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All Journal Jurnal Informatika dan Teknik Elektro Terapan Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta AL-TANZIM : JURNAL MANAJEMEN PENDIDIKAN ISLAM JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Building of Informatics, Technology and Science Jurnal Teknik Informatika C.I.T. Medicom MEANS (Media Informasi Analisa dan Sistem) Jurnal Informatika Terpadu JUSTIN (Jurnal Sistem dan Teknologi Informasi) D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Artificial Intelligence and Engineering Applications (JAIEA) J-Icon : Jurnal Komputer dan Informatika Jurnal Sistem Informasi dan Manajemen
Dikananda, Arif Rinaldi
Unknown Affiliation
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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Optimization of Classification of Tea Leaf Disease Images Using LBP–HOG and MobileNetV2 Ezar Qotrunnada; Nurdiawan, Odi; Dikananda, Arif Rinaldi; Putra, Aris Pratama; Nurhakim, Bani
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.v5i2.1861

Abstract

This study was motivated by the need for an accurate and efficient system for detecting tea leaf diseases, given that the current method Manual identification has limitations in terms of consistency, speed, and It also depends on expert labor. To address these challenges, the study It developed a classification model for detecting diseases in tea leaves using a combination of features Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) integrated with the MobileNetV2 architecture. The research method includes the following stages: importing the dataset, data partitioning, exploratory data analysis (EDA), preprocessing, features, and training four model scenarios: baseline MobileNetV2, LBP-based model, HOG-based model, and hybrid LBP–HOG model. Evaluation is done with the metrics of accuracy, precision, recall, and F1-score. The results show that the baseline model achieved 91.67% accuracy, the LBP model achieved 60.67%, the HOG model achieved 68.67% accuracy, and the hybrid model achieved 66.67% accuracy. These findings indicate that MobileNetV2 is still the most optimal model, but the integration of texture features and gradients provides a deeper understanding of the characteristics of disease patterns. This study emphasizes the importance of exploring classic features to enriching visual representation in lightweight CNN models, as well as providing a contribution to the development of plant disease diagnosis systems that are efficient.
Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm Aini, NoviFirda; Nurdiawan, Odi; Suprapti, Tati; Dikananda, Arif Rinaldi; 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 Seputra, Nenda Alfadil; Nurdiawan, Odi; Dikananda, Arif Rinaldi; Pratama, Denni; Kurnia, Dian Ade
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.
Co-Authors -, Martanto - ., Martanto abdullah, nur syarief Ade Rizki Rinaldi Ahmad Faqih Ahmad Rifai Aini, NoviFirda Almadina, Muhammad Fitrian Shousyade Ardhanur, Ichlas Artoti, Azzahra Rizky Bahrul Jawahir, Muhammad Cakranegara, Pandu Adi Dadang Sudrajat Ezar Qotrunnada Fasa, Saefullah Fathurrohman, Fathurrohman fatimah, lilis Fithriyani, Nurul Muna Gifthera Dwilestari Hamam, Moh Hardika Hardika, Hardika hermansyah, Muhammad ikhwan adholf Irfan Ali, Irfan Khoirunisa, Pitria khusnul khotimah Kurnia, Dian Ade Martanto Martanto . Mirna Mirna Muhamad Abdul Fatah Muhammad Hilman Mulyawan Mulyawan, Mulyawan Muzani, Muhamad Nining Rahaningsih Nur Hermawan, Ilham Nuraeni Nurcholis, Rifki Nurhakim, Bani Odi Nurdiawan Pratama, Denni Pratama, Fidya Arie Pura, Panji Adi Purnamasari, Ade Irma Putra, Aris Pratama Putra, Purniadi Putri, Haidah Putri, Niken Zeliana Raditya Danar Dana Ramadan, Muhamad Firly Rifa'i, Ahmad Rifa’I, Ahmad Rohman, Dede Rohmat, Cep Lukman Saeful Anwar, Saeful Seputra, Nenda Alfadil Subandi, Husein Syahri, Ibnu Nava Syifa, Nurkhasanah Fadhila Tati Suprapti Wijaya, Nurjana Adi Yunus, Shofian