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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik Penusa JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Teknologi Jurnal Infortech RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro IMTechno: Journal of Industrial Management and Technology

Amrin Amrin

Universitas Bina Sarana Informatika

Author-ID : 2543071

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

Published : 32 Documents Claim Missing Document

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Implementation of Logistic Regression Classification Algorithm and Support Vector Machine for Credit Eligibility Prediction Amrin - Amrin; Omar - Pahlevi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6220

Credit is a provision of money or bills that can be equated with it, the provision of loans or credit. A good credit analysis is very necessary, because it is one of the most important processes in the form of an investigation regarding the smooth or substandard credit repayments. The stages of identifying and predicting customers properly and correctly can be done before the loan process. This is done by examining the historical data of the customer's loan. At this time this activity is an effort made by the banking industry in dealing with credit risk problems. In this research, researchers will apply several data mining classification methods, including Logistic Regression algorithms and Support Vector Machines to predict creditworthiness. The dataset used 481 record motorized vehicle loan data, both problematic and non-problematic. The input variables in this study consisted of thirteen variables, including marital status, number of dependents, age, residence status, home ownership, occupation, employment status, company status, income, down payment, education, length of stay, and housing conditions. From the results of research and testing, the performance of the Logistic Regression model for predicting creditworthiness provided an accuracy rate of 94.81% with an area under the curve (AUC) value of 0.987. While the performance of the Support Vector Machine model provides an accuracy of 94.19% with an area under the curve (AUC) value of 0.978. Based on the T-Test test, the Logistic Regression method has the same performance compared to the Support Vector Machine.

Model Waterfall Untuk Rancang Bangun Sistem Informasi Pengadaan Mesin EDC Pada E-Channel Operations Perbankan Amrin Amrin; Muhammad Reza Aldiansyah
INSANtek Vol 2 No 2 (2021): November 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Dalam era globalisasi sekarang ini, teknologi informasi melaju dengan cepatnya. Adapun komputer yang merupakan peralatan yang diciptakan untuk mempermudah pekerjaan manusia, saat mencapai kemajuan baik di dalam pembuatan hardware maupun software. E-Channel Operations Perbankan membutuhkan sekali adanya suatu sistem informasi yang menunjang dan memberikan pelayanan yang memudahkan bagi para karyawannya. Sistem pengadaan yang digunakan E-Channel Operations Perbankan masih dilakukan secara manual, yaitu dengan membuat surat permintaan setiap kali melakukan pengadaan mesin baru. Perancangan sistem informasi pengadaan mesin EDC berbasis web merupakan solusi yang terbaik untuk memecahkan permasalahan- permasalahan yang ada pada perusahaan ini, serta dengan sistem yang terkomputerisasi dapat tercapai suatu kegiatan yang efektif dan efisien dalam menunjang aktifitas pada perusahaan ini. Metode yang digunakan untuk pengembangan perangkat lunak adalah metode waterfall. Hasil dari penelitian ini adalah dengan adanya aplikasi pengadaan mesin EDC, dapat mempermudah pegawai yang terkait dalam proses pengadaan secara real time, pembuatan laporan dapat dilakukan secara cepat dan tepat.serta penyimpanan data menjadi lebih aman.

Model Waterfall Untuk Perancangan Sistem Informasi Pengelolaan Data Arsip Gudang Pada Disdukcapil Kota Depok Amrin Amrin; David Ridwan Savero; Muhammad Alawi
INSANtek Vol 3 No 1 (2022): Mei 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pada era globalisasi teknologi komputer memegang peran yang sangat penting untuk membantu proses aktivitas kerja di instansi maupun di perkantoran baik dari lembaga pemerintahan maupun swasta. Dinas kependudukan dan pencatatan sipil (Disdukcapil) kota depok merupakan salah satu lembaga pemerintahan, dimana salah satu tugasnya adalah menyelenggarakan kegiatan pelayanan di bidang kependudukan dan pencatatan sipil, tentunya juga membutuhkan sebuah sistem informasi untuk memudahkan bagian gudang dalam mengelola data arsip. Pengelolaan data arsip yang masih manual yaitu pengelolaan data arsip masih menggunakan microsoft excel, proses penginputan data yang tidak efisien, mesin pencarian microsoft excel yang kurang akurat. Berdasarkan hal tersebut maka dalam penelitian ini dibuat sistem informasi pengelolaan data arsip gudang. Metode yang digunakan untuk pengembangan perangkat lunak adalah metode waterfall. Dengan adanya sistem ini diharapkan merupakan solusi yang baik untuk memudahkan bagian gudang yakni pegawai gudang dalam mengelola data arsip agar lebih efektif dan efisien, pencarian informasi data arsip yang cepat dan akurat serta menjamin keamanan informasi data arsip.

ANALISA KOMPARASI NEURAL NETWORK BACKPROPAGATION DAN MULTIPLE LINEAR REGRESSION UNTUK PERAMALAN TINGKAT INFLASI Amrin Amrin
JURNAL TEKNIK KOMPUTER AMIK BSI Vol 2, No 2 (2016): Jurnal Teknik Komputer AMIK BSI
Publisher : Universitas Bina Sarana Informatika

The inflation rate can not be underestimated in a country's economic system and businesses in general. If inflation can be predicted with high accuracy, of course, can be used as the basis of government policy making in anticipation of future economic activity. In this study will be used back propagation neural network method and multiple linear regression method to predict the monthly inflation rate in Indonesia, then compare which method is the better. The data used comes from the central statistical agency in 2006-2015, which is 80% as training data and 20% as testing data. In the results of the data analysis is concluded that the performance of multiple linear regression is better than back propagatin neural network, with a mean absolute deviation (MAD) is 0.0380, a mean square error (MSE) is 0.0023, and a Root Mean Square Error (RMSE) is 0.0481. Keywords: Inflation, neural network backpropagation, multiple linear regression, mean square error.

ALGORITMA C4.5 UNTUK DIAGNOSA PENYAKIT TUBERKULOSIS Amrin Amrin; Irawan Satriadi; Oki Rosanto
Jurnal Khatulistiwa Informatika Vol 7, No 2 (2019): Periode Desember 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jki.v7i2.6725

Penyakit tuberkulosis merupakan penyakit menular dan mematikan di dunia, bahkan World Health Organization (WHO) mencanangkan sebagai penyakit kedaruratan dunia (global emergency). Banyak gejala yang bisa terjadi pada seseorang yang terjangkit tuberkulosis, dan untuk menganalisa gejala tersebut bukan hal yang mudah, perlu dilakukan tes dahak pada penderita. Selain itu, dibutuhkan juga sebuah metode yang dapat mempermudah saat melakukan analisa dan menggali informasi pasien dari data rekam medik yang tersedia. Pada penelitian ini, penulis akan menerapkan metode klasifikasi data mining, yaitu Algoritma C4.5 untuk mendiagnosa penyakit tuberculosis. Berdasarkan hasil pengukuran performa dari model tersebut dengan menggunakan metode pengujian Cross Validation, Confusion Matrix dan Kurva ROC, diketahui bahwa algoritma C4.5 memiliki tingkat akurasi sebesar 84,56% dan nilai area under the curva (AUC) sebesar 0,938. Hal ini menunjukkan bahwa model yang dihasilkan termasuk kategori klasifikasi sangat baik karena memiliki nilai AUC antara 0.90-1.00.

Analisa Kelayakan Pemberian Kredit Mobil Dengan Menggunakan Metode Neural Network Model Radial Basis Function Amrin Amrin
Paradigma Vol 19, No 2 (2017): Periode September 2017
Publisher : LPPM Universitas Bina Sarana Informatika

Problems are often encountered in the provision of credit is to determine lending decisions to someone, while other issues are not all credit payments can run well. Among the causes are errors of judgment in making credit decisions. In this study will be used neural network with radial basis function method to analyze the feasibility of providing car loans. From the test results to measure the performance of the method is to use testing methods confusion matrix and ROC curve, it is known that the method of back neural network radial basis function has a value of 89,2% accuracy and AUC value of 0.9471. This shows that the model produced, including the classification is Exellent Clasification because it has the AUC values between 0.90- 1.00.

PENGARUH PEMBELAJARAN SISTEM MODUL TERHADAP HASIL BELAJAR MATEMATIKA SISWA SD Amrin Amrin
Paradigma Vol 12, No 1 (2010): Vol. 12 Nomor 1, Maret 2010
Publisher : LPPM Universitas Bina Sarana Informatika

The learning of module system is a model of learning that is designed and arranged in a systematically planned way and aimed to help students and teachers reach for learning goals. This research aims to know and to describe whether the learning of mathematics by using the module can show the students learning progress and whether it’s better than conventional models. This research uses a quantitative experimental approach. The population of this research is sixth level students from elementary school named Daar el Salam in Gunung Putri, Bogor. Then sampling taken in this research is 30 students by using purposive sampling method. Instruments used to collect data are: Test and Questionnaire. The data collected will be analyzed by descriptive statistical analysis and inferential statistical analysis. From the results of descriptive statistical analysis,it can be known that the interest of students in learning mathematics by using module is very good Then by inferential statistical analysis, it can be found that t count = -20.80 and t table = 2.00. By using the two-tailed test,it can be seen that the value of t count is outside the tolerance of acceptance Ho: -2 <t count <2, so that Ho is rejected and H1 is accepted. The conclusion can be taken that the learning of mathematics with the module system has a very significant impact . Or in the other words, the learning mathematics with the module system is better than the conventional method. Key Word : Impact, Learning of Mathematic, Module

DATA MINING DENGAN REGRESI LINIER BERGANDA UNTUK PERAMALAN TINGKAT INFLASI Amrin Amrin
Jurnal Techno Nusa Mandiri Vol 13 No 1 (2016): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Ma
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (821.266 KB)

In this study will be used multiple linear regression method to predict the monthly inflation rate in Indonesia. In the results of the data analysis is concluded that the model of multiple linear regression obtained in this study is Y= 0,241X1 + 0,164X2 + 0,271X3 + 0,07X4 + 0,040X5 + 0,060X6 + 0,169X7 - 0,010. The coefficient of regression value is 0,999 and coefficient of determination value is 0,997. the performance of multiple linear regression that formed by the training data and validated by testing data generates prediction accuracy rate is very good with a Mean Absolute Deviation (MAD) is 0.0380, a Mean Square Error (MSE) is 0.0023, and a Root Mean Square Error (RMSE) is 0.0481.

ANALISA KELAYAKAN PEMBERIAN KREDIT MOBIL DENGAN MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION Amrin Amrin
Jurnal Techno Nusa Mandiri Vol 12 No 1 (2015): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Ma
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.911 KB)

Problems are often encountered in the provision of credit is to determine lending decisions to someone, while other issues are not all credit payments can run well. Among the causes are errors of judgment in making credit decisions. In this study will be used back propagation neural network method to analyze the feasibility of providing car loans. From the test results to measure the performance of the method is to use testing methods Confusion Matrix and ROC curve, it is known that the method of back propagation neural network has a value of 89% accuracy and AUC value of 0.831. This shows that the model produced, including the classification is quite good because it has the AUC values between 0.8-0.9.

PERAMALAN TINGKAT INFLASI INDONESIA MENGGUNAKAN NEURAL NETWORK BACKPROPAGATION BERBASIS METODE TIME SERIES Amrin Amrin
Jurnal Techno Nusa Mandiri Vol 11 No 2 (2014): Techno Nusa Mandiri : Journal of Computing and Information Technology Periode Se
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (313.198 KB)

In this study will be used back propagation neural network method to predict the monthly inflation rate in Indonesia. In the results of the data analysis is concluded that the performance of back propagation neural network that formed by the training data and validated by testing data generates prediction accuracy rate is very good with a mean square error (MSE) is 0.0171. By using a moving average to forecast the independent variables obtained the rate of inflation in the month of July 2014 is 0.514, by using exponential smoothing to forecast the independent variables obtained by the rate of inflation in the month of July 2014 is 0.45, and by using seasonal method to forecast the independent variables obtained by the rate of inflation in the month of July 2014 is 0.93.

Co-Authors Andi Nana Mariana Budiarto, Balla Wahyu David Ridwan Savero Donny Dharmawan Eni Irfiani Frans Sudirjo Hafdiarsya Saiyar Harsih Rianto Irawan Satriadi Irawan Satriadi Luh Komang Candra Dewi Mita Diah Larasati Muhammad Alawi Muhammad Reza Aldiansyah Oki Rosanto Omar Pahlevi Priyono, Agil Hendro Rudianto Rudianto Satriadi, Irawan Sismadi , Sismadi Yopi Handrianto, Yopi

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