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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Science in Information Technology Letters
Nur Heri Cahyana
Universitas Pembangunan Nasional Veteran Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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TEXT MINING UNTUK MENDETEKSI PLAGIASI DOKUMEN DENGAN PENERAPAN STEMMING NAZIEF-ADRIANI DAN ALGORITMA SMITH-WATERMAN Alvika Meitaningsih; Agus Sasmito Aribowo; Nur Heri Cahyana
Telematika Vol 17, No 2 (2020): Edisi Oktober 2020
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v1i1.3377

Abstract

Plagiarisme adalah tindakan menjiplak karya orang lain dan mengakui sebagai hasil karya pribadinya. Saat ini sudah banyak algoritma yang membahas cara mendeteksi plagiarisme dokumen teks seperti Cosine, Smith Waterman. Hasil penelitian sebelumnya menyatakan bahwa algoritma Smith Waterman memiliki keakurasian yang rendah, sehingga pada penelitian ini dilakukan pengembangan dari Algoritma Smith Waterman. Algortima Smith Waterman biasa digunakan didalam bidang bioinformatika untuk menentukan kesamaan DNA, akan tetapi dalam penelitian ini Algoritma Smith Waterman dapat diimplementasikan untuk mendeteksi dokumen. Proses pendeteksian kemiripan dokumen pertama-tama dilakukan proses preprocessing untuk menghilangkan imbuhan guna memudahkan proses pendeteksian dokumen yaitu dengan menggunakan stemming. Stemming yang digunakan dalam penelitian ini adalah Stemming Nazief & Adriani dan untuk mengukur tingkat keakurasian pada proses pendeteksian dokumen dilakukan perhitungan menggunakan algoritma Smith Waterman untuk mendapatkan hasil persentase kemiripan antar dokumen. Dari uji coba yang dilakukan penambahan preprocessing yaitu stemming mempengaruhi waktu proses pengujian karena pada proses preprocessing ini kata yang berimbuhan akan dikembalikan ke kata dasar dan dicocokan dengan data kamus yang ada didalam database.
Feasibility study for banking loan using association rule mining classifier Agus Sasmito Aribowo; Nur Heri Cahyana
International Journal of Advances in Intelligent Informatics Vol 1, No 1 (2015): March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v1i1.8

Abstract

The problem of bad loans in the koperasi can be reduced if the koperasi can detect whether member can complete the mortgage debt or decline. The method used for identify characteristic patterns of prospective lenders in this study, called Association Rule Mining Classifier. Pattern of credit member will be converted into knowledge and used to classify other creditors. Classification process would separate creditors into two groups: good credit and bad credit groups. Research using prototyping for implementing the design into an application using programming language and development tool. The process of association rule mining using Weighted Itemset Tidset (WIT)–tree methods. The results shown that the method can predict the prospective customer credit. Training data set using 120 customers who already know their credit history. Data test used 61 customers who apply for credit. The results concluded that 42 customers will be paying off their loans and 19 clients are decline
Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification Awang Hendrianto Pratomo; Nur Heri Cahyana; Septi Nur Indrawati
Science in Information Technology Letters Vol 4, No 1 (2023): May 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i1.1182

Abstract

Convolutional Neural Networks (CNNs) have gained significant traction in the field of image categorization, particularly in the domains of health and safety. This study aims to categorize the utilization of face masks, which is a vital determinant of respiratory health. Convolutional neural networks (CNNs) possess a high level of complexity, making it crucial to execute hyperparameter adjustment in order to optimize the performance of the model. The conventional approach of trial-and-error hyperparameter configuration often yields suboptimal outcomes and is time-consuming. Genetic Algorithms (GA), an optimization technique grounded in the principles of natural selection, were employed to identify the optimal hyperparameters for Convolutional Neural Networks (CNNs). The objective was to enhance the performance of the model, namely in the classification of photographs into two categories: those with face masks and those without face masks. The convolutional neural network (CNN) model, which was enhanced by the utilization of hyperparameters adjusted by a genetic algorithm (GA), demonstrated a commendable accuracy rate of 94.82% following rigorous testing and validation procedures. The observed outcome exhibited a 2.04% improvement compared to models that employed a trial and error approach for hyperparameter tuning. Our research exhibits exceptional quality in the domain of investigations utilizing Convolutional Neural Networks (CNNs). Our research integrates the resilience of Genetic Algorithms (GA), in contrast to previous studies that employed Convolutional Neural Networks (CNN) or conventional machine learning models without adjusting hyperparameters. This unique approach enhances the accuracy and methodology of hyperparameter tuning in Convolutional Neural Networks (CNNs). 
Co-Authors Agus Sasmito Aribowo Alvika Meitaningsih Awang Hendrianto Pratomo Septi Nur Indrawati