Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC)
Yudianingsih Yudianingsih
Universitas Respati Yogyakarta
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Deep-RIC: Plastic Waste Classification using Deep Learning and Resin Identification Codes (RIC) Latifah Listyalina; Yudianingsih Yudianingsih; Adjie Wibowo Soedjono; Evrita Lusiana Utari; Dhimas Arief Dharmawan
Telematika Vol 19, No 2 (2022): Edisi Juni 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i2.7419

Abstract

In this study, the authors designed an algorithm based on deep learning that can automatically classify plastic waste according to Resin Identification Codes (RIC). The proposed algorithm is built through several stages as follows. In the first stage, image acquisition of plastic waste is carried out, which is the input of the designed algorithm. The acquired plastic waste image must display the resin code of the plastic waste to be classified. Furthermore, the acquired image is divided into two sets, namely training and testing sets. The training set contains images of plastic waste used in the training phase of the deep learning architecture DenseNet-121 to identify the resin code of each plastic waste image and classify it into the appropriate class. The training phase is run for 100 epochs, and at each epoch, the cross-entropy loss function is calculated, which expresses the performance of the deep learning architectures in classifying plastic waste images. In the next stage, a trained deep learning architecture is used to classify the plastic waste images from the test set. Classification performance in the test set is also expressed as the cross-entropy loss function value. In addition, the accuracy value has also been calculated, which shows the percentage of the number of plastic waste images successfully classified correctly to the total number of plastic waste images in the test set, which the best accuracy is equal to 85%.
Analisis Perancangan Digital Nutrition Scale Berbasis Sensor Load Cell Latifah Listyalina; Kusuma Mayasari; Yudianingsih yudianingsih
Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Vol 5, No 2 (2023): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v5i2.1767

Abstract

Timbangan merupakan alat yang dipakai dalam melakukan pengukuran massa suatu benda. Ada bermacam-macam jenis timbangan yang dikelompokkan fungsinya. Salah satunya timbangan nutrisi. Timbangan nutrisi menghitung kalori, karbohidrat, dan lemak. Sebuah sistem yang dapat mengukur kalori dan gizi pada makanan sehari-hari dapat membantu pasien dan ahli gizi untuk mengukur dan mengelola jumlah asupan makanan sehari-hari. Pada penelitian ini, diukur nilai nutrisi makanan karbohidrat dengan keluaran nilai kalori yang akan ditampilkan di LCD menggunakan sensor load cell. Alat timbangan nutrisi ini menghitung nilai kalori suatu kelompok makanan berdasarkan pengukuran berat, khususnya nutrisi karbohidrat, yaitu pada jenis makanan nasi putih, nasi merah dan kentang. Hasil analisis dari perancangan ini ialah besarnya nilai akurasi perbandingan massa dari timbangan digital dibandingkan dengan massa hasil pembacaan Digital Nutrition Scale. Akurasi adalah tingkat kedekatan hasil pengukuran alat terhadap nilai yang sebenarnya. Hasil pengujian menunjukkan pengukuran yang baik dengan akurasi 99%. Hal tersebut menunjukkan bahwa alat yang dirancang dapat bekerja dengan baik.
Co-Authors Adjie Wibowo Soedjono Dharmawan, Dhimas Arief Evrita Lusiana Utari Kusuma Mayasari Latifah Listyalina Latifah Listyalina