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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer METIK JURNAL Infotek : Jurnal Informatika dan Teknologi Jurnal Teknimedia: Teknologi Informasi dan Multimedia PELS (Procedia of Engineering and Life Science) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen

Muhammad Rudyanto Arief

Magister Teknik Informatika Universitas Amikom Yogyakarta

Author-ID : 4391874

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

Published : 6 Documents Claim Missing Document

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Articles

Prediksi Indeks Harga Konsumen Komoditas Makanan di Kota Surabaya menggunakan Support Vector Regression Ayu Adelina Suyono; Kusrini Kusrini; Muhammad Rudyanto Arief
METIK JURNAL Vol 6 No 1 (2022): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v6i1.339

In data mining, predictions are known to find knowledge about what will happen in the future. Predictions are usually made on time-series data. The Consumer Price Index (CPI) is an index value derived from daily consumer price data. The results of the CPI calculation are derived from observations of commodity prices at the household consumer level, which are carried out routinely on a daily, weekly, bi-weekly, and monthly basis. CPI prediction can be done using a data mining algorithm, namely Support Vector Regression (SVR). SVR is part of the Support Vector Machine algorithm that functions to solve regression cases. SVR is a reliable algorithm in the case of regression because it can handle data overfitting well. The data used as input in this paper comes from 34 food commodity prices, and the output data is obtained from the CPI value data. The food commodity price data used is from Surabaya City. The data period used is from 2014-2020. The results of the implementation of SVR with 4 kernels show that the Polynomial kernel has the best error rate with a MAPE value of 4.31%.

Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease Abdul Jalil Rozaqi; Muhammad Rudyanto Arief; Andi Sunyoto
Procedia of Engineering and Life Science Vol 1 No 1 (2021): Proceedings of the 1st Seminar Nasional Sains 2021
Publisher : Universitas Muhammadiyah Sidoarjo

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

Stroke Prediction Using Machine Learning Method with Extreme Gradient Boosting Algorithm Abd Mizwar A Rahim; Andi Sunyoto; Muhammad Rudyanto Arief
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 3 (2022)
Publisher : LPPM Universitas Bumigora

Based on data obtained from WHO, stroke is a disease that ranks as the second most deadly disease. The cause of a stroke is when a blood vessel is hit or ruptured, resulting in a part of the brain not getting the blood supply that carries the oxygen it needs, leading to death. By utilizing technology in the health sciences, especially in the health sector, machine learning models can adjust and make it easier for users to predict certain diseases. Previous studies have had problems with low accuracy when used in healthcare. The purpose of this research is to increase accuracy by proposing the application of one of the ensemble learning algorithms, namely the Xtreme Gradient Boosting algorithm. This stroke prediction research uses the Xtreme Gradient Boosting Algorithm; the application of this method with split data Training data and 70/30 test data, 70% of the training data is 3582, 30% of the test data is 1536, and the results are 96% accuracy with these results having good results. This study increase accuracy in predicting stroke cases and get better accuracy than previous studies.

PERGERAKAN NILAI AKTIVA BERSIH (NAB) BERDASARKAN EVALUASI KESALAHAN METODE DOUBLE EXPONENTIAL SMOOTHING PADA REKSA DANA BNI-AM DANA LANCAR SYARIAH Muhammad Noor Arridho; Kusrini Kusrini; Muhammad Rudyanto Arief
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 3 No. 2 (2022): Desember 2022
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v3i2.64

Mutual funds are a place to raise public funds managed by legal entities which are then invested in se-curities in the form of stocks, bonds and money markets. In essence, investing can increase welfare in the future. However, the interest of the Indonesian people in investing is relatively low. Along with the devel-opment of mutual fund technology, it has become known to the wider community through the presence of capital market service application providers. although, mutual funds have a small risk, as capital increas-es the risk increases. In this study, the researcher predicts the movement of net asset value (NAV) in the BNI-AM Dana Lancar Syariah mutual fund using the Double Exponential Smoothing method with 1 varia-ble to give preference in minimizing investment risk.. Predictions were made based on historical data for the period from January to March 2022 and an evaluation of the MAPE prediction error of 0.0107% and MAD 0.171248 using an alpha weighting of 0.4.

Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Untuk Klasifikasi Kelayakan Pemberian Pinjaman Amir Bagja; Kusrini Kusrini; Muhammad Rudyanto Arief
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.20059

Cooperatives are social organizations or economic bodies that have a very important role in the growth, development of economic potential and community success. One of the cooperative activities is the provision of credit or loans to community members. Cooperative credit is one of the most important banking activities and serves to provide credit to the community. In practice, errors often arise due to inaccurate credit analysis, or the behavior of the customers themselves. The purpose of this research is to compare the accuracy results between the Naive Bayes algorithm and Support Vector Machine (SVM), where the best accuracy results can later be used as a reference to determine the profitability of lending. The attributes used in this study consist of 11 attributes, namely: Gender, marital status, occupation, relatives, nominal income, income criteria, loan amount, loan term, interest rate, installments and class as income characteristics. The dataset used in this study includes 166 members of the Daru Nahdla Capita Shari'ah cooperative. The results of testing the naive bayes algorithm after dividing the data five times, dividing the data set 70% as test data and 30% as training data, obtained a precision value of 97.00%, recall 100.00%, F1 score 99.00%. and accuracy 98.00%. Thus, the Naive Bayesian algorithm is an algorithm that shows accurate classification and prediction

Aplikasi Perhitungan Jumlah Kendaraan Dengan Menggunakan Google Maps API Dibyo Sudarsono; Kusrini; Muhammad Rudyanto Arief
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 21 No 2 (2023): Mei 2023
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v21i2.14

Proses penghitungan kendaraan memberikan informasi yang tepat tentang arus lalu lintas, dan waktu puncak lalu lintas di jalan raya. Makalah ini menyajikan penghitung kendaraan dengan menggunakan data dari Google Maps API. Penghitungan kendaraan akan menggunakan rumus rasio densitas dari metode Greenshield, Greenberg dan Underwood. Implementasi dari teknik yang diusulkan telah dilakukan dengan menggunakan bahasa pemrograman PHP dan framework codeigniter. Hasil dari penelitian ini adalah sebuah aplikasi penghitung kendaraan berbasis web yang diharapkan dapat membantu dinas terkait dalam meningkatkan pelayanan kepada masyarakat sebagai pengguna jalan

Co-Authors Abdul Jalil Rozaqi Abdul Mizwar A. Rahim Amir Bagja Andi Sunyoto Ayu Adelina Suyono Dibyo Sudarsono Kusrini Kusrini Kusrini Kusrini Kusrini Muhammad Noor Arridho

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