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SMART MANGROVE TOURISM: TRANSFORMASI EKOWISATA BAKAU DENGAN SISTEM INFORMASI BERBASIS AR Aranski, Alvendo Wahyu; Dermawan, Aulia Agung; Aritonang, Mhd Adi Setiawan
MINDA BAHARU Vol 9, No 2 (2025): Minda Baharu
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/jmb.v9i2.8364

Abstract

Program Smart Mangrove Tourism bertujuan untuk meningkatkan kesadaran masyarakat dan pengunjung tentang pentingnya ekosistem mangrove serta mendukung pelestariannya melalui penerapan teknologi Augmented Reality (AR). Teknologi AR diterapkan untuk memberikan informasi interaktif secara real-time mengenai flora dan fauna yang ada di kawasan mangrove. Program ini menggunakan pendekatan partisipatif yang melibatkan masyarakat dalam setiap tahap perencanaan dan pelaksanaan, mulai dari sosialisasi hingga evaluasi. Selain itu, pelatihan literasi digital dan penggunaan teknologi AR juga diberikan kepada masyarakat untuk meningkatkan kapasitas mereka dalam mengelola ekosistem mangrove dengan cara yang lebih efektif. Program ini tidak hanya bertujuan untuk meningkatkan pengetahuan masyarakat mengenai mangrove dan pentingnya konservasi, tetapi juga untuk meningkatkan kualitas infrastruktur ekowisata yang ada di kawasan tersebut. Penerapan sistem AR di kawasan mangrove telah berhasil meningkatkan partisipasi aktif masyarakat dalam pemantauan kondisi lingkungan dan pengelolaan ekosistem secara berkelanjutan. Keberhasilan program ini diharapkan dapat memperkuat ekonomi lokal melalui pengembangan ekowisata yang berkelanjutan dan menciptakan peluang baru bagi masyarakat setempat. Dengan melibatkan teknologi modern dan kearifan lokal, program ini memberikan dampak positif terhadap kesejahteraan ekonomi dan pelestarian lingkungan. Program ini juga menjadi contoh bagaimana teknologi dapat dimanfaatkan untuk mendukung konservasi alam serta pemberdayaan masyarakat dalam pengelolaan ekosistem mangrove secara berkelanjutan.
PEMBERDAYAAN KELOMPOK PKK DALAM PENERAPAN SMART BIOPORI IOT UNTUK PENINGKATAN DAYA RESAP AIR DAN KETAHANAN LINGKUNGAN DI BATAM Caniago, Deosa Putra; Mardiansyah, Yopy; Jabnabillah, Faradibah; Candra, Samuel; Faizila, Reva Unieq; Syah, Andrian; Aritonang, Mhd Adi Setiawan
MINDA BAHARU Vol 9, No 2 (2025): Minda Baharu
Publisher : Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/jmb.v9i2.8382

Abstract

Permasalahan genangan air kronis dan minimnya infrastruktur resapan di Perumahan Cipta Green View, Kota Batam, menuntut solusi konservasi air yang modern. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk memberdayakan Kelompok PKK sebagai agen mitigasi lingkungan melalui implementasi Smart Biopori berbasis Internet of Things (IoT) dan edukasi komprehensif tentang pengelolaan lahan hijau. Metode pelaksanaan meliputi sosialisasi, bimbingan teknis instalasi 30 unit Smart Biopori, dan pelatihan alih kelola teknologi monitoring real-time kepada kader lingkungan. Evaluasi keberdayaan mitra menggunakan uji Paired Sample T-Test menunjukkan hasil Sig. (2-tailed) sebesar 0.000, yang mengindikasikan adanya peningkatan pemahaman yang sangat signifikan. Secara kualitatif, program ini berhasil mencapai 100% target keberdayaan pada aspek Manajemen (kemampuan operasional dan alih kelola teknologi) dan aspek Sosial Kemasyarakatan (pembentukan agen perubahan dan kesadaran kolektif). Hasil ini menunjukkan program ini sukses mentransformasi mitra PKK menjadi Kader Lingkungan Mandiri, menjadikannya model solusi cerdas dan terintegrasi untuk ketahanan lingkungan perkotaan.
IoT Application Development for Marine Debris Management in 3T Islands: Supporting a Circular Economy and Community Empowerment Hernando, Luki; Lawi, Ansarullah; Dermawan, Aulia Agung; Aritonang, Mhd Adi Setiawan; Ad, Roni; Kurniawan, Dwi Ely; Manurung, Putriana Carona; Putri, Intan Medisi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11699

Abstract

Marine debris is a serious problem, especially in the outermost, foremost, and least developed (3T) islands of Indonesia, where limited infrastructure and low public awareness are the main obstacles to effective waste management. This study aims to design, develop, and evaluate an Internet of Things (IoT)-based application integrated with a community-based web platform to support circular economy practices and community empowerment in marine debris management. The research method used is Research and Development (R&D) adapted from Borg & Gall, starting from the needs analysis stage to dissemination. An IoT module equipped with ultrasonic and GPS sensors is used to detect container capacity and location in real-time. Performance testing results show a response time of 1.8 seconds, a data transmission success rate of 98.7%, and a capacity detection accuracy of 96.2%, which meets the established technical standards. User acceptance testing using the Technology Acceptance Model (TAM) involving 15 respondents resulted in an average Perceived Usefulness (PU) score of 4.40 and Perceived Ease of Use (PEOU) of 4.23. Pearson's correlation analysis showed an r value of 0.84 (p = 0.0001), indicating a very strong and significant positive relationship between ease of use and perceived usefulness. This finding confirms that the developed system is technically reliable, easy to use, and capable of promoting environmental sustainability and economic opportunities in the 3T island communities.
Pelatihan Penanganan dan Pencegahan Downtime Server SIMRS dalam Meningkatkan Keandalan Layanan Rumah Sakit di RSU Mitra Medika Simanullang, Maradona Jonas; Sinaga, Frans Mikael; Aritonang, Mhd Adi Setiawan
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7887

Abstract

Sistem Informasi Manajemen Rumah Sakit (SIMRS) memiliki peran strategis dalam mendukung pelayanan kesehatan, pengelolaan data pasien, serta proses administrasi rumah sakit. Tingginya ketergantungan terhadap SIMRS menuntut ketersediaan sistem yang andal dan beroperasi secara berkelanjutan. Namun, permasalahan downtime server masih sering terjadi dan berdampak langsung terhadap kualitas pelayanan. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan kapasitas sumber daya manusia (SDM) teknologi informasi melalui pelatihan penanganan dan pencegahan downtime server SIMRS di RSU Mitra Medika. Metode pelaksanaan meliputi observasi lapangan, wawancara, diskusi kelompok terarah, analisis log server, pelatihan teknis, serta pendampingan penerapan prosedur operasional standar. Hasil kegiatan menunjukkan peningkatan pemahaman dan keterampilan peserta, penurunan frekuensi downtime sebesar 62,5%, serta pengurangan durasi downtime sekitar 60%. Selain itu, tingkat kepuasan pengguna terhadap stabilitas SIMRS meningkat secara signifikan. Kegiatan ini membuktikan bahwa pendekatan pelatihan dan pendampingan teknis yang kolaboratif efektif dalam meningkatkan keandalan SIMRS dan mendukung peningkatan kualitas layanan kesehatan.
Natural Language Processing (NLP) and Support Vector Machine (SVM) Optimization in Detecting Phishing Website URLs Aritonang, Mhd Adi Setiawan; Simanulang, Maradona Jonas; Batubara, Toras Pangidoan; Zega, Imanuel; Afrizal, M Hafis
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5334

Abstract

Phishing remains one of the most pervasive cyber-threats, with recent reports indicating a sharp rise in both volume and sophistication of attacks. According to the Anti‑Phishing Working Group, phishing incidents reached nearly 1 million in Q4 2024. To address this evolving threat, this study aims to develop an automated phishing-URL classification system based on Natural Language Processing (NLP) and Support Vector Machine (SVM). We utilised the Kaggle "PhiUSIIL Phishing URL Dataset" comprising 256,795 URL records and applied comprehensive preprocessing, feature extraction (structural URL features plus NLP-based keyword analysis), and SVM training with grid search optimisation. Evaluation was performed via confusion matrix and standard metrics of accuracy, precision, recall and F1-score. The best model achieved an accuracy of 99.99%, precision of 99.98%, recall of 100%, and F1-score of 99.99%. These results demonstrate that the combined NLP + SVM approach can effectively distinguish phishing from legitimate URLs with very high reliability. The proposed system contributes to cybersecurity by offering a feasible AI-based solution for real-time URL screening that can be integrated into browser extensions or enterprise email filters to bolster phishing defences.
Evaluasi Performa Jaringan pada Lingkungan Virtualisasi dengan Pendekatan SNMP Simanullang, Maradona Jonas; Sihotang, Ameliana; Simorangkir, Elsya Sabrina Asmita; Aritonang, Mhd Adi Setiawan; Gurusinga, Tria Adelia Putri Br; Halawa, Yudisa
Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer) Vol 6 No 2 (2026): Jurnal Pustaka Data (Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitekt
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakadata.v6i2.1845

Abstract

Penelitian ini bertujuan untuk mengevaluasi kinerja jaringan pada lingkungan virtualisasi dengan memanfaatkan Simple Network Management Protocol (SNMP) sebagai sistem monitoring. Metode yang digunakan adalah pendekatan kuantitatif melalui eksperimen dengan membandingkan kondisi jaringan sebelum dan sesudah implementasi monitoring berbasis SNMP. Pengumpulan data dilakukan selama lima hari dengan total 720 sampel menggunakan tools ping, iPerf, serta monitoring berbasis SNMP. Parameter yang dianalisis meliputi latency, packet loss, throughput, jitter, dan availability. Hasil penelitian menunjukkan adanya peningkatan pada indikator kinerja jaringan, di mana latency menurun sebesar 37,9%, packet loss menurun sebesar 68,4%, dan jitter menurun sebesar 44,3%, sementara throughput meningkat sebesar 23,7% serta availability meningkat dari 96,2% menjadi 99,1%. Peningkatan tersebut tidak secara langsung disebabkan oleh SNMP, melainkan berkaitan dengan peningkatan visibilitas jaringan dan efektivitas monitoring, sehingga memungkinkan deteksi dan penanganan gangguan jaringan secara lebih cepat. Dengan demikian, SNMP berkontribusi secara tidak langsung dalam meningkatkan kinerja dan keandalan jaringan pada lingkungan virtualisasi.
Design and Implementation of a Real-Time IoT-Enabled Embedded Monitoring Architecture for an Off-Grid Infant Incubator Candra, Joni; Aritonang, Mhd Adi Setiawan; Nazwan, Muhammad
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 2 (2026): Research Paper April 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i2.8323

Abstract

Reliable real-time monitoring of infant incubators is essential in off-grid and resource-limited environments, where unstable power supply and limited infrastructure often compromise continuous operation and data reliability. This study aims to design and implement a real-time IoT-enabled embedded monitoring architecture that addresses the lack of dependable data acquisition and remote monitoring for infant incubators operating under off-grid conditions. The proposed system is developed using a microcontroller-based embedded platform integrated with temperature and environmental sensors, wireless communication modules, and a cloud-based data service. An off-grid photovoltaic power system supports continuous operation, while the embedded architecture is designed with power-aware and real-time constraints. The system adopts an edge-to-cloud approach, enabling local data acquisition and processing at the embedded level and real-time data transmission to a remote monitoring interface. The research methodology includes system architecture design, embedded firmware development, IoT communication implementation, and experimental performance evaluation under continuous off-grid operation. System performance is quantitatively evaluated in terms of data acquisition reliability, communication latency, real-time responsiveness, and operational stability. Experimental results show that the system achieves stable real-time monitoring with an average end-to-end communication latency below 200 ms, a sampling rate of 1 Hz, and continuous operation reliability exceeding 99% uptime during extended off-grid testing. The results demonstrate that integrating real-time embedded systems with IoT-based architecture significantly enhances monitoring reliability for infant incubators in off-grid environments. This study contributes a scalable embedded–IoT monitoring framework that can be extended to other cyber-physical systems operating under constrained energy and infrastructure conditions
Studi Optimalisasi Deteksi Intrusi Jaringan NIDS Menggunakan XGBoost pada Dataset Netflow V2 Aritonang, Mhd Adi Setiawan; Marshall Al Karim, Muhammad; Roland, Roland; Irwan Gultom, Jefri; Enrico Sitompul, muel; Hendri , Hendri
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 6 No 1 (2026): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v6i1.1756

Abstract

This research is motivated by the increasing complexity of cyber attacks on modern networks, necessitating the need for an adaptive and accurate network intrusion detection system (NIDS) through the use of machine learning algorithms, specifically XGBoost. This research uses the NF-UQ-NIDS-v2 dataset with structured pre-processing stages, stratified data partitioning, and the development of an XGBoost-based multi-class classification model with optimized hyperparameter configurations. The test results show that the XGBoost model achieves an overall accuracy of 98.84% with excellent performance in the majority class, but still experiences a decrease in performance in the minority class due to data imbalance. The XGBoost-based NIDS model is proven to be effective and stable in detecting large-scale network attacks, although further strategies are needed to improve detection capabilities for rare types of attacks..