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Jurnal Teknologi Informasi dan Terapan (J-TIT)
Published by Politeknik Negeri Jember
ISSN : 2354838X     EISSN : 25802291     DOI : https://doi.org/10.25047
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
This journal accepts articles in the fields of information technology and its applications, including machine learning, decision support systems, expert systems, data mining, embedded systems, computer networks and security, internet of things, artificial intelligence, ubiquitous computing, wireless sensor networks, and cloud computing. The journal is intended for academics and practitioners in the field of information technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 235 Documents
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A Chatbot-Based Expert System for Food Crop Disease Diagnosis Saniyatul Mawaddah; Muhammad Turmudzi; Dewi Wulansari; Agus Wibowo
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.492

Abstract

The agricultural sector is very important for Indonesia's food security, however, plant diseases pose a serious threat that can significantly reduce crop yields. The limited availability of agricultural experts often hinders farmers from obtaining rapid and accurate diagnoses. To address this issue, this research develops a chatbot-based expert system using the forward chaining method to assist farmers in conducting self-diagnosis. This method works by drawing conclusions from the symptoms entered, while the chatbot provides real-time interactions that are easy to use. System testing shows good performance, black box testing ensures that all features operate without errors, with a diagnostic accuracy of 87.5 percent, as 42 out of 48 cases correspond with expert assessments. Furthermore, usability testing with 52 respondents yields a System Usability Scale score of 79.18, categorized as good. The results of this research indicate that the developed system is accurate, efficient, and practical, with the potential to serve as a widely applicable solution to help
Web-Based Smart Aquaculture: Comparative Analysis of Mamdani, Sugeno, and Tsukamoto Fuzzy Inference Systems for Shrimp Pond Water Quality Assessment Santi santi; Arna Fariza; Agus Indra Gunawan
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.493

Abstract

Indonesia possesses vast marine and aquaculture potential; however, national shrimp production in 2024 achieved only 56.67% of its target, largely due to suboptimal water quality management. To address this issue, an intelligent classification system capable of handling uncertainty in aquaculture environments is required. This study presents a comparative evaluation of three fuzzy inference systems (FIS), namely Mamdani, Sugeno, and Tsukamoto, for shrimp pond water quality classification based on four key parameters: temperature, pH, salinity, and dissolved oxygen (DO). Water quality conditions were categorized into four classes: Good, Medium, Bad, and Very Bad using trapezoidal membership functions and expert-defined reference labels derived from aquaculture water quality standards. The dataset consisted of 994 water quality records collected from shrimp ponds in Surabaya, Indonesia, during the period from December 2024 to April 2025. Experimental results indicate that the Mamdani method produced the highest consistency with the expert-defined reference rules, achieving an agreement accuracy of 0.800, precision of 0.825, recall of 0.800, and F1-score of 0.797. In comparison, both Sugeno and Tsukamoto produced lower performance with an accuracy of 0.700 and F1-score of 0.728, although they achieved slightly higher precision values of 0.880. The findings indicate that the Mamdani fuzzy inference system provides more stable and consistent inference behavior relative to the predefined aquaculture reference rules for shrimp pond water quality assessment. Furthermore, the proposed web-based monitoring system demonstrates the practical potential of fuzzy logic approaches in supporting sustainable smart aquaculture management and environmental monitoring.
Deep Learning-Based Tomato Leaf Disease Classification Using CNN, EfficientNetB0, and InceptionResNetV2 Rifqi Aji Widarso; Adi Sucipto; Dhony Manggala Putra; Tamara Maharani
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.494

Abstract

Tomato leaf diseases threaten agricultural productivity because symptoms such as early blight, late blight, leaf mold, septoria leaf spot, and yellow curl virus often produce visually similar color changes, necrotic lesions, and leaf deformation. Manual visual diagnosis is subjective and depends heavily on expert experience; therefore, image-based deep learning is a relevant approach for supporting preliminary disease identification. This study compares five deep learning architectures, namely a custom convolutional neural network, EfficientNetB0, MobileNetV2, DenseNet121, and InceptionResNetV2, for classifying six tomato leaf categories using 7,192 images from a Kaggle dataset. The research workflow includes dataset preparation, image resizing and normalization, model training using the Adam optimizer, and evaluation through accuracy, loss, precision, recall, F1-score, and confusion matrix analysis. Based on the notebook results, EfficientNetB0 achieved the best validation accuracy of 89.44% after 20 epochs, followed by MobileNetV2 at 85.12%, DenseNet121 at 82.77%, the custom CNN at 70.69% test accuracy, and InceptionResNetV2 at 45.76% test accuracy. The results indicate that lightweight transfer learning models are more suitable for medium-sized agricultural image datasets than large architectures trained for only a few epochs. Future work should validate the model using real field images, harmonize all models on the same test set, and report class-wise metrics to ensure reliability before deployment as a farmer-oriented diagnostic support system.
A Multi Criteria Framework for Ice Block Production Systems Integrating Machine Performance and Floating Photovoltaics A.A Chrisna Firman Alamsyah Putra; Ridho Hantoro
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.499

Abstract

The increasing demand for ice blocks in small island fisheries requires an efficient and sustainable production system that accounts for both technical and energy constraints. This study aims to identify the optimal ice block production system for Bungin Island by integrating machine characteristics, ice demand, and solar energy potential. Ice demand is estimated from fish catch data using a ratio-based approach, while technical evaluation includes criteria such as production capacity, unit weight, quantity, harvesting time, power consumption, specific energy consumption (SEC), and cost. A multi-criteria decision analysis (MCDA) framework combining the CRITIC and TOPSIS methods is applied to determine criteria weights and rank alternatives. The results indicate that harvesting time and energy efficiency (SEC) are the most influential factors in decision-making. Among the evaluated alternatives, machines with lower SEC and shorter production cycles demonstrate superior performance. Furthermore, the analysis confirms that integrating photovoltaic systems is feasible and can support energy requirements under spatial constraints. This study provides a systematic framework for optimizing ice block production systems, contributing to sustainable fisheries infrastructure development in small island regions.
LSTM Approached for Cassava Tapai Ripeness Identification Shabrina Choirunnisa; Muhammad Izza Alfiansyah; Khafidurrohman Agustianto; Rifda Hanifah Azzahra
Jurnal Teknologi Informasi dan Terapan Vol 13 No 1 (2026): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v13i1.500

Abstract

Tapai singkong (cassava tapai) is a traditional Indonesian fermented food product whose quality is highly dependent on precise control of the fermentation process. Inconsistent fermentation outcomes arise from fluctuating environmental conditions including temperature, humidity, and fermentation gas levels making it difficult to reliably determine ripeness status without objective measurement tools. This study addresses the challenge of automated ripeness prediction by providing a controlled, head-to-head comparison of four machine learning approaches Logistic Regression, Support Vector Machine (SVM), Random Forest, and LSTM-based Recurrent Neural Network (RNN) on a single, uniformly preprocessed dataset of 600 time-series observations across three ripeness classes (unripe, ripe, overripe), collected from 10 fermentation trials spanning 60 hours each. All models were evaluated under identical preprocessing and hyperparameter settings using accuracy, precision, recall, F1-score, and confusion matrices to reveal per-class behavior. LSTM yielded the best test performance (96.46% accuracy; macro F1 = 0.93), Random Forest followed closely (93.70% accuracy; macro F1 = 0.94), while SVM and Logistic Regression obtained 91.28% and 90.31% accuracy respectively. This paper discusses the trade-off between predictive performance, temporal modeling capability, and interpretability, and recommends LSTM for high-accuracy quality control deployments where temporal dependencies are critical, and Random Forest as a strong, interpretable alternative for resource-constrained environments. Per-class metrics and experimental artifacts are provided to support reproducibility and practical adoption in traditional food production monitoring.

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