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All Journal Paradigma: Jurnal Masalah Sosial, Politik, dan Kebijakan Jurnal Sisfokom (Sistem Informasi dan Komputer) International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Emerging Information Science and Technology WASATHON JKTi - Jurnal Keilmuan Teknologi Informasi Journal of Informatics, Intelligent Technology, Cybersecurity, and Software (JIITeCS) Jurnal Komunikasi dan Kajian Media
Ardiansyah Ardiansyah
Universitas Muhammadiyah Klaten
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Public Health Social Sciences

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Edukasi Sosial Media Literasi pada Siswa Kelas VI SD Muhammadiyah Tonggalan, Klaten Nisrina Akbar Rizky Putri; Noor Afy Shovmayanti; Ardiansyah Ardiansyah
WASATHON Jurnal Pengabdian Masyarakat Vol 3 No 01 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/wasathon.v3i01.1477

Abstract

The community service activity titled "Social Media Literacy Education for Sixth Grade Students at SD Muhammadiyah Tonggalan, Klaten" was designed to enhance the students' understanding and skills in utilizing information technology. Through education and direct guidance, this activity successfully improved students' knowledge in using social media, including crucial aspects such as data security and the accuracy of disseminated information. Additionally, the activity increased student’s awareness of the importance of privacy and security in technology usage. Although the results were positive, the report identifies several areas needing improvement, such as some students still sharing personal information, highlighting the need for broader collaboration with parties related to data security and information management skills, expanding participant reach, and developing more comprehensive educational materials
Penerapan Teknologi Informasi untuk Penyebaran Informasi dan Manajemen Data di Procarehapustato Klaten Ardiansyah Ardiansyah; Habib Ismail; Mustofa Romadhani; Fian Pandu Cahyadi; Muh. Ikhlazul Jihad
WASATHON Jurnal Pengabdian Masyarakat Vol 3 No 01 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/wasathon.v3i01.1517

Abstract

Information technology has received many positive reactions in use in various sectors, including in the field of MSME empowerment. The era of technology, which is often referred to as the 4.0 era, people tend to use technology in their daily lives so that MSMEs are forced to keep up with the times. However, in reality there are still many MSMEs that still cannot keep up with these developments due to several factors such as access to marketing or broad dissemination of information, or the quality of human resources owned by MSMEs. Procare Klaten is an MSME engaged in tattoo removal services. The problems found at Procare remove tattoos in Klaten, namely: Information dissemination, registration process, human resources (HR). So that designing an information system that can be used to provide information online so that it can be accessed by the public at large and accommodate the registration process is a solution. Furthermore, FGDs and intense meetings were held to assist and provide knowledge related to the latest technology to increase the productivity of Procare's human resources
Ensemble Deep Learning with Attention Mechanism and Explainable AI for Enhanced Brain Tumor Classification from MRI Images Krisna Nuresa Qodri; Ardiansyah Ardiansyah; Nisrina Akbar Rizky Putri; Adika Sri Widagdo; Fachruddin Edi Nugroho Saputro
Jurnal Internasional Teknik, Teknologi dan Ilmu Pengetahuan Alam Vol 8 No 1 (2026): International Journal of Engineering, Technology and Natural Sciences
Publisher : Universitas Teknologi Yogyakarta, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijets.v8i1.641

Abstract

Brain tumors represent a significant clinical challenge, with accurate classification being essential for treatment planning. Current deep learning approaches face two critical limitations: insufficient robust-ness across diverse imaging conditions and lack of clinical interpretability. Here we present an ensemble deep learning framework integrating three complementary architectures (MobileNetV2, EfficientNetB3, and DenseNet121) with spatial attention mechanisms and explainability features. Using 7,023 MRI images across four diagnostic categories (glioma, meningioma, pituitary tumor, and tumor-absent), our approach achieved 98.47% classification accuracy with balanced performance across all classes (F1-scores: 98.12% for glioma, 97.89% for meningioma, 98.76% for pituitary, 99.21% for tumor-absent cases). The ensemble demonstrated statistically significant improvement over individual models (p < 0.01, McNemar’s test), with gains of 1.24-1.75 percentage points. Integration of Gradient-weighted Class Activation Map-ping provided interpretable visual explanations with activation patterns consistently focusing on tumor regions. The findings demonstrate the potential of combining ensemble learning, attention, and visual explanation for brain tumor classification. However, the results represent internal validation on a single partition of aggregated public datasets and require confirmation through repeated validation and independent clinical evaluation.
Pendekatan Deep Learning untuk Deteksi Kantuk dengan YOLOv12 Diesti Hidayani; Mustofa Romadhani; Ardiansyah Ardiansyah
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1681

Abstract

Drowsiness while driving is a significant contributor to traffic accidents. To mitigate such occurrences, a precise and real-time drowsiness detection system is essential. This research aims to create a computer vision-based drowsiness detection system utilizing the YOLOv12 algorithm. The dataset was sourced from Kaggle and manually annotated with the help of Roboflow. It was categorized into two groups: drowsy and non-drowsy, with the original 5,000 images augmented to a total of 6,976 images. The model training utilized the AdamW optimizer (learning rate=0.001667, momentum=0.9) over 100 epochs and a batch size of 4. Performance assessment indicates that the model attained an mAP@50 of 0.732 and an mAP@50-95 of 0.62, alongside a precision of 0.648 and a recall of 0.928. These findings illustrate that YOLOv12 can successfully identify drowsiness in real-time. Nevertheless, the performance of the model is significantly influenced by the quality and balance of the dataset. Consequently, enhancing the structure and distribution of the dataset is vital for improving detection accuracy.
Deteksi Ekspresi Wajah Real-Time Menggunakan YOLOv12 Rizal Adimas; Garet Al Firmansyah; Ardiansyah Ardiansyah
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 1 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i1.1682

Abstract

This research focuses on the development of a real-time facial expression detection system using YOLOv12. The study utilizes a secondary dataset from Kaggle, consisting of 1000 images categorized into two classes: "Happy" and "Not Happy." The dataset undergoes preprocessing steps, including Gabor filter bank for key facial feature identification and geometric augmentation to enhance data quality. The YOLOv12 model is trained with 100 epochs, a batch size of 4, and the AdamW optimizer, achieving a mean Average Precision (mAP@0.5) of 0.89 for both expression classes. The system demonstrates real-time performance with an average processing speed of 15 FPS on CPU-based devices, adapting well to varying lighting conditions and angles, though accuracy decreases by 5-7% in low-light environments. The results highlight the model's potential applications in mental health, human-computer interaction, and security. Limitations include the restricted dataset and challenges with micro-expressions. Future work suggests expanding the dataset to include more expression classes and integrating post-processing models to reduce false positives.
Ekstraksi informasi kartu identitas menggunakan arsitektur multimodal learning berbasis data sintetis Muhammad Nashiruddin; Fiusyam Dhaza Noor Praditya; Agiel faiz Mufazzal; Ardiansyah Ardiansyah
Journal of Informatics, Intelligent Technology, Cybersecurity, and Software (JIITeCS) Vol. 1 No. 2 (2026)
Publisher : LPPM Universitas Muhammadiyah Papua

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67618/jiitecs.v1i2.43

Abstract

Studi ini menyarankan sistem ekstraksi informasi Kartu Tanda Penduduk (KTP) berbasis pipeline yang menggabungkan data sintetis, praproses citra, pemodelan multimodal, dan OCR. Dataset sintetis digunakan untuk mengatasi keterbatasan akses data nyata yang bersifat sensitif, dengan menghasilkan 1100 citra KTP dalam 11 variasi kondisi seperti pencahayaan, blur, dan distorsi. Tahap praproses meliputi transformasi perspektif, grayscale, adaptive thresholding, dan denoising. Tiga metode berbeda digunakan untuk menilai OCR: grayscale, binerisasi, dan bounding box-guided OCR. Hasil menunjukkan bahwa metode berbasis bounding box memiliki akurasi pembacaan hingga 100%, sementara metode berbasis praproses citra memiliki kinerja yang lebih buruk. Selain itu, ekstraksi entitas berbasis multimodal menggunakan model LayoutXLM dengan menggunakan teks, posisi spasial, dan fitur visual. Semua atribut menerima nilai F1-score 1,00 dari evaluasi.
Integrasi Netiquette dan Resiliensi Digital sebagai Strategi Preventif Cyberbullying pada Remaja Awal di Era Disrupsi Teknologi Noor Afy Shovmayanti; Ardiansyah Ardiansyah
Jurnal Komunikasi dan Kajian Media Vol. 9 No. 1 (2025): JURNAL KOMUNIKASI DAN KAJIAN MEDIA
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/jkkm.v9i1.2996

Abstract

This study aims to analyze the integration of internet etiquette and digital resilience as a preventive strategy against cyberbullying among adolescents in the era of technological disruption. A qualitative descriptive-exploratory approach was employed, involving 56 elementary school students, teachers, and parents through questionnaires, semi-structured interviews, and thematic analysis. The findings reveal that students’ understanding of digital ethics is relatively high, with 92.8% reporting consistent application of ethical online behavior. However, digital resilience remains limited, as 69% of students feel lonely without social media and 57% are easily influenced by negative comments. Cyberbullying experiences were reported by 30.8% of respondents, with most adopting passive responses. These results highlight the urgency of digital literacy programs that emphasize not only courtesy and privacy but also emotional management, healthy communication, and coping skills in digital interactions. Integrating internet etiquette and digital resilience can foster an ethical, safe, and resilient digital ecosystem for adolescents.
Understanding Burnout Experiences in Social Media Discourse: Evidence from YouTube User Comments Putri, Nisrina Akbar Rizky; Ardiansyah, Ardiansyah; Widyastuti, Erma; Azizah, Laila Ma'rifatul
Emerging Information Science and Technology Vol. 7 No. 1 (2026): May
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v7i1.31343

Abstract

Burnout has become an important psychological concern that is increasingly discussed through social media, providing valuable textual data for understanding public experiences of emotional exhaustion, workplace pressure, and coping. This study analyzes sentiment in burnout related YouTube comments using DistilIndoBERT and examines the contribution of back translation to classification performance. The initial dataset consisted of 2,931 comments collected from 5 YouTube videos published between 2021 and 2025 was subjected to a data quality audit that removed exact duplicates, promotional content, spam, and nonmeaningful comments, resulting in 2,829 relevant records. Sentiment labels were assigned through a semi automated process and reviewed by the researchers into positive, neutral, and negative categories. The dataset was divided using stratified sampling into 70% training data, 15% validation data, and 15% test data. Back translation was applied exclusively to the positive and neutral classes in the training set to prevent data leakage, expanding the training data from 1,980 to 3,003 records. Negative sentiment was dominant, accounting for 1,430 comments or 50.55%, followed by neutral sentiment with 846 comments or 29.90% and positive sentiment with 553 comments or 19.55%. DistilIndoBERT achieved 82.4% accuracy, 81.9% macro precision, 81.5% macro recall, and 81.6% macro F1 score on the original dataset. After augmentation, the respective scores increased to 87.1%, 86.8%, 86.2%, and 86.4%. These observed improvements demonstrate the potential of training focused back translation to strengthen DistilIndoBERT classification of burnout discourse.
Co-Authors Adika Sri Widagdo Adika Sri Widagdo Agiel faiz Mufazzal Agiel Faiz Mufazzal Al-Baihaqi, Muhammad Zulfikhar Azizah, Laila Ma'rifatul Banna, Dion Al Choiril Hana Mustofa Diesti Hidayani Erma Widyastuti Fachruddin Edi Nugroho Saputro Fachruddin Edi Nugroho Saputro Fian Pandu Cahyadi Fiusyam Dhaza Noor Praditya Garet Al Firmansyah Habib Ismail Habib Ismail Hanindya Aisyah Hidayani, Diesti Krisna Nuresa Qodri Krisna Nuresa Qodri Muh. Ikhlazul Jihad Muhammad Nashiruddin Muhammad Nashiruddin Mustofa Romadhani Mustofa Romadhani Nisrina Akbar Rizky Putri Nisrina Akbar Rizky Putri Nisrina Akbar Rizky Putri Noor Afy Shovmayanti Noor Praditya, Fiusyam Dhaza Putri, Nisrina Akbar Rizky Radit Widianto Rizal Adimas Rizky Putri, Nisrina Akbar Romadhani, Mustofa Sri Widagdo, Adika Syahputri, Rezyana Budi