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All Journal Jurnal Pendidikan Teknologi dan Kejuruan KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Sains dan Teknologi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan SINTECH (Science and Information Technology) Journal WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Teknologi Informasi dan Pendidikan Jurnal Bali Membangun Bali Unri Conference Series: Community Engagement Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Jurnal Ilmu Komputer Dan Informatika Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Artificial Intelligence and Digital Business Sains Data Jurnal Studi Matematika dan Teknologi Manufaktur: Publikasi Sub Rumpun Ilmu Keteknikan Industri KOMTEKS: Komputer dan Teknologi Sains
I Nyoman Saputra Wahyu Wijaya
Universitas Pendidikan Ganesha
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Synthetic Data Quantity in Animal Classification Fieter Brain Pasaribu; Luh Joni Erawati Dewi; I Nyoman Saputra Wahyu Wijaya; Ni Wayan Marti; Ni Putu Novita Puspa Dewi
Jurnal Teknologi Informasi dan Pendidikan Vol. 20 No. 1 (2027): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v20i1.1137

Abstract

The need for large datasets remains a challenge in deep learning-based classification models such as Convolutional Neural Networks. Limited data can lead to overfitting, where the model performs well on training data but poorly on testing data. To address this, numerous studies have been conducted using both traditional augmented data and synthetic data generated by Generative Adversarial Networks (GANs). GANs have proven effective across a wide range of dataset domains. However, no research has focused on the effect of the amount of synthetic data on model classification accuracy, leading to confusion about how much should be used to avoid overfitting. Therefore, this study will review the effectiveness of GANs by examining the effect of synthetic data ranging from small amounts to 20 times the original dataset size. The synthetic image results from GANs were then tested using them as supplementary datasets for MobileNetV2 and EfficientNetB0. The results show that the effectiveness of synthetic data fluctuates significantly. At certain amounts, the use of synthetic data can improve accuracy, such as in MobileNetV2, which increased from 73% to 77%. However, at certain amounts, the use of synthetic data also decreases accuracy from 73% as the baseline to 66%. This fluctuating condition is caused by the synthetic data from GANs not being consistent in representing the original data well. The lack of quality of GAN is caused by the architecture used, which is still too simple to capture data patterns with a wide variety of perspectives
Pengenalan Emosi Manusia Melalui Suara Menggunakan Metode Convolutional Neural Network dan Transformer Ida Bagus Sebali Bayu Nandana; Ni Putu Novita Puspa Dewi; I Nyoman Saputra Wahyu Wijaya
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 2 (2026): Edisi Oktober
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Communication between humans and machines currently lacks a natural emotional component, since systems generally understand verbal content without capturing the emotional meaning conveyed by tone and timbre. This study aims to design a Speech Emotion Recognition model by integrating Convolutional Neural Network (CNN) and Transformer architectures to capture spatial features as well as long-range temporal dependencies in speech signals. The dataset combines four public corpora, namely RAVDESS, CREMA-D, SAVEE, and TESS, comprising 12,798 audio files across seven emotion classes (angry, disgusted, fearful, happy, neutral, sad, and surprised). The audio signals were processed through Mel Spectrogram feature extraction along with delta and delta-delta coefficients, balanced through data augmentation, and trained using a 5-Fold Stratified Cross Validation scheme. Testing on an independent test set shows that the hybrid model achieves a 5-fold average accuracy of 75.80%, while the best-performing single-fold model reaches 77.00% accuracy with a Macro Average F1-Score of 78.00%, and all emotion classes obtain an AUC value above 0.90. Applying Ensemble Learning (Soft Voting) across the five fold models further improves the testing accuracy to 81.00% with a Macro Average F1-Score of 0.82, representing the highest overall result achieved in this study. These results demonstrate that the integration of CNN and Transformer is effective and generalizes well on heterogeneous speech data.
RANCANG BANGUN SMART HOME BABY MONITORING BERBASIS INTERNET OF THINGS (IoT) K Arista Arya Dwijaya; I Ketut Resika Arthana; I Nyoman Saputra Wahyu Wijaya
KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) Vol. 15 No. 3 (2026): ONGOING : Karmapati Vol 15 No 3 Tahun 2026
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/karmapati.v15i3.120128

Abstract

The development of Internet of Things (IoT) technology has encouraged the implementation of smart home systems to improve safety and convenience in daily life, including infant monitoring. Parents' limited ability to continuously supervise their babies creates the need for a system capable of real-time monitoring and providing notifications whenever conditions requiring immediate attention are detected. This study aims to design and develop an Internet of Things (IoT)-based Smart Home Baby Monitoring system that can monitor both infant activities and environmental conditions in real time. The research employed the Rapid Application Development (RAD) method, which consists of the stages of requirement planning, design workshop, construction, and implementation. The proposed system utilizes an ESP32 as the main server and two Wemos D1 Mini boards as sensor nodes integrating a PIR HC-SR501 motion sensor, KY-037 sound sensor, DHT11 temperature sensor, and MQ-2 smoke sensor. Sensor data are transmitted via a Wi-Fi network to the ESP32, which subsequently forwards the data to an MQTT Broker using the MQTT protocol and sends notifications to users through a Telegram Bot. The experimental results demonstrate that all sensors functioned according to the system design, MQTT-based data communication operated reliably, and the Telegram Bot successfully delivered real-time notifications when motion, sound, smoke, or temperature changes were detected. Therefore, the proposed system effectively assists parents in remotely monitoring their infants while supporting the implementation of Internet of Things-based smart home technology.
Co-Authors A. A. Gede Yudhi Paramartha Agus Aan Jiwa Permana Agus Seputra I Ketut Anak Agung Candra Widyaningsih Anggaradiva Bendesa Atmaja, Ketut Jaya Chiki Dwi Putri Sibarani Devina Marsya Rani Fieter Brain Pasaribu I Gusti Ayu Agung Diatri Indradewi I Gusti Made Ngurah Desnanjaya I Ketut Purnamawan I Ketut Resika Arthana I Made Aditya Nugraha I Made Putrama I Md. Dendi Maysanjaya I Putu Adi Pratama Ida Bagus Ary Indra Iswara Ida Bagus Sebali Bayu Nandana K Arista Arya Dwijaya Kadek Yota Ernanda Aryanto Luh Joni Erawati Dewi Made Donita Maharani Ngakan Gde Satria Abirama Ni Ketut Kertiasih Ni Putu Novita Puspa Dewi Ni Putu Novita Puspa Ni Putu Novita Puspa Ni Wayan Marti Ni Wayan Yulya Wiani Putu Mandiasa Putu Praba Santika Sandika, I Kadek Budi Trihana Santhi Valensia Natalia Wayan Gede Suka Parwita Yudhantara, I Kadek Prasta