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All Journal Jurnal Teknologi Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Majalah Ilmiah Bahari Jogja Edsence: Jurnal Pendidikan Multimedia Gema Wiralodra Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Teknika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia semanTIK
Hanafi Hanafi
Universitas Amikom Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Transportation Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi Hanafi; Alva Hendi Muhammad; Ike Verawati; Richki Hardi
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

Abstract

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection
Enhance Document Contextual Using Attention-LSTM to Eliminate Sparse Data Matrix for E-Commerce Recommender System - Hanafi; Anik Sri Widowati; - Jaeni; Jack Febrian Rusdi
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1233

Abstract

E-commerce has been the most important service in the last two decades. E-commerce services influence the growth of the economic impact worldwide. A recommender system is an essential mechanism for calculating product information for e-commerce users. The successfulness of recommender system adoption influences the target revenue of an e-commerce company. Collaborative filtering (CF) is the most popular algorithm for creating a recommender system. CF applied a matrix factorization mechanism to calculate the relationship between user and product using rating variable as intersection value between user and product. However, the number of ratings is very sparse, where the number of ratings is less than 4%. Product Document is the product side information representation. The document aims to advance the effectiveness of matrix factorization performance. This research considers to the enhancement of document context using LSTM with an attention mechanism to capture a contextual understanding of product review and incorporate matrix factorization based on probabilistic matrix factorization (PMF) to produce rating prediction. This study employs a real dataset using MovieLens dataset ML.1M and Amazon information video (AIV) to observe our ATT-PMF model. Movielens dataset represents of number sparse rating that only contains below 4% (ML.1M). Our experiment report shows that ATT-PMF outperforms more than 2% on average than previous work. Moreover, our model is also suitable to implement on huge datasets. For further research, enhancement of product document context will be a good factor in eliminating sparse data problems in big data problems.
Co-Authors - Jaeni Abdul Malik Zuhdi Abdul Samad Hasan Basari Abidullah S. Salim Aflahah Apriliyani Ahmad Febri Diansyah Ahmad Jaisy Rahman Alva Hendi Muhammad Andri Pranolo Anggit Dwi Hartanto Anik Sri Widowati Asfa Dhevi Azzumzumi Cahyarini Maulida Tri Yunanda Catur Riyono Heri Wibowo D. Diffran Nur Cahyo Ema Utami Fahmi Rusdi Al Islami Fendy Prasetyo Nugroho Fidya Farasalsabila Fidya Farasalsabila Ike Verawati Jack Febrian Rusdi Jatmiko, Burham Dwi Kusrini Kusrini Kusrini Kusrini Kusrini Leonel Hernandez Marwan Noor Fauzy Moch Mashuri Muh Adha Muhammad Akbar Maulana Muhammad Fajrian Noor Muhammad Fatahillah Muhammad Ilham Fadila Muhammad Rusdi Rahman Muqorobin Muqorobin Nanang Fitriana Kurniawan Nanna Suryana Nur Avin Maulana Reflan Nuari Richki Hardi Rizky . Robi Wariyanto Abdullah Rona Guines Purnasiwi Sri Wulandari Taqwa Hariguna Thedjo Sentoso Tutik Lestari Verra Budhi Lestari Widdi Djatmiko Windha Mega Pradnya Dhuhita Windha Mega Pradnya Dhuhita Yingchi Mao Yoga Dwi Pambudi Yudhi Setiyantara Yulita Fatma Andriani Zul Hisyam