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All Journal Jurnal Teknologi Informasi dan Terapan (J-TIT)
Adi Sucipto
Politeknik Negeri Jember, Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Implementation of the Template Matching Algorithm for Smart Light Control through Speech Recognition for People with Disabilities Sholihah Ayu Wulandari; Adisty Pramudita Putri Rudi; Adi Sucipto; Bekti Maryuni Susanto; Dhony Manggala Putra
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.449

Abstract

Voice control systems in smart homes provide significant convenience for people with disabilities, especially in operating household devices such as lights without physical interaction. This study develops a voice-based light control system that runs locally on IoT devices using the template matching method. This system utilizes Mel-Frequency Cepstral Coefficients (MFCC) for voice feature extraction and Dynamic Time Warping (DTW) to match test voices with pre-recorded templates. Out of 66 voice samples tested, the system successfully recognized 13 out of 22 voices belonging to the primary user and rejected 43 out of 44 voices from other users, with an accuracy rate of 84.85%. Thus, this system shows potential as an inclusive, efficient, and disability-friendly voice control solution for smart home environments
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.
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.
Co-Authors Adisty Pramudita Putri Rudi Agus Hariyanto Ahmad Fahriyannur Rosyady Bekti Maryuni Susanto Dhony Manggala Putra Nicolas Chenu Rifqi Aji Widarso Sapta Nugraha Sholihah Ayu Wulandari Tamara Maharani