Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal INTECOMS: Journal of Information Technology and Computer Science INTI Nusa Mandiri
Muhammad Rafi Muttaqin
STT Wastukancana
Computer Science & IT

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

Found 3 Documents
Search

 1 
Penentuan Penerimaan Bantuan Pangan Nontunai Dengan Metode Simple Additive Weighted Mochzen Gito Resmi; Muhammad Rafi Muttaqin; Meriska Defriani
INTECOMS: Journal of Information Technology and Computer Science Vol 4 No 1 (2021): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v4i1.2102

Abstract

Bantuan Pangan Non Tunai (BPNT) merupakan bantuan pangan yang diberikan oleh pemerintah kepada masyarakat miskin yang ada di Indonesia. Bantuan tersebut bertujuan untuk mengurangi beban pengeluaran dan memberikan asupan nutrisi yang lebih baik kepada masyarakat miskin secara tepat sasaran dan tepat waktu. Berdasarkan hal tersebut, Dinas Sosial Kabupaten Purwakarta khususnya Bidang Balinsos harus mampu menentukan peserta Keluarga Penerima Manfaat (KPM) sesuai dengan peraturan yang telah ditentukan oleh Presiden tentang penyaluran bantuan sosial non tunai. Dalam melakukan proses pemilihan peserta Keluarga Penerima Manfaat (KPM) diperlukan kriteria-kriteria untuk memilih calon peserta yang akan terpilih menjadi Keluarga Penerima Manfaat (KPM) Bantuan Pangan Non Tunai. Penelitian ini mengunakan metode Simple Addtive Weighted (SAW) dan metode pengembangan sistem Waterfall. Dengan dibuatnya Sistem Pendukung Keputusan untuk menentukan peserta Keluarga Penerima Manfaat (KPM) Bantuan Pangan Non Tunai, Dinas Sosial Kabupaten Purwakarta lebih mudah dalam memberikan alternatif keputusan siapa peserta yang lebih berhak menerima. Hasil alternatif keputusan dilengkapi dengan nilai prioritas yang mengacu pada kriteria-kriteria pengambilan keputusan yang telah ditetapkan. Kata Kunci – Simple Addtive Weighted (Saw), Sistem Pendukung Keputusan, Waterfall, Bantuan Pangan Non Tunai (BPNT).
KLASIFIKASI KONDISI BAN KENDARAAN MENGGUNAKAN ARSITEKTUR VGG16 ahmad fudolizaenun nazhirin; Muhammad Rafi Muttaqin; Teguh Iman Hermanto
INTI Nusa Mandiri Vol 18 No 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4270

Abstract

Tyres are the main component that a vehicle needs to work with reducing vibration due to uneven road surfaces, protecting the wheels from wear to provide stability between the vehicle and the ground helping to improve acceleration to facilitate travel while driving. Wear ensures stability between the vehicle and the ground helps improve acceleration for easy movement and driving. Caused including components that are often used, tires can experience damage such as the appearance of cracks in the tires. Cracks in tires can be triggered by factors such as age or the cause of the road that has been exceeded. Detection of tire cracks at this time is still carried out conventionally, where users see directly the state of the tire whether the tire is in good condition or cracked. Conventional methods are important because they maintain tire quality and rider safety. The Conventional Method certainly has weaknesses because vehicle users must have good vision and the ability to distinguish normal tires or cracked tires, but this method is considered less effective because it still uses human labor, causing the risk of human error (human negligence) which can hinder the process of identifying tire cracks. Based on this problem, this study will develop a deep learning model that can classify cracked tires using the VGG16 architecture. In this study, the model was created using 8 scenarios by changing the value of epochs, to get the best parameters in making the model. The results of the 8 scenarios carried out in this study are the best scenario obtained in scenarios 1,3,4 which get 98% accuracy in model testing
KLASIFIKASI KONDISI BAN KENDARAAN MENGGUNAKAN ARSITEKTUR VGG16 ahmad fudolizaenun nazhirin; Muhammad Rafi Muttaqin; Teguh Iman Hermanto
INTI Nusa Mandiri Vol. 18 No. 1 (2023): INTI Periode Agustus 2023
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v18i1.4270

Abstract

Tyres are the main component that a vehicle needs to work with reducing vibration due to uneven road surfaces, protecting the wheels from wear to provide stability between the vehicle and the ground helping to improve acceleration to facilitate travel while driving. Wear ensures stability between the vehicle and the ground helps improve acceleration for easy movement and driving. Caused including components that are often used, tires can experience damage such as the appearance of cracks in the tires. Cracks in tires can be triggered by factors such as age or the cause of the road that has been exceeded. Detection of tire cracks at this time is still carried out conventionally, where users see directly the state of the tire whether the tire is in good condition or cracked. Conventional methods are important because they maintain tire quality and rider safety. The Conventional Method certainly has weaknesses because vehicle users must have good vision and the ability to distinguish normal tires or cracked tires, but this method is considered less effective because it still uses human labor, causing the risk of human error (human negligence) which can hinder the process of identifying tire cracks. Based on this problem, this study will develop a deep learning model that can classify cracked tires using the VGG16 architecture. In this study, the model was created using 8 scenarios by changing the value of epochs, to get the best parameters in making the model. The results of the 8 scenarios carried out in this study are the best scenario obtained in scenarios 1,3,4 which get 98% accuracy in model testing
Co-Authors ahmad fudolizaenun nazhirin Meriska Defriani Resmi, Mochzen Gito Teguh Iman Hermanto