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All Journal ELKHA : Jurnal Teknik Elektro Semantik Proceeding SENDI_U JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informatika dan Rekayasa Perangkat Lunak Abdimasku : Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknik Informatika (JUTIF) Jurnal Informatika: Jurnal Pengembangan IT INOVTEK Polbeng - Seri Informatika
Fauzi Adi Rafrastara
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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SUPER ENKRIPSI TEKS KRIPTOGRAFI MENGGUNAKAN ALGORITMA HILL CIPHER DAN TRANSPOSISI KOLOM Megantara, Rama Aria; Rafrastara, Fauzi Adi
Proceeding SENDI_U 2019: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.969 KB)

Abstract

Dalam kemajuan teknologi keamanan data adalah salah satu faktor yang sangat penting. Perancangan kriptografi baru menjadi alternatif apabila metode pengamanan informasi lain sudah ada kriptanalisisnya. Hill Cipher merupakan salah satu algoritma kriptografi yang memanfaatkan matriks sebagai kunci untuk melakukan enkripsi dan Dekripsi dan aritmatika modulo. Setiap karakter pada plaintext ataupun ciphertext dikonversikan kedalam bentuk angka. Enkripsi dilakukan dengan mengalikan matriks kunci dengan matriks plaintext, sedangkan Dekripsi dilakukan dengan mengalikan invers matriks kunci dengan matriks ciphertext. Transposisi kolom yaitu teknik membagi plainteks menjadi blok-blok dengan panjang kunci (k) tertentu yang kemudian blok- blok tersebut disusun dalam bentuk baris dan kolom. Metode SuperEnkripsi dengan metode Hill Cipher dan Transposisi Kolom agar di dapatkan Cipher text yang bersifat aman. Untuk mempermudah penghitungannya, proses Super Enkripsi menggunakan PHP Native serta Javascript
A Combination of Hill CIPHER-LSB in RGB Image Encryption Megantara, Rama Aria; Rafrastara, Fauzi Adi; Mahendra, Syafrie Naufal
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 4, No 3, August 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1401.076 KB) | DOI: 10.22219/kinetik.v4i3.785

Abstract

The progress of the development of digital technology today, many people communicate by sending and receiving messages. However, along with extensive technological developments, many crimes were committed. In avoiding these crimes, data security needs to be done. Form of data security in the form of cryptography and steganography. One of the cryptographic techniques is the hill cipher algorithm. Hill ciphers include classic cryptographic algorithms that are very difficult to solve. While the most popular steganography technique is Least Significant Bit (LSB). Least Significant Bit (LSB) is a spatial domain steganography technique using substitution methods. This study discusses the merging of message security with hill cipher and LSB. The message used is 24-bit color image for steganography and text with 32, 64 and 128 characters for cryptography. The measuring instruments used in this study are MSE, PSNR, Entropy and travel time (CPU time). Test results prove an increase in security without too damaging the image. This is evidenced by the results of the MSE trial which has a value far below the value 1, the PSNR is> 64 db, the entropy value ranges from 5 to 7 and the results of travel time <1 second.
TAXONOMY BOTNET DAN STUDI KASUS: CONFICKER Adhitya Nugraha; Fauzi Adi Rafrastara
Semantik Vol 1, No 1 (2011): Prosiding Semantik 2011
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (381.813 KB)

Abstract

Botnet merupakan salah satu ancaman yang nyata bagi pengguna komputer saat ini, khususnya bagi mereka yang terhubung pada jaringan, baik itu jaringan lokal maupun global (internet). Botnet tergolong sebagai program berbahaya karena dia merupakan bentuk hybrid dari beberapa malware, dimana dia menggabungkan mekanisme Command & Control (C&C) sekaligus. Untuk mengatasi ancaman botnet ini memang tidak mudah, mengingat diperlukan pengetahuan dan pemahaman yang mendalam terlebih dahulu tentang botnet itu sendiri. Dalam paper ini, penulis membahas tentang botnet beserta taxonominya. Pembahasan mengenai taxonomi ini akan memberikan gambaran tentang karakteristik yang dimiliki oleh botnet beserta penggolongannya. Selanjutnya, study case dilakukan untuk memperoleh gambaran detail tentang suatu botnet berdasarkan taxonominya. Dalam studi case ini, penulis menggunakan salah satu botnet yang cukup popular saat ini, yaitu Conficker. Dengan demikian, pembaca akan memperoleh gambaran yang lebih jelas tentang karakteristik botnetconficker tersebu.Kata kunci : Botnet, Taxonomy, Conficker, C&C
BOTNET DETECTION SURVEY Adhitya Nugraha; Fauzi Adi Rafrastara
Semantik Vol 1, No 1 (2011): Prosiding Semantik 2011
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (382.176 KB)

Abstract

Di antara berbagai bentuk malware, Botnet merupakan salah satu ancaman yang paling serius terhadap cyber-crime saat ini. Hal ini disebabkan karena Botnet mampu menyediakan platform yang dapat didistribusikan pada kegiatan ilegal seperti serangan-serangan di internet, termasuk spam, phishing, clickfraud, pencurian password dan Distributed Denial of service(DDoS) attack.Akhir-akhir ini, deteksi Botnet telah menarik perhatian para peneliti untuk dijadikan topik penelitian dalam usaha pencegahan terhadap cyber-crime. Dalam paper ini, penulis melakukan studi literature untuk mengkaji beberapa penelitian sebelumnya yang membahas tentang teknik-teknik yang digunakan untuk mendeteksi keberadaan Botnet didalam suatu sistem. Beberapa teknik yang dibahas dalam paper ini yaitu signature-based, anomaly-based DNS-based, dan mining-base. Kajian komprehensif ini diharapkan dapat memberikan gambaran yang lebih jelas tentang teknik-teknik mendeteksi Botnet dengan memaparkan kelebihan dan kekurangan dari masing-masing metode tersebut yang selanjutnya dapat digunakan sebagai langkah awal dalam usaha prefentif terhadap serangan Botnet.Kata kunci : Botnet, deteksi Botnet, cyber-crime
Optimasi Algoritma Random Forest menggunakan Principal Component Analysis untuk Deteksi Malware Fauzi Adi Rafrastaraa; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Etika Kartikadarma; Usman Sudibyo
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.854

Abstract

Malware is a type of software designed to harm various devices. As malware evolves and diversifies, traditional signature-based detection methods have become less effective against advanced types such as polymorphic, metamorphic, and oligomorphic malware. To address this challenge, machine learning-based malware detection has emerged as a promising solution. In this study, we evaluated the performance of several machine learning algorithms in detecting malware and applied Principal Component Analysis (PCA) to the best-performing algorithm to reduce the number of features and improve performance. Our results showed that the Random Forest algorithm outperformed Adaboost, Neural Network, Support Vector Machine, and k-Nearest Neighbor algorithms with an accuracy and recall rate of 98.3%. By applying PCA, we were able to further improve the performance of Random Forest to 98.7% for both accuracy and recall while reducing the number of features from 1084 to 32.
Optimasi Investasi di Pasar Saham Indonesia: Meningkatkan Keputusan Investasi dengan Prediksi IHSG menggunakan Decision Tree Dwi Eko Waluyo; Cinantya Paramita; Hayu Wikan Kinasih; Fauzi Adi Rafrastara; Dewi Pergiwati
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1876

Abstract

Pasar saham Indonesia merupakan pilar ekonomi yang vital, memfasilitasi perolehan modal bagi perusahaan serta menawarkan peluang investasi bagi individu hingga korporasi besar. Keberhasilan investasi sangat dipengaruhi oleh kemampuan memahami faktor-faktor yang menentukan pergerakan harga saham. Teknologi dan analisis data, khususnya melalui algoritma Decision Tree, dapat membantu memprediksi pergerakan Indeks Harga Saham Gabungan (IHSG), sehingga mendukung keputusan investasi yang lebih baik. Pengabdian masyarakat bertajuk "Optimasi Investasi di Pasar Saham Indonesia" dirancang untuk meningkatkan literasi investasi di kalangan mahasiswa, pengusaha dan pemegang saham, melalui pengembangan system analisis berbasis Decision Tree untuk prediksi IHSG. Program ini mencakup penelitian awal, pengembangan dan validasi model prediksi, pelatihan dan edukasi, implementasi, serta evaluasi dan penyempurnaan berbasis MOS, dengan tujuan akhir meningkatkan keberhasilan investasi di pasar saham Indonesia, seraya mengintegrasikan pengetahuan di bidang komputer, AI, dan keuangan. Materi pelatihan mencakup dasar analisis teknikal dan fundamental, analisis Decision Tree, optimasi portofolio, dan strategi manajemen risiko, dilengkapi dengan alat machine learning. Evaluasi pasca pelatihan menggunakan metode Mean Opinion Score (MOS) menunjukkan tingkat kepuasan tinggi dengan skor 97.08% untuk Fungsionalitas, 96.09% untuk Keandalan, dan 98.09% untuk Kegunaan, menekankan efektivitas algoritma Decision Tree dalam memprediksi IHSG dan meningkatkan keputusan investasi.
Website-Based System Prototype Development for Classify Student Characteristics Kencana, Lisdi Inu; Rafrastara, Fauzi Adi; Paramita, Cinantya
Journal of Intelligent Computing & Health Informatics Vol 3, No 1 (2022): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i1.10364

Abstract

Student characteristics are important attributes in understanding their academic abilities and ways of thinking. In the teaching and learning process, the right learning strategy is very important to implement. According to the Hippocrates-Galenus Typology, personality types are categorized into four categories, including sanguinis, choleric, melancholics, and phlegmatics. The Classification of student characteristics using experience and intuition methods often gives inaccurate results and takes a long time to understand their behavior and way of thinking. In our research, we developed a prototype cognitive system website to classify student characteristics at SD Wijaya Kusuma 02 Semarang. There are several stages of the proposed method, including, communication, rapid planning, rapid design modeling, prototype construction, and delivery & feedback deployment. The C4.5 algorithm is applied as the modeling of student characteristics classification. The results showed a fairly good accuracy of 90.08%. It can be concluded that the C4.5 algorithm can classify student characteristics well.
Malware Detection Using K-Nearest Neighbor Algorithm and Feature Selection Supriyanto, Catur; Rafrastara, Fauzi Adi; Amiral, Afinzaki; Amalia, Syafira Rosa; Al Fahreza, Muhammad Daffa; Abdollah, Mohd. Faizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6970

Abstract

Malware is one of the biggest threats in today’s digital era. Malware detection becomes crucial since it can protect devices or systems from the dangers posed by malware, such as data loss/damage, data theft, account break-ins, and the entry of intruders who can gain full access of system. Considering that malware has also evolved from traditional form (monomorphic) to modern form (polymorphic, metamorphic, and oligomorphic), a malware detection system is needed that is no longer signature-based, but rather machine learning-based. This research will discuss malware detection by classifying the file whether considered as malware or goodware, using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features was reduced using the Information Gain and Principal Component Analysis (PCA) feature selection methods. The performance of kNN with PCA and Information Gain will then be compared to get the best performance. As a result, by using the PCA method where the number of features was reduced until the remaining 32 PCs, the kNN algorithm succeeded in maintaining classification performance with an accuracy of 95.6% and an F1-Score of 95.6%. Using the same number of features as the basis, the Information Gain method is applied by sorting the features from those with the highest Information Gain score and taking the 32 best features. The result, by using this Information Gain method, the classification performance of the kNN algorithm can be increased to 96.9% for both accuracy and F1-Score.
Performance Comparison of k-Nearest Neighbor Algorithm with Various k Values and Distance Metrics for Malware Detection Rafrastara, Fauzi Adi; Supriyanto, Catur; Amiral, Afinzaki; Amalia, Syafira Rosa; Al Fahreza, Muhammad Daffa; Ahmed, Foez
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6971

Abstract

Malware could evolve and spread very quickly. By these capabilities, malware becomes a threat to anyone who uses a computer, both offline and online. Therefore, research on malware detection is still a hot topic today, due to the need to protect devices or systems from the dangers posed by malware, such as loss/damage of data, data theft, account hacking, and the intrusion of hackers who can control the entire system. Malware has evolved from traditional (monomorphic) to modern forms (polymorphic, metamorphic, and oligomorphic). Conventional antivirus systems cannot detect modern types of viruses effectively, as they constantly change their fingerprints each time they replicate and propagate. With this evolution, a machine learning-based malware detection system is needed to replace the existence of signature-based. Machine learning-based antivirus or malware detection systems detect malware by performing dynamic analysis, not static analysis as used by traditional ones. This research discusses malware detection using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features is reduced using the Information Gain feature selection method. The performance of kNN with Information Gain will then be measured using the evaluation metrics Accuracy and F1-Score. To get the best score, some adjustments are made to the kNN algorithm, where 3 distance measurement methods will be compared to obtain the best performance along with the variations in the k values of kNN. The distance measurement methods compared are Euclidean, Manhattan, and Chebyshev, while the variations of k values compared are 3, 5, 7, and 9. The result is, kNN with the Manhattan distance measurement method, k = 3, and using information gain features selection method (reduction until 32 features remain) has the highest Accuracy and F1-Score, which is 97.0%.
Pendampingan Pembuatan Konten Youtube Bagi Siswa SMA At Thohiriyyah Semarang astuti, yani parti; Subhiyakto, Egia Rosi; Dolphina, Erlin; Sutojo, Totok; Rafrastara, Fauzi Adi; Kartikadarma, Etika
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2261

Abstract

The extraordinary use of YouTube at this time indicates that developments in the world of technology are very rapid. But it must always be aware that technological developments also affect the psychological development of children. As is the case at the age of children - teenagers who sometimes cannot control. The development of the use of technology, information and communication in the digital world has had various impacts on our lives. As happened at SMA At Thohiriyyah Semarang, which has a middle to lower economic background and is located on the outskirts of East Semarang. At that high school, the students still don't understand the correct use of YouTube. They just watch content that is sometimes not useful. With these problems, they must be given assistance on how to use YouTube properly. For this reason, they must be made active in using YouTube by having an account and being able to create useful content for other people. Apart from that, they are expected to be able to entertain other people through the content they create
Co-Authors Abdollah, Mohd. Faizal Abdussalam Abdussalam, Abdussalam Adhitya Nugraha Adhitya Nugraha Ahmed, Foez Al Fahreza, Muhammad Daffa Amalia, Syafira Rosa Amiral, Afinzaki Anargya, Muhammad Alden Nayef Astuti, Yani Parti Bintoro, Jevan Catur Supriyanto Catur Supriyanto Catur Supriyanto Cinantya Paramita Cinantya Paramita Dewi Pergiwati Dwi Eko Waluyo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Firmansyah, Muhammad David Foez Ahmed Ghozi, Wildani Hayu Wikan Kinasih Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ilma, Zidni Kencana, Lisdi Inu L. Budi Handoko Latifah, Ines Aulia Lisdi Inu Kencana Mahendra, Syafrie Naufal Megantara, Rama Aria Nohan, Rajendra Osman, Waleed Mahgoub Paramita, Cinantya Pergiwati, Dewi Pulung Nurtantio Andono Putra Hartanto, Octaviano Ryan Eka Ramadhan Rakhmat Sani Ramadhani, Arsabilla Ricardus Anggi Pramunendar Rosyada, Salma Saputra, Ardi Sudibyo, Usman T. Sutojo Usman Sudibyo Widyatama, Thierry Wildanil Ghozi Yani Parti Astuti Yani Parti Astuti Yassin, Warusia