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All Journal Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Algoritme Jurnal Mahasiswa Teknik Informatika
Andreas Saputra, Andreas
Universitas Kristen Duta Wacana
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Analisis Sentimen Publik Terhadap Keberadaan Juru Parkir Liar Menggunakan Naïve Bayes Dengan Teknik SMOTE Daniel, Daniel; Saputra, Andreas; Al Rivan, Muhammad Ezar
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i1.8154

Abstract

The continuous growth of YouTube is increasingly leveraged by users to convey information, including critiques and suggestions about illegal parking attendants. The method used in this research is data classification using the Naïve Bayes Classifier (NBC). The system is developed using internal data collected from the internet/YouTube to determine whether sentences are positive or negative opinions. This determination is classified as a classification process. The data is processed using SMOTE to balance the dataset, followed by classifying comments into two classes: positive and negative. This classification employs the Naïve Bayes algorithm. This classification provides convenience for users to view both positive and negative opinions. The accuracy test results for the Naïve Bayes method without SMOTE for classification yielded an average of 86.93%, while the accuracy test results for the Naïve Bayes method with SMOTE technique yielded an average of 91.99%.
Deteksi Penyakit Daun Teh Berdasarkan Citra Menggunakan Deep Learning Saputra, Andreas; Hermanto, Dedy
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 2 (2026): APRIL 2026
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.v10i2.5657

Abstract

Tea plant (Camellia sinensis) originates from China and is one of the most widely consumed beverages in the world. Tea plants are vulnerable to leaf diseases such as Tea Leaf Blight, Tea Red Leaf Spot, and Tea Red Scab, which can reduce the quality and productivity of the harvest. Manual disease identification is still commonly used, but this method has many limitations, such as dependence on farmers’ experience and inaccuracy in early detection. This study aims to apply the YOLOv11 algorithm as an object detection method to automatically, quickly, and accurately detect four classes of tea leaf conditions (three diseases and one healthy). The dataset used consists of 3,960 high-resolution tea leaf images that have undergone segmentation, augmentation, and normalization processes. The research was carried out through image preprocessing, YOLOv11 model training, and model performance evaluation using precision, recall, F1-score, and mean Average Precision (mAP) metrics. The results of tea leaf disease detection using YOLOv11 achieved an average precision of 97.2%, recall of 98.2%, mAP@0.5 of 98.8%, and mAP@0.5:0.95 of 95.5%. This model can be used to help farmers identify tea leaf diseases more quickly and reduce the risk of crop yield losses.
Co-Authors Daniel Daniel Dedy Hermanto M Ezar Al Rivan