Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization Charity : Jurnal Pengabdian Masyarakat Journal of Artificial Intelligence and Digital Business
Felix Corputty
Telkom University
Arts Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Exploration of The Impact of Kernel Size for YOLOv5-based Object Detection on Quadcopter Rissa Rahmania; Felix Corputty; Suryo Adhi Wibowo; Dany Eka Saputra; Annisa Istiqomah
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.898

Abstract

Drones or quadcopters have been widely used in various fields based on deep learning, especially object detection. However, drone vision characteristics such as occlusion and small objects are still being explored for performance in terms of accuracy and speed detection. The YOLO architecture is very commonly used for cases requiring high-speed detection. To overcome the limitations of drone vision, in this paper, we explore the size of the YOLOv5s backbone kernel in the shallowest convolutional layer to achieve better performance. The kernel is a filter that has a main role in the feature map, and it defines the size of the convolution matrix, and the resulting features in the shallowest convolutional layer are more representative of the case of object detection and recognition. The techniques can be divided into three major categories: (1) data preprocessing, which involves augmentation and normalization of the data, (2) kernel size exploration in the shallowest convolutional layer of the YOLOv5s, and (3) model implementation in the real environment using the quadcopter. The dataset consisted of four classes representing dragon fruit, snake fruit, banana, and pineapple, with a total of 8000 data. Exploration results with kernel size give promising results. Kernel sizes 5 and 7 give an mAP of 0.988. Through these results, modification of the kernel size provides an opportunity for more in-depth investigations, such as with the epoch parameter, padding scheme, and other optimization techniques.
Pelatihan Pembuatan Media Pembelajaran Berbasis Artificial Intelligence Untuk Guru TK di IGKTI Bandung Timur Corputty, Felix; Setiawan, Dhoni Putra; Ramadha, Ade Aditya; Renata, Ganis Widya; Ghalyndra, Farrel Ardya; Disiulina, Octlivatua Patricia; Putro, Bagas Eko Tjahyono
Charity : Jurnal Pengabdian Masyarakat Vol. 8 No. 2 (2025): Charity-Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v8i2.9469

Abstract

Perkembangan teknologi Artificial Intelligence (AI) memberikan peluang besar dalam meningkatkan kualitas pembelajaran di berbagai tingkatan pendidikan, salah satunya pada tingkat Taman Kanak-Kanak (TK). Namun, masih banyak guru TK yang belum memahami pemanfaatan teknologi AI dikarenakan keterbatasan literasi teknologi dan minimnya pelatihan teknologi yang relevan. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan pengetahuan dan keterampilan para guru TK di bawah naungan IGTKI Bandung Timur dalam menciptakan media pembelajaran berbasis AI yang menarik, interaktif, efektif, dan sesuai dengan perkembangan anak usia dini. Metode kegiatan meliputi analisis kebutuhan, pelatihan dan pendampingan langsung, praktik pembuatan media ajar, serta evaluasi pasca pelatihan. Tools yang diperkenalkan dalam pelatihan meliputi DeepAI untuk pembuatan ilustrasi gambar, Canva untuk desain lembar kerja, serta Google Translate untuk mendukung penyesuaian bahasa prompt. Hasil pelatihan menunjukan bahwa seluruh peserta berhasil menghasilkan minimal satu media ajar berbasis AI, Serta menunjukan antusiasme tinggi selama sesi berlangsung. Evaluasi kuantitatif menunjukan bahwa 65 responden menyatakan pelatihan ini sesuai dengan tujuan kegiatan, dengan 42% menyatakan setuju dan 58% sangat setuju. Selain itu, responden juga merasa bahwa pelatihan ini sesuai dengan kebutuhan mereka, terdiri dari 52% setuju dan 48% sangat setuju. Kegiatan ini memberikan dampak positif terhadap peningkatan literasi teknologi guru TK dan membuka potensi berkelanjutan berupa pelatihan berkala dan kolaborasi riset pendidikan di masa depan. Kata Kunci : Artificial Intelligence, Guru TK, Literasi Teknologi, Media Pembelajaran, Pelatihan.
Evaluation of Machine Learning Models for Customer Churn Prediction Using LIME-Based Explainable AI Corputty, Felix; Putra, Verindra Hernanda
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.7294

Abstract

Customer attrition forecasting has become a critical challenge in highly competitive industries such as telecommunications, where retaining existing customers is more cost-effective than acquiring new ones. Although machine learning techniques have been widely applied to identify customers at risk of churn, many models operate as black boxes, limiting their interpretability and usability. To address this issue, this study proposes an integrated framework that combines predictive modeling with Explainable Artificial Intelligence (XAI) using the Local Interpretable Model-Agnostic Explanations (LIME) technique. Unlike conventional approaches that treat explainability as a post-hoc analysis, the proposed framework embeds LIME directly into the modeling pipeline to ensure both accurate and interpretable predictions. The method consists of several stages, including data preprocessing, feature selection, model training, performance evaluation, and model interpretation. Experiments were conducted using the Telco Customer Churn dataset obtained from Kaggle. Three classification algorithms, namely Logistic Regression, Decision Tree, and Random Forest, were evaluated using accuracy, precision, and recall metrics. The results show that Logistic Regression achieved the highest accuracy of 0.8211, followed by Random Forest with 0.7928 and Decision Tree with 0.7289. Furthermore, LIME-based analysis identifies contract type, internet service, monthly charges, tenure, and additional services such as online security and technical support as key factors influencing churn. These results demonstrate that integrating machine learning with XAI enhances model transparency and provides actionable insights for more effective customer retention strategies.
Comparative Analysis of YOLOv8 and YOLOv11 for Wildlife Detection in Natural Habitats FELIX CORPUTTY; AHMAD SULTHON JAUHARI; DARIELE ZEBADA SANUWU GEA; IVAN FEBRIANTO LALO; RIFKI RAHMAN NUR IKHSAN; ADE ADITYA RAMADHA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 3: Published July 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i3.288

Abstract

Wildlife monitoring in natural habitats remains difficult due to occlusion, camouflage, and varying illumination, which limit the performance of conventional object detection systems. Although recent YOLO-based models have shown strong potential, systematic comparisons between newer architectures in real ecological environments are still limited. This study evaluates YOLOv8 and YOLOv11 using 6,165 images from 14 wildlife classes collected at Taman Buru Gunung Masigit Kareumbi, with data augmentation applied to improve robustness. Performance is evaluated using a quantitative comparative approach based on precision, recall, mAP 0.5, and mAP 0.5:0.95. The results indicate that YOLOv11 achieves higher detection accuracy with an mAP 0.5:0.95 of 0.894.
Co-Authors Ade Aditya Ramadha AHMAD SULTHON JAUHARI Annisa Istiqomah Dany Eka Saputra DARIELE ZEBADA SANUWU GEA DHONI PUTRA SETIAWAN Disiulina, Octlivatua Patricia Ghalyndra, Farrel Ardya IVAN FEBRIANTO LALO Putra, Verindra Hernanda Putro, Bagas Eko Tjahyono Renata, Ganis Widya Rifki Rahman Nur Ikhsan Rissa Rahmania Suryo Adhi Wibowo