Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Informatika Pertanian
Ikhlas, Ariza
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MESIN PEMBELAJARAN ENSEMBLE UNTUK IDENTIFIKASI VARIETAS PADI Ikhlas, Ariza; Abdullah, Abdullah; Prasetyo, Dwi Yuli
Informatika Pertanian Vol 29, No 2 (2020): DESEMBER
Publisher : Sekretariat Badan Penelitian dan Pengembangan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/ip.v29n2.2020.p123-130

Abstract

Setiap varietas padi memiliki karakter tertentu dengan anjuran tanam berbeda. Petani umumnya kesulitan memilih varietas padi yang cocok untuk ditanam di lahan mereka karena kurangnya kemampuan identifikasi. Algoritma klasifikasi merupakan solusi mengatasi masalah ini karena mampu mengidentifikasi varietas padi melalui citra digital. Tujuan penelitian ini adalah menerapkan dan mengevaluasi beberapa algoritma klasifikasi untuk mengidentifikasi varietas padi menggunakan fitur warna dan tekstur. Penelitian dilakukan di kabupaten Indagiri Hilir Riau pada tahun 2018. Mesin pembelajaran dibangun dengan cara menggabungkan beberapa algoritma klasifikasi (classifier), yaitu Support Vector Machine, k-Nearest Neighbors, Logistic Regression, dan Decision Tree. Varietas yang diteliti adalah IR42, Inpara-9. dan Batang Piaman. Berdasarkan tingkat ketelitian masing-masing algoritma, k-Nearest Neighbors memberikan hasil lebih baik dibanding algoritma lainnya, baik dengan maupun tanpa normalisasi data. Terdapat enam sampel Inpara-9 yang diprediksi benar (true positive) dan lima sampel diprediksi salah (false positive). Pada varietas Batang Piaman terdapat delapan sampel yang diprediksi benar (true positive). Pada IR42 terdapat lima sampel yang diprediksi benar.
Literature Review: A Comparative Study of Waste Classification using Deep Learning Algorithms Ikhlas, Ariza; Hendrik, Billy
Sistemasi: Jurnal Sistem Informasi Vol 14, No 3 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i3.5163

Abstract

Waste type classification remains a daily challenge in modern waste management. Proper waste classification contributes significantly to environmental protection and enhances the efficiency of the recycling process. Unfortunately, manual waste classification is rarely performed by individuals, resulting in mixed waste that is difficult to separate into recyclable and non-recyclable categories. This leads to increased waste accumulation, which becomes harder to process over time. Therefore, automating this procedure using computer vision is of critical importance. This study adopts a Systematic Literature Review (SLR) methodology to analyze existing research conducted by previous scholars. The main objectives are to identify the most appropriate algorithms for waste type classification, determine the most suitable model architectures, and examine the correlation between dataset size, number of classes, and classification accuracy. The results of the literature review show that the Convolutional Neural Network (CNN) algorithm is widely used and considered highly effective for computer vision tasks. Among the best-performing models are: A standard CNN architecture achieving 100% accuracy with 150 data points and 3 classes, CNN with ResNet50 model achieving 99.41% accuracy on 2,527 data points and 6 classes, A combination of ResNet, k-Nearest Neighbors (kNN), and Neighborhood Component Analysis (NCA) achieving 99.35% accuracy on 13,089 data points and 1,672 classes, CNN with CapSA ECOC + ANN model reaching 99.01% accuracy on 1,515 data points and 12 classes. These findings indicate that numerous prior studies have successfully developed high-accuracy models for waste classification, which can serve as a solid foundation for building computer vision systems to automate the waste sorting process.
Co-Authors Abdullah Abdullah Dwi Yuli Prasetyo Hendrik, Billy