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All Journal Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian Masyarakat Bumi Raflesia Cyber Security dan Forensik Digital (CSFD) Masyarakat Berdaya dan Inovasi Jurnal Computer Science and Information Technology (CoSciTech) Humanism : Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika Jurnal Pengabdian kepada Masyarakat (DIASYA)
Murinto
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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.
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.
Peningkatan Pemahaman Mikrokontroler Melalui Pelatihan Arduino pada Siswa Rekayasa Perangkat Lunak SMK YPKK 1 Sleman Wiranata, Ade Davy; Hanief, Shofwan; Saputro, Wahju Tjahjo; Murinto; Muchlas
Jurnal Pengabdian kepada Masyarakat (DIASYA) Vol. 2 No. 1 (2026): Jurnal Pengabdian Kepada Masyarakat (DIASYA)
Publisher : Rumah Jurnal PT Citra Air Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71200/v99fz998

Abstract

Kegiatan Progam Pemberdayaan Umat (PRODAMAT) ini bertujuan untuk meningkatkan pemahaman dan keterampilan mikrokontroler siswa Rekayasa Perangkat Lunak (RPL) kelas XII SMK YPKK 1 Sleman. Latar belakang kegiatan ini didasari kebutuhan SMK YPKK 1 Sleman terhadap penguasaan teknologi embedded system sebagai bagian penting dari perkembangan industri dan teknologi digital, seperti Internet of Things dan otomasi. Namun, pembelajaran mikrokontroler di tingkat SMK masih menghadapi keterbatasan, baik dari sisi fasilitas, metode pembelajaran, maupun integrasi antara perangkat lunak dan perangkat keras. Pelatihan Arduino melibatkan 33 siswa RPL kelas XII, didampingi oleh narasumber yang kompeten di bidangnya. Metode pelatihan mengkombinasikan ceramah, demonstrasi, praktik langsung, dan diskusi interaktif. Materi pelatihan meliputi pengenalan konsep dasar mikrokontroler, penggunaan Arduino IDE, pemrograman dasar, perakitan rangkaian sederhana. Untuk mengukur efektivitas kegiatan, dilakukan evaluasi melalui pre-test dan post-test serta penilaian hasil praktik siswa. Hasil yang diharapkan dari kegiatan ini adalah meningkatnya pemahaman konseptual dan keterampilan praktis siswa dalam bidang mikrokontroler, meningkatnya minat dan motivasi belajar siswa terhadap teknologi embedded system.
Peningkatan Kualitas Citra Hilal Berdasarkan Kontras Menggunakan Metode Histogram Equalization, AHE, dan CLAHE Suprayitno, Ady; Murinto; Kartika Firdausy
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10376

Abstract

The determination of the beginning of the Hijri month is often aided by digital imaging technology, but the quality of the crescent images produced often faces the challenge of very low contrast. The faint light of the crescent is difficult to distinguish from the still bright background of the evening sky, exacerbated by atmospheric conditions and camera sensor noise that reduce visual quality. To improve the image, many still perform manual contrast enhancement. On the other hand, the selection of contrast enhancement methods is often without a measurable basis. This study aims to conduct a comparative performance evaluation between three contrast enhancement methods: Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE). The goal is to identify the most suitable technique for improving the quality of crescent images, the specific application of which has not been widely explored. A total of 30 crescent images were tested through a quantitative evaluation approach using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The results show that CLAHE provides the best performance with the lowest average MSE (89.97) and the highest PSNR (30.92 dB), demonstrating the best ability to balance contrast enhancement and distortion reduction. In contrast, the HE and AHE methods produce high MSE and low PSNR values, indicating significant visual distortion. Thus, CLAHE is recommended as the most reliable method for improving the quality of crescent images based on contrast in digital technology-based observation systems. For further research, it is recommended to explore the automatic determination of CLAHE parameters and the use of additional evaluation metrics such as SSIM (Structural Similarity Index Measure).
Co-Authors Abdul Fadlil Abdul Fadlil Ade Davy Wiranata 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 Kartika Firdausy Kencana, Nagala Wangsa Muchlas Muhammad Kunta Biddinika Nadia Wati Aprianti Nilam Tri Astuti Prambudi, Rizal Prambudi Raihan, Habib Aulia Rinday Zildjiani Salji Rusydi Umar Salsabilla Azahra Putri Shofwan Hanief Sunardi Suprayitno, Ady Suyahman Suyahman Wahju Tjahjo Saputro