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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
Informatics Study Program, Faculty Of Computer Science, Universitas Mercu Buana, Indonesia
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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Penerapan Bahasa Pemrograman HTML Python sebagai perangkat pendukung dalam pelayanan Masyarakat Pada Tim PKK Kelurahan Duri Kepa Kebon Jeruk Jakarta Barat Mohamad Yusuf; Roy Mubarak; Rushendra Rushendra; Siti Maesaroh; Nungky Awang Candra
Jurnal Abdimas Indonesia Vol. 5 No. 1 (2025): Januari-Maret 2025
Publisher : Perkumpulan Dosen Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34697/jai.v5i1.1327

Abstract

Tim Pemberdayaan dan Kesejahteraan Keluarga (PKK) di Kecamatan Duri Kepa berperan penting dalam menyebarkan informasi dan mendukung pengambilan keputusan tentang kesehatan masyarakat. Dengan meningkatnya kebutuhan akan solusi berbasis web, pengetahuan tentang teknologi seperti HTML, CSS, dan Python menjadi semakin krusial. Teknologi ini memungkinkan pengembangan sistem informasi yang lebih interaktif dan efektif, bahkan untuk pemula. Untuk menghadapi tantangan ini, program pelatihan telah disiapkan untuk memberikan anggota PKK keterampilan yang diperlukan dalam pengembangan web. Pelatihan ini menerapkan metode pembelajaran interaktif dan langsung di laboratorium universitas, dengan penekanan pada praktik HTML dan Python. Metode ini memberikan kesempatan bagi peserta untuk menerapkan keterampilan yang diperoleh dalam proyek berbasis web yang relevan dengan tugas mereka di PKK. Hasil dari kegiatan ini menunjukkan bahwa pelatihan berlangsung sukses dan peserta menunjukkan antusiasme yang tinggi. Mereka merasa nyaman dalam mengikuti pelatihan dan mampu menggunakan pengetahuan tentang HTML dan Python untuk membuat aplikasi sederhana. Program ini diharapkan dapat meningkatkan efektivitas intervensi kesehatan di tingkat komunitas serta mendukung pengambilan keputusan yang berbasis data dan berkelanjutan dalam konteks kesehatan masyarakat.
WORKSHOP PENGENALAN TOOL DEEP LEARNING UNTUK KLASIFIKASI GAMBAR UNTUK MENENTUKAN STATUS SOSIAL DAN REKOMENDASI BANTUAN SOSIAL Yusuf, Mohamad; Hakim, Lukman; Rushendra; Awang, Nungky
Jurnal Pengabdian Kepada Masyarakat Patikala Vol. 4 No. 4 (2025): Jurnal PkM PATIKALA
Publisher : Education and Talent Development Center of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/patikala.v4i4.3355

Abstract

This Community Service Activity was carried out as an effort to improve operational efficiency in travel service business actors, especially PT Swabina Gatra Travel, through optimizing the use of the Bookswantastic and Jurnal ID digital systems. Although both systems have been implemented to support the ticket booking process and financial transaction recording, the lack of integration of these systems still causes obstacles such as data duplication, price input errors, and increased employee workload. Through this activity, the community service team provides education-based solutions and assistance in the form of training on the use of more effective digital systems, preparation of internal Standard Operating Procedures (SOPs), and socialization of the potential for system integration through API technology. It is hoped that this activity can improve the understanding and skills of business actors in managing digital-based business processes efficiently, while strengthening the readiness for digital transformation in the travel service sector.
Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering Wijaya, Ody Octora; Rushendra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5819

Abstract

Sulawesi is a region in Indonesia known for its significant seismic activity, and its history of impactful earthquakes makes it an area of crucial importance for in-depth analysis. This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. DBSCAN is known for its effectiveness in identifying spatial clusters, especially in handling data with undefined density patterns. The primary aim of this research is to identify spatial earthquake occurrence patterns, classify regions with similar earthquake occurrence rates, describe the characteristics of the resulting spatial clusters, and identify seismic gap areas. The results of analysis and clustering using the DBSCAN algorithm have identified clusters with earthquake depth characteristics, which are expected to make a significant contribution to mapping and understanding earthquake vulnerability and distribution in this region. These findings can aid in more effective disaster mitigation planning, support sustainable development efforts, and enhance earthquake preparedness and response in Sulawesi. This study contributes to a better understanding of earthquake patterns and potential seismic gaps in Sulawesi, which is crucial for developing improved risk mitigation strategies and supporting sustainable development policies.
Optimizing DBSCAN Parameters for Depth-Based Earthquake Clustering Using Grid Search Rushendra, Rushendra; Wijaya, Ody Octora; Yusuf, Mohamad; Setiyaji, Andri; Prabowo, Djoko
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.
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 (On Progress)
Publisher : Education and Talent Development Center of 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
Co-Authors Aan Wahyu Anung YS Ardianto, Muhammad Rezanur Ariya Pramudita Awang, Nungky Dimas Surya Subrata Ellisa Agustina, Ellisa Firdyanto, Tri imaniah Aliat Lukman Hakim maniah Aliat5 Miftahul Huda Mohamad Yusuf Mohamad Yusuf Mohamad Yusuf Nungky Awang Candra Panca, Aji Permatasari, Eka Prabowo, Djoko Rahmad Hidayat Robbi Rahim Roy Mubarak Saipul Anwar Setiyaji, Andri Siti Maesaroh Wijaya, Ody Octora Wulandari, Ike Yuni Yakob Liklikwatil Yasin Efendi Z, Syauqii Fayyadh Hilal