measuring the performance of the three algorithms using Cross Validation testing methods, Confusion Matrix and ROC curves, it is known that the k-Nearest Neighbor algorithm optimized Genetic Algorithm has the AUC value and highest accuracy.
Analisa Kemanfaatan Dan Kemudahan Terhadap Penerimaan Sistem OPAC Menggunakan Metode TAM Citra Kharismaya; Linda Sari Dewi; Ester Arisawati; Frisma Handayanna
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 1, No 1 (2017): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (804.684 KB) | DOI: 10.30645/j-sakti.v1i1.27

Abstract

This study describes the reception OPAC Davis 1989 TAM variables namely perceived usefulness (perceived benefit), perceived ease of use (ease perceived) and accepted (acceptance) OPAC. In this study, data were collected through a questionnaire using Likert scale to 100 users si¬stem den response OPAC (Online Public Access Catalog). Sampling technique used is purposive sampling to determine the level of acceptance of the system OPAC (Online Public Access Catalog). Quantitative analysis includes the validity, reliability. In the classic assumption test, used test for normality, multicollinearity and heterokedastisitas with F and t hypothesis testing. Multiple linear regression analysis is used to determine the effect of the independent variables with the dependent variable. The results showed that perceived usefulness and perceived ease have a significant effect on the acceptance by the user system (R Square) amounted to 40.8%.
Co-Authors Citra Kharismaya Frisma Handayanna Linda Sari Dewi