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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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Analisis sentimen terhadap pelayanan Kesehatan berdasarkan ulasan Google Maps menggunakan BERT Ardiansyah; Adika Sri Widagdo; Krisna Nuresa Qodri; Fachruddin Edi Nugroho Saputro; Nisrina Akbar Rizky P
JURNAL FASILKOM Vol 13 No 02 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i02.5170

Abstract

The utilization of technology has developed in various scientific fields, without exception in health. Hospitals, health centers, and clinics are part of the health sector. Thus, it must evolve according to health service standards and patient measures or service user satisfaction that needs to be measured using sentiment analysis. The Media to give opinions to Health service providers is Google Maps. However, the anomaly is that the reviews and the given text are sometimes not correlated. Thus, The utilization of sentiment analysis using the scientific branch of artificial intelligence, namely Natural Language Processing (NLP), is an effective way to infer opinions. The research concluded that the BERT indobenchmark/indobert-base-p1 model has good performance to use of Indonesian text classification with a dataset of 4228 data after preprocessing, which at the beginning of the collection process obtained data as much as 4748 data. Split datasets into 3 data, namely training, validation, and test data, with a ratio of 70:30:30. The experimental results, The researchers found that the model allows the use of the model with other Indonesian texts. The results are 0.85 for accuracy and weighted avg, and macro avg 0.75 on the validation data training process. While the testing data training process is 0.86 for accuracy and weighted avg, the macro avg 0.73. In addition, researchers found that services are the most frequent topic in Health Services. Even though health services have improved, positive sentiment is the highest compared to other sentiment classes.
Manajemen Pendampingan Dan Edukasi Penghapusan Tato Metode Laser Habib Ismail; Ardiansyah; Choiril Hana Mustofa
WASATHON Jurnal Pengabdian Masyarakat Vol 1 No 03 (2023)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/wasathon.v1i03.641

Abstract

Tato atau yang sering disebut dengna seni mengukir pada bagian tubuh manusia telah ada ribuan tahun yang diawal kemunculannya diperkuat dengan ditemukan 57 tato pada tubuh mumi. Seni tersebut, dari waktu ke waktu, telah mengalami peningkatan yang sangat tinggi meskipun pembuatan tato dapat mengakibatkan penyakit atau virus seperti hepatitis B, hepatitis C. Di lain sisi, keinginan untuk menghapus tato juga meningkat dikarenakan adanya kesadaran diri dari masyakarat. Namun dengan meningkatnya keinginan masyarakat yang ingin menghapus tato tidak berjalan lurus dengan penyediaan jasa penghapusan tato. Hal ini dikarenakan tingginya biaya untuk menghapus tato. Metode dalam menghapus tato salah satunya adalah metode menggunakan laser. Permasalahan tersebut muncul dikarenakan masyarakat yang memiliki tato serta keinginan menghapus tato berada di kategori latar belakang secara ekonomi menengah kebawah. Dari survey yang dilakukan didapatkan peserta yang mengikuti kegiatan memiliki pendapatan 1juta sampai 2juta sebanyak 42% dan 61.9% merupakan lulusan SMA atau sederajat. Selain itu, hasil yang didapatkan telah banyak peserta yang memahami efek samping dari tindakan penghapusan tato. Hal tersebut diperkuat dengan peserta yang datang lebih dari sekali di event hapus tato sebesar 31%. Angka yang didapatkan tersebut perlu ditingkatkan dengan berbagai usaha seperti pemanfaatkan teknologi informasi untuk menyebarkan informasi event yang akan diselenggarakan.
Edukasi Literasi Digital Dalam Penggunaan Smartphone Siswa Kelas V Seklah Dasar Muhammadiyah Tonggalan Klaten Nisrina Akbar Rizky Putri; Noor Afy Shovmayanti; Ardiansyah Ardiansyah
WASATHON Jurnal Pengabdian Masyarakat Vol 2 No 01 (2024)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/wasathon.v2i01.865

Abstract

Technological progress is still happening on a massive scale and has been felt by all levels of society, including children. The generation of children born in 2010 is often called the Alpha generation, the generation most familiar with technology and internet facilities. Therefore, assistance is needed regarding the use of digital media, in line with what has been carried out by the government regarding the design of the School Literacy Movement (GLS). However, the facts obtained based on test results in the Program International Student Assessment (PISA) state that students in Indonesia have low literacy compared to other ASEAN countries. Poor literacy can impact students' psychology because children's emotions are still unstable. Elementary school students often conclude information without filtering to ensure whether the information received is valid or not. Therefore, it is necessary to raise awareness of digital literacy among students and parents to prepare them before accompanying their children. There is a need for digital literacy education to be provided in schools. The aim of implementing digital literacy for early school students is to help students understand digital literacy related to how students respond to problems in the digital world.
The Role of Social Media Literacy and Use in Determining Emotional Wellbeing Afy Shovmayanti, Noor; Rizky Putri, Nisrina Akbar; Ardiansyah, Ardiansyah
Jurnal Paradigma Vol 28, No 2 (2024): July 2024
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/paradigma.v28i2.13148

Abstract

Social media life is where communication and individuals and communities are characterized through social media networks. Concerns about the adverse effects of social media have directed public attention to media literacy as a potential solution. The SMILE Model is an effort to equip social media literacy that can influence social media use (in terms of exposure and self-expression), ultimately positively impacting individual/adolescent well-being. The results showed that most respondents preferred video content and a combination of video and text. Influencers' influence also influenced the use of social media, but only 25% of respondents felt that influencers had a significant impact on their use. Good social media literacy involves cognitive understanding and emotional regulation to improve emotional well-being. Balanced and healthy social media use can reduce loneliness and increase social support.
PEMANFAATAN SAM DAN YOLOV8 UNTUK DETEKSI DAN SEGMENTATION MRI TUMOR OTAK Ardiansyah, Ardiansyah; Qodri, Krisna Nuresa; Banna, Dion Al; Al-Baihaqi, Muhammad Zulfikhar
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 5 No. 1 (2024): Juni 2024
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v5i1.192

Abstract

The development of artificial intelligence (AI) is specific to the field of Computer Vision (CV) to obtain information based on data contained in visual media. AI in the healthcare field such as image recognition and Deep Learning (DL) is a discussion that is often used as an object of research and development. The health sector Limitation is the emergence of AI utilization in the health sector, which encourages DL research. Segmentation Anything Model (SAM) and YOLOv8 are new algorithms introduced. Thus, this research aims to measure the utilization of SAM and YOLOv8 for making the detection and segmentation of Brain Tumor MRI data. Before the training process, researchers first compared roboflow segmentation and the SAM model. The dataset was labeled with a Bounding Box by experts. The dataset contains 455 gliomas, 550 meningiomas, and 620 pituitaries. The research concluded that the utilization of SAM greatly simplified the annotation process. The segmentation YOLOv8 obtained Box accuracy results for all classes of 86% precision, 87% Recall, 89% mAP50, and 71% mAP 50-95. The mask performance evaluation gets the results of 86% precision, 87% Recall, 89% mAP50, and 70% mAP50-95. The research obtained the YOLOv8n-seg model to get excellent results even though it is a tiny model of YOLOv8. This study found the glioma tumor class to be the class with the lowest results because the dataset used was not much. The researcher encourages other researchers to use data augmentation to increase the use of datasets for each class to provide better results.
Implementasi OCR berbasis Tesseract untuk Ekstraksi data kartu mahasiswa UMKLA Muhammad Nashiruddin; Noor Praditya, Fiusyam Dhaza; Agiel Faiz Mufazzal; 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.v1i1.1688

Abstract

Manual data entry from student ID cards (KTM) is often inefficient and prone to errors. Therefore, automating this process is a crucial solution for educational institutions to improve accuracy and the speed of administrative services. This research aims to design and implement an Optical Character Recognition (OCR) system to automatically extract information from student ID card images of Universitas Muhammadiyah Klaten (UMKLA). The methodology involves image pre-processing using the OpenCV library to enhance image quality through grayscale conversion and Otsu's binarization. Subsequently, the Tesseract OCR Engine is used to convert the image into raw text, which is then parsed using Regular Expressions (Regex) to separate data fields such as Name, Student ID Number (NIM), and Program of Study. Test results indicate that the system can extract information with a good success rate, although accuracy is heavily influenced by image quality factors like lighting and text clarity. Fields with standard printed formats were found to have higher accuracy. In conclusion, this Tesseract-based system successfully demonstrates its feasibility for local automation of student ID card data. However, further development in the post-processing stage is required to handle more complex OCR output variations.
Performance Analysis of YOLOv8, YOLO11, and YOLOE in Detecting Patient Density under Complex Healthcare Conditions Ardiansyah, Ardiansyah; Syahputri, Rezyana Budi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2526

Abstract

Providing quality healthcare is a fundamental right of citizens as stipulated in the 1945 Constitution, making healthcare a national priority as outlined in the Ministry of Health's 2020-2024 Strategic Plan. However, high patient visitation rates can lead to overcrowding, impacting service efficiency and quality. Therefore, real-time patient monitoring technology is needed. Previous studies have shown promising results, but remain limited to ideal conditions for the machine. This study uses the YOLO algorithm to detect patient congestion in real healthcare facilities using CCTV footage from waiting rooms. This study uses three instance segmentation models — YOLOv8n-seg, YOLO11n-seg, and YOLOE-seg — that are tested on a custom dataset and compared with the official model. The results of training the custom dataset model are: YOLOv8n-seg Precision 96%, Recall 97%, mAP50 98%, mAP50-95 84%, and F1-score 97%. YOLO11n-seg precision 96%, Recall 97%, mAP50 98%, mAP50-95 84%, and F1-score 97%. and YOLOE-seg precision 96%, Recall 98%, mAP50 98%, mAP50-95 85%, and F1-score 97%. In addition, this study compared predictions with the official model, which found that all custom dataset models successfully detected objects with 100% density. In contrast, the official model correctly predicted density 70%-82% of the time. Therefore, this study concludes that models trained on custom datasets can improve the accuracy of patient density predictions, thereby enhancing the quality of real-time healthcare services.
Implementasi Deteksi Tumor Otak Menggunakan YOLOv11 dan Flask Ardiansyah, Ardiansyah; Sri Widagdo, Adika; Nuresa Qodri, Krisna; Hidayani, Diesti; Romadhani, Mustofa
JURNAL FASILKOM Vol. 15 No. 2 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i2.9703

Abstract

Kecerdasan buatan (AI) telah mengalami kemajuan yang sangat signifikan untuk membantu kehidupan masyarakat salah satunya adalah bidang kesehatan. Kemajuan AI didorong karena banyaknya kesalahan yang diakibatkan beberapa faktor fundamental dan tingginya permintaan dari masyarakat terhadap layanan kesehatan terus meningkat. AI juga mampu meminimalkan kesalahan diagnosa maupun pengobatan dalam praktik klinis pasien seperti deteksi tumor otak. Algoritma YOLO yang sering digunakan untuk deteksi objek karena akurasi yang tinggi. YOLO juga dapat digunakan untuk real-time diagnosa menjadi nilai tambah pada algoritma tersebut. YOLOv11 merupakan algoritma terbaru dan memiliki performa yang lebih baik dibandingkan seri sebelumnya. Meskipun begitu, tantangan terhadap keterbatasan dataset menjadi salah satu permasalahan yang perlu diselesaikan. Penelitian yang dilakukan memiliki tujuan yaitu meningkatkan jumlah dataset citra medis menggunakan Data Augmentasi dan mengintegrasikan algoritma YOLO dengan Flask untuk memberikan tampilan yang lebih baik kepada pengguna. Penelitian yang dilakukan menggunakan Data Augmentasi pada dataset menggunakan teknik Flip (Horizontal dan Vertical), 90° Rotate (Clockwise, Counter-Clockwise, Upside Down), serta penambahan Noise: Up to 1.5% of pixels. Hasilnya, diperoleh F1-score 0.951 dari 4 kelas (0.902 Glioma, 0.989 Meningioma, 0.915 Pituitary, dan 0.997 No tumor). Sehingga terbukti efektif mengatasi keterbatasan data. Selanjutnya, Integrasi YOLO dengan Flask dapat memberikan tampilan deteksi objek yang lebih baik tanpa menurunkan skor dari hasil deteksi objek tumor otak, sehingga Flask dapat dijadikan framework yang dipertimbangkan untuk pengembangan interface machine learning
Implementasi Hand Gesture Recognition untuk Bahasa Isyarat Indonesia berbasis MobileNetV2 Radit Widianto; Hanindya Aisyah; Ardiansyah Ardiansyah
JKTI Jurnal Keilmuan Teknologi Informasi Vol 2 No 1 (2026)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v2i1.2458

Abstract

Communication is a fundamental human necessity; however, for the deaf community, barriers to interaction with the general public remain a significant challenge due to limited literacy in sign language. This research aims to implement a hand gesture recognition system capable of translating the alphabet of the Indonesian Sign Language System (SIBI) in real-time. The MobileNetV2 architecture was selected as the base model due to its efficiency in processing data on resource-constrained devices without significantly compromising accuracy. The methodology involves several crucial stages, beginning with image pre-processing—including resizing and image normalization—to the application of data augmentation strategies such as rotation, shifting, and brightness adjustment to enhance the model's generalization capabilities in real-world conditions. The dataset comprises SIBI alphabet classifications from A to Z, collected with high variability to minimize the risk of overfitting. The results demonstrate that the use of depthwise separable convolutions in MobileNetV2 allows the system to perform gesture detection with high responsiveness and low computational overhead. Through hyperparameter optimization, this model is expected to achieve optimal accuracy, providing a practical and inclusive communication tool for the deaf community within social environments and public services.
Pemodelan Hybrid Prediksi Dampak Generative AI terhadap Retensi Keterampilan Belajar Mahasiswa Ardiansyah Ardiansyah; Noor Afy Shovmayanti
JKTI Jurnal Keilmuan Teknologi Informasi Vol 2 No 1 (2026)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v2i1.2468

Abstract

The growing adoption of Generative Artificial Intelligence (GenAI) technology in the field of education has sparked global concerns regarding the potential decline in students’ cognitive abilities and the loss of their analytical independence. On the other hand, the majority of previous studies have employed a one-size-fits-all approach that generalizes the impact of artificial intelligence without accounting for the specific behavioral heterogeneity of its users. This gap in the literature serves as the research gap for this study, which proposes Hybrid Machine Learning to predict fluctuations in the Skill Retention Score metric. The K-Means algorithm was implemented to segment the data, predictive modeling using the CatBoost Regressor through SHAP (Semi-Supervised Heterogeneous Adaptive Predictor) explainable AI. The segmentation results confirmed the existence of the following profiles: Cluster 0 (The Heavy Dependent) and Cluster 1 (The Traditional User). Based on these two clusters, the STEM field was found to be the top field in the use of Artificial Intelligence (AI) for the purpose of debugging computational code. Model evaluation revealed that AI adoption behavior variables significantly dictate skill degradation only among extreme users (R² = 0.3131), compared to conventional users (R² = 0.1797). Furthermore, the Shapley value analysis found that AI is proven to be safe as a support assistant or learning assistant if the dependency level remains between 1 and 6; however, if usage exceeds 6, it will affect cognitive retention. In other words, the Shapley value analysis successfully identified the tipping point of the cognitive offloading phenomenon. Nevertheless, a total ban on AI use was found to be ineffective in preserving academic retention scores. Therefore, a transition toward institutional regulations regarding the adoption of artificial intelligence is needed, one that is more adaptive, accountable, and specifically tailored to high-risk demographics, particularly in STEM fields.
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