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All Journal Jurnal Simetris Seruni - Seminar Riset Unggulan Nasional Inoformatika dan Komputer Speed - Sentra Penelitian Engineering dan Edukasi International Journal of Artificial Intelligence Research Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) J-SAKTI (Jurnal Sains Komputer dan Informatika) DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Prosiding University Research Colloquium Journal of Information Technology and Cyber Security Jurnal Indonesia : Manajemen Informatika dan Komunikasi Smart Dedication: Jurnal Pengabdian Masyarakat
Dimas Sasongko
Jurusan Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada Jl. Grafika No.2 Kampus UGM, Yogyakarta55281.
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network Resa Arif Yudianto, Muhammad; Sukmasetya, Pristi; Abul Hasani, Rofi; Sasongko, Dimas
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2575

Abstract

Garbage is one of Indonesia's most significant problems with an increase in waste each year reaching 187.2 million tonnes/year. Various efforts to reduce the amount of waste such as Garbage Banks have been encouraged. However, this program has not run well, because some people have difficulty distinguishing the type of waste. One solution to overcome this problem is that need a system that can classify the type of waste. The deep learning approach with the CNN algorithm is currently widely used to solve classification problems. This method requires a large number of datasets to increase the level of accuracy. Getting a garbage dataset is a particular problem in the training process because the dataset is unbalanced. The dataset used amounted to 2527 data consisting of 6 classes. Several treatments such as undersampling and image augmentation are applied to overcome imbalanced datasets. Other treatments such as the type of input image channel and the use of filters are combined into 24 experimental scenarios to achieve the highest accuracy. The results of the experiment get the best scenario, namely, the dataset is undersampling and then augmented with 5 geometric transformation parameters with the input image being RGB and applying a sharpening filter to get an accuracy value of 0.9919 with 20 epochs.
Implementasi Algoritma YOLO V8 (You Only Look Once) Dalam Deteksi Penyakit Daun Durian Putra, Raihan Restu; Maimunah, Maimunah; Sasongko, Dimas
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6136

Abstract

The durian plant (Durio zibethinus Murr.) is one of the leading tropical fruit agricultural products in Indonesia with high economic value. However, durian productivity is often disrupted by leaf disease attacks such as Algal Leaf Spot, Leaf Blight, and Leaf Spot, which results in a decrease in the quality of the crop and its quantity. As a step to address this problem, the goal of this study is to automatically detect durian leaf disease using the YOLOv8 algorithm, a new deep learning model developed to detect objects directly in real time. This study used a data set that included 420 images of durian leaf disease in four categories, Algal Leaf Spot, Leaf Blight, Leaf Spot, and No Disease. This study uses three dataset distribution scenarios (70:20:10, 75:15:10, and 80:10:10), with various epoch and batch size configurations. The results show that the 70:20:10 scenario with 100 epochs and batch size 16 produces the best performance, with a precision value of 0.994, recall of 0.989, mAP50 of 0.990, and mAP50-95 of 0.927. The model developed is able to detect durian leaf disease with high accuracy and fast inference time. The implementation of this model through the Roboflow platform allows efficient use and is ready to be implemented to support the sustainable increase in durian productivity. This research also makes a significant contribution to the development of deep learning-based agricultural technology in Indonesia.
Co-Authors Abul Hasani, Rofi Agung Suprapto, Agung Agus Eko Purnomo Ardhin Primadewi Arju, Muhammad Aryawan, Arif Berliana Kusuma Riasti Budi Yanto, Budi Cinta, Puspa Dono, Bagus Danar Dwi Ngatmono, Dwi Dwi, Shinta Eko Pramono Endah Ratna Arumi, Endah Ratna Estiarto Wahyu Sumirat Fahlefi, Muhamad Rizal Imam, Agung Khairul Isa Budi Kahono Jalaluddin, Alif Kridoyono, Agung Linda Kartika Sari Maharina, Maharina Maimunah, Maimunah Mubarak, Rosyid Muhammad Khoiruddin Harahap Muhammad Resa Arif Yudianto Muhammad Syarifudin Muhammad Wali, Muhammad Mukhtar Hanafi Noer Wahid Novita, Hilda Yulia Nur Marlena Nurudin, Anwar Nuryanto Nuryanto Ogidia Bella, Novica Prabowo, Gadhi Dwi Pratama, Rifky Purnomo, Alwan Badiq Purwono Hendradi Putra, Raihan Restu R Arri Widyanto Resa Arif Yudianto, Muhammad Sambodo Sriadi Pinilih Septiyati Purwandari Setiya Nugroho Sudaryanto, Aris Sukmasetya, Pristi Suyatno . Tri, Vero Uky Yudatama, Uky Wiratama, Lucky Satrya Wiratama, Lucky Satrya Zidan, Muh Zonyfar, Candra