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All Journal International Journal of Electrical and Computer Engineering JURNAL REKAYASA TEKNOLOGI INFORMASI ILKOM Jurnal Ilmiah Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Jurnal Bina Komputer Adopsi Teknologi dan Sistem Informasi
Joan Angelina Widians, Joan Angelina
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Dipterocarpaceae trunk texture classification using two-stage convolutional neural network-based transfer learning model Wati, Masna; Puspitasari, Novianti; Hairah, Ummul; Widians, Joan Angelina; Tjikoa, Ade Fiqri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6874-6882

Abstract

The importance of plant identification has been recognized by academia and industry. There have been several attempts to utilize leaves and flowers for identification. However, the trunk can also be helpful, especially for tall trees. In Borneo, the Dipterocarpaceae family are the main constituents of the tropical rainforest ecosystem. This research focuses on the classification of the dipterocarp family, which can reach a height of between 70 and 85 m. Leveraging convolutional neural network (CNN) models, this research proposes a two-stage transfer learning strategy. In the first stage, the pre-trained CNN models are refined by only modifying the classification layer while keeping the feature layer frozen. The second stage involves selecting and freezing several convolutional layers to adapt the model to classify dipterocarp stems. The dataset consists of 857 images of different dipterocarp species. Experiments show that the VGG16 model with a two-stage transfer learning strategy achieves a high accuracy of 98.246%. This study aims to accurately identify species, benefiting conservation and ecological studies by enabling fast and reliable tree species classification based on stem texture images.
Feature selection based on chi-square and ant colony optimization for multi-label classification Widians, Joan Angelina; Wardoyo, Retantyo; Hartati, Sri
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3303-3312

Abstract

Text classification is widely used in organizations with large databases and digital documents. In text classification, there are many features, most of which are redundant. High-dimensional features impact multi-label classification performance. Feature selection is a data processing technique that can overcome this problem. Feature selection techniques have two major approaches: filter and wrapper. This paper proposes a hybrid filter-wrapper technique combining two algorithms: Chi-square (CS) and ant colony optimization (ACO). In the first stage, CS is used to reduce the number of irrelevant features. The ACO method is in the second stage. The ACO is applied to select the efficient features and improve classifier performance. The experiment results show that CS-ACO, CS-grey wolf optimizer (GWO), CS, and without feature selection (FS) have a micro F1-score based multinomial naïve Bayes classifier including 80%, 79.75%, 79.64% and 77.78%. The result indicates that the CS-ACO algorithm is suitable for solving multi-label classification problems.
Co-Authors Abu Bakar Andi Maulana, Andi Astried, Astried Baiquni, Muhammad Rizky Caesar Budiman, Edy Dani, Riski Rama Fadila, Tandy Firmanda, Haga Daffa Aska Hairah, Ummul Islamiyah Islamiyah, Islamiyah Kurniawan, Tri Basuki Misinem, Misinem Muhammad Awaluddin, Muhammad Mursidin . Novianti Puspitasari Pakpahan, Herman Santoso Puspitasari, Novianti Retantyo Wardoyo Rizkyani, Farahdina Nur Rosmasari Rosmasari, Rosmasari Sari, Lili Kurnia Simbolon, R.H. Kimebmen Sri Hartati Tjikoa, Ade Fiqri Wati, Masna