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All Journal Paradigma: Jurnal Masalah Sosial, Politik, dan Kebijakan Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) WASATHON JKTi - Jurnal Keilmuan Teknologi Informasi
Ardiansyah Ardiansyah
Universitas Muhammadiyah Klaten
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science Medicine & Pharmacology Public Health Social Sciences

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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 Adimas, Rizal Agiel Faiz Mufazzal Al Firmansyah, Garet Al-Baihaqi, Muhammad Zulfikhar Banna, Dion Al Cahyadi, Fian Pandu Choiril Hana Mustofa Fachruddin Edi Nugroho Saputro Habib Ismail Habib Ismail, Habib Hanindya Aisyah Hidayani, Diesti Jihad, Muh. Ikhlazul Krisna Nuresa Qodri Muhammad Nashiruddin Mustofa Romadhani Nisrina Akbar Rizky Putri Nisrina Akbar Rizky Putri Noor Afy Shovmayanti Noor Praditya, Fiusyam Dhaza Radit Widianto Rizky Putri, Nisrina Akbar Romadhani, Mustofa Sri Widagdo, Adika Syahputri, Rezyana Budi