Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Indonesian Mining Journal Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Indonesian Mining Journal
firhad firmansyah
Unknown Affiliation
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
sebuah Pengukuran Daya Dukung Tanah Terhadap Rencana Pembangunan Jalan Sebagai Upaya Harmonisasi Perusahaan Tambang dengan Masyarakat Kalirejo firhad firmansyah; Rizqi Prastowo
Retii 2022: Prosiding Seminar Nasional ReTII ke-17
Publisher : Institut Teknologi Nasional Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penambangan merupakan kegiatan kegiatan, memuat dan mengangkut material dari depan tambang menuju tempat-tempat penjualan. Kegiatan pengangkutan materi tentunya membutuhkan prasarana sebagai akses menuju tempat penjualan, akses tersebut adalah jalan. Jalan pada suatu pedesaan pada dasarnya di desain sesuai dengan peruntukkannya, yang mana jalan desa hanya mampu menerima <8 ton (PP No. 43 tahun 1993). Berdasarkan dilapangan jalan pedesaan juga digunakan sebagai jalan untuk pertambangan yaitu pengangkutan bahan tambang, akibat jalan rusak.Penelitian ini bertujuan untuk mengetahui daya dukung tanah (DDT) pada ruas jalan sepanjang 1.3Km dengan 13 lokasi titik pengukuran langsung dilapangan menggunakan DCP (Dynamic Cone Penetration) dan didapatkan nilai DDT terkecil 5.6 kg/cm2 dan terbesar 10,2 kg/cm2 sehingga nilai daya dukung tanah dibandingkan dengan beban alat angkut menunjukan ketidak serasian. Upaya kajian DDT untuk rencana perkerasan jalan perlu dilakukan, sehingga tercipta harmonisasi dengan harapan jalan tersebut dapat dimanfaatkan untuk kegiatan pertambangan maupun kegiatan masyarakat dalam jangka waktu yang cukup lama
PENILAIAN DAN PREDIKSI JARINGAN SYARAF TIRUAN TERHADAP KECEPATAN PARTIKEL YANG DIINDUKSI PELEDAKAN - STUDI KASUS PENAMBANGAN BATUGAMPING Prastowo, Rizqi; Hendro Purnomo; Firhad Firmansyah; Ipmawan, Vico Luthfi
Indonesian Mining Journal Vol 27 No 1 (2024): Indonesian Mining Journal, April 2024
Publisher : Balai Besar Pengujian Mineral dan Batubara tekMIRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30556/imj.Vol27.No1.2024.1531

Abstract

In recent decades, generation of ground vibrations results from blasting activities in mining sector has been identified as a significant cause of extensive harm to nearby structures, vegetation, and individuals. Hence, it is imperative to closely monitor and accurately forecast the uncertain levels of vibration, and implement the appropriate steps to mitigate their potentially harmful impact. The objective of this study was to establish a correlation between the peak particle velocity and the various parameters that influence it. This study employed the deployment of the artificial neural network approach to assess and forecast the uncertain ground vibrations. In this study, a multilayer perception neural network with three layers and a feed-forward back-propagation architecture was employed. The network consisted of five input parameters, namely the distance from the blast face, maximum charge per delay, spacing, burden, and depth hole. The output of interest was the peak particle velocity. The neural network was trained using the Levenberg–Marquardt algorithm, and the training dataset comprised 29 experimental records and blast event data obtained from the limestone mine in Indonesia. In order to assess the effectiveness and the precision of the artificial neural network model that was created, a total of four conventional predictor models were utilized. These models were proposed by reputable sources such as the US Bureau of Mines, Ambraseys–Hendron, Langefors–Kihlstrom, and the Bureau of Indian Standards. The results collected from the demonstrate study show that the artificial neural network model suggested in this research has the ability to provide more precise estimations of ground vibrations in comparison to existing conventional prediction models. The artificial neural network model yielded a coefficient of determination (R2) of 0.9332 and a root mean square error (RMSE) of 0.4763.
PENILAIAN DAN PREDIKSI JARINGAN SYARAF TIRUAN TERHADAP KECEPATAN PARTIKEL YANG DIINDUKSI PELEDAKAN - STUDI KASUS PENAMBANGAN BATUGAMPING Prastowo, Rizqi; Hendro Purnomo; Firhad Firmansyah; Ipmawan, Vico Luthfi
Indonesian Mining Journal Vol 27 No 1 (2024): Indonesian Mining Journal, April 2024
Publisher : Balai Besar Pengujian Mineral dan Batubara tekMIRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30556/imj.Vol27.No1.2024.1531

Abstract

In recent decades, generation of ground vibrations results from blasting activities in mining sector has been identified as a significant cause of extensive harm to nearby structures, vegetation, and individuals. Hence, it is imperative to closely monitor and accurately forecast the uncertain levels of vibration, and implement the appropriate steps to mitigate their potentially harmful impact. The objective of this study was to establish a correlation between the peak particle velocity and the various parameters that influence it. This study employed the deployment of the artificial neural network approach to assess and forecast the uncertain ground vibrations. In this study, a multilayer perception neural network with three layers and a feed-forward back-propagation architecture was employed. The network consisted of five input parameters, namely the distance from the blast face, maximum charge per delay, spacing, burden, and depth hole. The output of interest was the peak particle velocity. The neural network was trained using the Levenberg–Marquardt algorithm, and the training dataset comprised 29 experimental records and blast event data obtained from the limestone mine in Indonesia. In order to assess the effectiveness and the precision of the artificial neural network model that was created, a total of four conventional predictor models were utilized. These models were proposed by reputable sources such as the US Bureau of Mines, Ambraseys–Hendron, Langefors–Kihlstrom, and the Bureau of Indian Standards. The results collected from the demonstrate study show that the artificial neural network model suggested in this research has the ability to provide more precise estimations of ground vibrations in comparison to existing conventional prediction models. The artificial neural network model yielded a coefficient of determination (R2) of 0.9332 and a root mean square error (RMSE) of 0.4763.
Co-Authors Hendro Purnomo Rizqi Prastowo Vico Luthfi Ipmawan