PHYSICAL AND SOCIAL VULNERABILITY IN THE HOUSHOLD LEVEL TOWARDS TSUNAMI IN SADENG COASTAL AREA, GUNUNGKIDUL

Tsunami occurrence in Indonesia has continued to increase until 2018. The southern coast of Java is one of the tsunami-prone areas because it is located in a subduction zone. Study location is Sadeng coastal area which is located in the south coast of DIY Province. Disaster vulnerability studies at the household level is still limited, so this paper aims to identify physical and social vulnerability to tsunami hazard at the household level. The data of this research was obtained by invterviewing household respondents and observing physical condition of building. Identification of physical vulnerability was performed using modified SCHEMA and PTVA-3 method, while social vulnerability assessment considered demographic and socio-economic parameters. Total vulnerability was retrieved from matrix analysis of physical and social vulnerability classification. The study shows that 64 % households in Sadeng coastal areas are classified to moderate vulnerability, 30% of households are high vulnerability and 6 % of households are low vulnerability. High vulnerability is characterized by households which occupy non-permanent houses, have no economic assets, and have a high dependency ratio. Moderate vulnerability is characterized by households which occupy semi-permanent house, have economic assets, but have high dependency ratio. Low vulnerability is characterized by households which live in government-owned buildings, have economic assets, and have low dependency ratio. Keywords: tsunami, vulnerability, building`s physical vulnerability, social vulnerabilityKejadian tsunami di Indonesia terus mengalami peningkatan hingga tahun 2018. Pesisir selatan Jawa merupakan salah satu kawasan yang terpapar bahaya tsunami karena terletak pada zona subduksi. Lokasi kajian adalah kawasan pesisir Sadeng yang berlokasi di pesisir selatan Propinsi DIY. Kajian kerentanan bencana di tingkat rumah tangga belum banyak dilakukan, sehingga tulisan ini bertujuan untuk melakukan identifikasi kerentanan fisik bangunan dan sosial terhadap bencana tsunami di tingkat rumah tangga. Perolehan data penelitian dilakukan dengan wawancara responden rumahtangga dan observasi kondisi fisik bangunan. Identifikasi kerentanan fisik bangunan dilakukan dengan metode SCHEMA dan PTVA-3 yang dimodifikasi, sedangkan penilaian kerentanan sosial mempertimbangkan parameter kependudukan dan sosial ekonomi. Nilai total kerentanan diperoleh dari analisis matriks klasifikasi kerentanan sosial dan fisik bangunan. Kajian menunjukkan bahwa sebesar 64 % rumahtangga di kawasan pesisir Sadeng termasuk dalam kelas kerentanan sedang, 30 % rumahtangga dalam kerentanan tinggi dan 6 % rumahtangga dalam kerentanan rendah. Tingkat kerentanan tinggi dicirikan dengan rumahtangga yang menempati rumah tinggal non-permanen, tidak memiliki asset ekonomi, dan memiliki angka ketergantungan yang tinggi. Tingkat kerentanan sedang dicirikan dengan rumahtangga yang menempati rumah tinggal semi permanen, memiliki asset ekonomi namun memiliki angka ketergantungan yang tinggi. Tingkat kerentanan rendah dicirikan dengan rumahtangga yang tinggal pada bangunan milik pemerintah, memiliki asset ekonomi, dan memiliki angka ketergantungan yang rendah. Kata kunci: tsunami, kerentanan, kerentanan fisik bangunan, kerentanan sosial

BLocate: A Building Identification Scheme in GPS Denied Environments Using Smartphone Sensors

Indoor localization systems assume that the user’s current building is known by the GPS (Global Positioning System). However, such assumptions do not hold true in GPS denied environments or where the GPS cannot determine the user’s definite location. We present a novel solution to identify the building where the user is present now. The proposed building identification method works on the pervasive magnetic field using a smartphone. The accelerometer data determines the user’s activity of being stationary or walking. An Artificial Neural Network is used to identify the user’s activities and it shows good results. The magnetometer data is used to identify the user’s current building using the fingerprinting approach. Contrary to a traditional fingerprinting approach which stores intensity values, we utilize the patterns formed by the magnetic field strength in the form of a Binary Grid (BG). The BG approach overcomes the limitation of Dynamic Time Warping (DTW) whose performance is degraded when the magnitude of the magnetic data is changed. The experiments are performed with Samsung Galaxy S8 for eight various buildings with different altitudes and number of floors in Yeungnam University, Korea. The results demonstrate that the proposed building identification method can potentially be deployed for building identification. The precision, UAR (Unweighted Average Recall), F score, and Cohen’s Kappa values are used to determine the performance of the proposed system. The proposed systems shows very promising results. The system operates without any aid from any infrastructure dependent technologies like GPS or WiFi. Furthermore, we performed many experiments to investigate the impact of isolated points data to build fingerprint database on system’s accuracy with 1 m and 2 m distance. Results illustrate that by trading off a minor accuracy, survey labor can be reduced by 50 percent.