Nurfalah, Ridan
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Identifikasi Citra Beras Menggunakan Algoritma Multi-SVM Dan Neural Network Pada Segmentasi K-Means Nurfalah, Ridan; Dwiza Riana; Anton
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v5i1.2721


Indonesia is a country with high rice needs because it is a staple food for more than 90% of populations. High demand requires high stock so imports are carried out in accordance with Permendagri Number 19/M-DAG/PER/3/2014 which explains rice import standards. There are many types of rice imported into Indonesia with various quality, color and import requirements such as for health or price stabilization. In terms of colors, imported white rice is the most consumed rice by Indonesians. One example is jasmine rice from Thailand. Meanwhile, in terms of imports, both for health and stabilizing the price of japonica rice (Japan) and Basmati (Pakistan) are the most imported to Indonesia. But there are still many who are not familiar with those three rices. In this research, the three types of rice were identified by comparing the Multi-SVM algorithm and Neural Network algorithm. Image acquisition is done using a flatbed scanner which produces 90 images divided into 63 training images and 27 testing images. K-Means becomes an image segmentation method and image binary converts. Feature extraction using morphological features with the regionprop method combined with the Gray Level Co-Occence Matrix (GLCM) produces 9 features that can produce 96.296% accuracy for Multi-SVM and 88.89% Neural Network
The Analysis of Adult Autism Spectrum Disorders Screening Using Neural Network Nurfalah, Ridan; Rahayu, Sri; Akbar, Muhammad Faittullah
Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (874.302 KB) | DOI: 10.33395/sinkron.v4i1.10148


One of the increasing developmental disorders in Indonesia is Autism Spectrum Disorder (ASD), developmental disorder characterized by difficulties to conduct verbal and non-verbal communication and social interaction. This disorder cannot be tolerated and requires early treatment to reduce its development. However, ASD treatments required ineffective treatment costs and waiting times diagnosis were lengthly. One effective alternative diagnosis isto use the screening technology to determine the early symptoms of ASD disorders. The rapid development of the number of ASD cases around the world required researchers to determine a dataset with behavioral properties to update the screening process. Thus, the purpose of this study is to predict the success of screening performed on adults with Autism Spectrum Disorder (ASD) using the researchers’ results dataset, so that the dataset could be used as a benchmark for the success of the ASD screening process. The method used is machine learning neural network method with 100 training cycle, learning rate 0,01 and momentum 0,9 resulted in a classification accuracy of 96.00%
Penerapan Deep Learning dalam Pendeteksian Autism Toddler Ambarsari, Diah Ayu; Nurfalah, Ridan; Kuryanti, Sandra Jamu
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 4, No 1 (2019): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v4i1.1593


Health is a very important thing. Everyone can overcome health problems. Children's health is the dream of every parent. During the growth period the child will switch several times which can stop their development. Parents must be more sensitive and have extensive knowledge in health. The problem that often occurs is that parents do not know the initial autism symptoms that occur in the baby, so more parents assume if it is okay, this situation accelerates the diagnosis process, whereas autism disorders can be detected early by looking at growing habits child development every time an autism transfer is a developmental development in children, autism must facilitate quickly, because with autism treatment quickly and quickly will help autistic patients grow back to normal. To help understand the children mengamalim autism, the authors conducted research with new methods. In a previous study, Fades Tahbatan conducted research to ascertain whether the child was autistic or not using a tool. But it only produces data sets., It turns out to have attributes that are not yet precise, which increases the level of accuracy. In this research, use the method of deep learning and improve accuracy, the application used is fast miners. The variables are then processed so as to produce a prediction model from the data set obtained. Accuracy values that can be processed are sufficient while accuracy = 98.96% precision = 96.74%, recall = 98.49% with AUC of = 0.90 Keywords: Autism, deep learning, toddlers  
MULTIMEDIA LEARNING FOR WUDHU AND SHOLAT PROCEDURES ANDROID BASED AT TK PERTIWI 01 SERANG Widiastuti, Widiastuti; Masturoh, Siti; Kahfi, Ahmad Hafidzul; Saelan, M Rangga Ramadhan; Nurfalah, Ridan; Fakhriza, Muhammad Hilman
Techno Nusa Mandiri: Journal of Computing and Information Technology Vol 17 No 1 (2020): TECHNO Period of March 2020
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1382.401 KB) | DOI: 10.33480/techno.v17i1.1290


Wudhu is one way to purify oneself from uncleanness and suffering. Performing ablution perfectly in accordance with Islamic Shari'a is the key to receiving prayer. The introduction of religious activities such as ablution and prayer from an early age is considered necessary. Learning ablution and prayer is usually done by parents repeatedly and by example. In one study, 8 out of 10 children aged 5-6 years did not recognize ablution when they were praying. The method of developing multimedia systems by Luther-Sutopo is one of the system development methods used by multimedia application developers. Therefore it will be built an Android operating learning media that uses Adobe Flash technology to display an animated image, motion, and audio in a 2-dimensional form. This learning media will display 2-dimensional objects of ablution movements, namely intentions, washing both feet and prayer after ablution, and prayer movements from beginning to end and added a few daily prayers. The results of this study are in the form of learning applications for ablution and five-time prayer based on Android. In this application using elements of text, images, animations, and sounds to attract and make it easier for children to remember lessons on how to perform ablution and prayer and various kinds of daily prayers.
KOMPARASI ALGORITMA NAIVE BAYES, RANDOM FOREST DAN SVM UNTUK MEMPREDIKSI NIAT PEMBELANJA ONLINE Agustyaningrum, Cucu Ika; Gata, Windu; Nurfalah, Ridan; Radiyah, Ummu; Maulidah, Mawadatul
Jurnal Informatika Vol 20, No 2 (2020): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v20i2.2402


Beberapa tahun terakhir ini, penggunaan e-commerce atau toko online sangat meningkat. Bermacam-macam toko online yang bermunculan di internet, baik berskala kecil maupun yang berskala besar. Hal ini memiliki pengaruh yang sangat penting pada penggunaan waktu yang efektif dan tingkat angka penjualan. Maka dari itu e-commerce atau toko online harus mempunyai kemampuan menilai sarana yang digunakan untuk mengetahui dan mengklasifikasikan niat pembelanjaan online sehingga menghasilkan keuntungan bagi toko tersebut. Niat pembelanja online dapat dilakukan pengklasifikasian menggunakan beberapa algoritma, seperti Naive Bayes, Random Forest dan Support Vector Machine. Dalam penelitian ini perbandingan algoritma dilakukan menggunakan aplikasi WEKA dengan mengetahui nilai F1-Score, Akurasi, Kappa Statistic dan Mean Absolute Error. Terdapat perbedaan antara hasil pengujian, untuk nilai F1-Score, Akurasi, Kappa Statistic menghasilkan pengujian algoritma Random Forest-lah yang paling baik dibandingkan Naive Bayes dan Support Vector Machine. Sedangkan pada nilai Mean Absolute Error hasil pengujian algoritma Support Vector Machine merupakan nilai terbaik dari pada Naive Bayes dan Random Forest. Sehingga berdasarkan penelitian ini Algoritma Random Forest merupakan algoritma yang paling baik dan tepat untuk diterapkan sebagai pengklasifikasian niat pembelanja online, karena algoritma Random Forest yang paling mendominasi dalam mengetahui nilai kriteria seperti F1-Score, Akurasi, Kappa Statistic dan Mean Absolute Error.