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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK INFORMATIKA Jurnal Teknologi dan Manajemen Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Inovasi Teknologi Pendidikan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Paradigma Jurnal Ilmiah FIFO JURNAL MEDIA INFORMATIKA BUDIDARMA SMARTICS Journal Faktor Exacta Rang Teknik Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JURNAL TEKNOLOGI INFORMASI Jurnal Mantik Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Scientific Journal of Informatics CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal of Intelligent Decision Support System (IDSS) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Indonesia : Manajemen Informatika dan Komunikasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Ilmiah Multidisiplin (JUKIM) JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) SAGA: Journal of Technology and Information Systems Jurnal Indonesia : Manajemen Informatika dan Komunikasi Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal
Ratih Titi Komalasari
Informatika, Universitas Nasional Jakarta
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DETEKSI ANOMALI PERGERAKAN KAPAL MENGGUNAKAN ISOLATION FOREST DAN ONE CLASS SVM DI PELABUHAN TANJUNG PRIOK Sari Ningsih; Panca Dewi Pamungkasari; Tunggul Puliwarna; Babag Purbantoro; Endah Tri Esti Handayani; Ratih Titi Komala Sari; Ahmad Rifqi; Fauziah Fauziah; Muhammad Zahran Alfarizi; Erina Rahmazani
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 6, No 3 (2026): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v6i3.2907

Abstract

Ship movement activities in busy port areas have the potential to generate abnormal movement patterns that may disrupt maritime security and operational efficiency. The main problem addressed in this program is the need for an early detection system capable of identifying vessel movement anomalies quickly and accurately. This Community Service Program (PKM) aimed to develop an anomaly detection model for ship movements at Tanjung Priok Port using the Isolation Forest and One Class Support Vector Machine (OC-SVM) methods. The novelty of this program lies in the integration of these two methods into a maritime traffic data-based anomaly detection system developed collaboratively with the Indonesian Maritime Security Agency (Bakamla RI) to support smarter and more adaptive maritime surveillance. The implementation method consisted of ship movement data collection, data preprocessing, machine learning model development, system testing, and dissemination of results to the Indonesian Maritime Security Agency (Bakamla RI) as the partner institution. The results showed that the developed model was able to identify abnormal ship movement patterns, such as sudden direction changes, unusual speed, and movement outside designated shipping lanes. The resulting system provided faster data visualization and anomaly information, thereby supporting more effective and efficient maritime surveillance processes. This activity also created opportunities for broader implementation across Indonesian waters. ABSTRAKAktivitas pergerakan kapal di kawasan pelabuhan yang padat memiliki potensi terjadinya pola pergerakan tidak normal yang dapat mengganggu keamanan dan kelancaran operasional maritim. Permasalahan utama yang dihadapi adalah kebutuhan akan sistem deteksi dini yang mampu mengidentifikasi anomali pergerakan kapal secara cepat dan akurat. Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan mengembangkan model deteksi anomali pergerakan kapal di Pelabuhan Tanjung Priok menggunakan metode Isolation Forest dan One Class Support Vector Machine (OC-SVM). Kebaruan kegiatan ini terletak pada penerapan dan integrasi kedua metode tersebut dalam sistem deteksi anomali berbasis data pelayaran yang dikembangkan bersama Bakamla RI untuk mendukung pengawasan maritim secara lebih cerdas dan adaptif. Metode pelaksanaan dilakukan melalui tahapan pengumpulan data pergerakan kapal, praproses data, pemodelan menggunakan algoritma machine learning, pengujian sistem, serta sosialisasi hasil kepada mitra Badan Keamanan Laut Republik Indonesia (Bakamla RI). Hasil kegiatan menunjukkan bahwa model yang dikembangkan mampu mengidentifikasi pola pergerakan kapal yang menyimpang dari rute normal, seperti perubahan arah mendadak, kecepatan tidak wajar, dan pergerakan di luar jalur pelayaran. Sistem yang dihasilkan memberikan visualisasi data dan informasi anomali secara lebih cepat sehingga mendukung proses pengawasan maritim yang lebih efektif dan efisien. Kegiatan ini juga membuka peluang implementasi sistem pada wilayah perairan Indonesia secara lebih luas.
Deteksi dan Klasifikasi Sampah Real-Time Berbasis Yolov11 untuk Manajemen Sampah Cerdas Sari Ningsih; Panca Dewi Pamungkasari; Agus Setiawan; Ratih Titi Komalasari; Endah Tri Esti Handayani; Djarot Hindarto; Asrul Sani; Erina Rahmazani; Muhammad Aldinugroho Abdullah; Ferdiansycah Ferdiansycah
Masyarakat Mandiri : Jurnal Pengabdian dan Pembangunan Lokal Vol. 3 No. 4 (2026): Oktober: Masyarakat Mandiri : Jurnal Pengabdian dan Pembangunan Lokal
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/masyarakatmandiri.v3i4.3607

Abstract

This community service activity aims to develop an automated waste sorting system based on Deep Learning using the YOLOv11 architecture for real-time object detection. The system is designed to identify six categories of waste, namely cardboard, glass, metal, paper, plastic, and trash through direct camera input. The dataset used was obtained from Kaggle, consisting of 2,467 images which were subsequently expanded to 10,108 images through data augmentation techniques to improve model generalization and reduce the risk of overfitting. The training process employed an 80% training set and a 20% validation set, with optimization using the Stochastic Gradient Descent (SGD) algorithm, momentum, and cosine learning rate scheduling over 100 epochs. Model performance was evaluated using the standard object detection metric, Mean Average Precision (mAP). The results show that the model achieved an mAP@0.5 of 95.8% and an mAP@0.5:0.95 of 82.4%, with an average inference speed of 45 frames per second (FPS) on standard hardware. These findings indicate that the system is capable of performing waste classification quickly, accurately, and efficiently, supporting the implementation of smart waste management in urban environments, particularly in collaboration with waste management partners in Depok.
Co-Authors Abdul Aziz Hasibuan Adinne Islamiyati Aditya Prasetya Afif Maulana Agung Triayudi Agus Setiawan Ahmad Fahreza Ahmad Khuzaifi Ahmad Rifqi Aldisa, Rima Tamara Alfian Cutryanto Alma Bryan Fitri Finika Andriana, Septi Andrianingsih Anwar Anwar Arief, Arya Asep Nurdin Asrul Sani Asrul Sani Babag Purbantoro Bayu Abrianto Risnadi Bayu Yasa Wedha Darmawan, Dika Rizki Dede Wandi Dika Rizki Darmawan Diranisha, Virly Djarot Hindarto Doddy Prasetyo Endah Tri Esti Handayani Erina Rahmazani Fachreza, Sodi Farhany, Nadia Muis Fauziah Fauziah Faza Nadhira Ferdiansycah Ferdiansycah Fikar Wahyu Tyas Tono Finika, Alma Bryan Fitri Firdhani Novrizal Firman Firman Ghirrid, Aria Andros Gusti Karnawan Hasibuan, Abdul Aziz Hindarto, Djarot Ikbal Danu Setiawan Indriawan, Rizal Ira Diana Sholihati Ira Diana Sholihati Irfan Hadi Putra Kiki Vebiant Ladika, Anwar Wali M Alwi Saepul Zaman Mardiani, Eri Mochamad Hariadi Mohammad Iwan Wahyuddin Muhamad Reza Pahlevi Muhammad Aldinugroho Abdullah Muhammad Hafizhan Muhammad Ikbal Muhammad Ikbal Muhammad Nurdin Muhammad Nurdin Muhammad Rafli Muhammad Zahran Alfarizi Mustofa Kamal Syarifudin Naqiyah, Shofy Novi Dian Nathasia Nur Aprilia Nur Hayati Panca Dewi Pamungkasari Pandyawan Eka Rizqullah Prasetya, Aditya Puspita, Anna Thasyia Putra, Irfan Hadi Raditya, Muhammad Maheswara Raffi Dima Sampurno Rahmat Azyad Samallo Raja Timor Purba Raka Adji Setiawan Ravi Anwar Rena Cahya Hutama Reza Rifqi Maulana Rifqi Aldy Al Hafizh Harahap Rifqi Naufal Senja Pratama Risnadi, Bayu Abrianto Rizal Maulana Yusuf Effendi Rizki Akbar Mahdafiki Salsabilah, Salsabilah Sampurno, Raffi Dima Sapto Wibowo Sari Ningsih Septi Andriana Septi Andryana Setiawan, Ikbal Danu Setiawan, Raka Adji Shobur Abdusalam Sholihati , Ira Diana Sodi Fachreza Suryana, Muhamad Fajar Teguh Riyono Adi Tunggul Puliwarna Tyas Tono, Fikar Wahyu Ucuk Darusalam Vebiant, Kiki Wa'asaro Telaumbanua Wahyu Suratman Wahyuddin, M Iwan Wahyuddin, Moh. Iwan Wahyuddin, Mohammad Iwan Wedha, bayu Yasa Wildan Alfy Syahry Wilsen Wilsen Wilsen Wilsen, Wilsen Wulan Widhari Yanuar Bimantoro Yusuf Effendi, Rizal Maulana Zaman, M Alwi Saepul