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All Journal International Journal of Electrical and Computer Engineering MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) INFORMAL: Informatics Journal INTEGRITAS : Jurnal Pengabdian TEKIBA : Jurnal Teknologi dan Pengabdian Masyarakat Room of Civil Society Development Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Proceeding International Conference Of Innovation Science, Technology, Education, Children And Health Room of Civil Social Development
Gayatri Dwi Santika
Program Studi Informatika, Universitas Jember
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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Journal : INFORMAL: Informatics Journal

 1 
Implementasi Metode Hybrid AHP dan TOPSIS pada Sistem Pendukung Keputusan Pemilihan Lokasi Tempat Pembuangan Sampah Sementara Bayhaqqi Bayhaqqi; Saiful Bukhori; Gayatri Dwi Santika
INFORMAL: Informatics Journal Vol 6 No 2 (2021): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v6i2.25648

Abstract

Temporary Waste Disposal Site (TPSS) is a place to collect waste from various community activities which will later be transported to the final disposal site by garbage trucks. There are many considerations in choosing a TPSS location, so the selection of a TPSS location is very important in supporting the collection of waste that will be transported to final disposal. The Jember Regency Environmental Service is an agency in charge of waste management, including the selection of TPSS locations. Choosing the location of TPSS so far is still manual, where manual selection cannot be separated from human error, so that choosing the location of TPSS is not accurate can cause new problems in the community. In addition, there is no standardized assessment system in the TPSS selection process, so a decision support system is needed that can be used to assist the process of selecting the best TPSS location recommendations. In making this research system, we implemented the hybrid method of AHP and TOPSIS. Where the AHP method is used to determine the weight of the criteria while the TOPSIS method is used for the selection process for TPSS candidates.
Segmentasi Citra Tanda Tangan Menggunakan Fitur Titik SURF (Speeded Up Robust Features) dan Klasifikasi Jaringan Syaraf Tiruan hidayat, muhamad arief; retnani, windy eka yulia; Firmansyah, Diksy Media; Santika, Gayatri Dwi; Furqon, Muhammad ‘Ariful
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.53514

Abstract

Signature image classification is an important field of image processing. One of the stages of signature classification is segmentation. The segmentation process aims to detect image pixels that are part of the signature and separate them from text or logo pixels in a document image. There is a signature segmentation technique using interest points extracted using the SURF (Speeded Up Robust Features) algorithm [1] In this technique, a connected component pixel will be considered part of the signature if it has more SURF points in common with the database connected component pixel signature. Compared to the similarity with the database connected component non-signature pixels. This method is able to provide good accuracy results for signature pixel segmentation. However, the recall value is relatively low, namely 56%. This is because many connected component logos are considered as connected component signatures. In this study, signature segmentation was carried out using SURF points by adding two things: 1) using internal connected component characteristics as additional classification atributs: extent, solidity, ratio, and circularity 2) using an Artificial Neural Network classification algorithm to assist the segmentation process. The test results show that the proposed method improves segmentation quality by an average of 20.7% for an increase in accuracy, an average of 22.4% for an increase in precision, and an average of 18.6% for an increase in recall. When compared with the results reported in (Ahmed et al., 2012), the recall has increased by 38.3% - 42.8%
Co-Authors Agus Naba Allysa, Pupud Deanira Amalia, Karina Nine Bayhaqqi Bayhaqqi Farchanulhady, Reefadhinta Ubaidillah Firmansyah, Diksy Media Furqon, Muhammad Ariful Hidayat, Muhamad Arief mahmudy, wayan f Nugraha, Tri Agustina retnani, windy eka yulia S, M Zaki Saiful Bukhori Sari, Nadia Roosmalita Satrio Thoriq Shenny Syavira, Anintya Alisya Valiant Shabri Rabbani Wayan Firdaus Mahmudy