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Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 473 Documents
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Solar Radiation Prediction using Long Short-Term Memory with Handling of Missing Values and Outliers Syahab, Alfin Syarifuddin; Achamd, MS Hendriyawan
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1225

Abstract

The pyranometer sensor is an instrument for measuring Global Horizontal Irradiance (GHI) which is used as parameter for analyzing and predicting weather. GHI data which is processed into prediction model for photovoltaics is useful for determining the performance of solar power generation systems in distributed energy operations. However, GHI sensor data has weaknesses in missing values and outliers due to measurement errors. The research designed a GHI sensor data prediction model using data preprocessing by the imputation of missing values using linear, polynomial, and Piecewise Cubic Hermite Interpolating Polynomials (PCHIP) interpolation and eliminating outliers using Random Sample Consensus (RANSAC) on the dataset. Previous researches show that Long Short-Time Memory (LSTM) can improve the performance of predictions compared to machine learning. This research designs an LSTM prediction model with data preprocessing and without data preprocessing. The results of the imputation of missing values obtained the best performance in PCHIP with Mean Absolute Error (MAE) 39.708 W/m2, Root Mean Absolute Error (RMSE) 76.224 W/m2, Normalized Root Mean Absolute Error (NRMSE) 0.433, and Coefficient Determination (R2) 0.903 then imputation from outlier elimination obtained MAE 44.377 W/m2, RMSE 86.738 W/m2, NRMSE 0.500, and R2 0.886. RANSAC testing succeeded in eliminating 100% outliers. The results of LSTM with data preprocessing obtained better performance with the best evaluations on MAE, RMSE, NRMSE, and R2 for test data of 42.863 W/m2, 82.396 W/m2, 0.396 and 0.918. This study contributes to GHI prediction model that can handle missing values ​​and outliers from sensors to support solar power plants.
Design of a 20 Mbps OQPSK Modulator Based on Multiplexers Nugroho, Prapto
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1237

Abstract

This paper aims to delve into the design and implementation aspects of an OQPSK (Offset Quadrature Phase Shift Keying) modulator within the context of modem technology, underscoring its pivotal role in contemporary information technology infrastructure. The research primarily focuses on elucidating the theoretical foundations of OQPSK modulation, which represents an enhancement of QPSK modulation techniques. Central to the design is the utilization of a multiplexer to select one carrier signal out of four, which is subsequently modulated by data bits. Furthermore, the modulator employs two square wave signals phased 90 degrees apart, which are transformed into differential sinusoidal signals to constitute the carrier signal. Operating frequencies encompass a 5 MHz streaming data bits frequency and a 10 MHz clock frequency. Simulation results validate the efficacy of the OQPSK modulator by demonstrating its capability to generate modulated signals at a robust data transfer rate of 20 Mbps. This underscores the modulator's effectiveness in transmitting digital data over analog communication channels. In conclusion, the designed OQPSK modulator exemplifies its proficiency in efficiently modulating and demodulating signals, thereby bolstering connectivity and communication across diverse societal sectors. This research contributes significantly to the advancement of modem technology, which is indispensable for the expansion and maintenance of modern communication networks, ensuring robust connectivity in the digital age.
Revolutionizing Classroom Attendance: A Wireless Smart System Using ESP-NOW Protocol Onggo, Leonardo; Wibowo, Susilo; Ainul, Rafina Destiarti
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1259

Abstract

In an era where technological advancements drive improvements across various sectors, enhancing efficiency in educational management systems is crucial. This paper presents a novel wireless attendance system that leverages the ESP-NOW protocol, which offers advantages over traditional Wi-Fi by enabling low-power, low-latency, and direct device-to-device communication without the need for an intermediary network. The system employs ESP32 modules configured as both slave and master devices. Slave devices, positioned on the lecturer’s desk, interact with students' smartphones when the lecturer initiates the class, while master devices, strategically placed at multiple locations within the classroom, compile and consolidate attendance data for each room. The system incorporates RSSI-based restrictions via the ESP-NOW protocol to prevent overlapping attendance between rooms and ensure that students can only record their presence if they are physically within the designated classroom. Attendance data is automatically logged and made accessible in real-time through a dedicated mobile application for lecturers. Empirical testing demonstrates 100% accuracy in attendance recording, with an average verification time of less than 1 second and a data transmission rate ranging from 700 to 800 Bytes/seconds.
Desain dan implementasi Arus Keluaran Zeta Penaik-Penurun Inverter Jembatan-H menggunakan STM32F407VET6 Wijaya, Jonathan; Pratomo, Leonardus Heru
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1261

Abstract

The development of electrical power conversion equipment is increasing, along with the utilization of new and renewable energy sources. Power conversion equipment from DC to AC voltage, known as inverters, is extensively researched and implemented in this sector. These inverters commonly operate as step-down voltage in specific applications used as step-ups with limited operating ranges. A step-up-down inverter with a single power circuit is developed to overcome this issue. Still, the number of power switches used correlates with the complexity of its control strategy. This paper investigates a step-up-down inverter using the Zeta H-Bridge Inverter with the implementation of six power switches. Furthermore, this type of inverter is operated with a controlled output current utilizing the STM32VET407 microcontroller. The control method is derived based on possible operational modes. An HX10-P current sensor detects the output current. It maintains itself according to the current reference by installing a proportional-integral controller. The initial verification utilizes computational simulation with power simulator software, ensuring the system operates as intended. The final stage involves implementation in the laboratory and testing with standardized equipment. The test results meet the IEEE 519 standard, where the output current has a THD of 1.1%.
Application of Ensemble Machine Learning for Infectious Diseases with Vaccine Intervention: A Global COVID-19 Case Study Safitri, Egi; Fikri, Ruki Rizalnul; Nurlistiani, Rini
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1263

Abstract

The COVID-19 pandemic has posed significant challenges worldwide, especially in controlling the spread of the disease through vaccination and active case monitoring. This study aims to evaluate the effectiveness of various ensemble machine-learning models in predicting the number of daily vaccinations and the number of active cases of COVID-19 based on global data. The models used include Random Forest, Bagging, Gradient Boosting Machine (GBM), AdaBoost, and XGBoost. The evaluation results show that Random Forest provides the best performance in predicting both the number of daily vaccinations and active COVID-19 cases, with a MSE value of 4.7e+09, MAE of 16,971.1, and RMSE of 68,557.2 for daily vaccinations, as well as an R² Score of 0.989, indicating a high ability to explain data variability. The Bagging model also showed excellent results with MSE of 4.78e+09 and MAE of 17,039.8. In contrast, the AdaBoost model performed the worst in predicting both variables, with an MSE of 5.54e+10 and an MAE of 106,228.6. These findings suggest that Random Forest and Bagging are superior models for predicting the number of daily vaccinations and active COVID-19 cases. This study provides important insights into using machine learning to predict vaccination effectiveness and active case dynamics, aiding decision-making in global pandemic control efforts.
Optimasi Recursive Feature Elimination menggunakan Shapley Additive Explanations dalam Prediksi Cacat Software dengan klasifikasi LightGBM Hartati, Hartati; Herteno, Rudy; Faisal, Mohammad Reza; Indriani, Fatma; Abadi, Friska
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1159

Abstract

Software defect refers to issues where the software does not function properly. The mistakes in the software development process are the reasons for software defects. Software defect prediction is performed to ensure the software is defect-free. Machine learning classification is used to classify defects in software. To improve the classification model, it is necessary to select the best features from the dataset. Recursive Feature Elimination (RFE) is a feature selection method. Shapley Additive Explanations (SHAP) is a method that can optimize feature selection algorithms to produce better results. In this research, the popular boosting algorithm LightGBM will be selected as a classifier to predict software defects. Meanwhile, RFE-SHAP will be used for feature selection to identify the best subset of features. The results and discussion show that RFE-SHAP feature selection slightly outperforms RFE, with average AUC values of 0.864 and 0.858, respectively. Moreover, RFE-SHAP produces more significant results in feature selection compared to RFE. The RFE feature selection T-Test results are Pvalue = 0.039 < α = 0.05 and tcount = 3.011 > ttable = 2.776. On the contrary, the RFE-SHAP feature selection T-Test results are Pvalue = 0.000 < α = 0.05 and tcount = 11.91 > ttable = 2.776.
DETEKSI OBJEK ASET RUMAH SAKIT MENGGUNAKAN COMPUTER VISION DENGAN METODE GENERATIVE ADVERSARIAL NETWORKS Suakanto, Sinung; Hidayat, Muhammad Fahmi; Hamami, Faqih; Raffei, Anis Farihan Mat; Nuryatno, Edi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1277

Abstract

Hospital asset monitoring systems encounter significant challenges in managing partially occluded medical equipment, which affects inventory management and operational efficiency. Conventional object detection methods have shown limitations in accurately detecting occluded medical equipment, potentially leading to asset management inefficiencies. This study presents an integrated framework that combines Generative Adversarial Networks (GAN) inpainting with YOLOv8 to improve the detection accuracy of partially occluded medical equipment. The proposed system was evaluated using three distinct training configurations of 500, 750, and 1000 epochs on a comprehensive medical equipment dataset. The experimental results indicate that the 1000-epoch GAN model demonstrated superior reconstruction performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 39.68 dB, Structural Similarity Index Measure (SSIM) of 0.9910, and Mean Squared Error (MSE) of 7.0030. Furthermore, the integrated YOLOv8-GAN framework maintained robust detection performance with an F1-score of 0.933, comparable to the 0.938 achieved with unoccluded original images. The detection confidence scores exhibited improvement at higher epochs, ranging from 0.824 to 0.861, suggesting enhanced performance with extended training duration. The findings demonstrate that the integration of GAN inpainting with YOLOv8 effectively enhances occluded object detection in hospital environments, offering a viable solution for improved asset monitoring systems.
The Evaluation of Effects of Oversampling and Word Embedding on Sentiment Analysis Cahyana, Nur Heri; Fauziah, Yuli; Wisnalmawati, Wisnalmawati; Aribowo, Agus Sasmito; Saifullah, Shoffan
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1077

Abstract

Generally, opinion datasets for sentiment analysis are in an unbalanced condition. Unbalanced data tends to have a bias in favor of classification in the majority class. Data balancing by adding synthetic data to the minority class requires an oversampling strategy. This research aims to overcome this imbalance by combining oversampling and word embedding (Word2Vec or FastText). We convert the opinion dataset into a sentence vector, and then an oversampling method is applied here. We use 5 (five) datasets from comments on YouTube videos with several differences in terms, number of records, and imbalance conditions. We observed increased sentiment analysis accuracy with combining Word2Vec or FastText with 3 (three) oversampling methods: SMOTE, Borderline SMOTE, or ADASYN. Random Forest is used as machine learning in the classification model, and Confusion Matrix is used for validation. Model performance measurement uses accuracy and F-measure. After testing with five datasets, the performance of the Word2Vec method is almost equal to FastText. Meanwhile, the best oversampling method is Borderline SMOTE. Combining Word2Vec or FastText with Borderline SMOTE could be the best choice because of its accuracy score and F-measure reaching 91.0% - 91.3%. It is hoped that the sentiment analysis model using Word2Vec or FastText with Borderline SMOTE can become a high-performance alternative model.
User-Centered Design Approach in Mobile AR: Application for Hydrocarbon Visualization in Chemistry Course Fawwaz Illahi, Fariz Abqari; Laksitowening, Kusuma Ayu; Nurtantyana, Rio
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1221

Abstract

This study addresses the challenges in high school chemistry education, particularly in understanding hydrocarbon compounds, by designing a Mobile Augmented Reality (MAR) application using the User-Centered Design (UCD) method. The research focuses on enhancing the visualization of submicroscopic and symbolic aspects of chemistry, which students often find abstract and complex. Through iterative design processes involving teachers and students, the study developed an interactive MAR application that displays virtual ball-and-stick models of hydrocarbon compounds. The application was evaluated using the User Experience Questionnaire (UEQ), measuring six aspects of user experience: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Results showed significant improvements across all dimensions from the initial to the final iteration, with five out of six scales achieving "Excellent" ratings in the final version. The study demonstrates the effectiveness of UCD in creating an engaging and user-friendly educational tool, highlighting the potential of MAR technology to address longstanding challenges in chemistry education. The positive user perceptions suggest that when designed carefully considering user needs, MAR applications can significantly enhance the chemistry learning experience for high school students.
Improving the Accuracy of Concrete Mix Type Recognition with ANN and GLCM Features Based on Image Resolution Gasim, Gasim; Heriansyah, Rudi; Puspasari, Shinta; Irfani, Muhammad Haviz; Purnamasari, Evi; Permatasari, Indah; Samsuryadi, Samsuryadi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1201

Abstract

Concrete is an essential construction material that is often used due to its strength and durability, but its mix type identification often relies on conventional methods that are less efficient and accurate. This research aims to evaluate the effect of image resolution on the accuracy of concrete mix type recognition using Artificial Neural Network (ANN) and Gray-Level Co-Occurrence Matrix (GLCM) features. The method used involves analysing concrete images at various resolutions: 200 x 200, 300 x 300, 400 x 400, 500 x 500, 600 x 600, and 700 x 700 pixels. The experimental results show that higher image resolutions tend to improve recognition accuracy. all types of image sizes using 1,250 training data and 250 test data. Image sizes of 200 x 200 and 300 x 300 pixels give low accuracy of 42% and 45% respectively, while sizes of 400 x 400 and 500 x 500 pixels show an increase in accuracy to 60.5% and 62.5%. The higher resolutions of 600 x 600 and 700 x 700 pixels produced the highest accuracy of 68% and 70%, respectively. These results indicate that larger image resolutions are able to capture more details and characteristics required for more accurate concrete mix type recognition. This research has implications for improving efficiency and consistency in concrete inspection in the construction industry through the use of AI-based image recognition methods.

Page 44 of 48 | Total Record : 473

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