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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Computer Science and Information Technologies Indonesian Journal of Data and Science Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues The Indonesian Journal of Computer Science
Darwis, Herdianti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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Journal : Jurnal Teknik Informatika (JUTIF)

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COMBINATIONS OF FEATURE EXTRACTIONS AND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION Asfar, A. Muh. Fitrah; Hasnawi, Mardiyyah; Darwis, Herdianti
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2514

Abstract

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.
DIGITAL IMAGE CLASSIFICATION OF HERBAL LEAVES USING KNN AND CNN WITH GLCM FEATURES Zahirah, Dinna; Purnawansyah, Purnawansyah; Kurniati, Nia; Darwis, Herdianti
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1162

Abstract

Geographical position and having a tropical climate make Indonesia known for its abundant biodiversity, one of which is herbal leaves. Indonesia has more than 2039 species that fall into the category of herbal medicinal plants. Herbal leaves are plants that are used as an alternative to natural disease healing. The large number of herbal leaf plants makes it difficult for people to distinguish between herbal plants and non-herbal plants, except when herbal leaf plants bear fruit or bloom. With advances in technology, many studies have been conducted to identify types of herbal plants, one of which is to identify the characteristics of the leaves. In this study, image recognition of herbal leaves was carried out using the K-Nearest Neighbor and Convolutional Neural Network methods with feature extraction of the Gray Level Co-occurance Matrix. By using these 2 methods, the data collected in this study were 480 leaf images which were then divided into 80% testing data and 20% training data. The data used are in the form of Sauropus androgynus and Moringa leaves. Based on the test results, the Convolutional Neural Network method which is suggested in the herbal leaf image classification which has an accuracy value of 96%..
Co-Authors Adela Regita Azzahra Adnan, Adam Aji Prasetya Wibawa Alfitriana Riska Amaliah, Tazkirah Annisa Febrianti Arfah, Jumrayanti Asfar, A. Muh. Fitrah Astuti, Wistiani Atmajaya, Dedy Atussaliha, Nur Almar' Azwan Triyadi Backar, Sunarti Passura Basri, Haerunnisa Basri, Nur Anisa Dian Fitrah Yusti Randana Harlinda, Harlinda Hasanuddin, Tasrif Haviluddin Haviluddin Irawati Irawati Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Magfirah, Magfirah Mardiyyah Hasnawi Moleo, Alif Safa Munaf, Adryan Dwiprawira Nafalski, Andrew Nia Kurniati Nur wahidah Purnawansyah Purnawansyah Purnawansyah Puspitasari, Rahma Rachman Manga, Abdul Rahma, Reyna Aprilia Rahmasari, Putri Aulia Raja, Roesman Ridwan Salim, Yulita Samir, Mutmainnah Sastra, Ramdan Siska Anraeni Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umar, Fitriyani Veithzal Rivai Zainal Wanaspati, Nugraha Wati, Silmi Nur Zaskia Widyawati, Dewi Zahirah, Dinna Zulkifli, Muh. Rifqi