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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Ilmu Komputer BULLET : Jurnal Multidisiplin Ilmu
Danar Dana, Raditya
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Humanities Computer Science & IT Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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OPTIMALISASI PEMETAAN SUMBERDAYA DESA PEGAGAN MELALUI PENGEMBANGAN DESAIN APLIKASI GIS DENGAN METODE RAD Tri Mukti, Aryanto; Rahaningsih, Nining; Danar Dana, Raditya; Lukman Rohmat, Cep
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14065

Abstract

Pemetaan sumber daya Desa yang akurat dan real-time masih menjadi tantangan bagi pengelolaan Desa, terutama Pengelolaan sumber daya desa yang efektif memerlukan data spasial yang akurat dan mudah diakses. Namun, keterbatasan dalam pemetaan yang real-time masih menjadi kendala di banyak desa. Penelitian ini bertujuan untuk mengembangkan aplikasi WebGIS guna mendukung pemetaan sumber daya desa secara digital. Metode yang digunakan adalah Rapid Application Development (RAD) untuk merancang sistem yang interaktif dan efisien. Data diperoleh melalui observasi lapangan, wawancara dengan aparat desa, serta pemanfaatan OpenStreetMap. Aplikasi yang dikembangkan diuji menggunakan System Usability Scale (SUS) untuk mengukur kemudahan penggunaan dan efektivitas sistem. Hasil penelitian menunjukkan bahwa WebGIS mampu meningkatkan efisiensi pengelolaan data geografis, menyediakan peta interaktif yang memudahkan akses informasi bagi masyarakat dan pemerintah desa. Skor SUS sebesar 72,525 menunjukkan bahwa aplikasi ini tergolong baik (Good) dan user-friendly. Dengan demikian, WebGIS Desa Pegagan dapat menjadi model pengembangan sistem informasi geografis di desa lain untuk mewujudkan tata kelola yang transparan, partisipatif, dan berkelanjutan.
Optimizing Multimodal Health Chatbots through the Integration of Medical Text and Images Danar Dana, Raditya; Mulyawan, Mulyawan; Bahtiar, Agus; Nurdiawan, Odi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5328

Abstract

This study is motivated by the growing need for image-classification systems that remain accurate despite variations in image quality commonly found in real-world environments. Differences in image resolution often lead to decreased performance of Convolutional Neural Network (CNN) models, particularly in scenarios involving limited acquisition devices. This research aims to analyze the effect of image-resolution variations on CNN robustness by applying an adaptive augmentation strategy. An experimental approach was employed by manipulating independent variables namely image-resolution levels and augmentation techniques and observing their impact on accuracy, validation stability, and model generalization. The results show that medium-resolution images (128×128 px) combined with adaptive augmentation produce the best performance, yielding the highest validation accuracy and reduced overfitting compared to other configurations. The urgency of this study lies in its practical contribution to developing efficient image-classification models suitable for resource-constrained environments. Scientifically, the findings provide a structured mapping of the relationship between resolution, augmentation, and model stability, offering a foundation for designing more robust CNN architectures adaptable to real-world data variability.
Implementation of IndoBERT for Sustainability Impact Assessment in University Collaboration Information Systems Hamonangan, Ryan; Danar Dana, Raditya; Arie Wijaya, Yudhistira; Nurdiawan, Odi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5330

Abstract

University collaboration plays a critical role in enhancing institutional quality and supporting global sustainability agendas. However, many higher education institutions face challenges in managing Memorandum of Understanding (MoU), Memorandum of Agreement (MoA), and Implementation Agreement (IA) documents, particularly in monitoring implementation and assessing their alignment with sustainability goals. This study introduces a University Collaboration Information System enhanced with IndoBERT-based Natural Language Processing (NLP) to automate sustainability impact assessment. A synthetic corpus of 30 annotated collaboration documents was developed, covering multi-label Sustainable Development Goals (SDG) classification and span-level Named Entity Recognition (NER). Two approaches were evaluated: (1) baseline TF-IDF + Support Vector Machine (SVM) for SDG classification and rule-based NER, and (2) fine-tuned IndoBERT for both tasks. Experimental results show that IndoBERT significantly outperforms the baselines, achieving an average F1-score of 0.93 for SDG classification (+16.3%) and 0.96 for NER (+18.5%). The system integrates these models to generate automated entity extraction, sustainability dashboards, and document monitoring features. This work contributes to the advancement of informatics by demonstrating the effectiveness of Transformer-based NLP in processing institutional documents and by providing an integrated information-system framework that strengthens the role of NLP within the field of computer science.
Co-Authors Abdillah Fudholi, Luthfi Agustina, Inggit Arif Sofyan, Mohamad Arumsari, Rahayu Awaliyah, Lia Aziz Rafi, Muhamad Azizah, Maulidina Deva Rian, Rananda Dienwati Nuris, Nisa Elfrida Ratnawati Fajar, Miftahul Gita Budiarti, Mariani Gunawan Rahmat, Gun Gun Gusnanto, Ferdi Gustipartsani, Kamaludin Hafizh Fakhriza, Muhammad Hamonangan, Ryan Hayati, Umi Imam Muarif, Tiar Irma Purnamasari, Ade Kaswandi, Adi Kholil, Kholil AldiYatna Kurmasih, Masih Lathifah, Umi Lidia Putri, Teneshya Lukman Rohmat, Cep Mamluatul Hikmah, Lulu Maulana, Ajay Maulana, Guruh Maulidiyani, Nurul Muhamad Andika, Agus Mukhlashin, Khairul Mulyana, Krisna Mulyawan, Mulyawan Munawar, Adi Nafilah, Mala Nining Rahaningsih Nur Afrilia, Mela Odi Nurdiawan Oop Sofiyah, Siti Parahita, Parahita Qodri M.A, M. Alifia Qotrun Nada, Herlyta R., Nining Rahaningsih , Nining Rinaldi Dikananda, Arif Roghib, Moh. Soni, Moh Suarna, Nana Sutra Safira, Meita Syafi’i Bachtiar, Mochammad Tohidi, Edi Tri Mukti, Aryanto Tri Suryani, Juliani Yudhistira Arie Wijaya Yulia Mustafa, Iva