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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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Fuzzy Sugeno Model for SNR-Based Adaptive Modulation in Underwater Acoustic Communication Sholihah Ayu Wulandari; Ahmad Haris Hasanuddin Slamet
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.466

Abstract

Underwater communication faces significant challenges due to the dynamic characteristics of the channel and is strongly influenced by the physicochemical parameters of the water. This study proposes channel quality modeling using the Sugeno Fuzzy Inference System (FIS) with input variables of temperature, salinity, dissolved oxygen (DO), and turbidity. The system produces a Signal-to-Noise Ratio (SNR) output that is used as a basis for channel quality mapping, Bit Error Rate (BER) estimation, and the selection of adaptive modulation techniques (BPSK, QPSK, or 16QAM). Simulation results show that the Sugeno fuzzy model is able to follow the theoretical pattern well, where increasing temperature, salinity, and turbidity decrease the SNR value, while DO plays a role in maintaining channel stability. Based on the test results, at high SNR (≥ 15 dB) the system recommends 16QAM, at medium SNR (11–15 dB) QPSK, and at low SNR (≤ 10 dB) BPSK. This approach has proven effective in suppressing BER and increasing the reliability of underwater acoustic communications in fluctuating mangrove water environments.
Mobile App for Incubator Monitoring to Optimize Quail Egg Production Agus Nur Khomarudin; Indra Farhan; Rabby Nazli; Rina Novita; Romy Aulia; Sholihah Ayu Wulandari
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.v13i1.471

Abstract

Incubation is a crucial process in egg hatching, where eggs must be maintained under optimal temperature and humidity conditions. To ensure the stability of these parameters, a monitoring system is needed one that operates continuously, provides sufficient accuracy, and is easily accessible to users. This study aims to design a Mobile Application for Monitoring Temperature and Humidity in Quail Egg Incubators that is both valid and effective, as a means to enhance and optimize quail egg production. The research follows the Agile Method, consisting of the following phases: Plan, Design, Develop, Test, Deploy, Review, and Launch. The Mobile App for Incubator Monitoring to Optimize Quail Egg Production underwent product testing, including a validity test conducted by several experts in computer and mobile applications. The test resulted in a score of 0.78, which falls into the valid category. The effectiveness test yielded a score of 0.35, indicating a moderate level of effectiveness. The implementation of this monitoring application has shown a significant positive impact on operational efficiency in quail farming, particularly at Nisya Farm. User experience was evaluated through direct observation and interviews with respondents. Overall, users reported that the app is easy to use, thanks to its simple and intuitive interface. User feedback strengthens the argument that technological integration not only brings technical benefits but also boosts farmers’ confidence in managing the incubation process. This is essential, as the success of technology adoption heavily depends on how comfortable and user-friendly it is for its intended users.
Comparative Analysis of AES-GCM and ChaCha20-Poly1305 in IoT Data Encryption Based on ESP32 Dwi Dinda Meylani Angelina; Ronald David Marcus
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.v13i1.473

Abstract

Internet of Things (IoT) devices have become integral to modern applications, yet their resource constraints pose significant challenges for implementing robust security measures. While AES-GCM and ChaCha20-Poly1305 are widely recognized Authenticated Encryption with Associated Data algorithms, their comparative performance on resource-limited microcontrollers like ESP32 remains underexplored, particularly regarding execution time, memory usage, power consumption, and throughput. This research aims to conduct a comprehensive performance analysis of both algorithms for securing IoT data transmission on ESP32-WROOM-32D microcontrollers. The study implements both algorithms using Arduino IDE 2.3.6, leveraging ESP32's hardware acceleration for AES-GCM and optimized software implementation for ChaCha20-Poly1305. Performance evaluation encompasses various payload sizes (16, 64, and 256 bytes) with precise measurements of execution time using micros() function, memory usage via ESP.getFreeHeap(), and power consumption through shunt resistor analysis. The results reveal distinct performance characteristics between the two algorithms across all evaluated metrics, providing valuable comparative insights for IoT developers to select optimal cryptographic solutions based on specific application requirements and resource constraints, thereby enhancing security implementation on embedded systems.
Management of Potential Mental Health and Behavioral Disorders for College Students Using Integrated Applications: Implementation of Human-Centered Design Rinda Nurul Karimah; Dia Bitari Mei Yuana; Reza Putra Pradana; Prawidya Destarianto; Dhyani Ayu Perwiraningrum
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.v13i1.476

Abstract

Symptoms of mental disorders among college students have risen recently. A comprehensive synthetic review using a socio-ecological model as a guiding framework reveals that college students' mental health is influenced by several dynamic and interconnected factors at the individual, interpersonal, institutional, community, and policy levels, all of which can contribute to stress, anxiety, and depression. Several information and communication technology (ICT) services have been developed to improve healthcare, including the Depression, Anxiety, and Stress Scale 21 (DASS21) instrument. However, these applications do not receive continual monitoring and support from mental health professionals such as psychologists. To address the mental health difficulties of adolescent college students, it is critical that the services established provide appropriate interventions and successfully identify, detect, and address student mental health concerns. As a result, when creating an application interface, a Human-Centered Design (HCD) approach is required, which prioritizes human interaction to provide a more intuitive, precise, and user-friendly user experience. The success of the HappyMind app design was demonstrated by testing it on target users, namely college students, and end users, especially psychologists who served as evaluators. The results demonstrate that the HappyMind application design achieved an average score of 4 or 5, particularly for simplicity of use, text clarity, comfort, and visual appeal.
Design of a Naive Bayes–Based Adaptive Modulation Model in a Time-Varying Channel Environment Rosabella Ika Yuanita; Sholihah Ayu Wulandari; Taufiq Rahman Humaidi
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.v13i1.477

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) systems require an effective modulation adaptation mechanism to maintain transmission reliability over dynamic and noise-affected channels. This study proposes a machine learning–based adaptive modulation method using Naive Bayes classification to select the most appropriate modulation scheme—BPSK, QPSK, or 16-QAM—based on Signal-to-Noise Ratio (SNR) values. The Naive Bayes model is trained using the probabilistic performance distributions of each modulation scheme, enabling optimal modulation mode prediction under various channel conditions. Simulation results demonstrate that the proposed adaptive method achieves a lower Bit Error Rate (BER) compared to fixed modulation schemes, particularly under low to medium SNR conditions. Furthermore, the Naive Bayes–based approach exhibits more stable performance, especially in recovering transmitted messages. BER curves and demodulated message results indicate that the artificial intelligence–based adaptive scheme using Naive Bayes improves the reliability of transmitting the text message “HELLO WORLD” across an SNR range of –5 dB to 15 dB. These findings confirm that integrating intelligent methods into adaptive OFDM modulation provides an effective solution for wireless communication in fluctuating channel environments.
An Intelligent IoT-Enabled Vermiculture Monitoring and Control System Based on Fuzzy Inference Approach (Case Studi : Siscamling) Nur Hayati Mufarrihah; Hadi Prayitno; Isa Ma'rufi
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.v13i1.480

Abstract

Cultivating earthworms necessitates meticulous environmental control to ensure optimal growth, reproduction, and substrate integrity. Conventional vermiculture approaches depend significantly on manual monitoring, resulting in irregular moisture and pH management, hence diminishing production and resource efficiency. This research presents an intelligent Internet of Things (IoT)-facilitated monitoring and control system utilising a Sugeno fuzzy inference method for automated environmental management in vermiculture. The system amalgamates sensors for soil moisture, temperature, soil pH, water pH, and total dissolved solids with a microcontroller and cloud platform to facilitate real-time monitoring and remote oversight. Fuzzy logic is utilised to manage environmental uncertainty and ascertain suitable control measures, including irrigation, fertiliser application, and ventilation. Experimental findings indicate that the proposed system proficiently sustains substrate moisture within the ideal range of 15–30% and regulates pH values between 6.0 and 7.2. Automated reactions were effectively initiated under diverse environmental conditions, including irrigation activation at a 40% moisture threshold and nutrient correction in mildly acidic settings. The system attained dependable sensor functionality with negligible transmission latency and enhanced resource efficiency by minimising excessive irrigation and substrate waste. The amalgamation of IoT with fuzzy control offers a scalable, adaptive, and sustainable approach to smart vermiculture management.
Culturally Adaptive AI System for Wayang Character Visualization and Recognition for Children Yoga Rarasto Putra; Reza Fitriansyah; Lyscha Novitasary
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.v13i1.482

Abstract

This study developed an artificial intelligence–based drawing and classification system to support the visual reinterpretation of wayang characters for children while preserving their cultural identity. The research addressed the declining interest of younger generations in traditional cultural heritage by introducing a visually engaging and culturally adaptive digital approach. A generative model based on StyleGAN-3 was trained to produce child-friendly visual adaptations of ten wayang characters, while a ResNet-18 classification model was implemented to recognize character images. The dataset consisted of 400 training images and 60 testing images, including children’s drawings used to evaluate model generalization. Image preprocessing and data augmentation techniques were applied to improve model robustness. The classification model achieved an overall accuracy of 87%, indicating strong capability in recognizing distinctive visual characteristics of wayang characters across varied visual styles. In addition, a visual preference evaluation involving children showed that several generated characters received positive responses, particularly those with balanced proportions and expressive features. The results demonstrated that the proposed system can function as an interactive cultural learning medium and provide an innovative strategy for introducing traditional wayang characters to digital-native children.
Development of a Machine Learning Cumulative GPA Prediction Model using Explainable AI Fathinah Izzati; Ulva Elviani; Rizki Hikmawan
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.v13i1.488

Abstract

This study aims to develop an accurate, transparent, and interpretable model for predicting students’ Cumulative Grade Point Average (GPA) using an Educational Data Mining approach. The study adopts the Knowledge Discovery in Databases (KDD) framework, which includes data preprocessing, Z-transformation normalization, and feature selection. Three machine learning algorithms, namely Random Forest, XGBoost, and Support Vector Machine (SVM), are compared to determine the best-performing model. Model evaluation is conducted using a 10-fold cross-validation scheme with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics to ensure generalization capability. To address the black-box nature of machine learning models, Explainable Artificial Intelligence (XAI) techniques are applied using SHAP and LIME to provide both global and local interpretability of the predictions. The results indicate that XGBoost Regression achieves the best performance with the lowest error values. Previous GPA, attendance rate, and study duration are identified as the most influential predictors. The integration of XAI enables deeper insights for educators in supporting data-driven decision-making. Therefore, the proposed model has strong potential to be implemented as an early warning system for more effective and measurable academic interventions.
Machine Learning–Based Recommendation System for Optical Distribution Point Placement in Fiber Access Networks Widiatry Widiatry; Nova Noor Kamala Sari; Aprilita Aprilita
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.v13i1.489

Abstract

The rapid expansion of the digital economy requires reliable telecommunication infrastructure, particularly fiber optic access networks that provide high-speed broadband connectivity. One critical component in these networks is the Optical Distribution Point, which functions as a distribution node connecting optical infrastructure to end users. However, ODP placement is often determined manually, leading to inefficient resource utilization and inconsistent decision-making. This study aims to develop a data-driven recommendation system for optimal ODP placement. The proposed approach integrates spatial feature engineering with supervised machine learning techniques to analyze infrastructure capacity, spatial distance, and customer distribution. Several algorithms were evaluated, including Random Forest, Logistic Regression, K-Nearest Neighbors, Gradient Boosting, and a Stacking Ensemble model, while Synthetic Minority Oversampling Technique was applied to address class imbalance. Model performance was evaluated using Precision, Recall, F1-score, ROC-AUC, and Normalized Discounted Cumulative Gain. The results show that Gradient Boosting achieved the highest performance with an F1-score of 0.8986 and ROC-AUC of 0.96, while the Stacking Ensemble model demonstrated stable ranking performance with a mean NDCG of 91.75%. The proposed system improves the efficiency and accuracy of ODP placement planning and supports data-driven telecommunication infrastructure development.
Multivariate LSTM with SLO-Aware Loss for Virtual Machine Workload Prediction on Cloud Data Center Agus Hariyanto; Ahmad Fahriyannur Rosyady; Adi Sucipto; Bekti Maryuni Susanto; Sapta Nugraha; Nicolas Chenu
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.v13i1.490

Abstract

Accurate virtual machine (VM) workload prediction is a key component of cloud resource management, particularly to support auto-scaling and to maintain Service Level Objectives (SLOs). In conventional prediction models that rely on symmetric loss functions such as Mean Squared Error (MSE), under-prediction errors are treated equivalently to over-prediction errors, even though under-prediction carries significantly more severe operational consequences — it directly triggers capacity shortages and SLO violations. This study proposes a CPU workload prediction approach based on a multivariate Long Short-Term Memory (LSTM) network enhanced with an SLO-aware loss, an asymmetric loss function that penalizes under-prediction ten times more heavily than over-prediction. Experiments are conducted on a subset of 25,000 rows from the Bitbrain GWA-T-12 fastStorage dataset with four input features (CPU, memory, network received, network transmitted), using a fixed random seed for reproducibility. Two models are trained and compared: one with SLO-aware loss and one with standard MSE as baseline, both sharing identical architecture and hyperparameters. The primary evaluation metric is the under-prediction rate, which directly quantifies SLO violation risk. Results show that the SLO-aware model achieves an under-prediction rate of 0.04%, compared to 0.16% for the MSE baseline — a fourfold reduction. These findings empirically confirm that SLO-aware loss effectively directs the model toward conservative predictions that protect SLO compliance, establishing loss function design as a critical and actionable dimension in cloud VM workload prediction.

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