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All Journal Jurnal Nalar Pendidikan CommIT (Communication & Information Technology) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Knowledge Engineering and Data Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Mantik Progresif: Jurnal Ilmiah Komputer Infotekmesin Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Madani : Indonesian Journal of Civil Society Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Tadris : Jurnal Penelitian dan Pemikiran Pendidikan Islam Journal of Dinda : Data Science, Information Technology, and Data Analytics Prosiding Seminar Nasional Pengabdian Kepada Masyarakat IJCOSIN : Indonesian Journal of Community Service and Innovation Proceeding of International Conference Health, Science And Technology (ICOHETECH) eProceedings of Engineering Madani : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal MediaTIK Journal of Mechatronics and Artificial Intelligence Transaction on Informatics and Data Science
Ummi Athiyah
Institut Teknologi Telkom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : Infotekmesin

 1 
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.
Komparasi Model Analisis Sentimen Pada Twitter Terhadap Kemahalan Minyak Goreng dengan Metode Naive Bayes dan Support Vector Machine Moh. Aminullah Al Fachri; Ummi Athiyah
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1759

Abstract

At the end of 2021, people are shocked by the drastically reduced supply of cooking oil and high prices. This makes people talk about it a lot through social media like Twitter. Freedom on Twitter raises many responses from the public. The number of negative and positive responses on Twitter makes comparisons between the two responses difficult to observe. This study aims to determine the comparison of positive responses and negative responses. Machine learning with the naïve Bayes method and support vector machine is able to overcome this problem. The research conducted examines how the comparison between positive responses and negative responses and which method has higher accuracy. The data used is 10,000 Indonesian language tweets. Model testing was carried out with 1839 test data. the Naive Bayes method gets an accuracy of 74.06% with the results of predicting two positive tweets and 1837 negative tweets. The SVM method was tested on linear, polynomial, RBF, and sigmoid kernels. The kernel with the highest accuracy value is the sigmoid kernel with an accuracy of 81.8% with the predicted results of 266 positive tweets and 1573 negative tweets.
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.
Komparasi Model Analisis Sentimen Pada Twitter Terhadap Kemahalan Minyak Goreng dengan Metode Naive Bayes dan Support Vector Machine Al Fachri, Moh. Aminullah; Athiyah, Ummi
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1759

Abstract

At the end of 2021, people are shocked by the drastically reduced supply of cooking oil and high prices. This makes people talk about it a lot through social media like Twitter. Freedom on Twitter raises many responses from the public. The number of negative and positive responses on Twitter makes comparisons between the two responses difficult to observe. This study aims to determine the comparison of positive responses and negative responses. Machine learning with the naïve Bayes method and support vector machine is able to overcome this problem. The research conducted examines how the comparison between positive responses and negative responses and which method has higher accuracy. The data used is 10,000 Indonesian language tweets. Model testing was carried out with 1839 test data. the Naive Bayes method gets an accuracy of 74.06% with the results of predicting two positive tweets and 1837 negative tweets. The SVM method was tested on linear, polynomial, RBF, and sigmoid kernels. The kernel with the highest accuracy value is the sigmoid kernel with an accuracy of 81.8% with the predicted results of 266 positive tweets and 1573 negative tweets.
Co-Authors Adam Ikbal Perdana Adela Putri Handayani Aditya Dwi Putro Aditya Dwi Putro Wicaksono Adytia Abi Restianto Agus Priyanto Agustyawan, Arif Ahmad Muslih Syafi'i Al Fachri, Moh. Aminullah Alam Patria Utama Alameka, Faza Alifta Salma Shafira Alika, Shintia Dwi Amalia, Hasna Shafa Andreas Rony Wijaya Arif Wirawan Muhammad Arif Wirawan Muhammad Arnelka Hananta Atika Ratna Dewi Azhari, Ahmad Diandra Chika Fransisca Dwi Setiawan, Brandon Elisabeth Angeline Wilhelmina Bakowatun Erlina Marfianti, Erlina Faisal Dharma Adhinata Faiz Rizky Fahlevi Felia Citra Dwiyani Putri Rosyadi Firda Millennianita Firda Millennianita Habiburrahman, Muhammad Quthb Hafidz Daffa Hekmatyar Hasan Nizar Hikmah Quddustiani Hulqi, Filfimo Yulfiz Ahsanul Irmayatul Hikmah Ismail , Moh Izzati Muhimmah Jannah , Uzlifatul Juvandio Aufaresa Kholidiyah Masykuroh Luthfi Rakan Nabila Made Riza Kartika Maya Nurachmawati Adiningtias Moh. Aminullah Al Fachri Muhammad Alvi Awliya Muhammad Nur Faiz Muhammad Nur Faiz Muhammad Quthb Habiburrahman Muhammad Yusril Aldean Naden, Yoga Nikmatul Khayati Novanda Alim Setya Nugraha Novantri Prasetya Putra Novian Adi Prasetyo Oktavia Jazilatus Sa’adah Pangestu, Happy Gery Puguh Ika Listyorini Rafian Ramadhani Rara Nur Salsabila Rayhan Hidayat Regina Putri Wanda Zahirah Reno Agil Saputra Rheni Aprilia Ningrum Ridha Berlianny Sulistiaputri Sa’adah, Oktavia Jazilatus Saputro, Satria Nur Sausan Silalahi, Indri Monica Cristiani Sinaga, Rifaldo Yohannes Siti Khomsah, Siti Sudianto Suryani, Ajeng Ayu Taufik Maulidi Theo Felix Harianto Purba Tri Ginanjar Laksana Trihastuti Yuniati Tufail Akhmad Satrio Ulya, Fadilla Zundina Vico Meylana Eka Putra Warto Yehezekiel Ramasyah Putra Haloho Yohani Setiya Rafika Nur Yunita Wisda Tumarta Arif