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All Journal International Journal of Electrical and Computer Engineering Telematika Jurnal Akuntansi dan Manajemen Bisnis (JAMAN)
Taufik Hidayat
Gunadarma University
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering

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Identification of Plant Types by Leaf Textures Based on the Backpropagation Neural Network Taufik Hidayat; Asyaroh Ramadona Nilawati
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (482.905 KB) | DOI: 10.11591/ijece.v8i6.pp5389-5398

Abstract

The number of species of plants or flora in Indonesia is abundant. The wealth of Indonesia's flora species is not to be doubted. Almost every region in Indonesia has one or some distinctive plant(s) which may not exist in other countries. In enhancing the potential diversity of tropical plant resources, good management and utilization of biodiversity is required. Based on such diversity, plant classification becomes a challenge to do. The most common way to recognize between one plant and another is to identify the leaf of each plant. Leaf-based classification is an alternative and the most effective way to do because leaves will exist all the time, while fruits and flowers may only exist at any given time. In this study, the researchers will identify plants based on the textures of the leaves. Leaf feature extraction is done by calculating the area value, perimeter, and additional features of leaf images such as shape roundness and slenderness. The results of the extraction will then be selected for training by using the backpropagation neural network. The result of the training (the formation of the training set) will be calculated to produce the value of recognition accuracy with which the feature value of the dataset of the leaf images is then to be matched. The result of the identification of plant species based on leaf texture characteristics is expected to accelerate the process of plant classification based on the characteristics of the leaves.
Detection and Classification of Vehicles on the Bekasi Toll Road Using the Gaussian Mixture Models Method and Morphological Operations Rifki Kosasih; Hidayat Taufik Akbar
Telematika Vol 15, No 1: February (2022)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v15i1.1222

Abstract

Traffic surveillance was initially carried out directly using CCTV, but this kind of surveillance was not possible for a full day by the security forces. In addition, with the increasing growth of vehicles in Indonesia, a method is needed that can be used to assist security forces in monitoring traffic such as detecting and automatically counting the number of vehicles. Therefore, in our research, we propose a method that can detect vehicles, and count the number of vehicles from video recordings on the Bintara Bekasi toll road using background substraction methods such as gaussian mixture models and morphological operations. The results showed that the vehicle detection accuracy rate was 86.3636%, the precision was 89.0625%, and the recall was 96.6101%. In this study, vehicle classification was also carried out based on the detection results into two types of vehicles, namely cars and trucks. From the results of the research, the classification accuracy rate was obtained at 85.9649%.
Pengaruh Citra Merek dan Kualitas Produk Terhadap Keputusan Pembelian Konsumen Studi Kasus Pada MR. DIY Samarinda Aditya Rian Ramadhan; Taufik Hidayat
Jurnal Akuntansi dan Manajemen Bisnis Vol. 5 No. 3 (2025): Desember: Jurnal Akuntansi dan Manajemen Bisnis
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/jaman.v5i3.2363

Abstract

Kajian ini bermaksud menelaah akibat citra merek dan mutu produk terhadap aksi beli pelanggan di toko MR. DIY Samarinda. Mengaplikasikan metode kuantitatif dengan teknik angket, informasi dihimpun dari para pembeli yang pernah bertransaksi di MR. DIY Samarinda melalui daftar pertanyaan. Pengujian hipotesis riset dilakukan menggunakan analisis regresi linear berganda. Output riset memperlihatkan bahwa baik citra merek maupun mutu produk memberikan dampak positif dan penting terhadap aksi beli pelanggan di MR. DIY Samarinda. Hal ini menandakan bahwa makin baik impresi merek MR. DIY dan makin unggul kualitas produk yang dirasakan pembeli, maka makin besar pula tendensi pembeli untuk melakukan transaksi. Implikasi terapan dari riset ini yaitu urgensi bagi manajemen MR. DIY Samarinda untuk terus memantapkan citra merek dan menaikkan standar produk demi mendongkrak aksi beli pelanggan.
Co-Authors Aditya Rian Ramadhan Asyaroh Ramadona Nilawati Rifki Kosasih