Role of Forensic Anthropology in Disaster Victim Identification (DVI)

DNA profiling is one of the most dependable and well-organized methods for recognizing bodies or losing body parts in disaster victim identification (DVI). This necessitates the collection of a post-mortem DNA sample and an antemortem DNA sample from the alleged victim or a biological related people. Collecting an acceptable ante mortem sample is usually effortless, but because of the varying degree of preservation of the human remains after any disaster and very high risk of cross-contamination of DNA, obtaining an adequate standard post mortem sample under cold DVI conditions is difficult. Various post mortem DNA samples from a deceased person in DVI can be obtained from muscle, bone including femur and ribs, teeth, and bone marrow with the slightest possibility of contamination. DVI (disaster victim identification) has been used to identify deceased people in various famous disasters like the 9/11 attack of the terrorist group al-Qaeda against the United States, Malaysia Airlines Flight 17 from Amsterdam to Kuala Lumpur that was shot down on 17 July 2014 while flying over eastern Ukraine. All 283 passengers and 15 crew members were killed; the 26/11 attack on Mumbai in 2008 led by terrorist organizations caused 166 deaths, excluding nine terrorists. According to Interpol protocol, four steps for identification are given importance: 1 – Site examination, which lasts for days to weeks. 2 – Post-mortem data include fingerprints, odontology, DNA profiling and physical indication. 3 – Ante-mortem data collected from victim houses. 4 – Reconciliation where specialists identify the victim from the data collected.

Implementasi Algoritma You Only Look Once (YOLO) untuk Deteksi Korban Bencana Alam

<p class="Abstrak">Bencana alam merupakan suatu peristiwa yang dapat menyebabkan kerusakan dan menciptakan kekacuan. Bangunan yang runtuh dapat menyebabkan cidera dan kematian pada korban. Lokasi dan waktu kejadian bencana alam yang tidak dapat diprediksi oleh manusia berpotensi memakan korban yang tidak sedikit. Oleh karena itu, untuk mengurangi korban yang banyak, setelah kejadian bencana alam, pertama yang harus dilakukan yaitu menemukan dan menyelamatkan korban yang terjebak. Penanganan evakuasi yang cepat harus dilakukan tim SAR untuk membantu korban. Namun pada kenyataannya, tim SAR mengalami kendala selama proses evakuasi korban. Mulai dari sulitnya medan yang dijangkau hingga terbatasnya peralatan yang dibutuhkan. Pada penelitian ini sistem diimplementasikan untuk deteksi korban bencana alam yang bertujuan untuk membantu mengembangkan peralatan tim SAR untuk menemukan korban bencana alam yang berbasis pengolahan citra. Algoritma yang digunakan untuk mendeteksi ada atau tidaknya korban pada gambar adalah <em>You Only Look Once</em> (YOLO). Terdapat dua macam algoritma YOLO yang diimplementasikan pada sistem yaitu YOLOv3 dan YOLOv3 Tiny. Dari hasil pengujian yang telah dilakukan didapatkan <em>F1 Score</em> mencapai 95.3% saat menggunakan YOLOv3 dengan menggunakan 100 data latih dan 100 data uji.</p><p class="Abstrak"> </p><p class="Abstrak"><strong><em>Abstract</em></strong></p><p class="Abstrak"> </p><p class="Abstract"><em>Natural disasters are events that can cause damage and create havoc. Buildings that collapse and can cause injury and death to victims. Humans can not predict the location and timing of natural disasters. After the natural disaster, the first thing to do is find and save trapped victims. The handling of rapid evacuation must be done by the SAR team to help victims to reduce the amount of loss due to natural disasters. But in reality, the process of evacuating victims of natural disasters is still a lot of obstacles experienced by the SAR team. It was starting from the difficulty of the terrain that is reached to the limited equipment needed. In this study, a natural disaster victim detection system was designed using image processing that aims to help find victims in difficult or vulnerable locations when directly reached by humans. In this study, a detection system for victims of natural disasters was implemented which aims to help develop equipment for the SAR team to find victims of natural disasters based on image processing. The algorithm used is You Only Look Once (YOLO). In this study, two types of YOLO algorithms were compared, namely YOLOv3 and YOLOv3 Tiny. From the test results that have been obtained, the F1 Score reaches 95.3% when using YOLOv3 with 100 training data and 100 test data.</em></p>

DESIGN AND DEVELOPMENT OF TETOS APPLICATION FOR LOCATION DETECTION IN SUPPORTING DISASTER VICTIM SEARCHING BASED ON GLOBAL POSITIONING SYSTEM (GPS)

<div><p class="Els-history-head">Indonesia is a country with a high level of disasters where during 2005-2015 there were 11,648 hydrometeorological disasters and 3810 geological disasters. In terms of technology, Indonesia is using mobile devices with high growth according to data from the Ministry of Communication and Information. One of the ways to increase the use of mobile devices in assisting disaster management in Indonesia can be developed in the location-based discovery of disaster victims. The location in a mobile device uses a Global Positioning System (GPS) where the existing data is in the form of Latitude and Longitude data. The aim of this research is that the application is expected to assist in the discovery of disaster victims based on the last GPS location. This study focuses on developing a disaster victim search application based on the Android operating system. Development uses the Agile Model by using Black-box Testing and White-Box Testing techniques, where the type of testing uses Unit Testing, Integration Testing, and Acceptance Testing with qualitative data. The results of the research obtained an application in the form of a prototype where there are several corrective inputs obtained from Acceptance Testing. This study is then expected that the application can be applied in disaster management systems in Indonesia which of course can be supported by binding rules so that the data obtained can be utilized optimally.</p></div>