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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan Jurnal Telematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal ICT : Information Communication & Technology Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Pengabdian Masyarakat Sultan Indonesia
Rushendra, Rushendra
Universitas Mercu Buana
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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The Efficiency of Machine Learning Techniques in Strengthening Defenses Against DDoS Attacks, Such as Random Forest, Logistic Regression, and Neural Networks Z, Syauqii Fayyadh Hilal; Rushendra
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14502

Abstract

Distributed Denial of Service (DDoS) attacks are one of the most common cybersecurity concerns brought on by the quick development of digital technology. By flooding servers with too many requests, these assaults interfere with online services, highlighting the necessity of strong detection systems. Using the well-known CIC-DDoS2019 dataset, this study explores the use of machine learning algorithms—Random Forest (RF), Logistic Regression (LR), and Neural Networks (NN)—to improve DDoS assault detection. A comprehensive preprocessing procedure that comprised feature selection, normalization, and duplication removal was applied to dataset in order to ensuring optimal algorithm performance. With an accuracy of 97% on the entire test dataset and 99.13% on the training and validation datasets, RF showed exceptional performance. While NN successfully managed intricate data patterns, attaining an accuracy of roughly 94%, LR demonstrated impressive results with an accuracy of 98.65%. Because of its ensemble method, which minimizes overfitting and improves model generalization, the RF algorithm performed better than the others. This study highlights how machine learning may be used to solve practical cybersecurity issues by offering insightful information about how to optimize algorithms for real-time DDoS detection. The results improve the stability and resilience of digital infrastructures by aiding in the creation of effective intrusion detection systems. Future research can explore integrating advanced neural network architectures and hybrid methods to further improve detection rates and adaptability to evolving cyber threats.
IMPLEMENTATION OF LOAD BALANCING WITH PER CONNECTION CLASSIFIER AND FAILOVER AND UTILIZATION OF TELEGRAM BOT (CASE STUDY : PT TUJUH MEDIA ANGKASA) Ariya Pramudita; Rushendra, Rushendra
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1165

Abstract

For customers of PT. Seven Media Angkasa, which is engaged in providing fast online shopping services in Indonesia, definitely needs stable internet to process requests from customers. Even though it already has 2 ISPs, sometimes there are frequent downtimes which will disrupt the service process for customers who want to shop. In this case, one of the Load Balancing methods is the Per Connection Classifier (PCC) which is able to specify a packet to the gateway of a particular connection. Failover for backing up The weakness of the PCC method is Failover which can switch automatically if one of the systems fails so that it becomes a backup for the system that has failed. Added Telegram Bot as a DHCP Alert which can detect if there is a DHCP Rouge. By using the PCC method, it is able to maximize bandwidth usage and minimize the occurrence of downtime in sending or receiving data. So with the addition of the Failover method, if there is a temporary delay when many incoming requests can interfere with performance, Failover can move manually or automatically if one of the systems fails so that it becomes a backup for a failed system. If gateway 1 is disconnected, the backup gateway will replace gateway 1. If gateway 1 returns to normal, the connection path is used again to become gateway 1. Likewise with gateway 2 when it is disconnected. From testing on the speedtest.net tools, it was found that the Load Balancing applied was able to combine 2 ISPs into one, namely Download to 19.18 Mbps and Upload to 18.47 Mbps. The Telegram Bot is able to send notifications when there is a counter DHCP Server with the contents of the message successfully getting a Mac Address or unknown server from the counter DHCP server, namely DC: 2C: 6E: 81: CF: 34.
Implementation of Intrusion Detection System with Rule-Based Method on Website Firdyanto, Tri; Rushendra, Rushendra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4256

Abstract

The aim of this research is to implement an intrusion detection system using rule-based methods on websites. The approach in this research is the development of an intrusion detection system (IDS). research results after implementation, testing, and acceptance of test results, conclusions can be drawn. The detection system can be implemented well in website-based applications using a rule-based method.
Optimalisasi Produktivitas Kerja Untuk Manajemen Program Sosial PKK di Kecamatan Kembangan Dengan Pemanfaatan AI dan Prinsip Keamanan Siber Yusuf, Mohamad; Rushendra
Jurnal Pengabdian Kepada Masyarakat Patikala Vol. 5 No. 2 (2025): Jurnal PkM PATIKALA
Publisher : Pusat Pengembangan Pendidikan dan Bakat Indonesia/Education and Talent Development Center of Indonesia (ETDC Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/patikala.v5i2.4030

Abstract

This community service program aimed to enhance the work productivity of Pembinaan Kesejahteraan Keluarga (PKK) cadres in Kembangan District, West Jakarta, through the integration of artificial intelligence (AI) technology and cybersecurity principles. It addressed critical challenges, including low digital literacy, inefficient manual administration, and limited digital marketing capabilities. The program was executed in structured phases: a needs assessment survey (January–February 2025), data identification and literature review (February 2025), training module development and delivery of AI and cybersecurity workshops (February 2025), followed by monitoring and evaluation (March 2025–June 2026), and final report publication (July–August 2025). Fifty PKK cadres participated in the training. Results showed that 85% adopted digital systems using Google Drive and spreadsheets, reducing data loss risk by 90% and accelerating monthly reporting by 50%. Additionally, 65% utilized digital marketing platforms, with 40% of supported micro-enterprises reporting a 20% revenue increase. Evaluation revealed a knowledge score improvement from 45 to 82, with 80% of cadres proficient in digital tools and 70% effectively applying AI—without any data breaches. The initiative improved administrative efficiency, business competitiveness, and digital literacy while promoting community participation through a Merdeka Curriculum-based approach. It established a sustainable, replicable model for community empowerment applicable to other regions
Penerapan Tools Artificial Intelligence dalam peningkatan skill Guru Science di SD Islam Terpadu Al Hikmah, Pamulang, Tangerang Selatan Rushendra Rushendra; Mohamad Yusuf
Jurnal Pengabdian Masyarakat Sultan Indonesia Vol. 1 No. 2 (2024): Abdisultan
Publisher : Sultan Publsiher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/abdisultan.v1i2.339

Abstract

Pada tahun 2023, penerapan Kurikulum Merdeka di Sekolah Dasar semakin gencar dilaksanakan, dengan fokus pada metode pembelajaran intrakurikuler yang beragam dan memberikan kesempatan bagi siswa untuk lebih mendalami konsep serta meningkatkan kompetensi mereka. Para guru diberikan kebebasan untuk memilih alat ajar yang relevan dengan kebutuhan dan minat siswa, sambil menekankan pada pengembangan soft skills, karakter, materi inti, dan pembelajaran yang fleksibel. Meskipun begitu, implementasi kurikulum ini menghadapi beberapa tantangan, seperti kebutuhan peningkatan materi pembelajaran yang selaras dengan prinsip Kurikulum Merdeka, beragamnya latar belakang pendidikan guru—terutama dominasi pendidikan umum yang memiliki pemahaman teknologi informasi yang terbatas—serta kurangnya keterampilan guru dalam mengintegrasikan teknologi, terutama dalam pengajaran sains. Kegiatan ini bertujuan untuk memperluas wawasan dan pengetahuan guru sains dalam menciptakan bahan ajar yang inovatif dan menarik. Selain itu, kegiatan ini juga bertujuan untuk meningkatkan keterampilan guru dalam memanfaatkan alat berbasis kecerdasan buatan (AI) serta menerapkan ilmu pengetahuan dan teknologi (IPTEK) dalam praktik, untuk mencari solusi terhadap masalah yang dihadapi dalam dunia pendidikan.
PREDIKSI PENYAKIT DIABETES BERDASARKAN PERBANDINGAN KLASIFIKASI METODE K-NEAREST NEIGHBOR, NAÏVE BAYES, DAN DECISION TREE MENGGUNAKAN RAPID MINER Ardianto, Muhammad Rezanur; Rushendra, Rushendra
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6079

Abstract

Pada era digital seperti saat ini kegiatan manusia dipermudah dengan adanya teknologi yang tak terkecuali dalam bidang penjualan makanan dan minuman, namun dengan kemudahan tersebut mengakibatkan kesulitan masyarakat dalam melihat gizi dari makanan dan minuman yang mengakibatkan terjangkitnya penyakit Diabetes, akan tetapi penyakit tersebut banyak faktor yang dapat memengaruhinya . Oleh sebab itu penelitian ini dilakukan sebuah prediksi terjangkitnya penyakit Diabetes dengan melakukan perbandingan algoritma K-NN, Naïve Bayes, dan Decision Tree. Hasil dari perbandingan algoritma yang paling cocok pada kondisi default yaitu Decision Tree dengan tingkat akurasi 93,60%, namun untuk menghindari overfitting dan underfitting perlu dilakukan optimasi K cross validation pada K=5 sampai K=10, kemudian dilakukan optimasi nilai Konstanta K pada K=10. algoritma K-NN dengan K=2, sehingga didapatkan hasil algoritma K-NN lebih cocok untuk prediksi penyakit diabetes dengan nilai akurasi 96.13%.
Analisis Performansi Codec G.711 Dan G.729 Berbasis Issabel Menggunakan Metode MOS E-Model Aji Panca; Rushendra Rustam
JOINS (Journal of Information System) Vol 8 No 2 (2023): Edisi November 2023
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v8i2.8534

Abstract

Komunikasi telah menjadi komponen penting dalam menjalankan bisnis, terutama dalam bisnis layanan terpadu (call center) di mana mungkin diperlukan untuk melakukan panggilan ke ribuan klien dalam satu hari karena kemajuan sistem telekomunikasi. Dalam kasus jaringan yang menangani transmisi VoIP, memastikan kualitas panggilan yang sesuai sangatlah penting. Penelitian ini bertujuan untuk mencari referensi codec audio terbaik untuk kebutuhan komunikasi suara sistem VoIP berbasis Issabel Server. Antara call codec G.729 dan G.711 dilakukan analisis mean opinion score dengan menggunakan teknik MOS E-MODEL (ITU-T G.107) menggunakan skenario call dengan durasi 30 detik, 60 detik, dan 120 detik dari ekstensi lokal ke jaringan PSTN. Codec G.729 dan G.711 memiliki nilai MOS 4.25061 pada waktu panggilan 30 detik. Codec G.711 bekerja lebih baik daripada codec G.729 dengan durasi panggilan 60 detik, dengan selisih persentase 0,153% (nilai MOS 4,24739) dibandingkan dengan nilai MOS codec G.729 sebesar 4,24087. Codec G.711 bekerja lebih baik daripada codec G.729 pada durasi panggilan 120 detik, dengan selisih persentase 0,018% (nilai MOS 4,24866) dibandingkan dengan nilai MOS codec G.729 sebesar 4,24787. Codec G.711 memiliki kualitas panggilan yang superior dibandingkan dengan codec G.729. Codec G.729 menggunakan sumber daya lebih efektif daripada codec G.711 meskipun kualitas panggilannya serupa dengan codec G.711.
Optimizing DBSCAN Parameters for Depth-Based Earthquake Clustering Using Grid Search Rushendra Rushendra; Ody Octora Wijaya; Mohamad Yusuf; Andri Setiyaji; Djoko Prabowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6521

Abstract

This study addresses the challenge of accurately clustering earthquake events based on depth to better understand seismic activity patterns in Sulawesi from 2019 to 2023. Traditional clustering algorithms often fail to capture the complex spatial and depth-based structures of earthquake data. To overcome this, we employed the DBSCAN algorithm, which is well-suited for identifying irregularly shaped clusters and handling noise in spatial datasets. A key focus of this research is the systematic optimization of DBSCAN’s parameters—epsilon (ε) and minimum samples (min_samples)—using a grid search approach. Epsilon values varied from 0.1 to 0.5, and min_samples ranged from 6 to 60. The optimal parameters, determined using the Calinski-Harabasz (CH) index, were ε = 0.4 and min_samples = 54. Compared with previous heuristic settings, the optimized configuration produced better separated and more interpretable clusters. Using the optimized parameters, nine distinct clusters were identified, capturing meaningful patterns in both depth and magnitude. The results revealed that shallow earthquakes (0–20 km) tend to exhibit greater magnitude variation, with some clusters averaging magnitudes up to 3.7. This suggests a higher seismic hazard potential associated with brittle crustal activity. The findings contribute to seismic hazard analysis by providing a more robust understanding of three-dimensional earthquake distribution, aiding regional risk assessment and disaster preparedness efforts. These insights can support agencies such as BMKG and BPBD in hazard mapping, sensor deployment, and contingency planning for high-risk zones.
Co-Authors Aan Wahyu Aji Panca Andri Setiyaji Anung YS Ardianto, Muhammad Rezanur Ariya Pramudita Awang, Nungky Dimas Surya Subrata Djoko Prabowo Ellisa Agustina Firdyanto, Tri imaniah Aliat Lukman Hakim maniah Aliat5 Miftahul Huda Mohamad Yusuf Mohamad Yusuf Mohamad Yusuf Nungky Awang Candra Ody Octora Wijaya Permatasari, Eka Rahmad Hidayat Robbi Rahim Roy Mubarak Saipul Anwar Siti Maesaroh Wijaya, Ody Octora Wulandari, Ike Yuni Yakob Liklikwatil Yasin Efendi Z, Syauqii Fayyadh Hilal