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All Journal Zeta - Math Journal TIN: TERAPAN INFORMATIKA NUSANTARA Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Rewina, Anggita Eka
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Implementation of the K-Means Method for Beverage Clustering Based on Calorie and Protein Rewina, Anggita Eka; Hapsari, Rinci Kembang; Putri, Chatarina Natassya; Lande, Gamaliel Virani Fofid; Aditya, Andre Fransisco; Alamsyah, Mochamad Tegar Bagas
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.19-29

Abstract

Recently, the number of coffee shops in big cities in Indonesia has increased. This makes it easier for coffee lovers to enjoy it. With the increasing public awareness of the importance of healthy drinking patterns in preventing diabetes and other diseases, consuming low-calorie drinks has become a prominent trend. This study aims to group the coffee drink menu at Starbucks based on the calorie and protein content of Starbucks drinks. It is grouped into 2 clusters, namely, high and low clusters. In this study, the clustering process of Starbucks drink menu data was carried out by applying the K-Means algorithm. The clustering results can identify members of Cluster 1 and members of Cluster 2. From the tests that have been carried out, it can group the drink menu into 2 clusters based on the amount of protein and calories from Starbucks drinks and help the public choose which drinks are better to consume.
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.
Penerapan Metode CNN (Convolutional Neural Network) dalam Mengklasifikasi Uang Kertas dan Uang Logam Rewina, Anggita Eka; Sulistyowati, Sulistyowati; Kurniawan, Muchamad; N, Muhammad Dinarta; Yunanda, Sita Fara
TIN: Terapan Informatika Nusantara Vol 4 No 12 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i12.5128

Abstract

Banknotes and coins are valuable assets that are used as legal means of payment in everyday life. The value of these two types of money has been determined and is printed on each piece of banknote when used in transactions and trade. Even though currently banknotes can be recognized using technology such as ATM machines, these machines are only able to recognize the value of the largest currency owned by a country. Computers require digital images as input to display the information contained therein because computers do not have the ability of the human eye to directly recognize or calculate the objects they see. Therefore, techniques or methods are needed that aim to obtain information from digital images to facilitate human interpretation. This research aims to design a system for detecting banknotes in images using the Convolutional Neural Network (CNN) architecture, which is a form of deep learning. . The system also integrates image pre-processing using user-based manual annotation techniques in Python program code. Using the CNN method, a test was carried out to detect the nominal amount of money in the input image. Test results using 29 banknote dataset samples and 31 coin money dataset samples show that the two types of money are divided into two classes, namely paper and coins. From the training carried out on banknotes and coins, an average accuracy of 98% was obtained, showing good results. Repetition of the detection process also shows consistent output probabilities. However, there are several denominations of money that show high accuracy values, so it can be concluded that the labeling annotation method is thought to be less effective.
Co-Authors A., M. Mahameru Abdullah, Ryan Gading Aditya, Andre Fransisco Alamsyah, Mochamad Tegar Bagas Dian Puspita Hapsari Kurniawan, Muhammad Andhika Lande, Gamaliel Virani Fofid Muchamad Kurniawan, Muchamad N, Muhammad Dinarta Putri, Chatarina Natassya Rinci Kembang Hapsari Sulistyowati Sulistyowati Yunanda, Sita Fara