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All Journal Jurnal INKOM Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal Teknik Informatika (JUTIF) eProceedings of Engineering Jurnal Abdimas LOGIC: Jurnal Penelitian Informatika Indonesian Journal on Computing (Indo-JC)
Tjokorda Agung Budi Wirayuda
Universitas Telkom
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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Improving Children's Computational Thinking Through a Combination of Unplugged and Plugged-in CT Techniques Tangible with Robot Games Gunawan, Putu Harry; Indwiarti, Indwiarti; Wirayuda, Tjokorda Agung Budi
Jurnal Abdimas Vol. 29 No. 1 (2025): June 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/j1j9cc66

Abstract

In today's digital era, introducing the concept of computational thinking ( CT ) from an early age is very important. Binekas School, as an educational partner at the Playgroup, Kindergarten (TK), and Elementary School (SD) levels, is committed to introducing CT to children starting at the age of 4. Currently, Binekas School only offers optional extracurricular coding activities at the elementary school level and uses a hard coding approach, which may be too challenging for most students. This school wants to prepare students with an introduction to the basics of coding from kindergarten using a more child-friendly approach, namely plugged-in with tangible robotics. The proposed solution includes an introduction to CT knowledge with a focus on algorithm development and CT training through tangible plugged-in techniques using robots for children aged 4 years and above. This training will not only improve children's understanding of the CT concept in an interactive and fun way, but will also prepare them for future educational challenges. The tools that will be used in this training are the Robotic Gigo Smart Brick, a robotic system designed for children so that they can learn basic programming concepts and computational thinking through interactive games. This outreach activity uses a combination of unplugged CT through card media and physical maps to train problem-solving mindsets accompanied by the opportunity to test proposed solutions with plugged-in CT using the Robotic Gigo Smart Brick. From the results of the activity evaluation through a questionnaire, it was found that 92% of students agreed that the application of the combination of unplugged CT and plugged-in CT was fun, the material was easy to understand and they were interested in getting further material.
Analisis Deteksi Masker Wajah menggunakan YOLOv8 dengan Dataset Facemask Arya Beta Putra Pratama; Wirayuda, Tjokorda Agung Budi; Febriyanti Sthevanie
LOGIC: Jurnal Penelitian Informatika Vol. 3 No. 2 (2025): Desember 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v3i2.9816

Abstract

Penelitian ini menyajikan analisis mendalam tentang deteksi dan klasifikasi masker wajah menggunakan YOLOv8 dan akan diuji coba pada dataset Facemask yang didalamya terdiri dari gambar-gambar yang dibagi dalam tahap pelatihan, pengujian, dan validasi dan melalui dua pendekatan, yaitu augmentasi dan non augmentasi. Penelitian ini menganalisis penilaian kinerja YOLOv8 dan menyoroti kemampuannya mengenali individu yang memakai masker wajah dan yang tidak memakai masker wajah. Tujuan utama dari penelitian ini adalah untuk menganalisis performa YOLOv8 dalam mendeteksi dan mengklasifikasikan penggunaan masker wajah. Hasil evaluasi berdasarkan tiga metrik utama yaitu Mean Average Precision (mAP), Precision, dan Recall. Hasil pada pendekatan non augmentasi model menunjukan Mean Average Precision (mAP) 93,1%, Precison 79,7% , dan Recall 95,9%. Hasil pada pendekatan augmentasi menunjukan menunjukan Mean Average Precision (mAP) 91,9%, Precison 76,6% , dan Recall 94,7%.
Customer Churn Prediction Pada Streaming Musics Platform Menggunakan Ensemble Learning Iqbal Saviola Syah bill haq; Wirayuda, Tjokorda Agung Budi
LOGIC: Jurnal Penelitian Informatika Vol. 3 No. 2 (2025): Desember 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v3i2.9946

Abstract

Churn prediction sangat penting bagi layanan berbasis subscriptions seperti KKBOX, yang mana merupakan sebuah streaming music platform terkenal di Asia. Meskipun terkenal, KKBOX menghadapi tantangan signifikan dengan churn customer, di mana ketika pelanggan membatalkan subscriptions mereka, yang berdampak langsung pada pendapatan dan pertumbuhan perusahaan. Penelitian ini mengeksplorasi pengembangan model churn prediction menggunakan ensemble machine learning. Churn prediction membantu mengidentifikasi pelanggan yang kemungkinan akan membatalkan subscriptions mereka, memungkinkan perusahaan untuk menerapkan retention strategies. Pentingnya topik ini terletak pada implikasi finansial dan pertumbuhan jangka panjang bagi bisnis. Churn predicition yang efektif dapat secara signifikan meningkatkan retention customers, karena mempertahankan hanya 5% dari pelanggan yang ada dapat meningkatkan keuntungan sebesar 25% hingga 95%. Penelitian ini menggunakan dataset dari KKBOX dan mengimplementasikan berbagai model machine learning, termasuk logistic regression, SVM, XGBoost, dan LightGBM, untuk memprediksi churn. Solusi ini melibatkan data exploration, data preparation, feature engineering, untuk meningkatkan model accuracy. Pada experiment ini LightGBM unggul dibanding model lainnya, dengan mencapai skor log loss terendah. Model-model ini menyediakan framework yang kuat untuk churn prediction, dapat meningkatkan retention strategies customers untuk subscription-based services seperti KKBOX. Experiment selanjutnya dapat mengeksplorasi features lainnya dan tuning hyperparameter untuk lebih meningkatkan model performances.
Integration of Thermal Images and Agricultural Data for Multi-Class Classification of Palm Seed Origin using MobileNet Nurrahman, Yusuf Abidin; Wijaya, Rifki; Wirayuda, Tjokorda Agung Budi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4879

Abstract

This research develops a palm kernel origin classification model by combining thermal images and numerical agricultural data using MobileNet architecture. The quality of palm kernels is highly influenced by origin and environmental conditions, but manual visual identification is difficult. Therefore, a machine learning-based approach is applied to improve classification accuracy. The dataset consists of 7.257 thermal images representing 73 seed origin classes, as well as supporting data in the form of soil, fruit, and socioeconomic information collected from plantations in Aceh, Indonesia. The MobileNet model was tested in two scenarios: using only thermal images, as well as a combination of thermal images with agricultural data. Results show that data integration provides significant performance improvement. The best model was obtained from MobileNet V3-Large with the optimal hyperparameter configuration (batch size 16, learning rate 0.001, and optimizer Adam), resulting in test accuracy of 99.04%, validation 97.25%, and training 98.77%. This finding opens up opportunities for the application of real-time classification technology in the plantation environment, especially to support precision and sustainable agriculture.
Employee Attendance System Based on Face Recognition and Liveness Detection Using MagFace Muhammad Idris; Rifki Wijaya; Tjokorda Agung Budi Wirayuda
Indonesian Journal on Computing (Indo-JC) Vol. 10 No. 2 (2026): February, 2026
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/indojc.v10i2.10294

Abstract

Face recognition-based attendance systems are vulnerable to spoofing attacks without effective liveness detection. This study proposes an employee attendance system that integrates CNN-based liveness detection with MagFace-based face recognition to enhance security. The liveness module serves as a preliminary filter to distinguish live faces from spoof attempts before identity verification. Experimental results show that the liveness detection module achieved accuracies of 98%, 96.28%, and 87.27% on training, validation, and testing datasets, respectively, with a False Positive Rate (FPR) of 6.0% on the testing dataset. The MagFace-based recognition module achieved an accuracy of 95.24%, with a False Acceptance Rate (FAR) of 4.64% and an Equal Error Rate (EER) of approximately 4.76%. These results indicate that the proposed system is suitable for employee attendance applications. However, the liveness detection module is intended as a baseline prototype and is not yet designed for high-security biometric authentication scenarios.
Co-Authors Adi Saputra Amna Rizky Anditya Arifianto Andri Arindiah Andrian Wijayana Arya Beta Putra Pratama Bambang Ari Wahyudi Bandy Cipta Nur Ramadhan Barsyah Dwi Idestio Bedy Purnama Bobby Yuliandra Brilian Aringga Prabowo Dian Masmawati Fahmi Rahmanthaha Nurdinsyah Fani Nuraini Febriyanti Sthevanie Febryanti Sthevanie Fitri Amalia Hadi Gugy Lucky Khamdani Gunawan Gunawan Gustav Nugraha Hafidz Al Djohari Hertog Nugroho Idestio, Barsyah Dwi Indwiarti Iqbal Saviola Syah bill haq Jondri Jondri Juwindar Honesty Simanjuntak Kurniawan Nur Ramadhani Muhammad Idris Muhammad Miftah Muslim Zulfikar Muhammad Mirza Muhammad Shafhi Kasyfillah Muhammad Zulfiqar Shafar Muslim Sidiq Natha, Si Gede Ngurah Chandra Adi Nurrahman, Yusuf Abidin Prasti Eko Yunanto Pratama, Arya Beta Putra Putu Harry Gunawan Retno Novi Retno Novi Reza Aulia Sadewa Rifki Wijaya Risky Diatama Rita Rismala Siti Sa'adah Sthevanie, Febriyanti Untari Novia Wisesty Yustar Pramudana