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Murinto
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Pengenalan Dan Pelatihan UI/UX Serta Jenjang Karir Di Masa Depan untuk Siswa Siswi SMK Informatika Wonosobo Fadlil, Abdul; Murinto; Firdaus, Asno Azzawagama; Rifaldi, Dianda
Humanism : Jurnal Pengabdian Masyarakat Vol 4 No 3 (2023): Desember
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/hm.v4i3.20285

Abstract

Artikel ini menyajikan kegiatan pengabdian yang dilaksanakan pada 12 Juni 2023 di SMK Informatika Wonosobo, Jawa Tengah. Kegiatan tersebut difokuskan pada pengenalan desain UI/UX dan pelatihan terkait desain UI/UX untuk membantu siswa mempersiapkan karir di bidang tersebut di masa depan. Sebanyak 20 orang siswa ikut serta dalam kegiatan ini yang didampingi oleh pihak sekolah. Peserta menunjukkan antusiasme yang tinggi selama kegiatan berlangsung. Kegiatan berupa sosialisasi dan tanya jawab hingga praktik langsung ini memang baru kali pertama diselenggarakan pada SMK Informatika Wonosobo tersebut sehingga siswa belum memiliki pemahaman mengenai desain UI/UX. Hal tersebut terlihat dari peningkatan skor akhir yang signifikan dalam evaluasi pra dan pasca pembekalan menggunakan pre test dan post test dengan metode perhitungan likert. Skor akhir meningkat dari 44,2% pada pre test menjadi 93,6% pada post test. Hasil ini menunjukkan bahwa kegiatan pengabdian ini berhasil meningkatkan pemahaman dan pengetahuan peserta dalam bidang desain UI/UX. Pihak sekolah mengharapkan kegiatan serupa dapat tetap dilaksanakan di SMK Informatika Wonosobo guna meningkatkan pengetahuan dan pemahaman siswa mengenai dunia kerja.
Pengenalan Kecedasan Buatan Untuk Pelajar Sekolah Menengah Pertama Muhammadiyah Al Mujahidin Agung Tri Lestari, agungyappi; Rinday Zildjiani Salji; Nadia Wati Aprianti; Deni Kuswandani; Nilam Tri Astuti; Murinto; Herman; Abdul Fadlil
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2025): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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Abstract

Program pengabdian masyarakat ini dirancang untuk memperkenalkan konsep dasar kecerdasan buatan (AI) kepada siswa Sekolah Menengah Pertama (SMP) di SMP Muhammadiyah Al Mujahidin. Latar belakang kegiatan ini adalah adanya kesenjangan antara pesatnya perkembangan teknologi digital, khususnya AI, dan minimnya pemahaman pelajar SMP tentang teknologi tersebut. Melalui serangkaian workshop interaktif dan edukatif, program ini bertujuan untuk memberikan pemahaman awal tentang konsep AI, mendemonstrasikan contoh aplikasi AI dalam kehidupan sehari-hari (seperti asisten virtual dan sistem rekomendasi), serta mendiskusikan dampak positif dan negatifnya. Metode implementasi melibatkan pemaparan materi yang disederhanakan dan menarik, penggunaan media visual, permainan interaktif, dan simulasi. Hasil yang diharapkan dari program ini adalah peningkatan literasi digital siswa, tumbuhnya minat dan rasa ingin tahu terhadap teknologi, serta terbentuknya pondasi untuk pemahaman yang lebih mendalam tentang AI di masa depan.
Siamese Neural Networks with Chi-square Distance for Trademark Image Similarity Detection Suyahman; Sunardi; Murinto; Arfiani Nur Khusna
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.4654

Abstract

Purpose: The objective of this study is to address the limitations of existing trademark image similarity analysis methods by integrating a Chi-square distance metric within a Siamese neural network framework. Traditional approaches using Euclidean distance often fail to accurately capture the complex visual features of trademarks, leading to suboptimal performance in distinguishing similar trademarks. This research aims to improve the precision and robustness of trademark comparison by leveraging the Chi-square distance, which is more sensitive to image variations. Methods: The approach involves modifying a Siamese neural network traditionally employing Euclidean distance with the use the Chi-square distance metric instead. This alteration allows the network to better capture and analyze critical visual features such as color and texture. The modified network is trained and tested on a comprehensive dataset of trademark images, enabling the network to learn and distinguish between similar and dissimilar trademarks based on subtle visual cues. Result: The findings from this study show a significant increase in accuracy, with the modified network achieving an accuracy rate of 98%. This marks a notable improvement over baseline models that utilize Euclidean distance, demonstrating the effectiveness of the Chi-square distance metric in enhancing the model's ability to discriminate between trademarks. Novelty: The novelty of this research lies in its application of the Chi-square distance in a deep learning framework specifically for trademark image similarity detection, presenting a novel approach that yields higher precision in image-based comparisons.
Comparative Analysis of CNN Architectures in Siamese Networks with Test-Time Augmentation for Trademark Image Similarity Detection Suyahman; Sunardi; Murinto
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13811

Abstract

Purpose: This study aims to enhance the detection of trademark image similarity by conducting a comparative analysis of various Convolutional Neural Network (CNN) architectures within Siamese networks, integrated with test-time augmentation techniques. Existing methods often face challenges in accurately capturing subtle visual similarities between trademarks due to limitations in feature extraction and generalization capabilities. The research seeks to identify the most effective CNN architecture for this task and to assess the impact of test-time augmentation on model performance. Methods: The study implements Siamese networks utilizing three distinct CNN architectures: VGG16, VGG19, and ResNet50. Each network is trained on a dataset of trademark images to learn deep feature representations that can discriminate between similar and dissimilar trademarks. During the evaluation phase, test-time augmentation (TTA) is applied to enhance model robustness by averaging predictions over multiple augmented versions of the input images. TTA includes transformations such as random rotations (up to 40%), width and height shifts (up to 20%), random shear transformations, zooming (up to 20%), horizontal and vertical flips, and random brightness adjustments. Result: Experimental findings reveal that the Siamese network based on VGG19 achieves the highest accuracy at 98.82%, outperforming the VGG16-based network with an accuracy of 97.07% and the ResNet50-based network with 50.00% accuracy. The application of TTA has improved performance across all models, with the VGG19 model receiving the highest improvement. The extremely low accuracy of ResNet50 can be attributed to its misinterpretation of original trademark images as close-forged ones, probably due to overfitting or lack of an efficient ability in generalizing very fine visual features. Novelty: The study conducted a comparative analysis of CNN architectures, namely VGG16, VGG19, and ResNet50 in Siamese networks for trademark image similarity detection.
Intelligent Monitoring of Smoking Prohibition in Public Spaces Using YOLOv8: Real-Time Detection and Telegram Notifications Putri, Salsabilla Azahra; Murinto; Sunardi
Jurnal Penelitian Pendidikan IPA Vol 11 No 4 (2025): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i4.10519

Abstract

This study aims to develop an intelligent monitoring system that supports the enforcement of smoking prohibition in public spaces by leveraging advancements in Artificial Intelligence (AI) and deep learning. Utilizing the YOLOv8 (You Only Look Once version 8) object detection model, the system is designed to identify smoking activities in real-time and promptly send alerts through the Telegram messaging platform. The proposed method integrates real-time object detection with an automated notification system, ensuring responsive enforcement across diverse environmental conditions, including normal lighting, low-light scenarios, and partially occluded views. The system architecture combines the YOLOv8 model for detection and a Python-based Telegram bot for communication. The model was evaluated using a test dataset collected from various public spaces. It achieved an F1-Score of 81% and a confusion matrix accuracy of 89%, indicating a high level of reliability and precision in identifying smoking behaviors. Additionally, the average notification response time via Telegram was 1.5 seconds, enabling near-instantaneous alerting for enforcement personnel. In conclusion, the results demonstrate that the system is both accurate and efficient in detecting smoking activities. Its robust performance across different conditions and rapid alert mechanism positions it as a practical and scalable solution to enhance compliance with smoking regulations in public areas.
Model Klasifikasi Emosi Berbasis Teks dengan Algoritma Decision Tree dan Support Vector Machine Raihan, Habib Aulia; Yuliansyah, Herman; Murinto
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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Abstract

Text-based communication has become a key means of interaction across various sectors. Previous studies have applied supervised learning algorithms to emotion classification in text. These studies used different datasets, but this diversity also introduced a risk of overfitting in text-based emotion classification models. Consequently, the use of cross-validation and hyperparameter optimization is required to ensure the model’s generalization ability. The aim of this research is to compare the performance of two supervised learning algorithms—Decision Tree (DT) and Support Vector Machine (SVM)—for emotion classification on an English-language text dataset of 16,000 labeled entries (anger, fear, joy, love, sadness, surprise) sourced from Kaggle. The dataset undergoes cleaning, tokenization, stopword removal, and lemmatization, after which features are extracted using TF-IDF. Both algorithms are evaluated with K-Fold and Stratified K-Fold cross-validation, then used to compute metrics of accuracy, precision, recall, and F1-score. Classification results show that the hyperparameter-tuned DT achieved an average accuracy of 88%, while the hyperparameter-tuned SVM achieved 89%. Meanwhile, Stratified K-Fold cross-validation yielded an accuracy variance of just 0.02% for DT and 0.15% for SVM. Therefore, it can be concluded that Stratified K-Fold performs better than standard K-Fold on imbalanced datasets, and that hyperparameter-tuned SVM outperforms hyperparameter-tuned DT.
Co-Authors Abdul Fadlil Abdul Fadlil Agung Tri Lestari, agungyappi Agus Akhtawan Andhi Yanto Wibowo Arfiani Nur Khusna Asno Azzawagama Firdaus Caesar Irjayana, Rizky Dedi Wijayanti Deni Kuswandani Dianda Rifaldi Febriyanto, Bayu Herman Herman Yuliansyah, Herman Imam Riadi Kencana, Nagala Wangsa Muhammad Kunta Biddinika Nadia Wati Aprianti Nilam Tri Astuti Prambudi, Rizal Prambudi Putri, Salsabilla Azahra Raihan, Habib Aulia Rinday Zildjiani Salji Rusydi Umar Salsabilla Azahra Putri Sunardi Suyahman Suyahman