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All Journal JIK Jurnal Ilmu Komputer Jurnal Sarjana Teknik Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Sebatik JURTEKSI Progresif: Jurnal Ilmiah Komputer Indonesian Journal of Electrical Engineering and Computer Science Jurnal Computer Science and Information Technology (CoSciTech) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Komtekinfo Jurnal Teknologi
Eka Praja Wiyata Mandala
Universitas Putra Indonesia YPTK Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Journal : Jurnal Komtekinfo

 1 
Prediksi Jumlah Pemberian Kredit kepada Nasabah di Bank Perkreditan Rakyat dengan Algoritma C 4.5 Eka Praja Wiyata Mandala; Dewi Eka Putri
Jurnal KomtekInfo Vol. 5 No. 1 (2018): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (628.335 KB) | DOI: 10.35134/komtekinfo.v5i1.7

Abstract

Bank Perkreditan Rakyat adalah lembaga keuangan bank yang hanya menerima simpanan dalam bentuk deposito berjangka, tabungan, dan/atau bentuk lain yang dipersamakan dengan itu dan menyalurkan dananya sebagai usaha BPR. Permasalahan yang dialami oleh BPR adalah pemberian kredit yang tidak tepat sasaran dan waktu tunggu keputusan nasabah yang lama. Maka dalam penelitian ini, kami mengusulkan suatu cara untuk memprediksi pinjaman yang dapat membantu BPR dalam mengambil keputusan yang akurat dan tepat sasaran. Metode yang digunakan untuk membuat prediksi tersebut adalah Algoritma C 4.5. Algoritma C 4.5 merupakan algoritma yang paling banyak digunakan untuk membuat prediksi dalam Data Mining. Jadi yang akan menjadi keputusan akhir dari prediksi pemberian kredit ini adalah keputusan besar atau kecilnya jumlah kredit yang akan diberikan kepada nasabah.
Hybrid Data Mining berdasarkan Klasterisasi Produk untuk Klasifikasi Penjualan Dewi Eka Putri; Eka Praja Wiyata Mandala
Jurnal KomtekInfo Vol. 9 No. 2 (2022): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (347.467 KB) | DOI: 10.35134/komtekinfo.v9i2.279

Abstract

Sales at minimarkets have had ups and downs since the Covid-19 pandemic. In this study, the problem was found, namely the number of products in minimarkets that were not sold or vice versa, there was a demand for products, but they were not available in minimarkets. Data mining can be a solution to solving this problem. This study proposes a hybrid data mining method by combining the K-Means and K-Nearest Neighbors algorithms. The combination of this method works in two stages, namely clustering the products sold which are then continued by classifying the sales of these products. The data used in this study is data for toiletries and washing products as many as 20 products. From the results of research conducted, there are 14 products that have many enthusiasts from 20 products. Of the 14 products used as training data, a sales classification was carried out for 1 new product with stock criteria 200, sold 100, and price Rp. 10,000. From the test results, the new product classification is High Sales with accuracy in the classification reaching 85.7143%. The use of a hybrid method between the K-Means and K-Nearest Neighbors algorithms has a significant influence in determining the classification results. For further research, it is recommended to have a prediction process from the classification results that have been found, so that they have a greater influence on the use of this hybrid data mining method.
Co-Authors Afdhal, Muhammad Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Dewi Eka Putri Emil Naf'an Faradila Mustika Laksamana, Ridho M Reza Tri Kurnia Mardhiah, Sitty Muhammad Reza Putra Muhammad Ridwan Muhammad, Abulwafa Musli Yanto Naf'an, Emil Putra, Dede Wira Trise Putri, Dewi Eka Radius Prawiro Rafli, M Nerdi Randy Permana Rhayatun Aviqah Rianti, Eva Ridho Laksamana Rini Sovia Rini Sovia Sarjon Defit Subrianto Chandra Sumijan Sumijan Yuhandri Yuhandri, Yuhandri Zulfikri, Rahmat