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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) 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 Pendidikan dan Kewirausahaan 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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Perancangan Enterprise Architecture Berbasis Togaf ADM Untuk Integrasi Sistem Menggunakan ODOO ERP pada PT Bromindo Mekar Mitra Hidayat, Suluh; Kirana, Heni Candra; Wicaksono, Himawan; Nugroho, Kristiawan
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.
Analysis of E-Government Implementation in Semarang City Based on Mayor Decree No.50/571 of 2023 on SPBE Architecture Determination Eka Ardhianto; Siti Sholihah Ari Susanti; Heribertus Yulianton; Widiyanto Tri Handoko; Kristiawan Nugroho
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.8802

Abstract

This study provides an in-depth analysis of the implementation of e-government in Semarang City, driven by Mayor Decree No.50/571 of 2023 concerning the establishment of the Electronic-Based Government System (SPBE) framework. The research evaluates the alignment of Semarang City's e-government strategies with national regulations, including PerPres No.95/2018 on SPBE, PermenPANRB No.5/2018 on SPBE evaluation criteria, and PerPres No.132/2022 on national SPBE architecture. Employing qualitative methods through literature reviews, interviews, surveys, and observations, this study examines e-government readiness, smart governance, digital service integration, and benchmarking practices. The results highlight significant progress in enhancing public service efficiency and transparency, though challenges such as system interoperability, cybersecurity risks, and public engagement remain. Recommendations include strengthening infrastructure, improving human resource capacity, and fostering citizen involvement for sustainable e-government development.
Sistem Rekomendasi Wisata di Pekalongan melalui Chatbot dengan Framework Rasa Fakhri; Kristiawan Nugroho
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 1 (2025): JANUARI-MARET 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i1.3000

Abstract

Pekalongan, a city renowned for its batik and rich in cultural and natural attractions, has great potential in the tourism sector. However, limited access to integrated and easily accessible information poses challenges for tourists planning their trips. The Rasa-based Telegram chatbot addresses these challenges as an innovative solution. Through interactive engagement, tourists can receive recommendations for destinations, culinary spots, and other relevant information tailored to their preferences. This system leverages Rasa Natural Language Understanding (NLU) to interpret user queries and provide appropriate responses. Comprehensive tourism data of Pekalongan is embedded into the system to ensure accurate and relevant recommendations. The chatbot's evaluation includes direct user testing to measure the accuracy of recommendations, user satisfaction, and ease of use. Results indicate that the Rasa-based Telegram chatbot can deliver personalized and accurate recommendations, enhancing the travel planning experience for tourists visiting Pekalongan.
Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm Mamet Adil Araaf; Kristiawan Nugroho; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 1 (2023): JCTA 1(1) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i1.9185

Abstract

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.
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.
INDONESIAN LANGUAGE CLASSIFICATION OF CYBERBULLYING WORDS ON TWITTER USING ADABOOST AND NEURAL NETWORK METHODS Nugroho, Kristiawan
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 Alfiqhyanto, Damas 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 De Rosal Ignatius Moses Setiadi Dhendra Marutho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Eka Ardhianto Eko Ariyanto Eko Prasetyo Eko Prasetyo Eksawati, Rini Endang Tjahjaningsih Eri Zuliarso Ermillian, Ade Faizi, Aditya Wahyu Nur Fakhri fakhri Farooq, Omar Fitrianto, Lindu Hakim, Mujibul Hari Murti Heribertus Yulianton Hermawan, Taufan Hidayat, Suluh Hussain Md Mehedul Islam Irawan, Sandy Isworo Nugroho Jusran, Alek Kasmari . Kirana, Heni Candra Kristhoporus Hadiono Kristianto, Taufik Fredy Kristiyono, Budi Kristophorus Hadiono Lie Liana Lie Liana . Linda Kartika Sari Mala, Hasda Nuril Mamet Adil Araaf Minantri Haika, Shara Muh Kholid Rizky Sapawi Muhamad Riski Atarik 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 Rahadiyanto, Cahyono Raharjo, Fajar Retnowati Rokhayadi, Wakhid Ruslana, Zauyik Nana Saputra, Roni Halim Saputro, Risky Wisnu Sariyun Naja Anwar Sarwo Edi, Sarwo Setyaningtyas, Elvanita Sholehudin, Mukti Ahmad Siti Sholihah Ari Susanti Sri Mulyani Sugeng Murdowo Suhana Suhana Sulastri Sulastri Sulistiyowati Sulistiyowati Sunardi Sunardi Suprapto, Yossy Suprihhartini, Suprihhartini SUTANTO, FELIX Syahroni Wahyu Iriananda, Syahroni Wahyu Teguh Khristianto Veronica Lusiana Vici Tiara Anjarsari Warto Warto Wicaksono, Himawan Widiyanto Tri Handoko Wijayanto, Wendhie Tri Wiratno, Amat Wismarini , Th. Dwiati Wiwien Hadi Kurniawati Yayi Suryo Prabandari Yoga Ryan Fatony Yoga Ryan Fatony