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All Journal Jurnal Media Infotama Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Kurniawan, Muhammad Andhika
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Computer Science & IT Electrical & Electronics Engineering

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A SISTEM PENDUKUNG KEPUTUSAN DENGANMETODE SIMPLE ADDITIVE WEIGHTING(SAW) DALAM MENENTUKAN BANTUAN SISWA MISKIN PADA SD NEGERI 36 KOTA BENGKULU MENGGUNAKAN VISUAL STUDIO Kurniawan, Muhammad Andhika; Kanedi, Indra; Fredricka, Jhoanne
Jurnal Media Infotama Vol 17 No 2 (2021)
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v17i2.1663

Abstract

Programming languages ​​and databases have a very important role in solving various problems faced by humans. With programming languages ​​and databases, human work can be quickly completed by creating a special application in the form of an information system. Currently, the process of selecting prospective BSM recipients is still influenced by the element of subjectivity of those who choose, so it is felt that there is less support for the process. If there is an inaccuracy by the assessment team, then the results will be feared that the provision of assistance to poor students will not be on target. The criteria used are: Parent's Income, Certificate of Inadequacy, Orphanage, Certificate of Good Behavior, and Living in an Orphanage Dormitory. In this research, the application creation tool uses Visual Basic 2015 and uses a database from Microsoft Access.
Klasifikasi Bangunan secara Otomatis Menggunakan Pembelajaran Mendalam dari Gambar Street-View Abdullah, Ryan Gading; A., M. Mahameru; Rewina, Anggita Eka; Kurniawan, Muhammad Andhika; Hapsari, Dian Puspita
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.6874

Abstract

Urban population density mapping or urban utility planning requires a classification map based on individual buildings that are considered much more informative. The goal of this research is to determine how to extract the fine-grained boundaries of individual buildings from a street-view dataset. This paper proposes a general framework for classifying individual building functionality using a deep learning approach. The proposed method is based on a Convolutional Neural Network (CNN) that classifies facade structures from street view images, such as Street-View images. From the experiments conducted, the CNN classifier with the ResNet architecture was able to classify the Street-View data group with an accuracy value of 86.79%. We construct a dataset to train and evaluate the CNN classifier. Furthermore, the method is applied to generate a building classification map at the urban area scale.
Co-Authors A., M. Mahameru Abdullah, Ryan Gading Dian Puspita Hapsari Fredricka, Jhoanne Indra Kanedi Rewina, Anggita Eka