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All Journal Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Sistem Informasi Galuh Journal of Mechatronics and Artificial Intelligence
Arif Wirawan Muhammad
Fakultas Informatika, Universitas Telkom Purwokerto
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Social Sciences

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Analisis Keamanan Browser Menggunakan Metode National Institute of Justice (Studi Kasus: Facebook dan Instagram) Ratri Ayunita Kinasih; Arif Wirawan Muhammad; Wahyu Adi Prabowo
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 11 No. 2 (2020): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v11i2.4678

Abstract

Pencurian data digital sangat meresahkan pengguna media sosial, terlebih pengguna Facebook dan Instagram yang merupakan media sosial dengan jumlah pengguna terbanyak. Browser yang digunakan untuk mengakses media sosial harus terjamin keamanannya. Analisis browser untuk mengetahui browser mana yang paling aman digunakan untuk mengakses media sosial sangat penting. Agar pengguna media sosial tidak perlu khawatir terjadi pencurian data. Browser yang akan dianalisis yaitu Google Chrome, Mozilla Firefox, dan Microsoft Edge. Penelitian ini dilakukan menggunakan skenario dengan menggunakan teknik live forensics agar data yang didapatkan masih terekam dalam Random Access Memory (RAM), khususnya data volatile seperti email dan password. Dalam penelitian ini didapatkan bukti digital seperti email, password, username, dan data-data pribadi lainnya dengan menggunakan tools FTK Imager. Kata kunci: Browser, Facebook, FTK Imager, Instagram, Live Forensics
Pengembangan Perangkat Lunak Untuk Deteksi DDoS Berbasis Neural Network Arif Wirawan Muhammad; Muhammad Nur Faiz; Ummi Athiyah
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1544

Abstract

System security issues are a vital factor that needs to be considered in the operation of systems and networks, which will later be used for disaster mitigation and preventing attacks on the network. Distributed Denial of Services (DDoS) is a form of attack carried out by individuals or groups to damage data through servers or malware in the form of flooding packets, therefore it can paralyze the network system used. Network security is a factor that must be maintained and considered in an information system. DDoS can take the form of Ping of Death, flood, Remote control attack, User Data Protocol (UDP) flood, and Smurf Attack. This study aims to develop software to detect DDoS attacks based on network traffic logs. The software has been tested and run according to the neural network algorithm. This software was developed with an interface that makes it easier for users to detect the source IP whether the IP is carrying out a DDoS attack or normal.
Rekayasa Fitur Berbasis Machine Learning untuk Mendeteksi Serangan DDoS Muhammad Nur Faiz; Oman Somantri; Arif Wirawan Muhammad
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 11 No 3: Agustus 2022
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v11i3.3423

Abstract

Distributed network attacks, also known as distributed denial of service (DDoS) are a major threat and problem for internet security. DDoS is an attack on a network aiming to disable server resources. These attacks increase every year with the current state of the COVID-19 pandemic. One of countermeasures to minimize the DDoS impact is the intrusion detection system (IDS) command. IDS techniques are currently still employing traditional methods so that they have many limitations compared to techniques and tools used by attackers because traditional IDS methods only use signature-based detection or anomaly-based detection models which cause many errors. Network data packet traffic has a complex nature, both in terms of sizes and sources. This research utilized the ability of artificial neural network (ANN) to detect normal attacks or DDoS. A classification technique with ANN method is a solution to these issues. Based on the shortcomings of the traditional IDS, this study aims to detect DDoS attacks using feeder machine learning-based feature engineering techniques to improve the IDS development. Using the UNSW-NB15 dataset with the ANN method, this study also aims to analyze and obtain training function combinations and the best hidden layer architectures of ANNs to solve the detection and classification problems of DDoS packets in computer networks. As a result, the training function combinations and hidden layer architectures of the ANN can provide a high level of DDoS recognition accuracy. Based on experiments conducted with three schemes and an ANN schema architecture technique with eight features as input, the highest accuracy was 98.22%. Feature selection plays an essential role in detection result accuracies and machine learning performances in classification problems.
Peningkatan Kapasitas Penjualan Pada Kader Pemberdayaan Masyarakat Desa Melalui Pelatihan Pemasaran Digital Ummi Athiyah; Shintia Dwi Alika; Atika Ratna Dewi; Muhammad Quthb Habiburrahman; Oktavia Jazilatus Sa’adah; Arif Wirawan Muhammad
Madani : Indonesian Journal of Civil Society Vol. 6 No. 2 (2024): Madani : Agustus 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v6i2.2193

Abstract

Empowering rural communities is essential for sustainable development, especially in the economic sector. This community service program aims to increase the sales capacity of the Sunyalangu Village Community Empowerment Cadres (KPMD) through digital marketing training. The main problems include simple packaging, conventional marketing methods, and poor business management practices. This program uses a community service method, Service Learning (SL), which involves practical steps such as product packaging training and digital marketing strategy workshops. This project significantly improved participants' skills in using sealer machines and promoting products online, especially on platforms like Shopee. The method of implementing strategic digital marketing communication training was carried out with a structured and interactive approach over two meetings. The results showed the importance of digital literacy in rural areas to achieve maximum business potential and improve economic sustainability. This training has successfully introduced participants to the world of online trading and provided them with practical skills in utilizing digital platforms to market processed products from the community.
Analisis Forensik Pada Instagram dan Tik Tok Dalam Mendapatkan Bukti Digital Dengan Menggunakan Metode NIST 800-86 Ali Diko Putra, Muhammad; Wirawan Muhammad, Arif; Parga Zen, Bita; Yunita Kisworini, Rianti; Rohayati, Tuti
Jurnal Sistem Informasi Galuh Vol 2 No 1 (2024): Journal of Galuh Information Systems
Publisher : Fakultas Teknik Jurusan Sistem Informasi Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/jsig.v2i1.3695

Abstract

Crime is currently increasing with the development of this smartphone, one of which is the crime of using social media. Instagram and Tik Tok are one of the most widely used social media applications in this era. The more users of this social media, it does not rule out the emergence of crimes against fellow users of this social media. Every activity carried out on social media, including criminal acts, leaves evidence or digital traces; whether deleted or not, all will be visible. Digital forensics is the study of how to obtain digital evidence obtained from digital devices. This study aims to determine the process of finding digital evidence on Instagram and Tik Tok social media applications that are accessed via smartphones using the National Institute of Standards and Technology (NIST) method. The stages in this digital forensic method include Collection, Examination, Analysis, and Reporting to obtain digital evidence on Instagram and TikTok using tools in the form of third-party software, namely MOBILedit Forensic Express, Autopsy, and FTK Imager tools. With the final result for the Instagram application tools, MOBILedit Forensic Express is able to acquire evidence with a percentage of 0.02%, for autopsy tools at 40%, and FTK Imager with a percentage of 57%. In the Tiktok application itself, MOBILedit Forensic Express gets a percentage of 27%, while an autopsy is 29%, and the last is the FTK Imager tool with a percentage of 71%. In this study, the FTK Imager tool is superior in the acquisition of evidence between the two tools.
Color Analysis on Indonesia Top e-Commerce Pinandita, Arsita; Nofrizaldi, Nofrizaldi; Muhammad, Arif Wirawan
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 4, No 4 (2021): Budapest International Research and Critics Institute November
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i4.2679

Abstract

The e-commerce usage is a must in the business industry; think more complex problems that require to always keeping up with the global developments which requires to taking a creative action. Statista reported (data.tempo.co) regarding data on the top 10 most visited Indonesia e-commerce sites, Shopee ranks first with 71.53 million clicks per month in the first quarter of 2020. Tokopedia is in the second place with 69 million clicks per month during the first quarter of 2020. The color identity usage in e-commerce has a strong perception in the brand image forming. In e-commerce, the color psychology usage is the one of supports to attract attention, selling products early, raise offer, build messages and product images, and showing identity. Color is universal languages that cross the cultural boundaries in technology which is currently called the global village. This study aims to provide an understanding of the color benefits in the brand identity form which is applied through e-commerce displays.
Comparison of Naïve Bayes Classifier and Support Vector Machine Methods for Sentiment Classification of Responses to Bullying Cases on Twitter Firda Millennianita; Ummi Athiyah; Arif Wirawan Muhammad
Journal of Mechatronics and Artificial Intelligence Vol 1, No 1 (2024): JMAI: June 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmai.v1i1.69959

Abstract

The rapid dissemination of information related to the K-Pop world, facilitated by social media, has made it easier to follow developments and controversies. One notable case that sparked extensive discussion on Twitter was the bullying allegations against Kim Garam of LE SSERAFIM. Researchers, using Twitter data, sought to analyze Indonesian public sentiment regarding this case through sentiment analysis, which classifies opinions as positive or negative. For processing textual data, text mining methods, particularly classification techniques, are employed. Two popular algorithms in text mining are the Naive Bayes classifier and the support vector machine (SVM). The Naive Bayes classifier is favored for its speed, simplicity, and high accuracy, while the SVM excels at identifying a hyperplane that maximizes the margin between classes. In this study, sentiment classification results were labeled as either positive or negative. The comparison between the Naive Bayes classifier and SVM for classifying responses to Kim Garam's bullying case on Twitter showed high accuracy rates: 93% for Naive Bayes and 97% for SVM. The higher accuracy of the SVM algorithm indicates its superiority over the Naive Bayes classifier in this context.
A Classification Data Packets Using the Threshold Method for Detection of DDoS Sukma Aji; Davito Rasendriya Rizqullah Putra; Imam Riadi; Abdul Fadlil; Muhammad Nur Faiz; Arif Wirawan Muhammad; Santi Purwaningrum; Laura Sari
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 1 (2024): JINITA, June 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i1.2224

Abstract

Computer communication is done by first synchronizing one computer with another computer. This synchronization contains Data Packages which can be detrimental if done continuously, it will be categorized as an attack. This type of attack, when performed against a target by many computers, is called a distributed denial of service (DDoS) attack. Technology and the Internet are growing rapidly, so many DDoS attack applications result in these attacks still being a serious threat. This research aims to apply the Threshold method in detecting DDoS attacks. The Threshold method is used to process numeric attributes so obtained from the logfile in a computer network so that data packages can be classified into 2, namely normal access and attack access. Classification results using the Threshold method after going through the fitting process, namely detecting 8 IP Addresses as computer network users and 6 IP addresses as perpetrators of DDoS attacks with optimal accuracy.
Analisis Efisiensi Metode K-Nearest Neighbor dan Forward Chaining Untuk Prediksi Stunting Pada Balita Pangestu, Happy Gery; Sinaga, Rifaldo Yohannes; Ulya, Fadilla Zundina; Athiyah, Ummi; Muhammad, Arif Wirawan; Alameka, Faza
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 18, No 2 (2023): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v18i2.10169

Abstract

Permasalahan gizi buruk yang terjadi di Indonesia menjadi sebuah tantangan yang nyata bagi pemerintah maupun masyarakat. Salah satu gizi buruk kronis yang menyerang balita di Indonesia yaitu stunting (tubuh pendek). Masalah stunting sering dialami oleh anak-anak di Indonesia. Dalam satu dekade terakhir, balita yang terindikasi stunting cenderung tidak mengalami perbaikan. Jika terus dibiarkan stunting akan mengakibatkan masalah serius yang dapat mengganggu pertumbuhan dari anak. Oleh karena itu, diperlukan pencegahan sejak dini terkait masalah stunting pada anak. Pada penelitian kali ini akan dibuat sebuah website untuk melakukan deteksi stuntung pada balita. Penelitian dilakukan dengan membandingkan metode sistem pakar forward chaining yang menggunakan pengkondisian manual pada bahasa pemrograman python dan machine learning menggunakan K-Nearest Neighbor. Hasil dari sistem pakar forward chaining memiliki akurasi prediksi yang lebih baik karena dapat memprediksi dengan benar 10/10 pengujijan yang diberikan, sedangkan dengan menggunakan machine learning K-Nearest Neighbour hanya dapat memprediksi benar 8/10 pengujian yang telah diberikan. Kemudian metode yang terpilih akan dilakukan deploying kedalam website. Pada tahap deploying, metode yang digunakan adalah forward chaining.
Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Sari, Laura; Faiz, Muhammad Nur; Muhammad, Arif Wirawan
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Co-Authors Abdul Fadlil Abdul Rohman Supriyono Achmad Zaki Yamani Ahmad Azhari Alameka, Faza Ali Diko Putra, Muhammad Alika, Shintia Dwi Amalia, Hasna Shafa Arsita Pinandita Atika Ratna Dewi Azhari, Ahmad Bita Parga Zen Busthomi, Iqbal Cik Feresa Mohd Foozy Davito Rasendriya Rizqullah Putra Dwi Januarita Ardianing Kusuma Feresa Mohd Foozy, Cik Firda Millennianita Foozy, Cik Feresa Mohd Imam Riadi Kamaruddin Malik bin Mohammed Laura Sari Maharrani, Ratih Hafsarah Malik, Kamaruddin Muhammad Afrizal Amrustian Muhammad Nur Faiz Muhammad Nur Faiz, Muhammad Nur Muhammad Quthb Habiburrahman Nofrizaldi Oktavia Jazilatus Sa’adah Oman Somantri Pangestu, Happy Gery Ratri Ayunita Kinasih Risa Eri Susanti Rusydi Umar Santi Purwaningrum Sarah Astiti S.Kom., M.MT Sari, Laura Sinaga, Rifaldo Yohannes Sukma Aji Sunardi Sunardi Supriyono, Abdul Rohman Tuti Rohayati Ulya, Fadilla Zundina Ummi Athiyah Wahyu Adi Prabowo Widi Widayat Yudha Saintika Yunita Kisworini, Rianti