http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/issue/feed JURNAL INFOTEL 2024-03-20T17:22:16+00:00 Eko Fajar Cahyadi, Ph.D. ekofajarcahyadi@ittelkom-pwt.ac.id Open Journal Systems <h2>About Jurnal INFOTEL</h2> <table border="0"> <tbody> <tr> <td><img src="https://ejournal.ittelkom-pwt.ac.id/public/site/images/journaladmin/cover_infotel.png" alt="telecommunication journal" width="180" height="250"></td> <td align="justify" valign="top"> <div style="background-color: #ebfeec; border: 1px solid #bae481; border-radius: 5px; text-align: justify; padding: 10px; font-family: sans-serif; font-size: 14px; margin-left: 10px;">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 <strong>informatics, telecommunication, and electronics</strong>. 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. <strong>Starting in 2018, Jurnal INFOTEL uses English as the primary language.</strong></div> </td> </tr> </tbody> </table> <div style="text-align: justify;"> <p>&nbsp;</p> <h4><strong>Important For Authors (Volume 16, No. 2, May 2024)<br></strong></h4> <p>Reminder for all the authors, you are expected to submit papers that:<br> 1. are original and have not been submitted to any other publication.<br> 2. have at least 20 references with 80 % of scientific Journals.<br> 3. use references published in the last 5 (five) years.<br> 4. structured using IMRaD format.<br> 5. use the template specified by Jurnal INFOTEL.<br> 6. use a reference manager <em>e.g.</em> Mendeley or others when managing the references.<br>7.Add all authors and complete affiliation in the metadata.<br>8.&nbsp;The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.<br>9.&nbsp;Willing to make improvements at the pre-review stage, a maximum of 14 days after the pre-review was carried out<br>Thank you.</p> <p>&nbsp;</p> </div> http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1081 Topic Sentiment Using Logistic Regression and Latent Dirichlet Allocation as a Customer Satisfaction Analysis Model 2024-03-13T08:33:34+00:00 Puji Winar Cahyo pwcahyo@gmail.com Ulfi Saidata Aesyi ulfiaesyi@gmail.com Bagas Dwi Santosa bagasdwisantosa87@gmail.com <p>Buying and selling goods now is more interesting through e-commerce or marketplaces because of the ease of carrying out online transactions. Each transaction usually generates a response from the customer. The transaction response on the Shopee platform is still in paragraph form and needs to be more specific. Therefore, this research aims to build a model analysis of customer satisfaction using the best algorithm between support vector machine (SVM), random forest, and logistic regression. This research method uses sentiment classification with logistic regression because the logistic regression algorithm has the best accuracy, with an accuracy of 90.5. Meanwhile, the SVM algorithm achieved an accuracy of 90.4, and random forest reached 90.2. The three algorithms were tested three times, splitting data train:test at 80:20, 70:30, and 60:40. The best results were obtained by splitting data at 60:40. The best model is used to predict data without labels. The prediction produces 12,844 positive sentiment comment data, 112 negative sentiment comment data, and 70 neutral sentiment comment data. The results of this research continued to topic modeling using latent dirichlet allocation (LDA) to generate a trending topic of customer satisfaction on sales products. Implications of discussing each trend topic can be used as a reference for improving products and services, especially in communicating with customers.</p> 2024-01-23T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1049 Resampling Strategies and their Influence on Heart Rate Variability Features in Low Sampling Rate Electrocardiogram Data 2024-03-13T08:44:32+00:00 Muhammad Zakariyah muhammad.zakariyah@staff.uty.ac.id Umar Zaky umar.zaky@staff.uty.ac.id Muhammad Nurjaman muhammad.5200411448@student.uty.ac.id Agil Ghani Istikmal agilistikmal3@gmail.com Hafizh Athallah Widianto athallah101hafizh@gmail.com <p>Heart rate variability (HRV) is a parameter to measure fluctuations in the interval between heartbeats. HRV provides essential insights into the cardiovascular function and autonomic nervous system. Electrocardiograms (ECG) on wearable devices are often recorded at low sampling rates, limiting temporal resolution and information. Resampling is a technique of changing the sampling rate from a high sampling rate to a lower sampling rate and vice versa. This research aims to evaluate the effect of resampling ECG data with a low sampling rate on HRV features. ECG data consists of 50 Hz and 100 Hz sampling rates. Data with a 50 Hz sampling rate is up-sampled up to 100 Hz, while 100 Hz data is down-sampled up to 50 Hz and up-sampled up to 250 Hz using the Fast Fourier Transform Interpolation Method. Upsampling from 50 Hz to 100 Hz shows unsatisfactory results, except for some HRV features such as NN20, pNN20, and CVI. Better results were found when up sampling from 100 Hz up to 250 Hz, with some HRV features showing good concordance values. However, downsampling from 100 Hz up to 50 Hz is unsuitable for HRV feature analysis. To obtain accurate HRV analysis results in all domains, it is highly recommended to use a sampling rate above 100 Hz.</p> 2024-01-25T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/988 Machine Learning Method to Predict the Toddlers’ Nutritional Status 2024-03-13T08:48:19+00:00 Rendra Gustriansyah rendra@uigm.ac.id Nazori Suhandi nazori@uigm.ac.id Shinta Puspasari shinta@uigm.ac.id Ahmad Sanmorino sanmorino@uigm.ac.id <p>Malnutrition is one of the leading health problems experienced by toddlers in various countries. Based on the 2022 Indonesian Nutritional Status Survey results, malnutrition in children under five in Indonesia is higher than the average malnutrition in Africa and globally. Therefore, a way is needed to predict the nutritional status of children under five early and quickly so that the Government (through District Health Office) can immediately provide the necessary treatment. This study aims to predict or classify the toddlers’ nutritional status based on age, body mass index (BMI), weight, and body length using various machine learning (ML) methods, namely naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbor, random forest, and support vector machine. The predictive performance of each ML method was evaluated based on accuracy, sensitivity, specificity, the area under curve, and Cohen's Kappa coefficient. The test results show that the RF method is the most recommended for predicting toddlers' nutritional status. The study's contribution is to obtain information about toddlers' nutritional status easier.</p> 2024-01-25T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1029 Foveal Avascular Zone Image Encryption using Pixel Scrambling Combination Technique for Medical Image Security 2024-03-13T10:29:42+00:00 Dewi Purnamasari dewipurnamasari@ivet.ac.id Didin Herlinudinkhaji didnt.aji@gmail.com Astrie Kusuma Dewi astrie.dewi@esdm.go.id Muhammad Zairon Mauludin muizai97@gmail.com <p><span style="font-weight: 400;">Data theft from year to year has increased in the era of big data and society 5.0. One area that requires data security is patient medical data. Medical image data security must be done to protect medical data security from data theft by third parties so that they cannot access the data. The development of Diabetic Retinopathy (DR) is also increasing every year. Determining the severity of DR is done by detecting the Foveal Avascular Zone (FAZ). Encryption is the process of changing a plain image into a cipher image. In this study, we compared the results of image quality and encryption time between the Vigenere Cipher method and a combination of pixel scrambling. The average encryption time of the tested FAZ images is 3.20 seconds. This result proves that the pixel combination method has a faster encryption time than the Vigenere Cipher. Vigenere Cipher encryption time is 4.96 seconds. The existence of the FAZ area with the pixel combination randomization method of the encryption process is also invisible, so third parties will not know about its existence.</span></p> 2024-02-02T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1070 Understanding Customer Perception of Local Fashion Product on Online Marketplace through Content Analysis 2024-03-13T10:48:31+00:00 Imam Adi Nata imamadinata@untidar.ac.id Muhammad Rifqi Maarif rifqi@untidar.ac.id <p>This research employs Natural Language Processing (NLP) techniques to evaluate customer reviews obtained from online marketplaces. It uses keyword extraction and clustering to identify thematic clusters in the data. These clusters reveal shared contextual significance and provide a higher-level perspective on customer perceptions of local fashion products. Sentiment analysis is also conducted within each theme to understand customer sentiment. This approach goes beyond binary sentiment classification and offers a more nuanced analysis. By incorporating keyword extraction, clustering, and sentiment analysis, this research offers a thorough framework for comprehending customer perceptions in the digital marketplace. It contributes to the field of e-commerce by offering a robust methodology for decoding customer sentiments towards local fashion products. The findings have substantial implications for marketers, designers, and platform providers in online marketplaces, leading to a more consumer-centric e-commerce ecosystem.</p> 2024-02-02T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/984 Imbalance Dataset in Aspect-Based Sentiment Analysis on Game Genshin Impact Review 2024-03-13T10:50:32+00:00 Prabowo Adi Perwira xanpbw@gmail.com Nelly Indriani Widiastuti nelly.indriani@email.unikom.ac.id <p>Sentiment analysis was commonly used to determine the polarity of the review text. However, there is a problem if some reviews have more than one aspect with different polarities, so the reviews have more than one polarity. That has happened in some reviews on the game Genshin Impact. Not merely that, the number of sentiments contained in a review is not always the same as other reviews will cause imbalanced data. So, this study will handle imbalance data with Random Under-Sampling and Random Over-Sampling on aspect-based-sentiment-analysis of Genshin Impact Review with Multinomial Naïve-Bayes, so that the classification prediction does not ignore the minority class due to the dominance of the majority class. The classification process used K-Fold Cross Validation (k=10) validation method and the Laplace smoothing technique on Multinomial Naïve Bayes. As a result, the conclusion is that Random Oversampling had better accuracy than Random Undersampling in handling imbalanced data on aspect-based sentiment analysis of Genshin Impact game Review in Indonesian with Naïve Bayes Multinomial, with the highest accuracy of 85.55%.</p> 2024-02-04T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1004 Butterfly Image Classification using Convolution Neural Network with AlexNet Architecture 2024-03-13T12:12:22+00:00 Ainin Maftukhah aninmaftukhah@gmail.com Abdul Fadlil fadlil@mti.uad.ac.id Sunardi Sunardi sunardi@mti.uad.ac.id <p><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kurangnya pengetahuan tentang kupu-kupu dapat menimbulkan masalah karena kupu-kupu berperan penting dalam ekosistem. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Urgensi dalam penelitian ini terkait dengan bidang biologi yaitu klasifikasi citra kupu-kupu dapat membantu dalam memahami pola migrasi, pola kawin, dan pola perilaku kupu-kupu dalam interaksinya dengan lingkungan sekitarnya. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Tujuan dari penelitian ini adalah untuk mengklasifikasikan spesies kupu-kupu. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Dataset yang digunakan adalah dataset citra kupu-kupu sebanyak 5.499 dengan total 50 spesies. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Metode yang diterapkan adalah convolution neural network (CNN) dengan arsitektur AlexNet. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Proses pelatihan menggunakan arsitektur AlexNet diawali dengan input dataset citra, dataset akan diproses terlebih dahulu seperti resizing dan RGB to grayscale.</span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kemudian lakukan filter atau kernel. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Output dari kernel digunakan untuk melakukan pooled convolution. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Konvolusi dan pooling dilakukan sebanyak lima kali. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu, terhubung sepenuhnya. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Tahap terakhir adalah citra dapat diklasifikasikan. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.</span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu, terhubung sepenuhnya. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Tahap terakhir adalah citra dapat diklasifikasikan. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.</span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu, terhubung sepenuhnya. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Tahap terakhir adalah citra dapat diklasifikasikan. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.</span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Tahap terakhir adalah citra dapat diklasifikasikan. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Tahap terakhir adalah citra dapat diklasifikasikan.</span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">dan hasil terakhir pengklasifikasian citra kupu-kupu. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. </span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">dan hasil terakhir pengklasifikasian citra kupu-kupu.</span></span></span><span style="vertical-align: inherit;"><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200.</span></span></span></span></p> 2024-02-04T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/974 Combination of Binary Particle Swarm Optimization (BPSO) and Multilayer Perceptron (MLP) for Survival Prediction of Heart Failure Patients 2024-03-13T12:14:23+00:00 Sutikno Sutikno sutikno.wae@gmail.com <p>Heart failure is a dangerous condition in which the heart cannot pump blood effectively and can lead to death. To improve this treatment, it needs methods to predict patient survival. This paper proposed combining wrapping features, namely Binary particle swarm optimization (BPSO) and a multilayer perceptron (MLP) classifier called BPSO-MLP. BPSO is used to determine the most relevant feature subset, and MLP is used to calculate its fitness. The experiment used a public dataset containing the medical records of 299 heart failure patients. This dataset comprises 13 features: age, anemia, high blood pressure, creatinine phosphokinase (CPK), diabetes, ejection fraction, platelets, gender, serum creatinine, serum sodium, smoking, time, and death events. The experiment results showed that the proposed method could produce an accuracy of up to 91.11%. The proposed method can increase accuracy by 8.89% compared to MLP (without BPSO). The addition of this wrapping feature has a significant influence on the accuracy results.</p> 2024-02-05T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1025 Temporal Sequential-Artificial Neural Network Enhancements for Improved Smart Lighting Control 2024-03-13T12:15:50+00:00 Aji Gautama Putrada ajigps@telkomuniversity.ac.id Maman Abdurohman abdurohman@telkomuniversity.ac.id Doan Perdana doanperdana@telkomuniversity.ac.id Hilal Hudan Nuha hilalnuha@telkomuniversity.ac.id <p>Several previous studies have proposed a temporal sequential-artificial neural network (TS-ANN) to convert PIR Sensor movement data into presence data and improve the performance of smart lighting control. However, such a temporal-sequential forecasting concept has a curse of dimensionality problem. This study aims to proposes the application of principal component analysis with TS-ANN (PCA-TS-ANN) as an intelligent method for controlling smart lighting with low dimensions. We have primary data directly from a smart lighting implementation that utilizes PIR sensors. We apply cross-correlation to the original dataset to find the optimum time step. Then we discover the optimum TS-ANN based on selected tuning parameter values through PCC. We then design and compare scenarios involving the combination of TS-ANN and PCA. Finally, we evaluate these scenarios using the metrics Accuracy, Precision, Recall, F1− Score, and Delay. The results of this study are the PCA-TS-ANN model with Accuracy, Precision, Recall, and F1−Score value of 0.9993, 0.9997, 0.9994, and 0.9996 respectively. The PCA method reduces the TS-ANN features from 1200 features to 36 features. The model size has also decreased from 3534kB to 807kB. Our model has a simpler complexity with TS-ANN that the µ ± σ Delay is 0.27±0.06 ms versus 0.34±0.11 ms.</p> 2024-02-13T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1057 Evaluation of Wireless Network Security with Penetration Testing Method at PT PLN UP2D S2JB 2024-03-13T12:17:38+00:00 Tamsir Ariyadi tamsirariyadi@binadarma.ac.id Irham Irham irhamirham081@gmail.com Eko Fajar Cahyadi ekofajarcahyadi@ittelkom-pwt.ac.id <p>Advances in information and communication technology continue to develop over time. This causes significant changes in social, economic, and political conditions. One of the companies that require strong network security is PT. PLN (Perusahaan Listrik Negara) Persero which is the leading energy company in Indonesia. In this case, the a need to evaluate network security at PT. PLN becomes very important. This evaluation will help identify vulnerabilities and security gaps that exist in PT. PLN's network infrastructure. Network security evaluation using the penetration testing execution standards (PTES) method can provide an overview of the vulnerabilities or weaknesses of the network system at PT. PLN UP2D S2JB which has quite a lot of gaps to be exploited. The parameters used in this study are attacking the infrastructure, The rogue access point, and ARP Spoofing to test the wireless network security system. This is evidenced by the results of the 15 tests carried out, only two failed, namely in the type of attack on the rogue access point. The results of penetration testing are very necessary and important as feedback for system managers in fixing existing vulnerabilities.</p> 2024-02-16T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/999 Weighted Voting Ensemble Learning of CNN Architectures for Diabetic Retinopathy Classification 2024-03-13T12:19:36+00:00 Anita Desiani anita_desiani@unsri.ac.id Rifkie Primartha rifkie@ilkom.unsri.ac.id Herlina Hanum herlina@mipa.unsri.ac.id Siti Rusdiana Puspa Dewi sitirusdiana@fk.unsri.ac.id Bambang Suprihatin bambangs@unsri.ac.id Muhammad Gibran Al-Filambany gibran098@gmail.com Muhammad Suedarmin muhammadsuedarmin@gmail.com <p>Diabetic Retinopathy (DR) is a diabetes disease that attacks the retina of the eye and can be recognized through retinal images. The process of assisting retinal images can be done by applying deep learning-based methods, one of which is the Convolutional Neural Network (CNN). CNN has many architectures that can perform image classification processes, namely ResNet-50, MobileNet, and EfficientNet. Weaknesses of each architecture can be overcome through ensemble learning methods that can add up the performance results of each classification method. The study applies the ensemble learning method to improve the performance of the ResNet-50, MobileNet, and EfficientNet architectures in paying for DR disease on the retina by weighted voting. The data used are the APTOS and EyePACS datasets. The method in this research is data collection, training, testing, and evaluation of each architecture and ensemble learning. The results of the superior ensemble learning performance in the value of accuracy, F1-Score, and Cohens Kappa were obtained respectively 93.3%, 93.42%, and 0.866. The best specificity value was obtained by Resnet-50 at 99.78% and the highest sensitivity value was obtained by EfficientNet at 96.2%. Based on the classification results of each architectural and ensemble learning, it can be interpreted that the proposed ensemble learning method is excellent to perform image classification for Diabetic Retinopathy.</p> 2024-02-19T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1103 Geo-Navigation in Museums: Augmented Reality Application in the Geological Museum Indonesia 2024-03-19T10:53:11+00:00 Alfian Akbar Gozali alfian@telkomuniversity.ac.id Fat'hah Noor Prawita fathah@telkomuniversity.ac.id Ihshan Gumilar igum002@aucklanduni.ac.nz Haidar Rashid Ramdana Putra haidar@gmail.com Muhammad Arief Fauzan arieffauzan@gmail.com <p>Navigational challenges in large buildings with multiple rooms, such as museums, often result in inefficient visitor experiences. Traditional signage and direction plans, while common, do not always effectively convey the necessary information. This paper introduces an innovative solution leveraging Augmented Reality (AR) technology to enhance navigation in such complex environments. We developed a mobile application utilizing the Immersal Software Development Kit (SDK) to facilitate interaction with the surroundings in the Bandung Geological Museum. The application serves as a digital guide, providing clear directions and route information to various rooms within the museum. Our study's findings reveal that the application not only facilitated easier navigation through its accurate room identification and route suggestions but also enhanced the overall visitor experience by making it more interactive and immersive. Furthermore, the user engagement and experience survey, encompassing a broad demographic range, highlighted a significant increase in visitor satisfaction and interaction. The application's intuitive and user-friendly interface played a key role in this enhanced engagement. The survey results reflect the application's success in meeting its main objectives, demonstrating usability, and offering an effective user interface.</p> 2024-02-21T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1041 Cloud-based Metabase GIS Data Analysis Platform Quality Management According to ISO 9126 Indicators 2024-03-19T12:10:02+00:00 Rani Purbaningtyas rpurbaningtyas@polije.ac.id Moh Munih Dian Widianta munihdian@gmail.com Mochammad Rifki Ulil Albaab mochrifki@polije.ac.id <p>Platform metabase GIS data analysis based on the cloud that has been successfully developed is an alternative solution for spatial-text data analysis. The output of this cloud-based platform not only provides accurate textual information but also precise location representation of the objects. This research examines the quality of the developed platform based on ISO 9126, which consists of six main indicators: functionality, reliability, feasibility, efficiency, maintainability, and portability. Each indicator has different sub-indicators, totaling 22 sub-indicators. The quality assessment results indicate that the platform for metabase GIS data analysis based on the cloud exhibits excellent quality, with an average test result based on the ISO 9126 indicators reaching 93%.</p> 2024-02-21T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1074 Water Meter Reading Application System Development using Image Processing: A Case Study from Sindangsari Village Water Services 2024-03-19T09:37:17+00:00 Umar Ali Ahmad umar@telkomuniversity.ac.id Ikbal Ramdani ikbal@mail.com Fath Muhammad Isham fath.m@mail.com R Roger Dwiputra S roger@mail.com Yusup Diva Pratama yusup@mail.com Rifdo Shah Alam rifdo@mail.com Fauzi Sofyan fauzi@mail.com Reza Rendian Septiawan reza.r@mail.com Ratna Astuti Nugraheni ratna@mail.com Angga Rusdinar angga@mail.com Ashri Dinimaharawati ashri@mail.com M Ammar Abdurahman ammar@mail.com <p>PAMDES is a drinking water company managed by local villagers. The water meter data are read and recorded manually without any technology, which is ineffective and inefficient. Digital image processing can be implemented to read and record the water meter data automatically. When implemented in the water meter, it can help PAMDES officers to read the data without the internet, without changing the conventional water meter device, and the water meter data can be read and recorded effectively and efficiently. This research used the agile method, one of the methods used in the Software Development Life Cycle (SDLC). The method is done repetitively within a short period of time. The output of this research is an application with a digital image processing model that can read water meter data up to 82% in normal conditions and still can be improved. This research aims to make the water meter data reading and recording more effective and efficient and to contribute to the transformation of Sindangsari village into a digitalized village.</p> 2024-02-04T00:00:00+00:00 ##submission.copyrightStatement## http://ejournal.ittelkom-pwt.ac.id/index.php/infotel/article/view/1075 Solar Panel Power Generator with Automatic Charging using PWM System based on Microcontroller 2024-03-20T17:22:16+00:00 Sulistyo Widodo sulistyo.widodo@undira.ac.id Erfiana Wahyuningsih erfiana.wahyuningsih@undira.ac.id Yohanes Galih Adhiyoga yohanes.galih.adhiyoga@undira.ac.id <p>Indonesia will become a net importer if it runs out of oil and gas reserves within the next 11 to 12 years if no alternative energy sources are developed. Therefore, there is a need to utilize alternative energy that is not dependent on oil or gas. One alternative energy is using solar light energy. This research designed a device that can convert the energy of sunlight into electrical energy and automatically store electrical energy in a battery with PWM (Pulse Width Modulation) so that it can be used as alternative energy, and help reduce the consumption of electricity from PLN. The battery used 12V 80Ah and a solar panel module 50W for energy storage and system resources. The research results show that systems can automatically charge energy using sunlight and turn the lights to 7W. Using the charging system automatically uses PWM to reduce the risk of damage to the battery because, in the charging process, battery conditions will be monitored. The maximum power generated from solar panel modules used is 35.57 W.</p> 2024-02-21T00:00:00+00:00 ##submission.copyrightStatement##