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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) LINK Jurnal Transformatika SemanTIK : Teknik Informasi SISFOTENIKA Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) International Journal of Informatics and Computation Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Pengabdian Dharma Bakti Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Berita Kedokteran Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Marselina Endah Hiswati
Universitas Respati Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Other

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Journal : International Journal of Informatics and Computation

 1 
Efficient Fruits Classification Using Convolutional Neural Network ADNAN ADNAN ABIDIN; Hamzah Hamzah; Marselina Endah
International Journal of Informatics and Computation Vol 3 No 1 (2021): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v3i1.31

Abstract

Classification of fruits is a growing research topic in image processing. Various papers propose various techniques to deal with the classification of apples. However, some traditional classification methods remain drawbacks to producing an effective result with the big dataset. Inspired by deep learning in computer vision, we propose a novel learning method to construct a classification model, which can classify types of apples quickly and accurately. To conduct our experiment, we collect datasets, do preprocessing, train our model, tune parameter settings to get the highest accuracy results, then test the model using new data. Based on the experimental results, the classification model of green apples and red apples can obtain good accuracy with little loss. Therefore, the proposed model can be a promising solution to deal with apple classification.
DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm Marselina Endah H.
International Journal of Informatics and Computation Vol 3 No 2 (2021): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v3i2.40

Abstract

Classification of skin cancer is a growing research topic with significant challenges in the image processing. Learning algorithms for classifying a kind of skin cancer have been presented in recent articles to accelerate the diagnosis process with a rapid and accurate diagnosis. However, effective detection of skin cancer requires extensive graphical data. Inspired by deep learning successful results in computer vision, A Convolutional Neural Network (CNN) is proposed in this study to build a skin cancer classification model. We conduct this experiment by collecting massive skin cancer datasets, conducting pre-processing, training models, and evaluating the performance. Based on the experiment result, the benign and malignant classification model can obtain a good accuracy with a slight loss. Therefore, the results obtained reached an accuracy of 54%.
Modern Web Semantic Application For Selling Fish Marselina Endah
International Journal of Informatics and Computation Vol 2 No 1 (2020): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v2i1.26

Abstract

Betta fish is famous as a fighter fish with a difference between betta fish with other types of fishes. Many people are interested in buying the Betta fish with a beautiful tail and attractive color with a giant belly. Thus, the paper aims to build a Semantic Web with API of delivery services. We design a system to enable and increase the betta fish sales of the Yogyakarta Community, Indonesia. We construct an application with REST of a web semantics to retrieve various data, including places and shipping cost of items to enable buyers to estimate fish prices and shipping costs.
Co-Authors ADNAN ADNAN ABIDIN Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Azijah, Izattul Basuki, Umar Dian Rhesa Rahmayanti Eka Damayanti Elisabeth Deta Lustiyati Erizal Erizal Fajar Y Zebua Fathinah, Azzah FAUZIYYAH, ANNUR HAFIZAH, IDA Hamzah Hamzah Hamzah Hamzah Huang J. Jie I Wayan Ordiyasa I Wayan Ordiyasa Kristian, Tadem Vergi Lestari, Nia Rizqi Lestariningsih Lestariningsih Lila Retnani Utami Lutfi Nur Wicaksono Meliala, Dyan Avando Mohammad Diqi Muflih Muflih Muflih, Muflih Ngaisyah, Rr Dewi Noverianus, Noverianus Ordiyasa, I Wayan Putra Wanda Putra Wanda Putra Wanda Putra Wanda, Putra Rahayu Widaryanti Rainbow Tambunan Romana Herlinda Salsabela, Marcella Sapariyah, Sapariyah Sri Hasta Mulyani Sriwidodo B, Sriwidodo Subandi, Yeyen Sudiyatmi, Sudiyatmi Supardi RS Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi