Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal JURSIMA Jurnal Sistem Informasi dan Manajemen
Widya Lelisa Army
STMIK “AMIK BANDUNG"
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN REKOMENDASI PESERTA BIDIKMISI DENGAN METODE ANALYTICAL HIERARCHY PROCESS Widya Lelisa Army
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 8 No 1 (2020): Jursima Vol. 8 No. 1, Juni Tahun 2020
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v8i1.189

Abstract

Penelitian yang dilakukan pada SMA Negeri 12 Padang di dalam menentukan rekomendasi pada bakal calon peserta bidikmisi secara konvensional sehingga menghasilkan calon pesertayang tidak sesuai maka dari itu dibuatlah Sistem Penunjang Keputusan di dalam Bidikmisi dengan metode Analytical Hierarchy Process (AHP) dan dibantu bahasa pemograman JAVA dengan database MySQL. Jadi dibuatlah aplikasiSistem pendukung Keputusan yang dapat membantu pihak sekolah SMAN 12 Padang dalam merekomendasikan siswa yang berhak,  berkompeten dan akurat dalam mengikuti program bidikmisi  dengan hasil keputusan yang mutlak.
ANALISA RISIKO KREDIT MACET DENGAN PENDEKATAN DATA MINING (STUDI KASUS: KOPERASI PUTRA KEMBAR) Widya Lelisa Army; Widya Jati Lestari; Rahimah Rahimah; Dedi Rahman Habibie
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 9 No 1 (2021): Jursima Vol. 9 No. 1, April Tahun 2021
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v9i1.241

Abstract

Data mining is a form of development of computerized methods that are widely used in various fields, especially those related to data. Data mining has the main function of turning data that seems meaningless into very valuable information. The use of data mining in credit risk analysis is very feasible to implement, one of the reasons is that data mining is able to recognize customer credit patterns originating from past data, resulting in information in the form of knowledge that can facilitate credit analysts in carrying out their duties. The purpose of this research is to simplIFy the task of credit analysts AND minimize misjudgment of prospective customers AND also shorten the time of assessment AND with accurate AND fast decision results. The assessment is carried out by utilizing past data based on predetermined criteria, THEN the data will be processed using the data mining method, namely the c4.5 algorithm. The results of the research are in the form of a knowledge rule: R1: IF Loan Amount = Small THEN Performing Loan (PL), R2: IF Loan Amount = Big THEN Non-Performing Loan (NPL), R3: IF Loan Amount = Medium AND Work= Employee THEN Performing Loan (PL), R4: IF Loan Amount = Medium AND Work=Entrepreneurial THEN Performing Loan (PL), R5: IF Loan Amount = Medium AND Work= PNS THEN Non-Performing Loan. From testing the new data testing with the value of Work ‘Employee’ Loan Amount ‘Rp. 7.000.000,-’ dependents ‘2’ length of loan ‘24 months’ with the results of the decision ‘Performing Loan (PL)’ . The result of the decision is based on the rule, namely R4: IF Loan Amount = Medium AND Work=Entrepreneurial THEN Performing Loan (PL).
SISTEM PENDUKUNG KEPUTUSAN PEMBERIAN REKOMENDASI PESERTA BIDIKMISI DENGAN METODE ANALYTICAL HIERARCHY PROCESS Widya Lelisa Army
JURSIMA Vol 8 No 1 (2020): Jursima Vol. 8 No. 1, Juni Tahun 2020
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v8i1.189

Abstract

Penelitian yang dilakukan pada SMA Negeri 12 Padang di dalam menentukan rekomendasi pada bakal calon peserta bidikmisi secara konvensional sehingga menghasilkan calon pesertayang tidak sesuai maka dari itu dibuatlah Sistem Penunjang Keputusan di dalam Bidikmisi dengan metode Analytical Hierarchy Process (AHP) dan dibantu bahasa pemograman JAVA dengan database MySQL. Jadi dibuatlah aplikasiSistem pendukung Keputusan yang dapat membantu pihak sekolah SMAN 12 Padang dalam merekomendasikan siswa yang berhak,  berkompeten dan akurat dalam mengikuti program bidikmisi  dengan hasil keputusan yang mutlak.
ANALISA RISIKO KREDIT MACET DENGAN PENDEKATAN DATA MINING (STUDI KASUS: KOPERASI PUTRA KEMBAR) Widya Lelisa Army; Widya Jati Lestari; Rahimah Rahimah; Dedi Rahman Habibie
JURSIMA Vol 9 No 1 (2021): Jursima Vol. 9 No. 1, April Tahun 2021
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v9i1.241

Abstract

Data mining is a form of development of computerized methods that are widely used in various fields, especially those related to data. Data mining has the main function of turning data that seems meaningless into very valuable information. The use of data mining in credit risk analysis is very feasible to implement, one of the reasons is that data mining is able to recognize customer credit patterns originating from past data, resulting in information in the form of knowledge that can facilitate credit analysts in carrying out their duties. The purpose of this research is to simplIFy the task of credit analysts AND minimize misjudgment of prospective customers AND also shorten the time of assessment AND with accurate AND fast decision results. The assessment is carried out by utilizing past data based on predetermined criteria, THEN the data will be processed using the data mining method, namely the c4.5 algorithm. The results of the research are in the form of a knowledge rule: R1: IF Loan Amount = Small THEN Performing Loan (PL), R2: IF Loan Amount = Big THEN Non-Performing Loan (NPL), R3: IF Loan Amount = Medium AND Work= Employee THEN Performing Loan (PL), R4: IF Loan Amount = Medium AND Work=Entrepreneurial THEN Performing Loan (PL), R5: IF Loan Amount = Medium AND Work= PNS THEN Non-Performing Loan. From testing the new data testing with the value of Work ‘Employee’ Loan Amount ‘Rp. 7.000.000,-’ dependents ‘2’ length of loan ‘24 months’ with the results of the decision ‘Performing Loan (PL)’ . The result of the decision is based on the rule, namely R4: IF Loan Amount = Medium AND Work=Entrepreneurial THEN Performing Loan (PL).
Co-Authors Dedi Rahman Habibie Rahimah Rahimah Widya Jati Lestari