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Eko Fajar Cahyadi
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Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Institut Teknologi Telkom Purwokerto Jl. D. I. Panjaitan, No. 128, Purwokerto 53147, Indonesia
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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 13 Documents
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Search results for , issue "Vol 17 No 1 (2025): February 2025" : 13 Documents clear
Optimasi Recursive Feature Elimination menggunakan Shapley Additive Explanations dalam Prediksi Cacat Software dengan klasifikasi LightGBM Hartati Hartati; Rudy Herteno; Mohammad Reza Faisal; Fatma Indriani; Friska Abadi
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 Sinung Suakanto; Muhammad Fahmi Hidayat; Faqih Hamami; Anis Farihan Mat Raffei; Edi Nuryatno
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 Nur Heri Cahyana; Yuli Fauziah; Wisnalmawati Wisnalmawati; Agus Sasmito Aribowo; Shoffan Saifullah
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 Fariz Abqari Fawwaz Illahi; Kusuma Ayu Laksitowening; Rio Nurtantyana
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; Rudi Heriansyah; Shinta Puspasari; Muhammad Haviz Irfani; Evi Purnamasari; Indah Permatasari; 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.
Optimization of Electric Multiple Unit Headway Ruliyanta Ruliyanta; Fahmi Idris; Adhyartha Usse Keraf
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.v17i1.1230

Abstract

The needs of the people of JABODETABEK, Indonesia, for fast, safe, and comfortable means of transportation, such as electric rail trains, are increasing. In 2023, the narrowing of the headway on the East Bekasi-Cikarang station route will cause frequent tripping of the traction substation. This is due to the increasing frequency of multiple electric unit trips and the lack of power capacity at the traction substations to supply electrical power. In addition to that, there is a voltage drop in the overhead power network because the distance between the traction substations is too long. The fastest headway is 3 minutes from the original 13 minutes. This research aims to optimize the power capacity of the traction substation in the LAA 1.10 Cikarang area. The method is load flow analysis using ETAP 19.0.1 software. Results of the design for adding the Tambun Insertion substation and the Telaga Murni Insertion substation. On a 3-minute headway, the average voltage drop increased by 22.5% on the East Bekasi - Cibitung route from 1,222 VDC to 1,497 VDC. Meanwhile, the Cibitung-Cikarang route, originally 1,282 VDC, became 1,494 VDC, or an increase of 16.5%.
Software Effort Coefficient Optimization Using Hybrid Bat Algorithm and Whale Optimization Algorithm Alifia Puspaningrum; Muhamad Mustamiin; Fauziah Herdiyanti; Kamaludin Noviyanto
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.v17i1.1250

Abstract

Software effort estimation is a crucial aspect in software engineering, especially in project management. It defines an effort required by a person to develop an application in certain of time. One of models which widely used for this purpose is Constructive Cost Model (COCOMO) II. In COCOMO II, two coefficients have a significant role in determining the accuracy of the effort estimation. Various methods have been conducted to estimate these coefficients to closely match the actual effort with the predicted values, such as particle swarm optimization, cuckoo search algorithm, etc. However, several metaheuristics has limit in exploration and exploitation to find optimal value. To overcome this problem, a hybrid metaheuristic combining the Bat Algorithm and Whale Optimization Algorithm (BAWOA) is proposed. This approach aims to optimize the two COCOMO II coefficients for better estimation accuracy. Additionally, the proposed method is compared with several other metaheuristic algorithms using the NASA 93 datasets. There are two evaluation criteria used in this research namely Magnitude of Relative Error (MRE) and Mean Magnitude of Relative Error (MMRE). With the optimal score among comparing method. proposed method achieves superior effort estimation, with an MMRE of 51.657%. It also proves that hybrid BAWOA can estimates predicted effort close to actual effort value.
Interpretation of Multi Sensor Measurement Results using Fuzzy Membership Function for Landslide Early Warning System Erna Alimudin; Arif Sumardiono; Muhamad Yusuf; Muhammad Mukhlisin; Roni Apriantoro; Aiun Hayatu Rabinah; Hany Windri Astuti
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.v17i1.1252

Abstract

Central Java has several areas prone to landslides. One of them is in Tembalang District in Semarang City, Central Java Province, Indonesia .Landslides can be caused by very high rainfall and there are no trees to support the soil, resulting in land shifting. Landslaezdide disasters are very dangerous because they cause many casualties. Therefore, there is a need for an early warning system for landslides. The landslide early warning system uses several sensors, namely rainfall sensors. Therefore, there is a need for an early warning system for landslides. The landslide early warning system uses several sensors, namely rainfall sensors, soil moisture sensors and soil movement. The sensor data will be processed using fuzzy logic so that the results can be more accurate. Early warning of landslides has several conditions, namely low risk to very high risk. Based on the results of real-time data collection in the landslide disaster early warning system, the results obtained were that the sensors were working well and communication sending data to the website was running well. Data processing has been carried out and can be processed via a controller with a fuzzy logic logic algorithm. The results obtained were that based on sensor data taken early warning of landslides still had a low risk with a value of 0.5375 and a medium risk with a value of 0.5875. This is due to moderate rainfall and high soil moisture, as well as ground movement ≥ 0.1
Kendali LQR berbasis inverse kinematik pada robot lengan 3 derajat kebebasan Adri Firmansya Sofyan; Erwin Susanto; Rafsanjani Nurul Irsyad; Alfitho Satya Prabaswara; Irham Mulkan Rodiana
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.v17i1.1257

Abstract

Linear Quadratic Regulator (LQR) is one of the optimal control methods on state space-based systems. The LQR control method is an option to be applied to the 3 DOF robot arm because multi-link systems such as robot arms are basically non-linear with quite complex modeling. Using conventional control methods has many trade-offs to find optimal stability between the parameters on the robot arm. System modeling is formulated using the Lagrangian dynamics and Euler-Lagrange method to obtain a nonlinear model of the system and then linearized it using Taylor series expansion. The values of the Q and R matrices can be adjusted to obtain a good system response for a particular trajectory. Tunning the Q and R parameters can also improve the stability of the system by reducing overshoot but causing the rise time of the system to increase
Optimization of Naive Bayes and Decision Tree Algorithms through the Application of Bagging and Adaboost Techniques for Predicting Student Study Success Endi Febriyanto; Wasilah Wasilah
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.v17i1.1258

Abstract

In the Education Assessment Center of the Ministry of Education and Culture, the average grade for each student's subject in the last few years has been below 70. This condition cannot be allowed to continue. A special analysis is needed regarding factors that can help improve student grades. Predictions of student study success are urgently needed. These predictions can anticipate negative impacts that occur, including increased risk of dropout, decreased student motivation to learn, and individual potential that does not develop. The Naive Bayes and Decision Tree algorithms have been used to predict student study success. However, among its advantages, these two algorithms still have several short comings. It can cause the algorithm's performance not to be as expected. Several methods in ensemble techniques can improve algorithm performance. Two methods that are often used and can help improve the performance of classification algorithms are Bagging and Adaboost. This Study will combine Bagging and Adaboost into the Decision Tree and Naïve Bayes algorithms to optimize the results in predicting student success. The stages carried out are initial Study, data collection, data pre-processing,data processing and evaluation model, and analysis of the results. The results show that Bagging and Adaboost techniques have been proven effective in improving accuracy, precision, recall, and F1-Score performance. Combining the naïve Bayes algorithm with Adaboost increases accuracy, precision and recall significantly by 1.95%, 28.98%, and 15.79%.

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