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All Journal Sinergi JURNAL PENDIDIKAN TAMBUSAI International Journal of Humanities Education and Social Sciences
Restianto, Mohammad Obie
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Humanities Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Analysis of Flood Susceptibility in Pati Regency Using Geographic Information Systems (GIS) and Analytical Hierarchy Process (AHP) Restianto, Mohammad Obie; Poniman, Aris; Supriyatno, Makmur
International Journal Of Humanities Education and Social Sciences (IJHESS) Vol 3 No 5 (2024): IJHESS APRIL 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhess.v3i5.849

Abstract

Floods are disasters that often occur in Indonesia. Society and the environment feel the negative impacts of flooding. Floods are ranked as the second most frequent disaster in Indonesia according to the Indonesian Disaster Information Data (DIBI) from the National Disaster Management Agency (BNPB). This research aims to conduct a flood susceptibility analysis in Pati Regency. The method used in this research is Analytical Hierarchy Process (AHP) combined with Geographic Information Systems (GIS). This research uses ten factors that influence flood susceptibility, there are Topographic Wetness Index (TWI), Elevation, Slope, Precipitation, Land Use and Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), Distance from Rivers, Distance from Roads, Drainage Density, and Soil Type. The results of this research show that there are four levels of flood susceptibility in Pati Regency, there are low level reaching 3.47%, moderate level reaching 64.85%, high level reaching 31.62%, and very high level reaching 0.07%.
Analisis Kerentanan Banjir di Wilayah Kota Ambon Menggunakan Sistem Informasi Geografis (SIG) dan Analytical Hierarchy Process (AHP) Restianto, Mohammad Obie; Poniman, Aris; Supriyatno, Makmur
Jurnal Pendidikan Tambusai Vol. 7 No. 3 (2023): Desember 2023
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v7i3.12205

Abstract

Banjir adalah bencana yang sering terjadi di Indonesia dan berdampak besar pada masyarakat dan lingkungan. Menurut Data Informasi Bencana Indonesia (DIBI) dari Badan Nasional Penanggulangan Bencana (BNPB), banjir menempati peringkat kedua bencana yang sering terjadi di Indonesia . Penelitian ini bertujuan untuk melakukan analisis kerentanan banjir di wilayah Kota Ambon. Metode yang digunakan dalam penelitian ini adalah Analytical Hierarchy Process (AHP) yang dipadukan dengan Sistem Informasi Geografis (SIG). Dalam penelitian ini menggunakan sepuluh faktor yang mempengaruhi kerentanan banjir yaitu Topographic Wetness Index (TWI), Ketinggian, Kemiringan Lereng, Presipitasi, Penggunaan Lahan atau Tutupan Lahan (LULC), Normalized Difference Vegetation Index (NDVI), Jarak dari Sungai, Jarak dari Jalan, Kerapatan Aliran, dan Jenis Tanah. Hasil penelitian ini menunjukkan bahwa kerentanan banjir di wilayah Kota Ambon ada empat jenis tingkatan, yaitu tingkat rendah mencapai 20.81%, tingkat sedang mencapai 72,84%, tingkat tinggi mencapai 6,32%, dan tingkat sangat tinggi mencapai 0,02%.
Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection Anggraini, Eca Indah; Nurdin, Fachdy; Restianto, Mohammad Obie; Dahsan, Sudarti; Ardhana, Andini Aprilia; Supriyadi, Asep Adang; Darmawan, Yahya; Arief, Syachrul; Ikhsanudin, Agus Haryanto
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.004

Abstract

Ensuring airport security is of paramount importance to safeguard the lives of passengers and prevent acts of terrorism. In this context, developing advanced technology for early terrorist detection is crucial. This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. Our system utilizes state-of-the-art artificial intelligence techniques to analyze facial features. Our research uses VGG architecture and pre-trained with face data as a CNN model. This model is used to extract face embedding features from the dataset. These embedding features are then compressed with Principal Component Analysis (PCA) to obtain the meaningful feature as training data for the ANN algorithm. We trained our system using data from 500 identities data with 60 data for each identity.  This training enables our system to recognize known terrorists and individuals on watchlists by comparing the facial features of individuals passing through security checkpoints with those in the database. The proposed CNN-ANN-based face recognition system not only enhances airport security but also significantly reduces the processing time for security checks. It can quickly identify potential threats, allowing security personnel to take appropriate actions in real time ensuring a rapid response to security concerns. We present the architecture, training methodology, and evaluation of the CNN-ANN model, achieving a high accuracy of 91.16% and precision of 91.36%. Through this research, we aim to increase airport security and strengthen efforts to combat terrorism, making air travel safer and more secure for all passengers. 
Co-Authors Anggraini, Eca Indah Ardhana, Andini Aprilia Arief, Syachrul ARIS PONIMAN Dahsan, Sudarti Ikhsanudin, Agus Haryanto Nurdin, Fachdy Supriyadi, Asep Adang Supriyatno, Makmur Yahya Darmawan