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All Journal JSI: Jurnal Sistem Informasi (E-Journal) International Journal of New Media Technology Jurnal ULTIMA InfoSys Jurnal ULTIMATICS
Raymond Sunardi Oetama
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

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Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining Ira Melissa; Raymond Sunardi Oetama
ULTIMA InfoSys Vol 4 No 1 (2013): UltimaInfoSys :Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1402.913 KB) | DOI: 10.31937/si.v4i1.238

Abstract

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova
Enhancing Decision Tree Performance in Credit Risk Classification and Prediction Raymond Sunardi Oetama
Ultimatics : Jurnal Teknik Informatika Vol 7 No 1 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.735 KB) | DOI: 10.31937/ti.v7i1.349

Abstract

This study is focused on enhancing Decision Tree on its capabilities in classification as well as prediction. The capability of decision tree algorithm in classification outperforms its capability in prediction. The classification quality will be enhanced when it works with resampling techniques such as Adaboost.
Prediksi prospek harga saham perusahaan perbankan Menggunakan Regresi Linear (Study Kasus Bank BCA Tahun 2015-2017) Merfin Keren; Raymond Sunardi Oetama
Jurnal Sistem Informasi Vol 11, No 1 (2019)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (752.858 KB) | DOI: 10.36706/jsi.v11i1.8105

Abstract

The method to be used in this paper is Linear Regression using Excel tools to perform calculations and SPSS 16.0 as a data mining tool. The research data taken is historical data of banking companies for 3 periods as a whole in the form of excel that has been downloaded from the Yahoo Finance website with the final results are in the form of MAPE charts in 3 years period, graphs of closing vs prediction price comparison and recommendations for investors to start shares.
Implementation of Backpropagation Method with MLPClassifier to Face Mask Detection Model Wendy Hendra Wijaya; Raymond Sunardi Oetama; Fransiscus Ati Halim
IJNMT (International Journal of New Media Technology) Vol 9 No 2 (2022): IJNMT : International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v9i2.2693

Abstract

Corona Virus Disease 2019 (COVID-19) is a virus that has spread widely and has become a global pandemic. The virus also can be spread through droplets made from coughs or sneezes. The Minister of Health of the Republic of Indonesia has issued a decision regarding this COVID-19 pandemic case, one of which is "Using personal protective equipment in the form of a mask that covers the nose and mouth to the chin”. This research aim is to detect masks on the face using the CRISP-DM framework and the backpropagation neural network method with MLPClassifier. The dataset is using RMFD (Real-World Masked Face Dataset. The dataset contains photos of human faces using mask and human faces without using mask. The result showed that the backpropagation neural network method can be used to detect mask on human faces with 94.4% accuracy. The accuracy from this research is outperform DNN algorithm. This research is expected to broaden the insight regarding the detection of masks to prevent the spread of COVID-19.
Co-Authors Ira Melissa Merfin Keren Wendy Hendra Wijaya