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All Journal Jurnal Informatika dan Teknik Elektro Terapan Infotech Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Teknik Komputer AMIK BSI Bina Insani ICT Journal Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics IndoMath: Indonesia Mathematics Education JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE JURIKOM (Jurnal Riset Komputer) Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) Respati Media Informatika Journal of Computer System and Informatics (JoSYC) Jurnal Sains Teknologi Transportasi Maritim Jurnal Sistem Komputer dan Informatika (JSON) Madani : Indonesian Journal of Civil Society MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknologi Informasi dan Komunikasi Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal Digit : Digital of Information Technology Mosharafa: Jurnal Pendidikan Matematika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jupiter Journal of Computer & Information Technology Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika dan Teknologi Informasi BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Smatika Jurnal : STIKI Informatika Jurnal
Odi Nurdiawan
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Transportation Other

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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.
Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm NoviFirda Aini; Odi Nurdiawan; Tati Suprapti; Arif Rinaldi Dikananda; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1883

Abstract

The rapid development of digital public services requires a deeper understanding of user perceptions and experiences regarding government applications, including Cek Bansos. This study aims to identify the polarity of user reviews by applying the Multinomial Naïve Bayes algorithm to review data collected from the Google Play Store. The methodology includes text preprocessing, sentiment labeling, feature extraction using TF–IDF, and model training and evaluation based on accuracy, precision, recall, and F1-score. The results show that the model achieves an accuracy of 79.5%, with very high performance in the negative class (recall 0.97) but poor performance in the neutral class due to data imbalance. The dominance of negative sentiment in the dataset indicates that users face significant technical difficulties, particularly in registration, verification, and service access. These findings demonstrate that Multinomial Naïve Bayes is effective as a baseline model for sentiment analysis; however, improving data balance and quality is necessary to produce a more stable, accurate, and representative model for evaluating digital public services.
Analysis of the Effectiveness of Manual Deployment and CI/CD Github Actions in the Braisee Application Nenda Alfadil Seputra; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade Kurnia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1916

Abstract

In the modern cloud-based software development ecosystem, the speed and reliability of the deployment process are critical elements. This study aims to evaluate the effectiveness of implementing Continuous Integration/Continuous Deployment (CI/CD) using GitHub Actions compared to manual methods for the machine learning API of the Braisee application hosted on Google Cloud Run. Using a quantitative approach with a comparative experimental design across ten testing iterations, this research measures deployment time efficiency, error rates, and system stability. The experimental results show a significant performance disparity, where the automated method based on GitHub Actions is considerably more efficient, with an average total duration of 111–167 seconds, reducing operational time by 40–60% compared to the manual method, which requires 297–364 seconds. In terms of reliability, the automated method achieves a 100% success rate with high consistency, whereas the manual method demonstrates substantial vulnerability to human errors such as mistyped project IDs and inconsistent image tagging. It is concluded that implementing CI/CD through GitHub Actions is a superior solution that improves time efficiency and ensures the stability of cloud-based applications compared to manual procedures.
Comparative Analysis of Serverless Container Service Performance Between Google Cloud Run and AWS App Runner in Cross-Cloud Architecture Muhammad Adithya Pratama; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade Kurnia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1919

Abstract

Research on the performance of serverless container services is becoming increasingly important as the need for modern distributed and cross-cloud architectures grows. This study analyzes the performance of two leading serverless services, Google Cloud Run and AWS App Runner, in a cross-cloud architecture scenario. Testing was conducted using identical parameters, including container configuration, region, memory, vCPU, and concurrency. Performance testing included p95 latency, throughput, and error rate metrics using loads of up to 1000 virtual users. The results showed that Google Cloud Run provided more stable performance with p95 latency of 47–71 ms, throughput of 436–438 RPS, and 0% error rate. In contrast, AWS App Runner showed p95 latency of 490–651 ms with throughput variation of 388–410 RPS and an error rate of 2–4.41%. The difference in performance was due to autoscaling mechanisms, cross-cloud communication overhead, and resource contention. This study provides empirical evidence for selecting the optimal serverless service for distributed architectures.
Optimisasi Model Backpropagation untuk Meningkatkan Deteksi Kejang Epilepsi pada Sinyal Electroencephalogram Odi Nurdiawan; Fathurrohman Fathurrohman; Ahmad Faqih
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 9 No 2 (2024): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2024)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v9i2.3187

Abstract

Epilepsy is a chronic neurological disorder characterized by recurrent seizures caused by abnormal electrical activity in the brain. Fast and accurate seizure detection is crucial to support medical intervention and improve patients' quality of life. Currently, Electroencephalogram (EEG) signals are widely used to diagnose epilepsy as they record brain electrical activity in real-time. However, manual analysis of EEG signals requires time and precision, necessitating a more effective automated solution. This study aims to optimize the Backpropagation model for detecting epileptic seizures using EEG data. The research involved collaboration between Telkom University, Sumber Waras Hospital, and the University of Bonn. The EEG data collected was processed through Discrete Cosine Transform (DCT) to extract important features before being used to train the artificial neural network (ANN) model. The model was trained and tested using varying numbers of epochs to measure its accuracy. The results show that the Backpropagation model achieved optimal accuracy of 91.15% at 100 epochs and increased to 93.05% at 200 epochs. Although accuracy improved with more epochs, the longer computational time posed a risk of overfitting. This research demonstrates that the Backpropagation algorithm can be optimized to detect epileptic seizures accurately and efficiently. The implication for Sumber Waras Hospital is that this model can be implemented in EEG monitoring systems to detect seizures in real-time, supporting faster medical intervention and reducing reliance on manual analysis. Thus, this study contributes to providing a more efficient diagnostic solution and enhancing healthcare services for epilepsy patients.
Penguatan Digital Marketing Berbasis Artificial Intelligence Bagi Produk Lokal UMKM Fatihanursari Dikananda; Edi Wahyudin; Odi Nurdiawan; Rudi Kurniawan
AMMA : Jurnal Pengabdian Masyarakat Vol. 4 No. 5 : Juni (2025): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a significant role in regional economic development but continue to face challenges in implementing digital marketing strategies. This community service program aimed to improve MSMEs' capabilities through mentoring on Artificial Intelligence (AI)-based digital marketing for five MSMEs in Cirebon City. The implementation included needs assessment, digital marketing training, AI utilization workshops, implementation assistance, and monitoring and evaluation. The results demonstrated improvements in social media management, content planning, AI-assisted copywriting, and digital promotional activities. The program enhanced participants' digital competencies and supported sustainable improvements in the competitiveness of local products.
Co-Authors Abdul Rauf Chaerudin Abdul Robi Padri abdullah, nur syarief Ade Irma Purnamasari Ade Irma Purnamasari Ade Irma Purnamasari Ade Kurnia, Dian Ade Rizki Rinaldi Adisty Tri Putra Agis Maulana Robani Agung Nugraha Agus Surip Ahmad Asyraful Hijrah Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Zam Zami Ainnur Rahman, Rizal Amar, Mohammad Rosihin Amarda, Juan Amri, Hajijin Ananda Rafly Andi Setiawan Andi Setiawan Anwar Musaddad Aria Pratama Arif Fitriyanto, Goffar Arif Rinaldi Dikananda Arif Rinaldi Dikanda baihaqqi, Farisky Bambang Irawan Basysyar, Fadhil Muhammad Basysyar, Muhammad Fadhil Cep Lukman Rohmat Cep Lukman Rohmat Cep Lukman Rohmat Dadang Sudrajat Danar Dana, Raditya Deasiva, Imanda Denni Pratama Dian Ade Kurnia Dias Bayu Saputra Dikananda, Arif Rinaldi Dilla Eka Lusiana Dita Rizki Amalia Dodi Solihudin Dwi Teguh Afandi Edi Tohidi Edi Wahyudin Eko Wiyandi ETI KURNIAWATI Fadhil M. Basysyar Fadrin Helmi FANDI ACHMAD Fathurrohman Fathurrohman Fathurrohman Fatihanursari Dikananda Faturrohman, Faturrohman Fauzi Fauzi Febriansyah, Feggy Fidya Arie Pratama Fidya Arie Pratama Firmansyah Firmansyah Fitriyani, Nur Sifa Gifthera Dwilestari Haidar Fakhri Hajijin Amri Hamonangan, Ryan Hanafi, Muhammad Salman Hayati, Umi Heliyanti Susana Herdiana, Ruli Herdiana, Rully Heriyawan, Ikhsan Himawan, Irvan Hira Wahyuni Azizah Ibnu Ubaedila Indah Diniarti Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Purnamasari, Ade Irvandi Irvandi IRVANDI, IRVANDI Jaelani Sidik Jamalul'ain, Abdul Jayawarsa, A.A. Ketut Julia Eka Yanti Juliadi, Diky Karlina, Lita Kaslani Khamim Surya Hadi Kusuma Al Atros Khoirul Insan, Moh Khoirul Kurniawan Fajar Abdulloh Laturrizqi, Washi Lukmanul Hakim M. Basyisyar, Fadhil M. Iqbal Fadhilah, Aji Mamluah, Karimatul Mariyani, Dinda Martanto . Mauludin, Muhammad Rifqi Medina Aprilia Putri Melia Melia Melia Melia Melisa Hikari Melva Regina Arpratika Mia Fijriani Muchamad Sobri Sungkar, Muchamad Sobri Muhalim, Alvy Muhammad Adithya Pratama Mulyana Mulyana Mulyawan Mulyawan Mulyawan, Mulyawan Musliyadi, Mar'i Nana Suarna Nana Suarna Nana Suarna Nanda Permatasari Nenda Alfadil Seputra Nining Rahaningsih Nining Rahaninsih Noval Salim NoviFirda Aini Nur Atikah Nurcholis, Rifki Nurdiawan, Rudi Nurhadiansyah Nurrohmat, Iman Pratama, Fidya Arie Pratama, Irfan Pratiwi, Fitriyani Prihartono, Willy Purnamasari, Ade Irma Putri, Haidah R, Nining Riansah, Adam Rinaldi Dikananda, Arif Rinaldi Dikanda, Arif Riyan Suryatana Riyan, Ade Bani Rizki, Dicky Miftakhul Rohmat, Cep Lukman Rokhmatan Khaerullah, Rizal Rudi Hartono Rudi Kurniawan Rudi Kurniawan Ruli Herdiana Ruli Herdiana Rully Pramudita Saeful Anwar Saeful Anwar Saeful Anwar, Saeful Saepul Hadi Salsa Billa Agistina Suarna, Nana Subandi, Husein Suripno Syafi'i, Syafi'i Tati Suprapti Taufik Hidayat Tengku Riza Zarzani N Tio Prasetiya Tio Prasetya TOMAS TOMAS Topan Hadi Tuti Hartati Tuti Hartati Willy Prihartono Wiyandi, Eko Yudhistira Arie Wijaya Yunus, Shofian