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All Journal Indonesian Journal of Geography Sinergi Journal on Education Jurnal TNI Angkatan Udara
Arief, Syachrul
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Aerospace Engineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering

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1. Analisis Studi IRNSS (Indian Regional Navigation Satellite System), Sebagai Referensi Pengembangan RNSS Di Indonesia Arief, Syachrul; Gultom, Rudi; Poniman, Aris
Jurnal TNI Angkatan Udara Vol 1 No 3 (2022): Jurnal TNI Angkatan Udara Triwulan Ketiga
Publisher : Staf Komunikasi dan Elektronika, TNI Angkatan Udara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62828/jpb.v1i3.2

Abstract

Regional navigasi satelit sistem (RNSS) adalah suatu sistem navigasi yang daya jangkaunya bersifat regional. Pemanfaatannya semakin hari semakin banyak orang menggunakan bahkan berbagai bidang hampir semuanya membutuhkan informasi navigasi. Penelitian ini mencoba mengeksplorasi sistem navigasi pada negara India yang skala jangkakunnya regional. Metoda penelitian yang digunakan adalah metode penelitian studi literarur, literarur diperoleh dari berbagai referensi yang sesuai dengan kebutuhan penelitian. Hasil yang diperoleh, bahwa sejarah pembangunan RNSS India bukan dibuat dalam waktu yang singkat, namun diperlukan berbagai persiapan yang membutuhkan waktu tidak sebentar. Mulai dari kajian segmen IRNSS, baik segmen darat, segmen ruang angkasa dan segmen pengguna, dipersiapkan seara mendetil dan sebaik mungkin. Berikutnya sistem referensi juga menjadi perhatian, referensi yang menentukan tata koordinat untuk keperluan koreksi atau acuan dalam berbagai aplikasi RNSS. Berikutnya masalah konstelasi satelit, berdasar kebutuhan dan kondisi akan mempengaruhi berapa jumlah satelit yang diperlukan untuk dapat mencakup area se Indonesia. Oleh karena hal tersebut analisis studi ini menjadi suatu referensi dalam rangka membangun suatu sistem navigasi yang mandiri, terlebih lagi jika dikaitkan dengan kebutuhan pertahanan dan keamanan.
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. 
Potensi Radar Cuaca dalam Pengamatan Aktivitas Gunung Berapi (Studi Kasus Letusan Gunung Lewotobi Laki-laki 8 November 2024) Heningtiyas, Hesti; Supriyadi, Asep Adang; Arief, Syachrul

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Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v7i2.8394

Abstract

The characteristics of ash volcanoes can be detected using weather radar by analyzing volcanic ash clouds. Weather radar reflectivity data can describe the estimated height of the column of volcanic ash cloud and the trajectory or direction of the eruption cloud. The BMKG's C-band single-polarization weather radar at Maumere Meteorological Station observed several eruptions of Mount Lewotobi Laki-Laki. Mount Lewotobi Laki-Laki is about 60 km from Maumere Weather Radar, which is still included in the radar coverage. From several eruption events, the eruption activity on 8 November 2024 of Mount Lewotobi Laki-Laki could be observed from the Maumere Weather Radar. The radar data processing produces Maximum (MAX) and VCUT (Vertical Cut) products to obtain information on the height of volcanic eruptions and eruption characteristics in more detail. The MAX and VCUT products of weather radar on the eruption event in this study show that the height of the column of volcanic ash cloud observed by weather radar is higher than the direct observation report by PVMBG.
Measuring the Height of Volcanic Clouds Using Weather Radar : Case Study Mount Sinabung Eruptions in Medan, Indonesia Heningtiyas, Hesti; Achmadi, Budhi; Supriyadi, Asep Adang; Arief, Syachrul; Charolydya, Rindita
Indonesian Journal of Geography Vol 57, No 2 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.96391

Abstract

Apart from being used to observe hydrometeorological phenomena, weather radar can also be used to observe volcanic eruptions. Weather radar reflectivity data can describe the estimated height of volcanic eruptions, while the HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model describes the trajectory or direction of distribution of eruption clouds. BMKG's single polarization C-Band weather radar at the Regional Office I in Medan was able to observe several eruptions of Mount Sinabung. Mount Sinabung is about 50 km from the Medan Weather Radar hence is still within radar coverage. Several eruptive activities of Mount Sinabung can be observed from the Medan Weather Radar, using Maximum (MAX) and VCUT (Vertical Cut) products to obtain information on the height of volcanic eruptions and eruption characteristics. While, the HYSPLIT model developed by ARL-NOAA is used to determine the direction of distribution of volcanic ash immediately after the eruption. The MAX and VCUT weather radar products and the results of NOAA HYSPLIT model for several events in this study show that the eruption height in weather radar observations is higher than the HYSPLIT model and the direction of volcanic ash distribution is different from Volcanic Observatory Notice for Aviation (VONA) observations.Received: 2024-05-24 Revised: 2025-06-26 Accepted: 2025-08-15Published: 2025-08-19  
Co-Authors Achmadi, Budhi Anggraini, Eca Indah Ardhana, Andini Aprilia ARIS PONIMAN Charolydya, Rindita Dahsan, Sudarti Gultom, Rudi Heningtiyas, Hesti Ikhsanudin, Agus Haryanto Nurdin, Fachdy Restianto, Mohammad Obie Supriyadi, Asep Adang Yahya Darmawan