Immanuel Freddy Augustino
Politeknik Perkapalan Negeri Surabaya

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Pengenalan Jenis Kapal Melalui Aktivitas Mewarnai Pada Anak Usia Dini di Taman Penitipan Anak Ridhani Anita Fajardini; Adristi Nisazarifa; Desrilia Nursyifaulkhair; Alief Nur Aisyi Maulidhia; Shultoni Mahardika; Immanuel Freddy Augustino
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 9 No. 2 (2026): April 2026
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v9i2.4163

Abstract

Abstract: Introducing the maritime world to early childhood is an approach to developing awareness and knowledge of the role of the sea as a natural resource in Indonesia. This can be accomplished, for instance, by introducing different types of ships and using coloring pages and images as an engaging way to learn. The activity was carried out in one of the day care located in Surabaya, with the par-ticipation of young children between the ages of 1 to 3 years old. The method applied in this community service activity is an educational method using a participatory approach. The activity was carried out in a number of phases, including preliminary observation and partner coordination, the creation of educational materials in the form of illustrations of various ship types, brief explanation of ship introduction, and ship coloring activities. Based on the activities that have been done, the children gave a positive response to learning and coloring activities. Using pictures and hands-on activities, the children tend to remember the shape and name of the ship that has been explained previously more easily. Coloring activities encourage fine-motor skills development and children's creativity. Keywords: coloring; early childhood; maritime; ship Abstrak: Pengenalan dunia maritim pada anak usia dini penting untuk menumbuhkan pengetahuan tentang peran laut sebagai sumber daya alam di Indonesia. Salah satu cara yang dapat dilakukan adalah melalui pengenalan jenis kapal dengan memanfaatkan media gambar dan aktivitas mewarnai sebagai bentuk pembelajaran visual yang menarik. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di taman penitipan anak yang berlokasi di Surabaya dengan melibatkan anak usia dini berusia 1–3 tahun. Metode yang digunakan adalah metode edukatif dengan pendekatan partisipatif. Pelaksanaan kegiatan meliputi beberapa tahapan, yaitu observasi awal dan koordinasi dengan mitra, persiapan media pembelajaran berupa gambar jenis-jenis kapal, penyampaian materi pengenalan kapal secara singkat, serta aktivitas mewarnai gambar kapal. Hasil kegiatan menunjukkan bahwa anak-anak memberikan respons positif terhadap kegiatan yang dilakukan. Dengan bantuan media visual dan aktivitas langsung, anak-anak lebih mudah mengenali bentuk dan nama kapal. Selain itu, kegiatan mewarnai juga mendukung perkembangan motorik halus serta kreativitas anak. Kata kunci: anak usia dini; kapal; maritime; mewarnai
SISTEM DETEKSI KERUSAKAN PANEL PLTS APUNG DI EMBUNG SIDOBANDUNG BERBASIS CONVOLUTIONAL NEURAL NETWORK DENGAN VISUALISASI AUGMENTED REALITY Thomas Brian; Immanuel Freddy Augustino; Parman Parman; Muhamad Sukarno
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.364

Abstract

This study aims to develop an Augmented Reality (AR) application integrated with a Convolutional Neural Network (CNN) as an interactive system for detecting damage in floating solar power plant (PLTS) panels at Embung Sidobandung in order to maintain the efficiency of the photovoltaic energy system. Conventional manual inspection methods are considered inefficient and prone to errors due to human factors. Therefore, a deep learning approach is employed to automatically and interactively detect and classify solar panel damage. AR technology is utilized to display panel condition information directly through a mobile device camera, enabling real-time damage monitoring. The dataset consists of 615 solar panel images, including 472 images of physical damage and 143 images of electrical damage. Experimental results show that the system is capable of classifying solar panel damage types in real time, achieving a precision of 93.48%, recall of 89.58%, and an F1-score of 91.49% for physical damage, and a precision of 70.59%, recall of 80.00%, and an F1-score of 75.00% for electrical damage, with an overall accuracy of 87.30%. Although the developed application provides interactive and informative visualization, varying lighting conditions in aquatic environments and differences in image acquisition angles remain challenges that affect system accuracy. Overall, the integration of CNN and AR has the potential to serve as an effective and efficient solution for developing damage detection systems for floating solar power plant (PLTS) panels.