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Jurusan Teknik Informatika, Fakultas Teknik Universitas Negeri Surabaya Kampus Ketintang, Gedung A10, lt.2, Surabaya-Indonesia, 60231.
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JIEET (Journal of Information Engineering and Educational Technology)
Published by Universitas Negeri Surabaya
ISSN : -     EISSN : 2549869X     DOI : http://dx.doi.org/10.26740/
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal Description: JIEET (Journal of Information Engineering and Educational Technology) is a scientific journal that publishes the peer-reviewed research papers in the field of Computer Engineering, Distributed and Parallel Systems, Business Informatics, Computer Science, Computer Security, System & Software Engineering and Educational Technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
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Search results for , issue "vol. 10 no. 01 (2026)" : 3 Documents clear
Homomorphically Encrypted Evaluation of The Autoregressive Component of an ARIMA Model Rini Deviani; Dini Handayani
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p1-13

Abstract

Across domains like healthcare, finance and environmental monitoring time series forecasting has many applications and often deals with sensitive data. Although ARIMA models are popular due to high interpretability and accuracy and the basic form of ARIMA does not need a large amount of data for modeling, it requires plaintext data access which raises problems in cloud computing with privacy. Homomorphically Evaluating the Autoregressive Component of ARIMA for Privacy-Preserving Forecasting through CKKS-based Fully Homomorphic Encryption (FHE) in the TenSEAL library A temperature dataset was used to train an ARIMA(2,1,1) model with the autoregressive coefficients in plaintext and more specifically, as part of plaintext and encrypted workflows. The CKKS Model Recent observations were encrypted with CKKS thus allowing forecasting on ciphertext without ever exposing the data. The outcomes indicate, that the prediction using ciphertext matched pretty well to the one with plaintext and had an absolute error of 0.000001 along with accuracy of 99.995%. Our findings validate that not only does CKKS-based FHE retain forecasting accuracy in a secure manner and enable the combination of FHE with classical forecasting models, but also gives rise to future work on encrypted ARIMA modelling, multivariate forecasting as well as privacy-preserving machine learning.
DETECTION OF TOBACCO LEAF QUALITY USING THE YOLOv11 ALGORITHM suwarno arieska; I Gusti Putu Asto Buditjahjanto; Wiyli Yustanti
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p26-38

Abstract

Accurate and efficient detection of tobacco quality is essential to identify the quality of tobacco leaves and improve farmers' tobacco yields. However, due to the high similarity between classes, significant intraclass differences and complex backgrounds among different tobacco leaves, accurately identifying tobacco quality through neural network models can pose significant challenges. To address this problem, this paper is presented with a fast and accurate method of detecting and identifying tobacco leaf quality using YOLO (You Only Look Once). This model uses YOLOv11 which incorporates an efficient detection head designed to detect the characteristics of tobacco leaves. In addition, an in-depth surveillance layer is introduced into the network along with incorporating and enhancing dynamic upsampling modules. Experimental data include public data sets of L1L, L2L, L3L, L3R, L4R, L10, L10F, and LND tobacco leaf quality. The results of the experiment showed that Yolov11 outperformed the Yolov8 and Yolov12 algorithms with a precision of 0.853, an F1 score of 0.73 mAP@0.5, 0.811 and mAP@0.5:0.95 of 0.631.
Performance Analysis YOLO11n Model for Chili Leaf Diseases Detection Rayyan Nur Fauzan; Ervin Yohannes; Ricky Eka Putra; Avirmed Enkhbat
JIEET (Journal of Information Engineering and Educational Technology) Vol. 10 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v10n01.p14-25

Abstract

Chili plants are a strategic agricultural commodity with high economic value, yet their production is often disrupted by disease attacks causing significant yield reduction. Early and accurate detection of chili leaf diseases is crucial for implementing precision agriculture practices. This research implements the YOLO11n (You Only Look Once version 11 nano) model for automated chili leaf disease detection using the "Chili Plant Leaf Disease and Growth Stage Dataset from Bangladesh" containing 1,856 high-resolution images across six categories: Bacterial Spot, Cercospora Leaf Spot, Curl Virus, Healthy Leaf, Nutrition Deficiency, and White Spot. The model was trained for 100 epochs on Google Colab with Tesla T4 GPU using 640×640 pixel input resolution. Evaluation results demonstrate excellent detection performance with precision of 83.7%, recall of 84.3%, mAP@0.5 of 92.3%, and mAP@0.5:0.95 of 74.5%. Per-class analysis reveals that Nutrition Deficiency achieved the highest performance (mAP@0.5 = 99.2%), while Curl Virus presented the greatest detection challenge (recall = 55.6%). The lightweight YOLO11n architecture with only 2.58 million parameters and 6.3 GFLOPs, making it highly suitable for deployment on edge devices such as agricultural drones, mobile applications, and IoT monitoring systems. This research contributes to smart agriculture applications by providing an efficient and accurate solution for automated chili leaf disease detection under real field conditions.

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