Reef fishes is an important part in maintaining the balance of various components in the coral reef ecosystem. The existence of reef fish on coral reef ecosystems is a marker of the ecosystem in good condition. Furthermore, it is important to observe the condition of reef fish in a coral reef ecosystem to determine the population and diversity of reef fish in the ecosystem. Observation of reef fish generally by performing a manual visual census by scuba diver. In entering the industrial revolution 4.0 era there is a need to develop technology that is used to monitor the condition of reef fish in a coral reef ecosystem. The development of technology will certainly help researchers, and later on ecosystem manager, in observing the condition of reef fish with automatic identification. The technological development that can be done to observe reef fish is by applying deep learning. In this research we used YOLO deep learning algorithm for automatic identification. YOLO has the advantage of faster object detection. Application of deep learning to identify fish automatically is illustrated using underwater video recording of reef fish.
Organoleptic assessment of fresh fish includes specifications for the quality of the eyes, gills, mucus, odor, texture and flesh (color and appearance). However, not everyone has knowledge about it. This research uses the tiny yolov2 to facilitate the determination of fish freshness levels (good quality, medium quality, poor quality) correctly and fast. There are a few stages in this research, included organoleptic test accompanied by taking fish eye image dataset every hour, processing organoleptic test data labeling, training, and validation. There are three types of fish used, consists of Rastrelliger, Euthynnus affinis, and Chanos chanos. Detection of fish freshness level for three species was successfully carried out with the result of average precision is 72.9%, average recall is 57.5%, and accuracy is 57.5%. The factors that affect the prediction results in this study is the collection of datasets before the training process is carried out consisting of fish samples obtained from traditional markets, which are considered inadequate so that it affects the organoleptic test process itself, the organoleptic test that was carried out as a reference for image sorting was considered inaccurate because it used less than 30 untrained panelists and dataset imbalance.
Daging merupakan salah satu sembako dan merupakan bahan pangan bernilai gizi tinggi, namun rentan mengalami kerusakan karena mikroba. Oleh karena itu, pengemasan produk daging potong juga perlu memenuhi standar informasi yang sebaiknya diketahui konsumen. UMKM YBS Supplier & Logistic adalah UMKM yang berlokasi di Kabupaten Gianyar, Provinsi Bali dan menjual produk daging potong. Sesuai hasil observasi awal, pengelola UMKM tersebut belum memiliki edukasi mengenai pengaturan penyimpanan maupun pengemasan produk. Tujuan dari kegiatan ini adalah untuk memberikan edukasi mengenai pengelolaan dan teknik penyimpanan serta pengemasan (termasuk logo dan label kemasan) produk daging potong pada UMKM YBS Supplier & Logistic. Pada tahap awal pengabdian, dilakukan penyuluhan dan pelatihan mengenai teknik penyimpanan produk. Selanjutnya dilakukan penyusun logo dan label kemasan produk yang didesain melalui proses diskusi dengan pengelola UMKM. Tahap akhir adalah pelatihan terkait penyimpanan dan pengemasan produk daging potong. UMKM YBS Supplier & Logistics telah memperoleh edukasi mengenai teknik penyimpanan produk daging potong sesuai tujuannya, yaitu melalui teknik refrigerasi atau teknik pembekuan. Dijelaskan pula mengenai tips untuk pemrosesan daging sebelum dilakukan penyimpanan. Kepada UMKM YBS Supplier & Logistics juga telah mendapat bantuan alat berupa freezer dan vacuum sealer. Terkait pengemasan produk, telah disusun logo yang merupakan perpaduan antara logogram dan logotype. Selanjutnya melalui diskusi juga didesain label kemasan produk yang informatif. Adapun keterangan yang disajikan berupa logo, jenis produk, berat, tanggal produksi, serta kontak (termasuk sosial media dan website). Melalui kegiatan pengabdian telah diberikan edukasi, pelatihan dan pemberian bantuan alat penyimpanan, serta telah dilakukan penyusunan logo dan label kemasan.
Data security issues are an important aspect of data and information communication over networks. In addition, it is also necessary to look at the security side of the software. In addition to software, computers have an internet protocol in the form of HTTP which is commonly used to access websites. On the LMS website, students have access to lecture materials, discussion forums with lecturers and access to assignments given by lecturers. Wireshark is used to analyze network protocols, can log all packets going through and display detailed data. The purpose of this study is to use a Wireshark application to sniff LMS and pinpoint vulnerabilities in the system. The results of the sniffing process carried out using Wireshark on an LMS that uses the HTTP protocol clearly indicate the absence of encryption and expose the risk of vulnerabilities to the system. Recommendations given to LMS are the use of HTTPS protocol, implementation of Multi Factor Authentication, website log monitoring and password management. Recommended password management are periodic password changes, standardization policies for the use of characters in passwords and password hashing. It is hoped that when the recommendations are implemented it will improve security on the LMS website and reduce risks in data communications
Seagrass is an Angiosperms that live in shallow marine waters and estuaries. The method commonly used to identify seagrass is Seagrass-Watch which is done by sampling seagrass or by carrying a seagrass identification book. Technological developments in the era of the industrial revolution 4.0 made it possible to identify seagrass automatically. This research aims to apply the deep learning algorithm to detect seagrass recorded by underwater cameras. Enhalus acoroides seagrass species identification was carried out using a deep learning method with the mask region convolutional neural networks (Mask R-CNN) algorithm. The steps in the research procedure include collecting, labeling, training, testing models, and calculating the seagrass area. This study used 6000 epochs and got a measure of value generated by the model of ± 1.2. The Precision value, namely the model’s ability to correctly classify objects, reached 98.19% and the model’s ability to find all positive objects, based on system testing was able to perform recall is 95.04% and the F1 Score value of 96.58%. The results showed that the MASK R-CNN algorithm could detect and segment seagrass Enhalus acoroides.
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