Article Per Year (5 Year)
p-Index From 2019 - 2024
1.288
P-Index
This Author published in this journals
All Journal Jurnal Penelitian Pendidikan Creative Information Technology Journal SISFOTENIKA Jurnal Eksplora Informatika Jurnal Ilmiah Sinus Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Perangkat Lunak JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Ekonomi dan Teknik Informatika Techno
HANIF AL FATTA
STMIK Amikom Yogyakarta
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Searching Similarity Digital Image Using Color Histogram Wahyu Wijaya Widiyanto; Kusrini Kusrini; Hanif Al Fatta
Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) Vol 20, No 1 (2019): Techno Volume 20 No.1 April 2019
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/techno.v20i1.3818

Abstract

In the era of globalization and modernization, as now, information technology is widely used in the fields of education, trade, animal husbandry, agriculture and even to the legal sector. One branch of science in the field of information technology that is growing rapidly is computer vision. One of the important roles of computer vision in everyday life is the use of computer vision. This can be applied in terms of face recognition, object detection, and can be applied to group images based on the order of similarity of the image, the ability of computer vision is applied to facilitate human work in selecting from several images to find the most similar images. In this study described the process of finding the similarity of an image with other images through several stages of research flow, the method used is to use RGB values that have been converted to grayscale, then the eucludian distance distance is calculated to determine the value of proximity of an image while calculating performance accuracy algorithm using confusion matrix. The search trial process resulted in an accuracy rate of 0.42, precision of 0.42 and recall 1 of 1000 datasets and 30 random data were taken. Found images that differ in color and shape but when converted into histograms the data has a fairly high similarity to the query. The disadvantage of this research is that images that have histograms similar to queries are displayed as similar images even though the reality is that images are very different from colors and shapes.Keywords: computer vision, similiarity, eucludian distance, grayscale, histogram
Sentiment Analysis Comments Covid-19 Variant Omicron on Social Media Instagram with Bidirectional Encoder from Transformers (BERT) Dody Pradipta; Kusrini Kusrini; Hanif Al Fatta
JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Vol 3 No 1: JTECS Januari 2023
Publisher : FAKULTAS TEKNIK UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jtecs.v3i1.3219

Abstract

Internet merupakan alat komunikasi yang banyak diminati karena pesatnya perkembangan teknologi dan informasi beberapa tahun belakangan ini. Ini adalah konteks untuk memodernisasi dan sepenuhnya mendigitalkan komunikasi. Salah satu perubahan dalam komunikasi digital adalah media sosial, platform digital yang memungkinkan orang berbicara satu sama lain, berbagi informasi, dan lainnya. Pada platform media sosial ini, yang dirancang untuk mendapatkan masukan inti dari pengguna atau konsumen secara efisien, komentar dapat digunakan untuk mengumpulkan opini dari pengguna. Instagram adalah salah satu platform media sosial paling populer saat ini, dan banyak penggunanya menggunakannya untuk menyuarakan pendapat (komentar) mereka tentang pandemi Covid-19. Menggunakan metode Bidirectional Encoder from Transformers (BERT), komentar masyarakat nantinya dapat diklasifikasikan menjadi sentimen positif, negatif, dan netral. Analisis sentimen mengungkapkan bagaimana perasaan orang tentang varian Omicron pandemi Covid-19. Prosedur scraping menghasilkan 1.052 data yang terdiri dari 663 komentar negatif, 388 komentar netral, dan 1 komentar positif. Hasil tes memiliki akurasi sebesar 0,632 (63%).
PERANGKINGAN DALAM PENENTUAN E-COMMERCE TERBAIK DENGAN METODE ANALYTICAL HIERARCHICAL PROCESS Arief Tri Nugroho; Kusrini Kusrini; Hanif Al Fatta
JURNAL PERANGKAT LUNAK Vol 5 No 3 (2023): Jurnal Perangkat Lunak
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/jupel.v5i3.2798

Abstract

Revolusi Industri 4.0 menyebabkan adanya pergeseran interaksi antar manusia, diantaranya dalam proses bertransaksi jual beli. Salah satu produk Revolusi Industri 4.0 yang dihasilkan untuk mempermudah proses bertransaksi jual beli adalah e-commerce. E-commerce diprediksi akan terus bertumbuh seiring berkembangnya ekonomi digital di Indonesia. E-commerce yang hadir di Indonesia sudah semakin menjamur dan bersaing dengan memberikan beragam fasilitas yang memanjakan konsumen diantaranya program diskon, cashback, dan, gratis biaya pengiriman. Semakin banyaknya pilihan e-commerce ini membuat konsumen cenderung bingung dan akhirnya mengikuti gelombang tanpa tahu tujuan sebenarnya dalam penggunaan e-commerce. Salah satu cara yang dapat digunakan konsumen untuk memilih e-commerce yang paling efektif dan efisien adalah dengan metode AHP (Analytical Hierarchical Process). Metode AHP akan memberikan rekomendasi e-commerce yang paling tepat dan layak digunakan berdasarkan bobot kriteria yang dihasilkan.
MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK Constantin Menteng; Arief Setyanto; Hanif Al Fatta
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 17 No. 2 (2023): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v7i2.8151

Abstract

Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network.
Co-Authors Abidarin Rosidi Arief Setyanto Arief Tri Nugroho Bety Wulan Sari Constantin Menteng Dimas Setiawan Dody Pradipta Donni Prabowo Ema Utami Fatimah Nur Arifah Hadi Sucipto Kusrini . Kusrini Kusrini M Suyanto Riska Dwi Handayani Sofyan Pariyasto Sri Lestari Rahayu Wahyu Wijaya Widiyanto Wira Dimuksa Wiwi Widayani