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All Journal JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Brilliance: Research of Artificial Intelligence Jurnal Informatika
Huda, Amirudin Khorul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Mathematics Other

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Enhanced security of Linux Server-based servers with a combination of iptables and Knocking Ports Ramadhani, Surya Tri Atmaja; Puri, Fiyas Mahananing; Huda, Amirudin Khorul
Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Vol 4 No 2 (2024): JTECS Juli 2024
Publisher : FAKULTAS TEKNIK UNIVERSITAS ISLAM KADIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/jtecs.v4i2.5541

Abstract

Menjamin keamanan server sangat penting untuk mengelola infrastruktur teknologi informasi secara efektif, terutama di era saat ini ancaman cyber meningkat. Keamanan server merupakan komponen kritis dalam manajemen infrastruktur teknologi informasi, terutama dalam menghadapi ancaman siber yang semakin canggih. Penelitian ini bertujuan untuk meningkatkan keamanan server berbasis Linux dengan mengimplementasikan kombinasi antara iptables dan teknik port knocking sebagai lapisan tambahan perlindungan terhadap serangan yang menargetkan port terbuka, khususnya port SSH. Metodologi yang digunakan dalam penelitian ini adalah Network Development Life Cycle (NDLC), yang melibatkan tahapan analisis, desain, simulasi, implementasi, dan monitoring. Simulasi dilakukan pada server Ubuntu Linux dengan skenario serangan yang menargetkan port SSH menggunakan teknik port scanning dan brute force. Hasil penelitian menunjukkan bahwa kombinasi iptables dan port knocking secara signifikan meningkatkan keamanan server dengan menyembunyikan port dari pemindaian dan mencegah akses tidak sah. Pengujian menunjukkan bahwa setelah implementasi kombinasi tersebut, tingkat keberhasilan serangan port scanning menurun dari 100% menjadi 0%, dan serangan brute force dari 60% menjadi 0%. Kesimpulannya, pendekatan ini efektif dalam melindungi port kritis seperti SSH tanpa mengorbankan kinerja server, menjadikannya solusi yang praktis dan mudah diimplementasikan untuk meningkatkan keamanan server.
Klasifikasi Data Gempa Bumi di Pulau Sumatera Menggunakan Algoritma Naïve Bayes Duha, Tobias; Laia, Mitranikasih; Huda, Amirudin Khorul; Jasuma, Agung
Jurnal Informatika Vol 2 No 1 (2023): Jurnal Informatika
Publisher : LPPM Universitas Nias Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57094/ji.v2i1.840

Abstract

Indonesia is one of the countries located in the Pacific Ring of Fire, where three tectonic plates meet. This makes Indonesia very vulnerable to natural disasters such as earthquakes, volcanic eruptions, and tsunamis. These natural phenomena occur very frequently, as evidenced by events such as those that have occurred on the island of Sumatra. This study aims to classify earthquake data in the Sumatra Islands based on hypocenter using the Naive Bayes Algorithm. The study uses earthquake datasets specifically from the Sumatra Islands, which are divided into training and testing data. The results of the study indicate that classification can be performed using the Naive Bayes Algorithm based on three categories, ranging from shallow earthquakes, moderate earthquakes, to deep earthquakes.
A Comparative Study of Naive Bayes, Vader, and TextBlob Methods in Sentiment Analysis of ShopeeFood on Twitter Huda, Amirudin Khorul; Ramadhani, Surya Tri Atmaja; Puri, Fiyas Mahananing
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5687

Abstract

Twitter API tweets were utilized to analyze sentiments surrounding ShopeeFood using its algorithmic attachment. A contended sample of 2,500 tweets was gathered for Shapshot-1 in sample focus and was later cleaned and translated into English. The methods employed for the analysis include TextBlob, VADER, and Naïve Bayes classifiers. The analysis reconsolidated, yet again, that tweets, which, by and large, had neutral sentiments attached to them, as confirmed by Naïve Bayes out of 83 per cent accuracy attained. VADER's classification resulted in 85.08% of tweets being categorized as neutral, positive 9.4%, and negative 5.52%. All three constructs captured presented similar results, but the Naive Bayes model proved to be more favourable in terms of sentiment classification; despite such successes with VADER and TextBlob, feature selection and the changes from the translation left them a flaw within the analysis. These problems highlight the challenges posed by social media data, which is rife with casual language, slang, and emoticons. To overcome these challenges, future work should focus on employing neural network techniques that would bolster performance for sentiment classification on large corpora. Practices such as the collection of social media opinion sentiment within the pre-processing stages need more focus. More sophisticated models and advanced pre-processing methods can yield more fine-grained sentiment and opinion expressions on Twitter.
Co-Authors Jasuma, Agung Mitranikasih Laia Puri, Fiyas Mahananing Ramadhani, Surya Tri Atmaja Tobias Duha