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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics INFOKAM Sisforma: Journal of Information Systems CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Rekam Medis dan Informasi Kesehatan Media Ilmu Kesehatan Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming SOSCIED Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Business and Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat SENTRI: Jurnal Riset Ilmiah Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Seminar Nasional Ilmu Terapan Jurnal Kabar Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sains dan Teknologi Informasi Journal of Future Artificial Intelligence and Technologies Proceeding of The International Conference on Mathematical Sciences, Natural Sciences, and Computing Jurnal Informatika Dan Tekonologi Komputer
Dian Kristiawan Nugroho
Universitas Stikubank
Aerospace Engineering Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition Rahadian Kristiyanto Rachman; De Rosal Ignatius Moses Setiadi; Ajib Susanto; Kristiawan Nugroho; Hussain Md Mehedul Islam
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10459

Abstract

In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow De Rosal Ignatius Moses Setiadi; Warto Warto; Ahmad Rofiqul Muslikh; Kristiawan Nugroho; Achmad Nuruddin Safriandono
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12376

Abstract

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
ANALISIS KOMPARATIF NILAI PASAR DAN PERFORMA PEMAIN: IDENTIFIKASI PEMAIN UNDERVALUED BERBASIS BIG DATA ANALYTIC Lintang Amarul Fatah; Hanacahyani Widya Asih; Mukti Diananingsih; Djoko Pitoyo; Kristiawan Nugroho; Eka Ardhianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7460

Abstract

The discrepancy between market value and actual player performance often creates financial inefficiencies in football club recruitment strategies. This study aims to identify undervalued players (high performance but low valuation) using Big Data Analytics on the Transfermarkt dataset. The initial dataset is large-scale, comprising 32,601 player records and 1,706,806 match appearance entries, reflecting high-volume data characteristics The research methodology follows four systematic stages: (1) massive data acquisition and integration covering match statistics and transfer history; (2) implementation of Feature Engineering to convert raw statistics into per-90-minute metrics while accounting for contract duration; (3) fair value modeling using the CatBoost Regressor algorithm optimized with Log-Transformation to handle skewed data distributions; and (4) model validation using 5-Fold Cross Validation and residual analysis to detect price anomalies. The results demonstrate the model's ability to precisely identify potential player segments overlooked by standard market valuations. It is concluded that integrating CatBoost with robust feature engineering serves as a strategic instrument for club management to enhance investment efficiency (Return on Investment). 
Perancangan Enterprise Architecture Berbasis Togaf ADM Untuk Integrasi Sistem Menggunakan ODOO ERP pada PT Bromindo Mekar Mitra Suluh Hidayat; Heni Candra Kirana; Himawan Wicaksono; Kristiawan Nugroho
SENTRI: Jurnal Riset Ilmiah Vol. 5 No. 1 (2026): SENTRI : Jurnal Riset Ilmiah, Januari 2026
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v5i1.5348

Abstract

Digital transformation requires organizations to implement integrated and adaptive information systems capable of supporting end-to-end business processes. PT Bromindo Mekar Mitra currently utilizes several applications, such as Firecek Backoffice, Bisma, Pembukuan, Trello, and Oteem to support its operations. However, these systems operate independently (stand-alone), resulting in data duplication, reporting delays, and information inconsistencies across divisions. This condition indicates an urgent need for a strategic and structured architectural approach to achieve system integration aligned with the company’s business requirements. This study aims to design an Enterprise Architecture (EA) as a foundation for system modernization and integration using the Odoo ERP platform. The TOGAF Architecture Development Method (ADM) framework is employed to analyze the As-Is architecture, develop the To-Be architecture, and formulate a comprehensive implementation roadmap. Data collection was conducted through interviews, business process observations, and analysis of internal documentation. The research results include integrated business, data, application, and technology architecture designs, as well as recommendations for Odoo ERP modules that align with PT Bromindo Mekar Mitra’s operational needs. The resulting EA blueprint and migration roadmap are expected to serve as strategic guidance for the company in implementing system integration in a gradual, measurable, and sustainable manner while minimizing the risks associated with ERP adoption.
Analisa Dan Perancangan Sistem Pendaftaran Pasien Rawat Jalan Di Klinik Mata Madiun Kristiawan Nugroho; Sandy Irawan; Cahyono Rahadiyanto; Lindu Fitrianto; Yossy Suprapto
Jurnal Rekam Medis dan Informasi Kesehatan Vol. 8 No. 1 (2025): MARET 2025
Publisher : Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jrmik.v7i2.12259

Abstract

Klinik merupakan fasilitas pelayanan kesehatan yang terdiri dari klinik pratama dan klinik utama, masing-masing menyediakan pelayanan non-spesialis dan spesialis. Klinik Mata di Madiun berlokasi di Jawa Timur, menyediakan berbagai layanan kesehatan mata seperti LASIK, operasi glaukoma, dan katarak. Untuk meningkatkan efisiensi pelayanan, klinik membutuhkan sistem informasi yang terintegrasi dengan Kementerian Kesehatan melalui platform Satu Sehat. Sistem informasi klinik ini, dikenal sebagai Simklinik, dirancang untuk mengelola data pendaftaran pasien, rekam medis, dan pengiriman data pasien secara terkomputerisasi. Hal ini dapat mencegah duplikasi data pasien dan mempercepat proses pelayanan. Penelitian ini bertujuan untuk menganalisis dan merancang sistem informasi pendaftaran rawat jalan di Klinik Mata Madiun. Metode yang digunakan adalah Waterfall, sebuah model pengembangan sistem informasi yang terdiri dari beberapa tahapan dalam System Development Life Cycle (SDLC). Data yang digunakan berdasarkan rekam medis dari Klinik Mata Madiun. Hasil pengujian sistem menggunakan metode black box menunjukkan keberhasilan dengan tingkat kesesuaian 100%, menandakan bahwa sistem ini dapat memenuhi kebutuhan klinik secara optimal dan efisien. 
INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLYING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS Kristiawan Nugroho
Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i2.54

Abstract

Cyberbullying is a very interesting research topic because of the development of communication technology, especially social media, which causes negative consequences where people can bully each other, causing victims and even suicide. The phenomenon of Cyberbullying detection has been widely researched using various approaches. In this study, the AdaBoost and Neural Network methods were used, which are machine learning methods in classifying Cyberbullying words from various comments taken from Twitter. Testing the classification results with these two methods produces an accuracy rate of 99.5% with Adaboost and 99.8% using the Neural Network method. Meanwhile, when compared to other methods, the results obtained an accuracy of 99.8% with SVM and Decision Tree, 99.5% with Random Forest. Based on the research results of the Neural Network method, SVM and Decision Tree are tested methods in detecting the word cyberbullying proven by achieving the highest level of accuracy in this study.
Co-Authors Achmad Nuruddin Safriandono Afandi , Afandi Afif, Randi Ahmad Fathoni Ajib Susanto Ajie, Ach. Ridlo Bayu Alex Chandra Iswanto Aminudin, Agus Anjis Sapto Nugroho Anton Sujarwo Anton Sujarwo Aprico, Fikky Apriyanti, Dewi Aquinia, Ajeng Arsyad , Muhammad Rafi Haidar budi hartono Budiarto, Indri Cahaya, Agus Indra Cahyono Rahadiyanto De Rosal Ignatius Moses Setiadi Dhendra Marutho Djoko Pitoyo Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Eka Ardhianto Eko Prasetyo Eko Prasetyo Eksawati, Rini Endang Tjahjaningsih Eri Zuliarso Ermillian, Ade Faizi, Aditya Wahyu Nur Fakhri fakhri Farooq, Omar Hanacahyani Widya Asih Hari Murti Heni Candra Kirana Heribertus Yulianton Hermawan, Taufan Hidayat, Suluh Himawan Wicaksono Hussain Md Mehedul Islam Isworo Nugroho Jusran, Alek Kasmari . Kirana, Heni Candra Kristhoporus Hadiono Kristianto, Taufik Fredy Kristophorus Hadiono Lie Liana Lie Liana . Linda Kartika Sari Lindu Fitrianto Lintang Amarul Fatah Mamet Adil Araaf Minantri Haika, Shara Muh Kholid Rizky Sapawi Muhamad Riski Atarik Mukti Diananingsih Mulyani , Wahyu Sri Mulyo Budi Setiawan Munna, Aliyatul Muslikh, Ahmad Rofiqul Niken Puspitasari Nofiyanto, Muhamat Nurmakhlufi, Alfin Ojugo, Arnold Adimabua Omar Farooq Palupi, Dian Perdana, Willy Yudha Prabowo, Ardian Adi Prihatin, Rudi Setyo R.M.Herdian Bhakti Radyanto, Mohammad Riza Rahadian Kristiyanto Rachman Raharjo, Fajar Retnowati Rokhayadi, Wakhid Ruslana, Zauyik Nana Sandy Irawan Saputra, Roni Halim Saputro, Risky Wisnu Sariyun Naja Anwar Sarwo Edi, Sarwo Setyaningtyas, Elvanita Siti Sholihah Ari Susanti Sri Mulyani Sugeng Murdowo Suhana Suhana Sulastri Sulastri Sulistiyowati Sulistiyowati Suluh Hidayat Sunardi Sunardi Suprihhartini, Suprihhartini SUTANTO, FELIX Syahroni Wahyu Iriananda, Syahroni Wahyu Teguh Khristianto Veronica Lusiana Vici Tiara Anjarsari Warto Widiyanto Tri Handoko Wijayanto, Wendhie Tri Wiratno, Amat Wismarini , Th. Dwiati Wiwien Hadi Kurniawati Yayi Suryo Prabandari Yoga Ryan Fatony Yoga Ryan Fatony Yossy Suprapto