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Bekti Maryuni Susanto
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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 221 Documents
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Efficient Intrusion Detection System Utilizing Ensemble Learning and Statistical Feature Selection in Agricultural IoT Networks Ahmad Fahriyannur Rosyady; Bekti Maryuni Susanto; Agus Hariyanto; Mukhamad Angga Gumilang
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.v12i1.448

Abstract

To enhance agricultural processes, smart agriculture combines a variety of devices,protocols, computing paradigms, and technologies. The cloud, edge computing, big data, andartificial intelligence all offer tools and solutions for managing, storing, and analyzing the vastamounts of data produced by various parts. Smart agriculture is still in its infancy and lacks severalsecurity measures, brought in the creation of numerous networks that are vulnerable to cyberattacks.The most well-known cyberattack is called a denial of service (DoS) attack, in which the attackersoverwhelm the network with massive amounts of data or requests, preventing the nodes fromaccessing the various services that are provided in that network. Intrusion Detection Systems (IDS)have shown to be effective defense mechanisms in the event of a cyberattack. The implementationof conventional intrusion detection systems (IDS) approaches in Internet of Things (IoT) deviceswas hindered by resource constraints, such as reduced computing capacity and low powerconsumption. In this paper, we used an ensemble learning and statistical based feature selectionstrategy to create a lightweight intrusion detection solution. The results show that the stackingensemble method is able to improve the performance of single machine learning in the classificationof anomalous events even though the computation time required is quite large compared to thecomputation time of single machine learning
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
Child Stunting Risk Analysis through Machine Learning Models using XGBoost Algorithm Nurul Renaningtias; Atik Prihatiningrum; Hardiansyah Hardiansyah; Yudi Setiawan; Arie Vatresia
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.v12i2.452

Abstract

Stunting is a chronic nutritional disorder that significantly affects child growth, development, and the overall quality of future human resources. According to the 2024 Indonesian Nutritional Status Survey (SSGI), the prevalence of stunting remains high at 19.8%, equivalent to approximately 4.48 million children under five. Early detection of stunting risk is essential for timely and data-driven interventions. This study employed the CRISP-DM methodology, encompassing business understanding, data collection, preparation, modeling, and evaluation phases. The dataset was processed through cleaning, variable encoding, and stunting status classification based on WHO standards. An XGBoost-based predictive model was developed and evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved 98% accuracy in predicting stunting risk. Feature importance analysis revealed that height is the most influential variable determining stunting risk.
Digital Image-Based Chili Quality Detection Using a Web-Based Convolutional Neural Network Jajang Jaya Purnama; Sri Rahayu; Ridwansyah Ridwansyah; Verry Riyanto
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.v12i2.453

Abstract

ABSTRACT Chili is one of the main horticultural commodities in Indonesia, with high economic value and stable market demand. Accurate determination of chili quality levels is an important factor in maintaining quality, selling price, and distribution efficiency. Until now, the process of assessing chili quality has generally been carried out manually through direct visual inspection by experts or field officers. This traditional approach has limitations, such as varying levels of accuracy due to assessor subjectivity and the limited availability of experts. Advancements in digital image processing technology, particularly deep learning, offer opportunities to develop more accurate and consistent automated detection systems. This study proposes a Convolutional Neural Network (CNN) model to classify chili quality levels based on digital images, which is then integrated into a web-based application. This study uses a dataset of 405 chili images from 11 varietal categories, each labeled with quality (good, pest-infested, or unknown), which undergoes preprocessing stages including resizing, normalization, and data augmentation. The CNN model was designed with convolutional layers, max-pooling, dense layers, and a Softmax activation function, and was trained using the Adam optimizer and Categorical Cross-Entropy Loss. The web application implementation was carried out using the Flask framework, allowing users to upload images and obtain prediction results in real time. The testing results showed that the developed CNN model achieved an accuracy of 1.000 on the test data, with reliable detection performance under variations in lighting and image backgrounds. This research contributes to the development of smart agriculture technology by providing an accurate, fast, and easily accessible solution for chili quality detection
Lightweight Deep Learning Approach for Sugarcane Leaf Disease Classification Using MobileNetV2 Cinantya Paramita; Rifky Bintang Pradana; Nurul Anisa Sri Winarsih; Ricardus Anggi Pramunendar
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.v12i2.456

Abstract

Sugarcane is one of Indonesia’s strategic crops, yet its productivity is frequently disrupted by leaf diseases such as yellow leaf, rust, and red rot. Previous studies have shown that deep learning models are promising for plant disease detection, but many of them rely on heavy architectures that limit deployment in real-world agricultural settings. To address this gap, this study applies MobileNetV2, a lightweight Convolutional Neural Network, for the classification of sugarcane leaf diseases. Using the publicly available Kaggle dataset, the model was trained and evaluated on four classes: healthy, yellow leaf, rust, and red rot. The results demonstrate that MobileNetV2 achieved 97.0% test accuracy, with strong precision, recall, and F1-scores across all categories. These findings highlight that efficient deep learning architectures can deliver reliable disease classification while remaining practical for implementation on mobile or edge devices. Compared with previous approaches, this study contributes by demonstrating that lightweight model like MobileNetV2 can provide a balance of accuracy and efficiency, making them suitable for supporting precision agriculture practices in resource-limited environments
Performance Comparison of CNN Transfer Learning Models for Coffee Bean Quality Classification Nur Muhammad Fadli; Prawidya Destarianto; Hendra Yufit Riskiawan; Bekti Maryuni Susanto; Satrio Adi Priyambada; Wawan Hendriawan Nur; Mukhamad Angga Gumilang
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.v12i2.457

Abstract

According to SNI Standard No. 01-2907-2008, accurate sorting of coffee beans is crucial for improving export value. Manual sorting is time-consuming, subjective, and error-prone, especially when visual differences are subtle between roast levels. This study proposes and evaluates an automatic, machine-learning based system to support quality assurance in coffee production. We compare three transfer-learning CNN architectures: Xception, MobileNetV2, and EfficientNet-B1 on a publicly available dataset of 1,600 coffee bean images divided into four classes (dark, medium, light, green). All models were trained with the same preprocessing and hyperparameter settings. EfficientNet-B1 achieved the highest test accuracy (100%), followed by Xception (99.5%) and MobileNetV2 (97%). We discuss trade-offs between accuracy and computational efficiency and recommend EfficientNet-B1 for high-accuracy applications and MobileNetV2 for edge/mobile deployment.
CNN Implementation in Progressive Web App for Automatic Garbage Classification using TensorFlow.js Eka Setyabudi; Noora Qotrun Nada; Mega Novita
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.v12i2.458

Abstract

The substantial and continuously increasing volume of global waste has become a critical environmental challenge, exacerbating the inherent inefficiency of conventional manual sorting techniques. This research addresses this problem by developing and evaluating an automated waste classification system using Convolutional Neural Networks (CNN), specifically the VGG16 architecture, integrated into a Progressive Web App (PWA) to enhance accessibility and sorting efficiency. Our primary goal is to deliver an intelligent, lightweight, and cross-platform solution capable of performing client-side inference on diverse devices. The VGG16 model was retrained using transfer learning on a validated public dataset of 10,365 images, comprising two classes (organic and inorganic waste). The trained model was converted to a browser-compatible format, TensorFlow.js, and deployed within the PWA framework which utilizes Service Workers for offline capabilities. Despite the significant challenge posed by the VGG16 model's large size, the system successfully performed client-side inference by prioritizing GPU acceleration and achieved 0.94 overall accuracy on the test dataset2. This result, supported by high F1-scores for both waste categories, confirms that deploying high-accuracy CNN models at the edge using PWA and TensorFlow.js is a feasible and promising strategy for practical, technology-based waste management and environmental education.
Spatial Mapping of Endemic Diseases in Lamongan Regency Using Natural Breaks Clustering Much Chafid; Ahmad Walid Hujairi; Muhammad Turmudzi; Arna Fariza; Mohammad Robihul Mufid; Shintia Dewi Rahmawati
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.v12i2.459

Abstract

Endemic diseases are health problems that require continuous monitoring to prevent their spread. However, the lack of interactive spatial data visualization can make it difficult for health agencies to analyze the distribution of diseases effectively. This study aims to build a spatial mapping system for endemic diseases in Lamongan Regency using the Natural Breaks clustering method. The data used in this study include sub-district data, population, and endemic diseases obtained from the Lamongan Regency health profile website. The system was developed using the Laravel 10 framework and MySQL database, with Metabase as a data visualization tool. The geovisualization method is applied to display the distribution of diseases in the form of interactive maps based on sub-districts. The system allows users to trace the spatial and temporal trends of endemic diseases more easily. The test results show that the system is able to display information accurately and interactively, so that it can help health agencies in monitoring and planning regional health policies more effectively
Mitigating Distributed Denial of Service Attacks on IoT Systems Using Gemstone Architecture Mohammad Robihul Mufid; Yogi Pratama; Arna Fariza; Saniyatul Mawaddah; Much Chafid; Agus Wibowo
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.v12i2.460

Abstract

One of the problems in the Internet of Things (IoT) system is the Distributed Denial of Service (DDoS) attack on the information technology infrastructure on the Internet network. This is because the IoT device system does not have a portal gateway configuration that is capable of providing the necessary security and privacy protection. In this study, the focus is on the mechanism for reducing the impact of DDoS attacks of the http flood type on the layer framework using PHP Swoole. PHP Swoole utilizes event-driven which provides several features to access the transport layer in onConnect so that it can be used to implement initial security such as access control lists, connection concurrency management, and optimizing server performance. The methodology used is to develop a TCP connection filtering algorithm by implementing a simple queue system by accepting 67% of connections to be forwarded to the next layer and 33% of connections will be queued for a timeout of 2 seconds. The results of this study show that the server can minimize the impact of DDoS and handle traffic specifically for http requests with an average latency of 871.8ms.
Clustering Analysis for Green Economy and Citizens-Based Social Forestry Business Development Model Pradityo Utomo; Dwi Nor Amadi; Rahmanta Setiahadi
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.v12i2.463

Abstract

This study aims to prove that clustering analysis can optimize the development model of social forestry businesses based on green economy and citizens. Clustering analysis can use machine learning methods. Some of these methods are K-Means and K-Medoids. First, the research data was obtained from the assessment results of forest edge residents. Residents assessed 13 green economy variables. The social forestry business development model based on green economy and citizens requires labeled data. Therefore, this study compares the performance of K-Means and K-Medoids to cluster the assessment data of forest edge residents. To determine its performance, this study uses three variations of k values, namely K = 4, K = 8, and K = 12. Performance testing uses the Davies Bouldin Index (DBI) method and computation time. Based on Davies Bouldin test, K-Means method is better than K-Medoids at K = 4, but K-Medoids method is better than K-Means at K = 8 and K = 12. Based on computation time test, K-Means method is better than K-Medoids. Based on this test, K-Means method is more suitable for big data and fast computing time.

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