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All Journal Journal Transformation of Mandalika Journal of Mandalika Literature
Tri Wahyana
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Physics Social Sciences

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SISTEM PENENTU KELAYAKAN PEMBERIAN PINJAMAN DANA MENGGUNAKAN FUZZY DI PT MUTIARA CEMERLANG FINANCE Tri Wahyana; Andreas Karina Manik
Journal of Mandalika Literature Vol. 5 No. 1 (2024)
Publisher : Institut Penelitian dan Pengembangan Mandalika (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jml.v5i1.2134

Abstract

PT Mutiara Cemerlang Finance merupakan koperasi yang bergerak dibidang jasa, salah satunya adalah peminjaman dana (kredit). Dalam pemberian kredit pihak PT Mutiara Cemerlang Finance perlu melakukan pengambilan keputusan yang cepat, tepat, dan akurat agar tidak terjadi kesalahan pengambilan keputusan dalam pemberian kredit kepada anggota PT Mutiara Cemerlang Finance. Permasalahan yang sering dihadapi antara lain kesulitan memutuskan peminjaman yang disetujui diberikan kredit dikarenakan harus memeriksa banyak berkas dan histori peminjaman anggota, apabila terjadi kesalahan pengambilan keputusan maka kemungkinan akan timbul kredit yang bermasalah atau kredit macet. Dalam hal peminjaman banyak pertimbangan yang harus diputuskan oleh pihak PT Mutiara Cemerlang Finance seperti dari gaji anggota, lama pinjaman, lama pinjaman, kelengkapan berkas, pinjaman lain, dan status anggota. Untuk dapat menyelesaikan masalah ini maka dibuatlah sebuah sistem penentu kelayakan pemberian pinjaman menggunakan fuzzy yang mampu membantu karyawan dan pimpinan dalam proses penilaian dan memberikan perhitungan yang tepat dalam menilai kelayakan pinjaman. Metode yang dapat mendukung keputusan adalah fuzzy, karena penilain yang bersifat subyektif atau non-linear dapat dimodelkan dan dihitung secara tepat dan akurat.
Penerapan Metode K-Nearest Neighbour Untuk Sistem Prediksi Kelulusan Siswa MTs Nurul Muslimin Berbasis Website Ari Sanjaya; Tri Wahyana
Journal Transformation of Mandalika, e-ISSN: 2745-5882, p-ISSN: 2962-2956 Vol. 3 No. 2 (2022): Februari
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jtm.v3i2.866

Abstract

Student graduation is that they are able to complete and meet the graduation requirements set out in the graduation meeting which is signed by the Principal through a decision letter from the results of the meeting. The student's graduation rate is a top priority because it involves school accreditation. For this reason, the right strategy is needed to boost student graduation rates. identification of existing problems, namely the school in predicting the graduation rate of students is not accurate, the school has difficulty predicting the graduation rate and the absence of a computer system in predicting the graduation rate at the school. From these problems, using the K-Nearest Neighbour (K-NN) method, and utilizing pre-existing data, processing MTS student graduation rate data is made to facilitate the school in predicting student graduation rates, both students who pass and students who do not pass. Methodology is a way of working that is used to build a new system. The research method used in this paper is descriptive research method. The method used to collect data in this research is Interview, Observation and Literature Study. Calculation of the K-Nearest Neighbour (KNN) algorithm is a method for classifying new objects based on (K) their closest neighbour. K-NN is a supervised learning algorithm, where the results of a new query instance are based on the results of the K-NN. The class that appears the most will be the predicted class. In this study, testing the stages of applying the method was done manually using Microsoft Excel. The training data used are 8 data and testing data are 3 data. The results of K-Nearest Neighbour can predict the processing of student graduation data. From the test results, it can be seen that the K-Nearest Neighbour method can make it easier to determine student graduation results. The K-Nearest Neighbour method can predict the number of students who will graduate.
Co-Authors Andreas Karina Manik Ari Sanjaya