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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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INDONESIA
Jurnal INFOTEL
Published by Institut Teknologi Telkom Purwokerto
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 409 Documents
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Model Siklus Waktu Lampu Lalu Lintas Cerdas Menggunakan Fuzzy Mamdani Mulki Indana Zulfa; Andreas Sahir Aryanto; Ari Fadli
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1106

Abstract

The growth of motorized vehicles in Indonesia has increased significantly. According to data from the Central Bureau of Statistics, the number of motorized vehicles in Indonesia has increased by around 10% each year in the last five years. One of the negative impacts of the increasing number of motorized vehicles is traffic congestion. Traffic congestion has become a serious problem in several cities in Indonesia. One of the causes is the increase in the number of vehicles at road intersections, which has an impact on congestion and the safety of road users. The rapid growth in the number of vehicles requires a more comprehensive strategy to reduce congestion and accidents at road intersections. Therefore, the need for Intelligent Transportation System, especially on the time-cycle configuration of intelligent red light is very important. This research aims to model the time-cycle of the red light using the Mamdani Fuzzy Inference System to simulate the green light time configuration so as to reduce the waiting time of road users at highway intersections. The simulation results show that the time-cycle configuration and green light time length of the Mamdani Fuzzy calculation are more varied relative to the number of vehicles. The values are relatively smaller than 6 to 54 seconds from the time configuration set by the local Department of Transportation. This shows a time efficiency for road users of up to 27%, which means that road users can complete trips 6 to 13 seconds faster.
Combination of Multi-Objective Optimization on The Basis of Ratio Analysis and ROC in The Selection of Extracurricular Activities Dyah Ayu Megawaty; Damayanti Damayanti; Adella Widiyanti; Setiawansyah Setiawansyah
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1108

Abstract

The selection of extracurricular activities for students often involves several challenges and problems. Some problems in the selection of extracurricular activities include limited time, interests and talents, limited facilities and infrastructure, and awareness of choices. Problems in the selection of extracurricular activities include limited time, interests and talents, limited facilities and infrastructure, and awareness of choices. The purpose of this study is to provide recommendations for a decision support system model for students in the selection of extracurricular activities by applying the ROC method as a method of weighting criteria and the MOORA method as an alternative ranking tool for extracurricular activities that become recommendations for students. The combination of MOORA and Rank Order Centroid (ROC) methods is an approach that can be used in complex multi-criteria decision analysis. This method combines the strengths of both methods to provide more comprehensive and effective solutions in decision-making. The results of the study on the selection of extracurricular activities using a combination of MOORA and ROC methods, recommendations for extracurricular activities for Futsal extracurricular activities with a value of 0.444 as recommendations based on the final calculation results with the MOORA method got rank 1, for rank 2 recommended extracurricular activities Basketball with a final value of 0.437, and rank 3 recommended extracurricular activities Karate with a final value of 0.426.
Identification of Evaluation Results in E-Banking Services Transaction for Product Recommendation using the BIRCH and Davies Bouldin Index Method Septian Eka Ady Buananta; Muhammad Aliif Ahmad; Jamilah Mahmood; Paradise Paradise
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1116

Abstract

E-banking transaction services in the banking world include many products offered to customers. However, the existence of regulatory factors may limit the extent to which banks can promote e-banking services, especially in cases where promotions involve incentives or special offers. Besides, there is a need for data analysis that is used to help the process of recommending product promos from these services. Recommendations for this product promo can be known from the evaluation process of data collected from e-banking transaction services for purchases and payments. The clustering method suitable for providing significant and influential results compared to other methods is BIRCH, which is assisted by the Davies Bouldin Index method to determine the list of product groups with the lowest value. The results of this evaluation process show that data can be grouped based on which services have low levels of use. The services in question are Deposits, Credit Cards on Mobile Services, OVB, and Inter-Bank Transfers on Mobile Services. Therefore, this service can be used as a reference to increase product promotion by the bank.
In-Depth Exploration and Comparison of Machine Learning Performances for Early-Stage Diabetes Risk Prediction Nor Kumalasari Caecar Pratiwi
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1117

Abstract

Abstract — Diabetes mellitus is distinguished by an inability of the human system to produce insulin on an ongoing basis, as well as by the inefficient utilization of the insulin hormone, resulting in an elevated level of blood glucose. Global diabetes rates have nearly doubled since 1980, reaching 9.3% among adults. Alarmingly, of the 463 million individuals with diabetes, 50.1% are unaware of their condition. Indonesia ranks seventh globally with 10.7 million diabetes cases. In 2019, it was fifth globally for adults (20–79 years) with undiagnosed diabetes. This silent epidemic demands urgent attention and comprehensive strategies for early detection and management. In recent years, researchers have increasingly studied machine learning for early diabetes recognition. In this study, we aim to predict early-stage diabetes risk by utilizing 16 health condition features. We explore 12 distinct machine learning algorithms, applying a hyperparameter grid to tune each algorithm. This involves systematically testing combinations of hyperparameters to identify the optimal settings for achieving the most accurate and reliable predictive models. The results indicate that the Light GBM algorithm achieved the highest accuracy of 0.9692. By contrast, the logistic regression and Naive Bayes algorithms demonstrated the lowest performance, each with an accuracy of 0.8923. The implications of these results underline the capability of employing machine learning algorithms to precisely and effectively detect individuals susceptible to diabetes, enabling the implementation of individualized healthcare approaches.
Bandwith and Gain Enhanced Hexagonal Patch Antenna Using Hexagonal Shape SRR Harfan Hian Ryanu; Syahna Hafizha; Azka Maulani; Aloysius Adya Pramudita; Bambang Setia Nugroho; Levy Olivia Nur; Rina Pudji Astuti; Dwiyanto Dwiyanto
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1118

Abstract

In the evolving digital era, the primary focus of the telecommunications industry is on the 5G network, expected to deliver high data rates, low latency, large network capacity, and improved connectivity. This article discusses efforts to adopt optimal frequencies for 5G, introducing techniques to enhance the characteristics of microstrip antennas using Double Negative (DNG) metamaterial properties. The hexagonal-shaped Split Ring Resonant (HSRR) metamaterial is considered a potential method to increase the bandwidth and gain of 5G antennas. Simulation of HSRR unit cells shows a positive impact on DNG characteristics. Meanwhile, the antenna design incorporating HSRR superstrate elements significantly increases gain to 4.47 dBi, and the implementation of HSRR structures on the groundplane results in a remarkable 368% increase in bandwidth compared to conventional antennas without metamaterial.
An Approach to a Group Movie Recommender System using Matrix Factorization-based Collaborative Filtering Faiha Adzra Darmawan; Z. K. A. Baizal
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1126

Abstract

The growth of online movie streaming platforms has driven the demand for recommender systems that are able to deal with the daunting challenge of users finding movies that match their preferences. However, these recommender systems tend to focus on the needs of individual users, whereas in the real world, there are circumstances in which recommendations are needed for a group of users. Therefore, this study proposes a Group Recommender Systems (GRS) using Matrix Factorization (MF) with aggregation model to recommend movies for a group of users. We employ three Matrix Factorization methods to three distinct group sizes, which are small, medium, and large. Our goal is to identify the most effective approach for each group size. To evaluate the performance, we use precision and recall as measurement metrics. The results show that the MF method, After Factorization (AF) outperforms the other MF methods, i.e., Before Factorization (BF) and Weighted Bfore Factorization (WBF) in terms of precision parameters for small groups (2-4 users), which achieving a score of 0.86. Meanwhile, BF method surpassing both AF and WBF in precision parameters for medium groups (5-8 users) with a score of 0.81.
Bahasa Inggris Yulinda Uswatun Kasanah; Syarif Hidayatuloh; Nabila Noor Qisthani
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1130

Abstract

Handling the distribution of broilers has special attention. The distribution of poultry should be done in the shortest possible time to decrease broiler mortality rate. Real-time monitoring of poultry distribution is expected to control the stress level of poultry due to the length of travel time by limiting the delivery time window. In order to control the distribution time and distance, it is necessary to integrate vehicle routing and mobile technology. By adopting the digital twin-enabled framework model and combining it with Google APIs, web and mobile applications are generated to accommodate the real-time traveling time, vehicle capacity, and pickup-delivery activity. There is a significant change in travel distance in one delivery cycle after implementing the developed technology. The total travel distance was 493 KM decreased to 436 KM, which means a decrease of 11.5% from the initial total travel distance. In addition, there was a change in the total penalty time (delay) from 102 minutes to 85 minutes, decreasing delay time by 16.6%. This mobile technology's development can indirectly increase vehicle utilization by not overloading the vehicle capacity and reducing vehicle travel distance.
Combatting Misinformation: Leveraging Deep Learning for Hoax Detection in Indonesian Political Social Media Yuliant Sibaroni; Shuhaimi Mahadzir; Sri Suryani Prasetiyowati; Aditya Firman Ihsan
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1139

Abstract

The rampant spread of hoax news in social media, especially in the political domain, poses a significant challenge that requires immediate attention. To address this issue, automatic hoax news detection using machine learning-based artificial intelligence has emerged as a promising approach. With the approaching presidential election in Indonesia in 2024, the need for effective detection methods becomes even more pressing.This research focuses on proposing an efficient deep learning model for detecting political hoax news on Indonesian social media. Word2vec feature representation and three deep learning models – LSTM, CNN, and Hybrid CNN-LSTM – are evaluated to determine the most effective approach. Experimental results reveal that the CNN-LSTM hybrid model outperforms the others, achieving an accuracy of 96% in detecting hoax news on Indonesian social media in the political domain. By leveraging state-of-the-art deep learning techniques, particularly the CNN-LSTM hybrid model, this study contributes to the advancement of hoax news detection in Indonesia's political landscape. The findings underscore the importance of utilizing sophisticated machine learning methods to combat the spread of misinformation, particularly during crucial political events such as elections.
Image Segmentation Performance using Deeplabv3+ with Resnet-50 on Autism Facial Classification Melinda Melinda; Hurriyatul Aqif; Junidar Junidar; Maulisa Oktiana; Nurlida Binti Basir; Afdhal Afdhal; Zulfan Zainal
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1144

Abstract

In recent years, significant advancements in facial recognition technology have been marked by the prominent use of convolutional neural networks (CNN), particularly in identification applications. This study introduces a novel approach to face recognition by employing ResNet-50 in conjunction with the DeepLabV3 segmentation method. The primary focus of this research lies in the thorough analysis of ResNet-50's performance both without and with the integration of DeepLabV3+ segmentation, specifically in the context of datasets comprising faces of children on the autism spectrum (ASD). The utilization of DeepLabV3+ serves a dual purpose: firstly, to mitigate noise within the datasets, and secondly, to eliminate unnecessary features, ultimately enhancing overall accuracy. Initial results obtained from datasets without segmentation demonstrate a commendable accuracy of 83.7%. However, the integration of DeepLabV3+ yields a substantial improvement, with accuracy soaring to 85.9%. The success of DeepLabV3+ in effectively segmenting and reducing noise within the dataset underscores its pivotal role in refining facial recognition accuracy. In essence, this study underscores the pivotal role of DeepLabV3+ in the realm of facial recognition, showcasing its efficacy in reducing noise and eliminating extraneous features from datasets. The tangible outcome of increased accuracy of 85.9% post-segmentation lends credence to the assertion that DeepLabV3+ significantly contributes to refining the precision of facial recognition systems, particularly when dealing with datasets featuring faces of children on the autism spectrum.

Page 41 of 41 | Total Record : 409

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