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INDONESIA
Telematika : Jurnal Informatika dan Teknologi Informasi
Published by Universitas Pembangunan Nasional Veteran Yogyakarta
ISSN : 1829667X     EISSN : 24609021     DOI : 10.31315
Core Subject : Engineering,
Electrical & Electronics Engineering
Arjuna Subject : -
Articles 361 Documents
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Performance Evaluation of Multiple Deep Learning Models for Wine Quality Prediction Fabiyanto, Dedik; Rianto, Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13007

Abstract

Research utilizing a dataset from the UCI repository evaluated the predictive accuracy of nine machine learning models for wine quality. The models employed include Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Support Vector Machine (SVM), Random Forest, XGBoost, LightGBM, CatBoost, and Gradient Boosting. The dataset comprises 1,599 samples with 12 chemical parameters. Data preprocessing, including oversampling, normalization, standardization, and seeding, was performed to enhance model performance.The study's findings indicate that the models with the highest accuracy values were LightGBM (87.80%), CatBoost (86.60%), and Random Forest (85.70%). A voting classifier combining these three models achieved an accuracy of 87.29%. Further analysis using a confusion matrix demonstrated that this combined model effectively predicts the "Good" and "Not Good" classes.In conclusion, the combination of LightGBM, CatBoost, and Random Forest models proves to be an effective approach for predicting wine quality based on chemical parameters, with an accuracy value of 87.29%.
Application of Fibonacci Pattern for Network QoS (Quality of Service) Management) gani, ahmad
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14329

Abstract

In managing network quality of service (QoS), this study uses Fibonacci patterns to optimize delay control and bandwidth allocation. QoS is essential in contemporary network management, especially given the increasing demand for stable and effective data services. This study prioritizes data based on traffic levels using a simulated Fibonacci algorithm. Each priority is assigned a value corresponding to the Fibonacci sequence, which allows allocating resources more in line with the network load. Simulations are performed under normal and overload conditions. The results show that conventional methods, such as round-robin and weighted fair queuing, can improve QoS efficiency with Fibonacci patterns by up to 15%. This improvement mainly concentrates on controlling important data packets such as real-time communication and video streaming and reducing delay. In addition, this technique is better at adjusting to traffic changes. The results show that the Fibonacci pattern can be an innovative method for managing network QoS, especially for complex prioritization requirements. It can be a reliable tool to improve user experience with modern network services if used properly. To find out how Fibonacci patterns relate to future network technologies such as 5G and the Internet of Things, further research is needed.
Introduction to the Solar System Based on Augmented Reality: An Interactive Learning Tool for Middle School Students alvina, tiya ivanka; Ratnawati, Dwi
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14353

Abstract

Penelitian ini bertujuan untuk mengevaluasi pengaruh teknologi Augmented Reality (AR) terhadap tingkat pemahaman siswa dalam pembelajaran tata surya di sekolah menengah. Perancangan penelitian ini menggunakan metode kuantitatif dengan pendekatan survei pada 30 siswa menengah di Indonesia. Data dikumpulkan melalui instrumen yang telah diuji validitas dan reliabilitasnya (Cronbach's Alpha = 0,719). Analisis regresi sederhana digunakan untuk menentukan hubungan antara penggunaan AR dan tingkat pemahaman siswa. Hasil menunjukkan bahwa teknologi AR memiliki pengaruh signifikan terhadap pemahaman siswa dengan nilai signifikansi 0,001. Persamaan regresi di mana setiap peningkatan satu unit penggunaan teknologi AR meningkatkan pemahaman siswa sebesar 0,300 unit. Keaslian penelitian ini terletak pada penerapan teknologi AR untuk materi tata surya, dengan hasil yang mendukung potensi AR sebagai alat pembelajaran interaktif modern, meskipun pengaruhnya dipengaruhi oleh faktor lain.
Development of Augmented Reality-based Space Building Learning Media Pengembangan Media Pembelajaran Bangun Ruang Berbasis Augmented Reality Noviantoko, Pangestu Lukasgi; Ratnawati, Dwi
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14370

Abstract

Konsep bangun ruang kerap menjadi tantangan bagi siswa kelas 7 SMP. Kesulitan dalam membayangkan bentuk tiga dimensi dari gambar dua dimensi seringkali menghambat pemahaman mereka. Salah satu tantangan utama adalah rendahnya pemahaman siswa terhadap konsep abstrak dalam bangun ruang. Penelitian ini bertujuan untuk memenuhi kebutuhan siswa untuk memahami konsep bangun ruang melalui pengembangan media pembelajaran interaktif yang lebih menarik dan mudah dipahami. Penelitian ini menggunakan metode R&D dengan model pengembangan ADDIE. Hasil dari penilitian ini mendapatkan penilaian dari berbagai aspek yaitu ahli media, ahli materi, dan siswa. Hasil dari penilaiannya mengindikasikan tingkat keberhasilan yang tinggi dengan persentase Ahli Media sebesar 92,39%. Kemudian Ahli Materi menunjukkan persentase sebesar 97,05%. Sedangkan penilaian dari siswa menunjukkan persentase sebesar 95,96%. Sehingga dapat diperoleh kesimpulan bahwa pengembangan media pembelajaran bangun ruang berbasis augmented reality (AR) untuk siswa kelas 7 SMP menunjukkan hasil yang sangat layak untuk digunakan.
Detection Of CT Kidney Disease Using GLCM Feature Extraction and Kernel Extreme Learning Machine (KELM) Classification Method Maulidiyah, Arifah Khairina
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14427

Abstract

Kidney disease includes a variety of disorders affecting renal function, such as kidney cysts, tumors, and stones. If left untreated, these conditions can progress to chronic kidney disease, posing significant health risks and potentially leading to mortality. This study aims to classify kidney diseases by using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and the Kernel Extreme Learning Machine (KELM) as a classification method, with renal CT images as the dataset. The classification process categorizes kidney conditions into four classes: Cyst, Normal, Stone, and Tumor. The dataset consists of 4,232 CT images, with 1,058 images per class, evenly divided into axial and coronal orientations. The study utilizes k-fold cross-validation with k = 5 and k = 10 and implements the Radial Basis Function (RBF) as the kernel function in the KELM model. An iterative tuning of parameters, including the kernel parameter () and the regularization constant (), was conducted to identify the optimal model configuration. The best classification performance was achieved at angle using k = 5, with an accuracy of 97.26%, sensitivity of 97.16%, and specificity of 99.05%. Furthermore, the model demonstrated high computational efficiency, requiring only 6.07 seconds.
Performance Comparison of VGG-19 and DenseNet-121 Architectures for Rice Plant Disease Megahaztuti, Istimewa
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14437

Abstract

Rice (Oryza sativa L.) is a major food source that often faces the challenge of crop failure due to various plant diseases. These diseases not only reduce productivity, but are also exacerbated by farmers' limited knowledge in recognizing symptoms and reliance on manual diagnosis that takes a long time. This study aims to compare the performance of two Convolutional Neural Network (CNN) architectures, namely VGG-19 and DenseNet-121, in classifying rice plant diseases based on image processing. Low accuracy and overfitting are problems that are often observed when small datasets are used to train deep learning models, such as Convolutional Neural Networks (CNN). In this study, modifications were made to the VGG-19 and DenseNet-121 architectures so that the model can achieve good accuracy and reduce the risk of overfitting despite using small datasets. The dataset consists of 11,790 images in 9 classes, which are divided into 7545 training data, 1887 validation data, and 2358 testing data. After the training data is segmented, the total number of images in the dataset is 23,580. Before modification, the DenseNet-121 model achieved the highest accuracy of 50.45% and F1-score of 44.83%, while VGG-19 achieved the highest accuracy of 13.84% and F1-score of 7.39%. After making modifications to both models, the test results show that DenseNet-121 achieved an accuracy of 97.76% and F1-score of 96.31%, while VGG-19 achieved an accuracy of 84.82% and F1-score of 87.52%. The advantage of DenseNet-121 lies in its ability to process features more efficiently, resulting in more accurate predictions than VGG-19. This research contributes to the selection of the best model architecture to support automatic diagnosis of rice plant diseases, which is relevant to the agricultural sector in Indonesia. 
Implementation of Forgy Initialization and K-Means++ Algorithms in the K-Means Clustering Method for Sales Data Analysis of Dazzle Store Abdillah, Muhamad Hilmi
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14468

Abstract

Objective: To determine the results of K-Means Clustering calculations by applying K-Means++ and Forgy initialization methods in analyzing sales data at Dazzle accessory store, as well as to identify the optimal number of clusters using the silhouette coefficient.Method: This study implements the Forgy initialization and K-Means++ algorithms in the K-Means Clustering method, along with an evaluation of the optimal number of clusters using the silhouette coefficient method.Results: The application of Forgy initialization and K-Means++ successfully improved clustering outcomes more optimally compared to the pure initialization method. The highest silhouette coefficient evaluation score was 0.9232095222373023 for K-Means++ and 0.8822890619277 for Forgy initialization. This result is clearly better than the pure initialization method, which only achieved a score of 0.8816344025002508.State of the Art: This study builds upon previous research. The innovation lies in the implementation of a combination of K-Means Clustering with Forgy initialization and K-Means++ initialization methods.
Pengembangan Website untuk Sistem Pengontrolan Reservoir Air Bersih Berbasis IoT pada Bandung City View I Fharist, Muhammad
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14514

Abstract

Perumahan Bandung City View I (BCV I) sering menghadapi masalah meluapnya air di reservoir atas, terutama saat penggunaan air warga rendah pada malam hari. Untuk mengatasi masalah ini, dikembangkan sebuah sistem pengontrolan reservoir air berbasis IoT yang dapat beroperasi secara kontinu selama 24 jam. Sistem ini menggunakan perangkat keras yang aman dan dapat diakses melalui web dan aplikasi mobile untuk mempermudah proses pemantauan dan pengontrolan reservoir air secara jarak jauh dan real-time. Pengembangan website untuk sistem ini melibatkan penggunaan HTML, CSS, dan JavaScript di bagian frontend, serta Laravel framework di bagian backend. Firebase Realtime Database digunakan untuk penyimpanan data dan sinkronisasi real-time. Pengujian sistem menunjukkan bahwa website yang dikembangkan dapat mengontrol pompa dan memonitor kondisi reservoir dengan efektif, mengurangi pemborosan air, dan menurunkan biaya operasional. Hasil ini mengindikasikan bahwa sistem pengontrolan reservoir berbasis IoT yang diakses melalui website dapat menjadi solusi yang efisien dan efektif dalam mengelola sumber daya air di perumahan BCV I.
Analisis Kualitas dan Klasifikasi Jenis Tanah Berbasis Pengolahan Citra: Teknik Image Sharpening dan CNN ResNet untuk pemetaan pemanfaatan Daerah Pesisir Harnelia, Harnelia; Saudi, Septiyani Bayu; Agsaria, Fabelina; Saputra, Rizal Adi
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14516

Abstract

Tujuan: Penelitian ini bertujuan untuk menganalisis kualitas dan klasifikasi jenis tanah di wilayah pesisir Teluk Kendari dengan menggunakan teknik penajaman gambar dan Convolutional Neural Network (CNN) ResNet152V2, guna mendukung pengelolaan sumber daya wilayah pesisir yang berkelanjutan.Perancangan/metode/pendekatan: Penelitian menggunakan pendekatan pengolahan citra digital dengan tahapan: pengumpulan dataset dari Kaggle dan lapangan, image preprocessing, image sharping, dan klasifikasi menggunakan model CNN ResNet152V2. Dataset terdiri dari 880 gambar dari Kaggle dan 110 gambar dari wilayah Teluk Kendari, dibagi menjadi data latih (80%), uji (10%), dan validasi (10%).Hasil: Model CNN ResNet152V2 berhasil mencapai akurasi klasifikasi sebesar 90.91% dalam mengidentifikasi delapan jenis tanah (Aluvial, Andosol, Entisol, Humus, Inceptisol, Laterit, Kapur, dan Pasir). Teknik penajaman gambar terbukti efektif meningkatkan kualitas citra visual, memperjernih detail tekstur tanah, dan memudahkan proses klasifikasi.Keaslian/ state of the art : Penelitian ini mengintegrasikan teknik penajaman gambar dan CNN ResNet untuk menganalisis tanah pesisir, yang sebelumnya belum banyak dilakukan di Indonesia. Pendekatan ini memberikan kontribusi dalam memahami kondisi tanah di wilayah pesisir dan mendukung strategi pengelolaan berkelanjutan
Decision Support System For Selecting The Best Non PNS Using Fuzzy Analytical Hierarchy Process Method Hashidiq, Agil Rasyid; Budiraharjo, Raden
Telematika Vol 22 No 2 (2025): Edisi Juni 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i2.14570

Abstract

The most important aspect of human resource management greatly impacts the success of the agency by selecting the best employees. The best employees are those who have certain skills that can contribute to the success of the agency. Non-Civil Servant Government Employees (PPNPN) are honorary personnel recognized by the Government and the State who are needed by agencies to assist a job in the unit in need. Problems occur when selecting employees with performance every month, namely the absence of a system that supports decisions to determine the selection of the best non-civil servants in Cipamokolan Bandung Village, so that the selection of the best employees is currently still done manually. Thus the selection of the best employees is not accurate and very subjective. The purpose of this research is to provide solutions to these problems by creating a decision-making system in selecting the best non-civil servants in Cipamokolan Bandung Village. The method used is by applying the Fuzzy Analytical Hierarchy Process method which can overcome uncertainty and inaccuracy in judgment. The research results obtained from the selection of non-civil servant government employees using the web-based Fuzzy Analytical Hierarchy Process method can create efficiency and effectiveness in selecting the best employees in Cipamokolan Bandung Village.

Page 35 of 37 | Total Record : 361

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