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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Teknologi dan Sistem Komputer International Journal of Artificial Intelligence Research Jurnal Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Mantik Penusa ILKOM Jurnal Ilmiah JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Jurnal ULTIMATICS JURTEKSI Jurnal ICT : Information Communication & Technology Jurnal ABDINUS : Jurnal Pengabdian Nusantara Computer Science and Information Technologies Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Teknik Informatika (JUTIF) Journal of Community Development Abdiformatika: Jurnal Pengabdian Masyarakat Informatika JAMAIKA: Jurnal Abdi Masyarakat
Hayaty, Mardhiya
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Nursing Public Health Social Sciences Other

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Journal : JUITA : Jurnal Informatika

 1 
Implementasi Algoritma Rabin-Karp untuk Pendeteksi Plagiarisme pada Dokumen Tugas Mahasiswa Asvarizal Filcha; Mardhiya Hayaty
JUITA : Jurnal Informatika JUITA VoL. 7 Nomor 1, Mei 2019
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (483.482 KB) | DOI: 10.30595/juita.v7i1.4063

Abstract

Perkembangan pada dunia teknologi informasi mengakibatkan perguruan tinggi mengurangi penggunaan kertas sehingga banyak tugas mahasiswa yang dikumpulkan dalam bentuk digital. Penggunaan digital menyebabkan semakin mudahnya mahasiswa untuk melakukan plagiarisme. Sehingga diperlukan sebuah sistem untuk melakukan pemeriksaan plagiarisme pada dokumen tugas antar mahasiswa dengan cepat dan tepat. Metode yang dapat digunakan adalah menggunakan algoritma Rabin-Karp. Algoritma Rabin-Karp memiliki keunggulan pencarian string dengan pola yang panjang. Algoritma Rabin-karp dalam sistem ini memiliki langkah - langkah text preprocessing yang terdiri case folding, tokenizing, punctuation removal, stopword removal dan stemming. Hasil dari text preprocessing inilah yang akan di proses menggunakan algoritma Rabin-karp. Hasil dari metode ini adalah nilai kemiripan dari tugas - tugas mahasiswa yang dihitung menggunakan dice coefficient. Perhitungan akurasi dengan melakukan 20 perbandingan antara sistem pendeteksi plagiarisme dan software Plagiarisme Checker X menggunakan confusion matrix menghasilkan tingkat keakuratan sebesar 90%.
Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method Aditya Salman; Mardhiya Hayaty; Ika Nur Fajri
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1150.687 KB) | DOI: 10.30595/juita.v10i2.13223

Abstract

In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.
Co-Authors Ade Rufaidah Mutmainah Aditya Salman Aditya Wiha Pradana Aini Nur Khasanah Anggit Dwi Hartanto Aprilia Nurkasanah Aqsal Harris Pratama Arfian Yogi Ferianto Arif Dwi Laksito Asvarizal Filcha Barka Satya DHANI ARIATMANTO Dwi Meylasari Dwi Nurani Fajri, Ika Nur Ghufran, Syed Muhammad Irawan, Restu Fajri Istiningsih Istiningsih Istiningsih Khafid Akbar Khasanah, Aini Nur Mei Maemunah Mei Maemunah Mohammad Khalis Purwanto Muhammad Farhan Juna Muhammad Rangga Aziz Nasution Nidal A.M Jabari Norhikmah . Nur’aini Qurrota A’yuna Itsnaini Rahma Widyawati Restu Fajri Irawan Rosyidah Jayanti Vijaya Siti Mutmainah Sri Mulyatun Sri Ngudi Wahyuni Sri Ngudi Wahyuni Sumarni Adi Sumarni Adi Teguh Efriyanto Teguh Efriyanto Timur Haryo Mahissanular Wisnu Dwi Harianto