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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 15 No 4 (2023): November 2023" : 13 Documents clear
Forecasting a museum visit post pandemic using exponential smoothing model Shinta Puspasari; Rendra Gustriansyah; Ahmad Sanmorino
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.949

Abstract

This paper aims to evaluate the performance of a machine learning model for predicting the number of visitors to a museum after the COVID-19 pandemic. The easing of policies that began to be implemented by the Palembang city government after the end of the pandemic at the end of 2022 became a momentum in predicting the number of visits to the SMBII museum. During the pandemic the museum experienced a very drastic decline due to closures and restrictions on activities at the museum and had an impact on achieving the museum's targets in the fields of tourism and education. Museum managers need to establish a strategy as an effort to achieve the targets set during the post-pandemic period. This study predicts the number of visits to the SMBII museum in post-pandemic years by applying the double exponential smoothing (ESM) model. The dataset used is SMBII museum visit data which is divided into three categories of visitors, namely students, local and foreign. The evaluation results show that the double ESM model has the best performance with MSE = 3.8 and a = 0.9. The phenomena that occurred in the student visitor category affected ESM's performance in predicting visits where MSE in the post-pandemic period had a 200% higher value than before the pandemic which was influenced by the implementation of post-pandemic policies in museums. With the forecasting results in this study, it is hoped that it can become information in developing strategies and improving the performance of post-pandemic museums
Development of a wearable resonator mask for breathing rate monitoring Yusnita Rahayu; Tasya Kirana; Jack Ping Soh
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.979

Abstract

As one of the processes of patient care, diagnosis, and monitoring are the most important steps in the medical field. Sleep apnea is a problem that affects about 25 million Americans, and 80% of them go untreated because it is not identified. Monitoring has a big role in making patient treatment decisions. So this research aims to produce a wearable resonator mask that can work as a breathing rate monitor. The proposed resonator will use the relative humidity generated during the respiration process. The resonator uses a textile jeans material that is flexible, comfortable, and fits on a mask. Testing is carried out in 3 different positions; lying down, sitting, and standing. There is a difference in the percentage of RH produced from each position based on the difference in the frequency range produced. The proposed resonator operates well at 3.9 GHz.
Implementation of association rule using apriori algorithm and frequent pattern growth for inventory control Imam Riadi; Herman Herman; Fitriah Fitriah; Suprihatin Suprihatin; Alwas Muis; Muhajir Yunus
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.980

Abstract

Business success is a business that is able to compete and grow keep abreast of developments in the business world. Especially in the retail sector, where competition is getting tighter. Business owners need to pay attention to the layout of goods and stock management to improve service and meet consumer needs because consumers often have difficulty in finding goods. On the other hand, shortages and excess stock often occur due to lack of goods management. Based on these problems, appropriate techniques are needed for the management of goods supply, one of which is to apply techniques found in the branch of science. Data mining is a technique of association rules. This study aims to find patterns of placement and purchase of goods in generating Association Rule using FP-Growth algorithm. The dataset in this study used data on sales of goods in clothing stores. The results of the study of 140 transactions there are 24 association rules consisting of 7 association rules with 2-itemsets and 17 association rules with 3-itemsets that most often appear in transactions. Based on the order of the highest support value, namely CKJ→STX^LK with a support value of 67%, while the highest confidence value, there are 3 association rules that get the same value, namely STX^CKJ→LK, STX^CAK→LK, STX^RI→LK with a value of 100%. Thus, the rules of association produced by the frequent itemset algorithm, FP-growth, can serve as decision support for the sales of goods in small and medium-sized retail businesses
Sistem radar lokasi koheren multi-pasif untuk jangkauan deteksi dan resolusi range-doppler di Tarakan Syahfrizal Tahcfulloh; Muhammad Jeki; Antonius Antonius
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1008

Abstract

The presence of many radio frequency illuminators in an area provides an opportunity to be used as a transmission source for a passive radar system called a passive coherent locator (PCL). Unlike the active radar, the passive radar provides advantages such as low cost without an active transmitter, potential for broad range measurements, portable in construction, anti-detectable by active radar or electronic counter measures (ECM), etc. Just like other areas where there are many PCL in Tarakan such as FM radio transmission (RRI), many base-stations (BTS) from 4G-LTE network, digital TV transmission, access-point (AP) from wireless fidelity (WiFi), and so on. This paper will present and analyze all of these PCLs which include predictions of performance and ambiguity function (AF). Performance prediction is related to range detection which provides information about target detection range, radar cross section, and range-velocity resolution. While AF analyzes the transmit waveform of all PCL which gives a limit of resolution to the range and Doppler of adjacent targets. The results of the evaluation and analysis of performance predictions show that FM radio transmission (RRI) has a range and velocity resolution of 1 km and 12 m/s, respectively and the detection range at a maximum SNR of 15 dB is around 4 km. While the results of the AF evaluation on the PCL obtained range and Doppler resolution of around 2.73 km and 500 Hz, respectively.
Analysis of voltage and frequency stability of electric power system network with photovoltaic-based generation penetration Rusilawati Rusilawati; Irfan Irfan; Gusti Eddy Wirapratama; Istiyo Winarno
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1022

Abstract

The operation of Distributed Generation (DG) with renewable energy sources integrated with distribution networks through microgrids poses challenges in terms of operation and control. If this is left unchecked, it can have a negative impact on system security and reliability in terms of voltage and frequency stability that will be disrupted because of frequent variations in power production and loading levels. This study investigates voltage and frequency stability in microgrids because of the penetration of DG with photovoltaic (PV) renewable energy sources in the power system using the Virtual Synchronous Generator (VSG) control technique. The VSG is a control alteration that enhances the capabilities of the power system so that voltage and frequency stability can be preserved and improved. The VSG control method with additional damping controllers that increase inertia with additional virtual inertia is used to simulate the speed of restoration of voltage and frequency stability of the power system due to the penetration of PV-based power plants. The simulation results show that at the time of penetration of PV-based power plants in the power system, there is a momentary instability in voltage and frequency, but it is immediately dampened by VSG control and can be quickly restored so that the stability of voltage and frequency is maintained.
Combining inception-V3 and support vector machine for garbage classification Intan Nurma Yulita; Firman Ardiansyah; Muhammad Rasyid Ramdhani; Mokhamad Arfan Wicaksono; Agus Trisanto; Asep Sholahuddin
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1027

Abstract

The global volume of trash has increased due to population growth and consumption, with a growing variety of materials and materials being generated. Inadequate garbage disposal practices, particularly in plastics, have led to environmental contamination and pollution in various regions. Artificial Intelligence (AI) technologies, particularly in machine learning, have demonstrated significant potential in trash sorting, particularly in the realm of machine learning. The Inception-V3 model and Support Vector Machines (SVM) are used in this study to extract relevant features and classify garbage categories. The Inception-V3 and SVM combination exhibits superior performance, with greater accuracy and F1 score compared to other methods. The radial basis function (RBF) kernel is the most optimal model of SVM, but it faces challenges in accurately categorizing the "trash" category due to limited data and resemblance to the "paper" class. The system developed in this study has a high level of effectiveness, with superior accuracy and F1 scores of 0.876 and 0.874, respectively.
Data preprocessing approach for machine learning-based sentiment classification Sunneng Sandino Berutu; Haeni Budiati; Jatmika Jatmika; Fornieli Gulo
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1030

Abstract

Public sentiment regarding a particular issue, product, activity, or organization can be measured and monitored with an application based on artificial intelligence. The data come from comments circulating on social media. However, the rules for writing comments on social media have yet to be standardized, so non-standard words often appear in these comments. Non-standard words affect the determination of sentiment into positive, negative, and neutral categories. Therefore, this study proposes a data preprocessing approach by inserting the Rabin-Karp algorithm to improve non-standard words. This research consists of several stages, namely crawling data, data preprocessing, feature extraction, model development (based on Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT) methods), and analysis of the results. The experimental results showed that the proposed approach influences the determination of the sentiment category composition. Then, model testing results showed that all models obtain the highest value in the Positive category for the precision parameter with a value 1. All models in the Neutral category obtain the highest value for the recall parameter, almost reaching 1. All models in the Neutral category achieve the highest value of the f1-score parameter, with an average value of 0.95. In general, the results of the performance analysis of the classification model showed that the NB and SVM-based models have better performance than the DT method.
The intelligent decision model for determine the best path of transportation on smart city using random forest algorithm and bayesian optimization (RF-BO) Ahmad Fali Oklilas; Milda Kamilia; Abdurahman abdurahman; Bita Parga Zen; Ari Widodo
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1036

Abstract

This study investigates various approaches and algorithms in the context of object detection and best path determination for managing vehicular traffic in an urban environment, particularly in Palembang city. This research is a step towards the development of smart city concept. In the object detection analysis, we applied the YOLOv3 method on video footage to identify vehicles, resulting in mAP accuracy rates between 72.72% to 79.35% for both motorcycle and car categories. The total detection accuracy of the model reached 76.03%. Next, we adopted the Random Forest algorithm to classify traffic conditions into three classes: smooth, moderate, and congested. After optimizing the algorithm with Bayesian Optimization, the model accuracy increased from 89% to 92%, while the classification accuracy increased from 91.66% to 92.36%. Results from the application of the A* Heuristic Search algorithm revealed that lane 5 (from SMK PGRI 1 Palembang to Bom Baru Jl Perintis Kemerdekaan Arah Charitas (STMIK MBC)) was selected most frequently in 9 out of 12time trials. The selection of this route was based on an evaluation of traffic levels that tended to be "smooth" and the shortest travel distance compared to other alternative routes. The decision in choosing the optimal path also considers the road width factor, where wider roads have the potential to reduce traffic density and the risk of congestion.
LSTM forecast of volatile national strategic food commodities Herlina Jayadianti; Vynska Amalia Permadi; Partoyo Partoyo
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1037

Abstract

Using the Long Short-Term Memory (LSTM) forecast, this study suggested a short-term projection model for national critical food pricing commodities. The model was trained using historical time-series data from each commodity price over the previous three years. The results demonstrated that the proposed LSTM architecture model was generalizable to all commodities and performed well in the majority of cases. This result indicates that the model is resilient and can be used to forecast commodity prices and offer accurate forecasts for most of the ten volatile national strategic foods, with an error value of less than 0.01 and an accuracy value of >95%. The model, however, failed to recognize the pricing pattern in cooking oil and beef commodities, both of which had increasing trend patterns. This shows that the model may be unable to effectively estimate commodity prices in the face of fast price fluctuations. The magnitude and quality of the dataset hampered the investigation. The time period selected also influenced the study. Future research should employ a more extensive and diversified dataset to increase the model's performance, allow it to learn more patterns and make more accurate predictions, and could use a more extended lookup date to improve forecast accuracy. This would enable the model to account for more recent pricing changes. Despite the limitations, the results of this study are promising and could be used to develop a more accurate and reliable food price prediction model. Policymakers and stakeholders could use the model to make informed food prices and inflation decisions.
Microcontroller-based smart foot as an educational tool for teaching reflexology nerve points to visually impaired massage trainers and trainees Dina Fitriana Rosyada; Krida Tri Wahyuli; Muhammad Hasani; Nur Rokhman
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1042

Abstract

The limited sense of sight makes it difficult for blind people to get a decent job. One of the jobs performed by the blind is a reflexology massager. Becoming a reflexology masseuse with visual limitations has several obstacles in learning reflexology. The purpose of this research is to make reflexology educational aids that are easily understood by blind masseurs so that blind masseurs can improve reflexology competence and determine the appropriate massage nerve points. The making of teaching aids is carried out using the Research and Development method with five stages, namely information gathering, planning, development, trials with students and coaches, and evaluation. The results of this study are a prototype in the form of a microcontroller-based smart demonstration leg called Smart Massage Tools. The output of the Smart Massage Tools prototype is sound which is suitable for use by blind people who want to study reflexology independently. Smart Massage Tools have higher time effectiveness and understanding than massage training using manual props.

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