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Steganalisis Blind dengan Metode Convolutional Neural Network (CNN) Yedroudj- Net terhadap Tools Steganografi Nurmi Hidayasari; Imam Riadi; Yudi Prayudi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020703326

Steganalisis digunakan untuk mendeteksi ada atau tidaknya file steganografi. Salah satu kategori steganalisis adalah blind steganalisis, yaitu cara untuk mendeteksi file rahasia tanpa mengetahui metode steganografi apa yang digunakan. Sebuah penelitian mengusulkan bahwa metode Convolutional Neural Networks (CNN) dapat mendeteksi file steganografi menggunakan metode terbaru dengan nilai probabilitas kesalahan rendah dibandingkan metode lain, yaitu CNN Yedroudj-net. Sebagai metode steganalisis Machine Learning terbaru, diperlukan eksperimen untuk mengetahui apakah Yedroudj-net dapat menjadi steganalisis untuk keluaran dari tools steganografi yang biasa digunakan. Mengetahui kinerja CNN Yedroudj-net sangat penting, untuk mengukur tingkat kemampuannya dalam hal steganalisis dari beberapa tools. Apalagi sejauh ini, kinerja Machine Learning masih diragukan dalam blind steganalisis. Ditambah beberapa penelitian sebelumnya hanya berfokus pada metode tertentu untuk membuktikan kinerja teknik yang diusulkan, termasuk Yedroudj-net. Penelitian ini akan menggunakan lima alat yang cukup baik dalam hal steganografi, yaitu Hide In Picture (HIP), OpenStego, SilentEye, Steg dan S-Tools, yang tidak diketahui secara pasti metode steganografi apa yang digunakan pada alat tersebut. Metode Yedroudj-net akan diimplementasikan dalam file steganografi dari output lima alat. Kemudian perbandingan dengan tools steganalisis lain, yaitu StegSpy. Hasil penelitian menunjukkan bahwa Yedroudj-net bisa mendeteksi keberadaan file steganografi. Namun, jika dibandingkan dengan StegSpy hasil gambar yang tidak terdeteksi lebih tinggi.AbstractSteganalysis is used to detect the presence or absence of steganograpy files. One category of steganalysis is blind steganalysis, which is a way to detect secret files without knowing what steganography method is used. A study proposes that the Convolutional Neural Networks (CNN) method can detect steganographic files using the latest method with a low error probability value compared to other methods, namely CNN Yedroudj-net. As the latest Machine Learning steganalysis method, an experiment is needed to find out whether Yedroudj-net can be a steganalysis for the output of commonly used steganography tools. Knowing the performance of CNN Yedroudj-net is very important, to measure the level of ability in terms of steganalysis from several tools. Especially so far, Machine Learning performance is still doubtful in blind steganalysis. Plus some previous research only focused on certain methods to prove the performance of the proposed technique, including Yedroudj-net. This research will use five tools that are good enough in terms of steganography, namely Hide In Picture (HIP), OpenStego, SilentEye, Steg and S-Tools, which is not known exactly what steganography methods are used on the tool. The Yedroudj-net method will be implemented in a steganographic file from the output of five tools. Then compare with other steganalysis tools, namely StegSpy. The results showed that Yedroudj-net could detect the presence of steganographic files. However, when compared with StegSpy the results of undetected images are higher.

Analisis Keamanan Website Open Journal System Menggunakan Metode Vulnerability Assessment Imam Riadi; Anton Yudhana; Yunanri W
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020701928

Open Journal System (OJS) merupakan perangkat lunak yang berfungsi sebagai sarana publikasi ilmiah dan digunakan diseluruh dunia. OJS yang tidak dipantau beresiko diserang oleh hacker. Kerentanan yang di timbulkan oleh hacker akan berakibat buruk terhadap performa dari sebuah OJS. Permasalahan yang dihadapi pada sistem OJS meliputi network, port discover, proses audit exploit sistem OJS. Proses audit sistem pada OJS mencakup SQL Injection, melewati firewall pembobolan password. Parameter input yang digunakan adalah IP address dan port open access. Metode yang digunakan adalah vulnerability assessment. Yang terdiri dari beberapa tahapan seperti information gathering atau footprinting, scanning vulnerability, reporting. Kegiatan ini bertujuan untuk mengidentifikasi celah keamanan pada website open journal system (OJS). Penelitian ini menggunakan open web application security project (OWASP). Pengujian yang telah dilakukan berhasil mengidentifikasi 70 kerentanan high, 1929 medium, 4050 low pada OJS, Total nilai vulnerability pada OJS yang di uji coba sebesar 6049. Hasil pengujian yang dilakukan menunjukkan bahwa pada OJS versi 2.4.7 memiliki banyak celah kerentanan atau vulnerability, tidak di rekomendasi untuk digunakan. Gunakanlah versi terbaru yang dikeluarkan oleh pihak OJS Public knowledge project (PKP). AbstractThe Open Journal System (OJS) is A software that functions as a means of scientific publication and is used throughout the world. OJS that is not monitored is at risk of being attacked by hackers. Vulnerabilities caused by hackers will adversely affect the performance of an OJS. The problems faced by the OJS system include the network, port discover, OJS system audit exploit process. The system audit process on the OJS includes SQL Injection, bypassing the firewall breaking passwords. The input parameters used are the IP address and open access port. The method used is a vulnerability assessment. Which consists of several stages such as information gathering or footprinting, scanning vulnerability, reporting. This activity aims to identify security holes on the open journal system (OJS) website. This study uses an open web application security project (OWASP). Tests that have been carried out successfully identified 70 vulnerabilities high, 1929 medium, 4050 low in OJS, the total value of vulnerability in OJS which was tested was 6049. The results of tests conducted showed that in OJS version 2.4.7 had many vulnerabilities or vulnerabilities, not on recommendations for use. Use the latest version issued by the OJS Public Knowledge Project (PKP).

Speech Classification to Recognize Emotion Using Artificial Neural Network Siti Helmiyah; Imam Riadi; Rusydi Umar; Abdullah Hanif
Khazanah Informatika Vol. 7 No. 1 April 2021
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v7i1.11913

This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centers, banks, education, and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral, and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.This study seeks to identify human emotions using artificial neural networks. Emotions are difficult to understand and hard to measure quantitatively. Emotions may be reflected in facial expressions and voice tone. Voice contains unique physical properties for every speaker. Everyone has different timbres, pitch, tempo, and rhythm. The geographical living area may affect how someone pronounces words and reveals certain emotions. The identification of human emotions is useful in the field of human-computer interaction. It helps develop the interface of software that is applicable in community service centres, banks, and education and others. This research proceeds in three stages, namely data collection, feature extraction, and classification. We obtain data in the form of audio files from the Berlin Emo-DB database. The files contain human voices that express five sets of emotions: angry, bored, happy, neutral and sad. Feature extraction applies to all audio files using the method of Mel Frequency Cepstrum Coefficient (MFCC). The classification uses Multi-Layer Perceptron (MLP), which is one of the artificial neural network methods. The MLP classification proceeds in two stages, namely the training and the testing phase. MLP classification results in good emotion recognition. Classification using 100 hidden layer nodes gives an average accuracy of 72.80%, an average precision of 68.64%, an average recall of 69.40%, and an average F1-score of 67.44%.

Development and Evaluation of Android Based Notification System to Determine Patient's Medicine for Pharmaceutical Clinic Imam Riadi; Sri Winiarti; Herman Yuliansyah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

The development of science in the field of health clinical pharmacy grows rapidly in recent years. Based on the data from information was obtained that needs to be done a reparation a learning process in clinical pharmacy to produce them who as requested by users pharmaceutical graduates. According to the results of the information there is a problem that in conducting the process of determining the pharmacys drug it can be made a mistake, especially in patients who have complications disease. The process of checking conducted repeatedly to make sure a medicine that is concocted in accordance with a list of the acts of treat a patient, while patient data not yet integrated into a system that could help them in analysis and determine a drug that in accordance. Notification system that developed using android platform this, the hope can become the tools in the form of a system that can give notification to the farmasis easily accessible at any time through gadgets. Based on the results of testing with the methods alpha test can be concluded the number of feasibility this system reached 88.75%. Thus notification system in the determination of medicine patients rule based as a medium learn students pharmaceutical clinic worthy to used.

ANALISIS STATISTIK LOG JARINGAN UNTUK DETEKSI SERANGAN DDOS BERBASIS NEURAL NETWORK Arif Wirawan Muhammad; Imam Riadi; Sunardi Sunardi
ILKOM Jurnal Ilmiah Vol 8, No 3 (2016)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v8i3.76.220-225

Distributed denial-of-service (DDoS) merupakan jenis serangan dengan volume, intensitas, dan biaya mitigasi yang terus meningkat seiring berkembangnya skala organisasi. Penelitian ini memiliki tujuan untuk mengembangkan sebuah pendekatan baru untuk mendeteksi serangan DDoS, berdasarkan log jaringan yang dianalisis secara statistik dengan fungsi neural network sebagai metode deteksi. Data pelatihan dan pengujian diambil dari CAIDA DDoS Attack 2007 dan simulasi mandiri. Pengujian terhadap metode analisis statistik terhadap log jaringan dengan fungsi neural network sebagai metode deteksi menghasilkan prosentase rata-rata pengenalan terhadap tiga kondisi jaringan (normal, slow DDoS, dan DDoS) sebesar 90,52%. Adanya pendekatan baru dalam mendeteksi serangan DDoS, diharapkan bisa menjadi sebuah komplemen terhadap sistem Intrusion Detection System (IDS) dalam meramalkan terjadinya serangan DDoS.

DETEKSI BUKTI DIGITAL ONLINE GAMBLING MENGGUNAKAN LIVE FORENSIK PADA SMARTPHONE BERBASIS ANDROID Andrian Sah; Imam Riadi; Yudi Prayudi
Cyber Security dan Forensik Digital Vol. 1 No. 1 (2018): Edisi Mei 2018
Publisher : Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Internet as media to do political movement. Since that, the Indonesian people understand internet-based activities until this current progress. Use of internet in Indonesia most frequently utilize units to access internet, namely, handphone, laptop/notebook, Personal Computer (PC), and tablet. Media used to access internet include mobile (47.6%), computer (1.7%) and both (50.7%). Use of internet increases so that criminal action rate is higher; for example, online gambling. In general, online gambling is done using smartphone. However, today smartphone can load more than one type of online gambling. So speedy progress of online gambling must contain criminal action. Criminal action is taken by involving smartphone having online gambling with impact on challenge to prove digital evidences and analyze it. Online gambling is a crime or criminal action being social problems resulting in negative impacts such as morale and mental disorders in society, especially young generation. Questions asked in this study are how to find characteristic of online gambling and digital evidences available to smartphone. This study focuses on characteristic and digital evidences in smartphone based on facts found in thing of evidence. Based on results of study, we found some types of online gambling in smartphone. Characteristic and digital evidences found in smartphone were found by using forensic media, namely, XRY. Forensic media of XRY were used to find thing of digital evidence in smartphone, such as, ID, Password and transaction of online gambling via social applications.

MASK DETECTION ANALYSIS USING HAAR CASCADE AND NAÏVE BAYES Imam Riadi; Abdul Fadlil; Izzan Julda D.E Purwadi Putra
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 7 No. 2 (2022)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v7i2.135

Coronavirus Disease (COVID-19) is a new virus variant that emerged in 2019. The World Health Organization (WHO) states that 394,381,395 people have been infected with COVID-19, and 5,735,178 have died. This epidemic has been found in Indonesia since March 2020. New cases in Indonesia are still increasing every day as a whole. The Government as a policy has imposed a policy on anyone who will be required to wear a mask and also carry out physical distancing so that they can work without the maker being exposed to the virus. In the midst of a pandemic, the use of masks has increased to prevent transmission. Various types of masks are easy to find, but not all masks are recommended to avoid transmission. Among them are the N-95 masks, which are recommended to prevent transmission. This application uses the haar cascade and naive bayes methods. The pycharm edition 2021.2 tools and python 3.8 are the detection systems used in this mask. The haar cascade method is also used in detecting objects with masks or not and naive Bayes, which is used as an accuracy calculation. This study uses a dataset of 1092, which is divided into 192 positive images and 900 negative images. Accuracy results using the haar cascade method are 100% more accurate, while the nave Bayes method is 76.6% less accurate.

Cyberbullying Analysis on Instagram Using K-Means Clustering Ahmad Muhariya; Imam Riadi; Yudi Prayudi
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Social Media, in addition to having a positive impact on society, also has a negative effect. Based on statistics, 95 percent of internet users in Indonesia use the internet to access social networks. Especially for young people, Instagram is more widely used than other social media such as Twitter and Facebook. In terms of cyberbullying cases, cases often occur through social media, Twitter, and Instagram. Several methods are commonly used to analyze cyberbullying cases, such as SVM (Support Vector Machine), NBC (Naïve Bayes Classifier), C45, and K-Nearest Neighbors. Application of a number of these methods is generally implemented on Twitter social media. Meanwhile, young users currently use Instagram more social media than Twitter. For this reason, the research focuses on analyzing cyberbullying on Instagram by applying the K-Mean Clustering algorithm. This algorithm is used to classify cyberbullying actions contained in comments. The dataset used in this study was taken from 2019 to 2021 with 650 records; there were 1827 words and already had labels. This study has successfully classified the tested data with a threshold value of 0.5. The results for grouping words containing bullying on Instagram resulted in the highest accuracy, which is 67.38%, a precision value of 76.70%, and a recall value of 67.48%. These results indicate that the k-means algorithm can make a grouping of comments into two clusters: bullying and non-bullying.

APPLICATION OF OWASP ZAP FRAMEWORK FOR SECURITY ANALYSIS OF LMS USING PENTEST METHOD Rusydi Umar; Imam Riadi; Sonny Abriantoro Wicaksono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5534

Learning Management System (LMS) is an application currently popular for online learning. The presence of LMS offers better prospects for the world of education, where its highly efficient use allows learning anywhere and anytime through the internet or other computer media. This study focuses on analyzing the security of the Learning Management System (LMS) on the domain e-learning.ibm.ac.id using the Pentest method with the Owasp Zap Framework. Security is a crucial step that needs to be considered by IBM Bekasi in protecting data and information from hacker threats. In this study, the method used is Pentest. Pentest is a series of methods used to test the security of a system by conducting literature studies, searching for data information, and domain information, followed by testing using Owasp Zap to find security-related vulnerabilities. The results of the testing using the Pentest method involve several stages of testing and scanning. The first step is checking domain information using Whois Lookup tools and then scanning using ZenMap on e-learning.ibm.ac.id. In this domain information search, the domain status serverTransferProhibited and clientTransferProhibited was found. The next stage is Vulnerability Analysis, where scanning is performed on the domain e-learning.ibm.ac.id using Owasp Zap tools. Based on the results from Owasp Zap scan, 16 vulnerabilities were found, with the breakdown being 2 high risk, 3 medium risk, 6 low risk, and 5 informational. In the exploitation stage using SQLMap, errors were found in the tested parameters, preventing injection.

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