cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Eko Fajar Cahyadi
Contact Email
ekofajarcahyadi@ittelkom-pwt.ac.id
Phone
+6285384848666
Journal Mail Official
infotel@ittelkom-pwt.ac.id
Editorial Address
Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Institut Teknologi Telkom Purwokerto Jl. D. I. Panjaitan, No. 128, Purwokerto 53147, Indonesia
Location
Kota bandung,
Jawa barat
INDONESIA
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
 40   41   42   43   44 
Recommender System for Group of Users using Matrix Factorization for Tourism Domain (Case Study: Bali) Prabowo, Ruh Devita Widhiana; Baizal, Z. K. A.
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1129

Abstract

Choosing a product that suits a customer's needs requires a recommendation system to provide suggestions on a collection of items of interest to the user. Recommendations can be applied in various fields such as entertainment, shopping sites, social networking, job portals, discovery of relevant web pages, and so on. There are many circumstances where recommendations are needed for a group such as in tourism and entertainment purposes. The development of a Group Recommendation System (GRS) was carried out in response to the need to provide several recommendations to a group of users. We conducted this research to build a GRS that can provide item recommendations using the Collaborative Filtering (CF) method with Matrix Factorization Technique, as well as three approaches, namely After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). Determine the best approach for the three categories of groups formed, namely small groups (three members), medium groups (five members), and large groups (ten members). The focus of this research is the tourism destination domain in Bali. In the evaluation results of the precision calculation, the medium group obtained the highest score for the AF, BF, and WBF approaches of 0.944. Meanwhile, in calculating recall, the small group achieved the highest scores for the AF, BF, and WBF approaches of 0.294, 0.259, and 0.259, respectively. From the results of this study, it appears that small groups are suitable for using the BF approach, while the AF method is more effective for large groups, and the best approach for medium groups is the WBF.
PREDIKSI KETERLAMBATAN TERHADAP PRESTASI SISWA SMK TELKOM LAMPUNG MENGGUNAKAN ARTIFICIAL NEURAL NETWORK Susanti, Desi; Triloka, Joko
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1163

Abstract

The analysis of student performance is crucial in vocational schools because it helps identify the challenges students face in preparing themselves for the workforce. By integrating data mining techniques such as Artificial Neural Networks (ANN), educators can enhance their understanding of factors that improve student learning outcomes. An artificial neural network (ANN) is composed of interconnected artificial neurons that can learn from input data and make complex predictions, including academic achievements. The structure and function of the human brain inspire ANN. This study compares the effec- tiveness of the artificial neural network (ANN) method with other methodologies, such as support vector regression (SVR), to predict student achievement at SMK Telkom Lampung. Primary data collected from SMK Telkom Lampung includes 4939 examples with 550 cases, 26 features, and 4 meta-attributes. Performance evaluation involves metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The coefficient of determination (R2) value of the Neural Network at 0.001 is higher than the R2 value of SVR, which reaches -0.036. Research find- ings indicate that the Artificial Neural Network model slightly outperforms the Support Vector Regression model, with lower prediction error rates and better ability to explain data variability.
Coverage hetnet based picocell and femtocell for uplink condition around building environment with single knife edge method Eska, Andrita Ceriana
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1166

Abstract

The development of HetNet (Heterogeneous Network) radio base stations has experienced many developments. This is indicated by the existence of microcells, macrocells, picocells, femtocells, and so on. In this research, the research is aimed at the propagation of user equipment communication systems in uplink conditions with HetNet picocells and femtocells. UE propagation is on a straight path between the building environment. The communication frequency used is 10 GHz. The communication between Tx and Rx is modeled as a diffraction mechanism, AWGN channel, and atmospheric attenuation. The Single Knife Edge (SKE) method is used to model the mechanism. The propagation channel is faced with AWGN (Additive White Gaussian Noise). The analysis of this research includes the SNR value, Adaptive Modulation and Coding (AMC) level, and percentage of communication coverage area. The AMC is based on the use of MCS (Modulation and Code Scheme). Some of the MCS modulations used QPSK, 16 QAM, and 64 QAM. As research results show that the percentage of communication coverage area obtained was gNB1 80.59 percent, gNB2 65.67%, and selection combining HetNet 95.52%.
Design and Implementation of Home Industrial-Based Automatic Granulated Food Weighing Machine Galina, Mia
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1168

Abstract

Automation activities are now required in manufacturing processes. This process can provide very effective and efficient performance results, particularly for industrial processes that require a specific standard, such as those used in the granulated food product industry, such as sugar, rice, coffee, and other products. Fast, sanitary packaging processes and standard weights are the main focus of the manufacturing process. This is not a significant barrier at the level of an established industry, whereas, in traditional processes, this is still the main impediment since automated weighing systems at reasonable prices are still challenging to come by in small and medium-sized businesses. This study proposes the development of a prototype weighing mode that can maintain the weight accuracy of powder materials (sugar, rice, and coffee) before they are placed in product packaging. The research employed the experimental method, utilizing a load cell sensor and a screw conveyor to precisely measure the weight of powder raw materials based on the user-specified size and parameters. It is also equipped with a data deviation memory. Some testing was conducted to ensure the accuracy of the prototype. Based on the test results, this system can work with an accuracy rate of 99.693% at parameters 120 grams and 99.677% when testing the weight of powder raw materials of 120 grams, 125 grams, and 175 grams, respectively.
Equal Incremental Cost Method dengan Adjustable Gamma Control untuk Menyelesaikan Penjadwalan Pembangkit Rahmat, Basuki; Wijaya, I Gede Putu Oka Indra; Ikhsan, Rifki Rahman Nur; Yustika, Lindiasari Martha; Raharjo, Jangkung
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1170

Abstract

Generator scheduling remains an intriguing issue within the energy industry. It relates to the optimization of production costs, where system operators must select the optimal combination of available resources to minimize production costs. This paper proposes an enhancement to the Equal Incremental Cost (EIC) Method using Adjustable Gamma Control (AGC) in generator scheduling. Iterations begin with an initial lambda value, then gradually increase with the application of the factor until power demand is met. A variable of 10% is used as an adjustment step in the optimization method. The proposed method is capable of achieving convergence with 100% accuracy, where the power generated by all generators precisely matches the load demand (2,650 MW), at a cost of USD 32,289.03. EIC-AGC ranks second-best after VLIM, albeit with the consequence of consuming 195 seconds. This method is expected to have a significant impact on designing highly accurate economic dispatch techniques. Thus, generator scheduling will lead to a reduction in operational costs compared to current practices.
Magazine Identification on SS1-V1 Assault Rifle using Web-based HX711 Load Cell Sensor Ismail, Yasikha Farras
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1173

Abstract

The SS1-V1 is an assault rifle model equipped with a magazine as one of its main components. The magazine plays a crucial role in storing and loading ammunition. However, magazines must be stored separately from the weapon as their integrated storage can pose a risk to a country’s security. Therefore, this research proposes a web-based system capable of identifying the presence of magazines in weapons in real-time. This system is supported by various hardware components, including a load cell sensor, HX711 sensor module, Arduino UNO R3, and an Ethernet shield for network connectivity. In addition, API is used for data management, which is then stored in the database. The results of this research indicate that the average response time for each rack within a cabinet is between 2.7s to 3.3s, while for racks serving as slaves, it ranges from 14.16s to 15.01s. Based on the results of the weight-based weapon identification testing, there is a weight difference of 0.1kg to 0.2kg. These results state that all tests were successfully identified by the web system according to the conditions of the weapons on the rack.
Dinamika Adopsi Pembayaran Digital: Studi Kasus Perkotaan tentang E-Money Menggunakan Model Penerimaan Teknologi Utomo, Rio Guntur; Yasirandi, Rahmat; Suwastika, Novian Anggis
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1174

Abstract

The utilization of e-money in Indonesia has surged, propelled by the expansion of digital payment platforms. Despite their growing prevalence, the dynamics of e-money acceptance within urban environments remain underexplored. This study innovatively extends the Technology Acceptance Model (TAM) by incorporating Perceived Security as a new variable, alongside traditional factors such as Perceived Usefulness, Perceived Ease of Use, Attitude Toward Using, Behavioral Intention to Use, and Facilitating Condition. The research focuses on Padang City, a representative urban landscape, where data was collected from 201 valid respondents through online platforms. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The integration of Perceived Security is a novel aspect of this study, reflecting its crucial role in the contemporary urban context of e-money utilization. Results reveal significant relationships among the studied variables, although the impacts of Facilitating Condition on Perceived Ease of Use and Perceived Usefulness on Behavioral Intention to Use were not supported. The findings underscore that positive attitude toward e-money significantly boost behavioral intentions to use it, primarily influenced by security perceptions and ease of use. These insights have substantial implications for policymakers and businesses focused on enhancing e-money adoption in urban settings. The study highlights the necessity of addressing user perceptions, particularly security, to foster broader acceptance. The limited influence of Facilitating Conditions suggests that improvements in infrastructure must be coupled with efforts to enhance user trust and ease of interaction with e-money platforms. This research contributes to the field by providing a deeper understanding of the factors driving e-money acceptance in urban areas, guiding targeted strategies for digital financial inclusion.
Klasifikasi Tumor Otak Berbasis MRI menggunakan ResNet-50 dan Regresi Softmax yang Dioptimalkan Musa, Muhammad Nazeer
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1175

Abstract

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.
Pembangunan Model Prediksi Potensi Kebakaran Hutan dan Lahan Menggunakan Algoritma Machine Learning Berdasarkan Data Patroli Santoso, Angga Bayu; Sitanggang, Imas Sukaesih; Hardhienata, Medria Kusuma Dewi
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1180

Abstract

Indonesia allocates 120 million hectares or 64% of its land area as forest areas. Indonesia's forests continue to experience deforestation; one of the causes is forest and land fires (karhutla). The government conducts forest and land fire prevention through integrated patrols with the Forest and Land Fire Prevention Patrol Information System (SIPP Karhutla) facility for patrol data management. However, the patrol data are still primarily used for data observation and simple spatial analysis in the spatial module. Patrol data has not been used for further forest and land fire prevention studies. Based on these problems, this research aims to build a prediction model of potential forest and land fires using SVM, Random Forest, and XGBoost algorithms and compare model performance to get the best model. The preprocessing stage uses the SMOTE-ENN method to handle data class imbalance, and the k-fold cross-validation stage and hyperparameter tuning use the random search method. The confusion matrix evaluation method to see the model performance in terms of accuracy is XGBoost (94.81%), Random Forest (90.23%), SVM-linear (79.58%), SVM-polynomial model (73.99%), SVM-rbf (74.26%), and SVM-sigmoid (35.04%). Therefore, the best prediction model is XGBoost (94.81%) with boosting technique. The results of this study have implications for helping early prevention of forest and land fires on the islands of Sumatra and Kalimantan.
Implementation of MOORA and MOORSA Methods in Supporting Computer Lecturer Selection Decisions Sitorus, Zulham; Karim, Abdul; Nasyuha, Asyahri Hadi; H. Aly, Moustafa
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1184

Abstract

The selection of computer science lecturers is an important process for educational institutions, requiring a balanced assessment of various criteria to find the most suitable candidates. This paper examines the implementation of Multi-Objective Optimization based on Ratio Analysis (MOORA) and its variant, namely Multi-Objective Optimization based on Ratio Analysis with a Subjective Attitude (MOORSA), as a tool to support decision making. in this case. This selection process is often complex, requiring consideration of various criteria, such as academic qualifications, teaching experience, research capabilities, and others. This research was conducted to support the decision-making process. by developing a Decision Support System (DSS) using the Multi-Objective Optimization on The Basic of Ratio Analysis (MOORA) and MOORSA methods. Many methods are used, such as SAW, AHP, Topsis and others. based on the calculation of the MOORA method, the highest result has been achieved by A1 worth 0.651819 and similarly, in the MOOSRA method the highest alternative result is A1 worth 0.592177.

Page 42 of 48 | Total Record : 473

 40   41   42   43   44 

Filter by Year

2009 2025


Reset
Filter By Issues
All Issue Vol 17 No 1 (2025): February 2025 Vol 17 No 3 (2025): August Vol 17 No 2 (2025): May Vol 16 No 4 (2024): November 2024 Vol 16 No 3 (2024): August 2024 Vol 16 No 2 (2024): May 2024 Vol 16 No 1 (2024): February 2024 Vol 15 No 4 (2023): November 2023 Vol 15 No 3 (2023): August 2023 Vol 15 No 2 (2023): May 2023 Vol 15 No 1 (2023): February 2023 Vol 14 No 4 (2022): November 2022 Vol 14 No 3 (2022): August 2022 Vol 14 No 2 (2022): May 2022 Vol 14 No 1 (2022): February 2022 Vol 13 No 4 (2021): November 2021 Vol 13 No 3 (2021): August 2021 Vol 13 No 2 (2021): May 2021 Vol 13 No 1 (2021): February 2021 Vol 12 No 4 (2020): November 2020 Vol 12 No 3 (2020): August 2020 Vol 12 No 2 (2020): May 2020 Vol 12 No 1 (2020): February 2020 Vol 11 No 4 (2019): November 2019 Vol 11 No 3 (2019): August 2019 Vol 11 No 2 (2019): May 2019 Vol 11 No 1 (2019): February 2019 Vol 10 No 4 (2018): November 2018 Vol 10 No 3 (2018): August 2018 Vol 10 No 2 (2018): May 2018 Vol 10 No 1 (2018): February 2018 Vol 9 No 4 (2017): November 2017 Vol 9 No 3 (2017): August 2017 Vol 9 No 2 (2017): May 2017 Vol 9 No 1 (2017): February 2017 Vol 8 No 2 (2016): November 2016 Vol 8 No 1 (2016): May 2016 Vol 7 No 2 (2015): November 2015 Vol 7 No 1 (2015): May 2015 Vol 6 No 2 (2014): November 2014 Vol 6 No 1 (2014): May 2014 Vol 5 No 2 (2013): November 2013 Vol 5 No 1 (2013): May 2013 Vol 4 No 2 (2012): November 2012 Vol 4 No 1 (2012): May 2012 Vol 3 No 2 (2011): November 2011 Vol 3 No 1 (2011): May 2011 Vol 2 No 2 (2010): November 2010 Vol 2 No 1 (2010): May 2010 Vol 1 No 2 (2009): November 2009 Vol 1 No 1 (2009): May 2009 More Issue