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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 Informatik : Jurnal Ilmu Komputer JURIKOM (Jurnal Riset Komputer) 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 Jurnal Teknimedia: Teknologi Informasi dan Multimedia Teknika Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Indonesian Journal Computer Science (ijcs) Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia SmartComp Jurnal Teknologi semanTIK
Hanafi Hanafi
Universitas Amikom Yogyakarta
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Transportation Other

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Journal : international journal of advances in intelligent informatics

 1 
Dynamic convolutional neural network for eliminating item sparse data on recommender system Hanafi Hanafi; Nanna Suryana; Abdul Samad Hasan Basari
International Journal of Advances in Intelligent Informatics Vol 4, No 3 (2018): November 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i3.291

Abstract

Several efforts have been conducted to handle sparse product rating in e-commerce recommender system. One of them is the inclusion of texts such as product review, abstract, product description, and synopsis. Later, it converted to become rating value. Previous researches have tried to extract these texts based on bag of word and word order. However, this approach was given misunderstanding of text description of products. This research proposes a novel Dynamic Convolutional Neural Network (DCNN) to improve meaning accuracy of product review on a collaborative filtering recommender system. DCNN was used to eliminate item sparse data on text product review while the accuracy level was measured by Root Mean Squared Error (RMSE). The result shows that DCNN has outperformed the other previous methods.
CAE-COVIDX: automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder Hanafi Hanafi; Andri Pranolo; Yingchi Mao
International Journal of Advances in Intelligent Informatics Vol 7, No 1 (2021): March 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i1.577

Abstract

Since the first case in 2019, Corona Virus has been spreading all over the world. World Health Organization (WHO) announced that COVID-19 had become an international pandemic. There is an essential section to handle the spreading of the virus by immediate virus detection for patients. Traditional medical detection requires a long time, a specific laboratory, and a high cost. A method for detecting Covid-19 faster compared to common approaches, such as RT-PCR detection, is needed. The method demonstrated that it could produce an X-ray image with higher accuracy and consumed less time. We propose a novel method to extract image features and to classify COVID-19 using deep CNN combined with Autoencoder (AE) dubbed CAE-COVIDX. We evaluated and compared it with the traditional CNN and existing framework VGG16 involving 400 normal images of non-COVID19 and 400 positive COVID-19 diseases. The performance evaluation was conducted using accuracy, confusion matrix, and loss evaluation. Based on experiment results, the CAE-COVIDX framework outperforms previous methods in several testing scenarios. This framework's ability to detect Covid-19 in various nonstandard image X-rays could effectively help medical employers diagnose Covid-19 patients. It is an important factor to decrease the spreading of Covid-19 massively.
IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism Hanafi Hanafi; Andri Pranolo; Yingchi Mao; Taqwa Hariguna; Leonel Hernandez; Nanang Fitriana Kurniawan
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.942

Abstract

An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical model to remove outliers in pre-processing, SDAE, responsible for reducing data dimensionality, and LSTM-Attention, responsible for producing attack classification tasks. The model was implemented into the NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics measures. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.
LUNGINFORMER: A Multiclass of lung pneumonia diseases detection based on chest X-ray image using contrast enhancement and hybridization inceptionresnet and transformer Hanafi, Hanafi
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1964

Abstract

Lung pneumonia is categorically a serious disease on Earth. In December 2019, COVID-19 was first identified in Wuhan, China. COVID-19 caused severe lung pneumonia. The majority of lung pneumonia diseases are diagnosed using traditional medical tools and specialized medical personnel. This process is both time-consuming and expensive. To address the problem, many researchers have employed deep learning algorithms to develop an automated detection system for pneumonia. Deep learning faces the issue of low-quality X-ray images and biased X-ray image information. The X-ray image is the primary material for creating a transfer learning model. The problem in the dataset led to inaccurate classification results. Many previous works with a deep learning approach have faced inaccurate results. To address the situation mentioned, we propose a novel framework that utilizes two essential mechanisms: advanced image contrast enhancement based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and a hybrid deep learning model combining InceptionResNet and Transformer. Our novel framework is named LUNGINFORMER. The experiment report demonstrated LUNGINFORMER achieved an accuracy of 0.98, a recall of 0.97, an F1-score of 0.98, and a precision of 0.96. According to the AUC test, LUNGINFORMER achieved a tremendous performance with a score of 1.00 for each class. We believed that our performance model was influenced by contrast enhancement and a hybrid deep learning model.
Co-Authors - Jaeni Abdul Malik Zuhdi Abdul Samad Hasan Basari Abidullah S. Salim Aflahah Apriliyani Ahmad Febri Diansyah Ahmad Jaisy Rahman Alva Hendi Muhammad Amir, Fail Andi Sunyoto Andri Pranolo Anggit Dwi Hartanto Anik Sri Widowati Asfa Dhevi Azzumzumi Asy'ari, Ahmad Hasyim B, Arijal Bambang Siswanto Cahyarini Maulida Tri Yunanda Catur Riyono Heri Wibowo D. Diffran Nur Cahyo Djatmiko, Widdi Ema Utami Fahmi Rusdi Al Islami Fendy Prasetyo Nugroho Fidya Farasalsabila Harahap, Awal Ridho Jack Febrian Rusdi Jatmiko, Burham Dwi KUSRINI Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Leonel Hernandez Lopes, David Dos Santos Pinto 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 Nugraha, Icha Nura Nur Avin Maulana Reflan Nuari Richki Hardi Rizky . Rona Guines Purnasiwi Sentoso, Thedjo Sri Ngudi Wahyuni, Sri Ngudi Sri Wulandari Sri Wulandari Sri Wulandari Taqwa Hariguna Tutik Lestari Verawati, Ike Verra Budhi Lestari Wariyanto Abdullah, Robi Widowati, Anik Sri Wijaya, Mochamad Rozikul Windha Mega Pradnya Dhuhita Windha Mega Pradnya Dhuhita Yingchi Mao Yoga Dwi Pambudi Yudhi Setiyantara Yulita Fatma Andriani Zubaedi, Umam Faqih Zul Hisyam