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All Journal SinarFe7 RESLAJ: Religion Education Social Laa Roiba Journal
Gatut Yulisusianto
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Religion Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Social Sciences

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Penentuan Koordinat Dan Jarak Dalam Operasi Tempur Guna membantu Strategi Penyerangan Menggunakan Regresi Linier Wiwin Winarno 1; Wahyu Dirgantara; Gatut Yulisusianto
SinarFe7 Vol. 1 No. 1 (2018): Sinarfe7-1B 2018
Publisher : FORTEI Regional VII Jawa Timur

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

Abstract

Alat pengintai ini bertujuan untuk mengetahui keberadaan musuh serta kekuatan dan jarak yang diduduki oleh musuh dari koordinat pengintai melalui data yang dikirim oleh pengintai ke komando atas. Dengan demikian alat ini menggunakan sensor GPS dan sensor jarak yaitu sensor yang dihubungkan ke Raspberry Pi 3 yang berfungsi sebagai penunjuk koordinat pengintai dan jarak musuh dari pengintai. Data yang di tampilkan akan dikirimkan kekomando atas secara real time agar komando atas dapat mengambil keputusan lebih tepat.
Rancang Bangun Alat Pendeteksi Kerusakan pada Bearing Kendaraan Angkut NPS 75 4x4 milik TNI Angkatan Darat Berbasis K- (Nearest Neightbor) Ferry Dahruyatsyah; Gatut Yulisusianto; Ade Setiawan; Dekki Widiatomoko
Reslaj: Religion Education Social Laa Roiba Journal Vol. 6 No. 7 (2024): RESLAJ: Religion Education Social Laa Roiba Journal 
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/reslaj.v6i7.2164

Abstract

The Indonesian National Army (TNI) is a key element in maintaining national defense. However, challenges in the TNI's performance arise due to the need to modernize the main defense system equipment (alutsista) especially related to the condition of the NPS75 4x4 transport vehicle. One of the problems that often occurs is damage to wheel bearings, which can threaten personnel safety. Traditional methods such as listening to the sound of the engine and carrying out a physical inspection have been used before, but with the development of electronic technology, automatic devices can be created that monitor the condition of vehicle bearings using vibration detection. In this research, we propose the use of the Wemos D1 Mini which is connected to the driver's mobile phone via a WiFi network. This device uses the K-Nearest Neighbor (KNN) method to classify damage to bearings based on learning data. The aim is to assist in automatically detecting and classifying bearing damage categories on NPS75 4x4 transport vehicles, so that timely and targeted maintenance actions can be taken.
Rancang Bangun Alat Pendeteksi Kerusakan pada Bearing Kendaraan Angkut NPS 75 4x4 milik TNI Angkatan Darat Berbasis K-(Nearest Neightbor) Ferry Dahruyatsyah; Gatut Yulisusianto; Ade Setiawan; Dekki Widiatomoko
Reslaj: Religion Education Social Laa Roiba Journal Vol. 6 No. 8 (2024): RESLAJ: Religion Education Social Laa Roiba Journal 
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/reslaj.v6i8.2253

Abstract

The Indonesian National Army (TNI) is a key element in maintaining national defense. However, challenges in the TNI's performance arise due to the need to modernize the main defense system equipment (alutsista) especially related to the condition of the NPS75 4x4 transport vehicle. One of the problems that often occurs is damage to wheel bearings, which can threaten personnel safety. Traditional methods such as listening to the sound of the engine and carrying out a physical inspection have been used before, but with the development of electronic technology, automatic devices can be created that monitor the condition of vehicle bearings using vibration detection. In this research, we propose the use of the Wemos D1 Mini which is connected to the driver's mobile phone via a WiFi network. This device uses the K-Nearest Neighbor (KNN) method to classify damage to bearings based on learning data. The aim is to assist in automatically detecting and classifying bearing damage categories on NPS75 4x4 transport vehicles, so that timely and targeted maintenance actions can be taken.
Co-Authors Ade Setiawan Dekki Widiatomoko Ferry Dahruyatsyah Wahyu Dirgantara Wiwin Winarno 1