Article Per Year (5 Year)
p-Index From 2021 - 2026
4.017
P-Index
This Author published in this journals
All Journal Jurnal Algoritma Jurnal Informatika dan Teknik Elektro Terapan PROCEEDING IC-ITECHS 2014 SMARTICS Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING INTECOMS: Journal of Information Technology and Computer Science Jiko (Jurnal Informatika dan komputer) KOMPUTIKA - Jurnal Sistem Komputer JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi Infotech: Journal of Technology Information SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Proceeding of International Conference Health, Science And Technology (ICOHETECH) Innovative: Journal Of Social Science Research Journal of Software Engineering and Information System (SEIS) SmartComp
Pradana, Afu Ichsan
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Other

Published : 31 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Algoritma

 1 
Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa Wijiyanto, Wijiyanto; Pradana, Afu Ichsan; Sopingi, Sopingi; Atina, Vihi
Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-1.1618

Abstract

A student's ability to complete courses is influenced by various factors, including academic and non-academic aspects. Understanding the factors that influence it is very important to know in order to anticipate and prevent the possibility of failure in the study. It turns out that non-academic factors also have a big influence on student success, especially family factors, such as the level of education obtained by parents, the employment status of parents and the income of both parents. To be able to understand these factors, studies are needed to predict student performance based on family background factors using machine learning models, support vector machine algorithms, naïve Bayes, neural networks and decision trees. The data used was 365 records and 11 attributes, separated by 70% for train data and 30% for test data, which was then used by kfold cross validation to evaluate the results using the parameters n_split=10 and random_state=42. In the confusion matrix parameters, the average (mean) accuracy value for the support vector machine model was 87.68%, naïve Bayes was 90.97%, neural network was 87.95% and decision tree was 85.75%. Meanwhile, the best fold result for SVM is located at the 10th fold with an accuracy of 94.44%, for NB it is located at the 4th fold with an accuracy value of 97.29%, for NN it is located at the 4th fold with an accuracy value of 94.59% and for DT is located on the 5th fold with an accuracy value of 91.89%. Thus, evaluation using k-fold cross validation can be used to predict student performance based on family attributes using the 4th fold which has the highest accuracy of 97.29% in the naïve Bayes model algorithm in order to graduate on time.
Deteksi Rambu Lalu Lintas Real-Time di Indonesia dengan Penerapan YOLOv11: Solusi Untuk Keamanan Berkendara Pradana, Afu Ichsan; Harsanto, Harsanto; Wijiyanto, Wijiyanto
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.2106

Abstract

This research aims to formulate and assess a real-time traffic sign detection framework in the context of Indonesia, using YOLOv11. Given the heterogeneous nature of traffic signs and road conditions in Indonesia, there is an urgent need for a robust and precise model to improve driving safety. The findings show that the model successfully achieved a Mean Average Precision (mAP) of 0.99, simultaneously demonstrating high accuracy across a wide range of traffic sign classifications. Evaluation using Confusion Matrix, shed light on the negligible error rate, signaling that the model has sufficient reliability for real-world applications. The potential applications of this technology are crucial in strengthening Indonesia's driving safety and intelligent transportation systems.
Optimalisasi Akurasi Model Identifikasi Penyakit Pada Daun Padi Dengan Fine-Tuning YOLOv11 Untuk Ketahanan Pangan Berkelanjutan Harsanto; Pradana, Afu Ichsan; Wahyu Pamekas, Bondan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2945

Abstract

Rice is one of Indonesia's main food commodities, whose productivity often declines due to leaf disease. Early detection of rice leaf disease is an important aspect of maintaining sustainable food security. This study aims to optimize the accuracy of early identification of rice leaf disease by fine-tuning the YOLOv11 model. The research stages included dataset collection, annotation, data preprocessing, data augmentation, model training, fine-tuning, and model performance evaluation. The results showed an improvement in model performance after fine-tuning, with the overall recall value increasing from 0.760 to 0.788 and mAP from 0.764 to 0.785. The confusion matrix also shows a more stable prediction distribution in the fine-tuned model compared to the initial model. Thus, fine-tuning YOLOv11 has proven to be effective in improving the accuracy of early identification of rice leaf diseases and has the potential to support the application of artificial intelligence in the agricultural sector to strengthen food security in Indonesia.
Co-Authors Aboobaider, Burhanuddin bin Mohd Aji, Sindhu Purnomo Akmal, Baasith Khoiruddin arsanto, arsanto Ashidiqi, Andhyka Yoga Atina, Vihi Azizah, Shelvi Bagaskara, Aditya Dwi Hartanti Eko Purwanto Harjanto, Prabandaru Pramu Harsanto Harsanto, Harsanto Harsanto, Malika Indriyani, Tiara Insaan, Nashiirotul Jordan, Alles Tio Junantoro, Hagai Kusuma, Adri Surya Maulindar, Joni Nugroho, Nasrun Nurchim Nurchim Pamekas, Bondan Wahyu Permana, Aditya Candra Permatasari, Hanifah Pipin Widyaningsih Pratama, Fandi Aziz Pujiati Edy Santoso, Elysa Mei Putra, Enggar Wijaya Putra, Wihan Perkasa Nugraha Rakadipa, Dhimas Arya Rengga, Nikodemos Rifky, Raja Saputra, Ilham Tristadika Setiawan, Gilang Sopingi Sopingi, Sopingi Tory, Alfa Rado Andre Yusa Saka Wahyu Pamekas, Bondan Wahyudi Wahyudi Wijanarko, Restu Gilang Wiji Lestari Wijiyanto Wijiyanto, Wijiyanto Yusuf Maulana, Yusuf