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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengabdian UNDIKMA Journal of Applied Smart Electrical Network and System (JASENS)
Yudi Andika
Politeknik Perkapalan Negeri Surabaya
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Pelatihan Dasar Robotika Berbasis STEM bagi Siswa Sekolah Dasar Al-Islah Surabaya Yudi Andika; Mirza Ardiana; Aulia Rahma Annisa; Dwi Sasmita Aji Pambudi; Mustika Kurnia Mayangsari; Sholahuddin Muhammad Irsyad
Jurnal Pengabdian UNDIKMA Vol. 7 No. 1 (2026): February
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i1.18201

Abstract

This community service program aims to enhance elementary school students’ understanding and basic skills in STEM-based robotics (Science, Technology, Engineering, and Mathematics) at SD Al-Islah Surabaya, thereby fostering adaptive, creative, and innovative character development in response to the challenges of globalization. The program implementation included the preparation of instructional modules, delivery of theoretical materials, basic programming activities, equipment demonstrations, hands-on practice, and evaluation using observation sheets to assess participant performance. The activity was conducted at SD Al-Islah Surabaya and involved 94 third- and fourth-grade students accompanied by their homeroom teachers. The results indicate that 50% of the students were able to operate the robots they assembled. In addition, students demonstrated high levels of enthusiasm throughout the activities, actively asked questions, and were able to independently attempt and operate simple robotic systems. Overall, the program effectively improved students’ problem-solving skills and critical thinking abilities, while also developing their foundational understanding of STEM concepts and robotics techniques. This initiative is expected to serve as an initial step toward integrating robotics education into the curriculum of the partner school.
Analisis Kinerja Sistem Kontrol Hybrid Electric Vehicle (HEV) Menggunakan Metode Neuro-fuzzy Aulia Rahma Annisa; Yudi Andika; Sholahuddin Muhammad Irsyad; Dwi Sasmita Aji Pambudi
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/f5y40s25

Abstract

Electric cars are an environmentally friendly vehicle alternative developed to reduce exhaust gas emissions and air pollution. One example is the Hybrid Electric Vehicle (HEV), which combines an Internal Combustion Engine (ICE) and an electric motor (DC motor) to improve efficiency and torque performance. HEVs generally have a smaller capacity compared to conventional vehicles, making them more fuel-efficient and energy-efficient. The system in an HEV is complex and nonlinear, requiring dynamic model approaches and appropriate control methods to maintain optimal performance. This research aims to analyze the performance of the inverse model neuro-fuzzy control system predictor implemented on an HEV. The test results show that applying a neuro-fuzzy controller can significantly improve the system's ability to achieve a response that matches the reference model. The performance of the DC motor is able to help reduce the speed error difference by up to 50 rpm with a Root Mean Square Error (RMSE) value of 0.582%. Additionally, there is a 1.411% decrease in error value compared to when the ICE is operating without using a neuro-fuzzy controller. Based on these results, the neuro-fuzzy method has proven effective in improving the accuracy and stability of the control system in HEVs.
Hyperparameter Optimization of CNN Based Open Set Speaker Verification Using MFCC and Speaker Embedding for Voice Biometric Security Mirza Ardiana; Mat Syai’in; Alief Nur Aisyi Maulidhia; Aulia Rahma Annisa; Yudi Andika; Sholahuddin Muhammad Irsyad; Fauzan Izzul Haq
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13027

Abstract

The development of voice based biometric security systems has increased the demand for authentication methods capable of operating accurately and securely in open set speaker verification scenarios. In this scenario, the system is required not only to recognize registered users but also to reject unknown users who are not included in the system database. This study focuses on hyperparameter optimization in a Convolutional Neural Network Embedding based speaker verification system using Mel Frequency Cepstral Coefficient (MFCC) features and speaker embeddings. The optimization process was conducted through several experimental stages, including MFCC parameter tuning, CNN architecture tuning, embedding dimension tuning, and audio augmentation analysis. The dataset consisted of Indonesian speech recordings from 8 registered speakers and 1 unknown speaker, sampled at 16 kHz under controlled recording conditions. The dataset was divided into training, enrollment, and testing subsets to support open set speaker verification evaluation and reduce data leakage. System performance was evaluated using accuracy, validation loss, False Acceptance Rate (FAR), False Rejection Rate (FRR), best threshold, and inference time. The experimental results show that the best configuration was achieved using the MFCC-C parameters (N_MFCC = 40, N_FFT = 1024, HOP_LENGTH = 256, N_MELS = 40), the CNN-E architecture with three convolution blocks (32-64-128), an embedding dimension of 64, and lightweight augmentation consisting of noise injection, pitch shifting, and time stretching. This configuration achieved stable system performance with a test accuracy of 96.43% and a FAR of 8.7%, while maintaining lightweight computational complexity and real time inference capability. The results also indicate that excessive augmentation may increase embedding overlap between speakers, thereby reducing system security performance. However, the study was conducted on a limited scale dataset and has not yet evaluated robustness against spoofing attacks, replay attacks, or adversarial synthesized voice attacks. Overall, the study indicates that hyperparameter optimization influences the balance between accuracy, computational efficiency, and biometric security performance in lightweight CNN based voice biometric authentication systems under limited scale evaluation conditions.
Co-Authors Alief Nur Aisyi Maulidhia Aulia Rahma Annisa Aulia Rahma Annisa Dwi Sasmita Aji Pambudi Fauzan Izzul Haq Mat Syai’in Mirza Ardiana Mustika Kurnia Mayangsari Sholahuddin Muhammad Irsyad