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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 15 Documents
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Search results for , issue "Vol 16 No 1 (2024): February 2024" : 15 Documents clear
Metode Pembelajaran Mesin untuk Memprediksi Status Gizi Balita Rendra Gustriansyah; Nazori Suhandi; Shinta Puspasari; Ahmad Sanmorino
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.988

Abstract

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.
Foveal Avascular Zone Image Encryption using Pixel Scrambling Combination Technique for Medical Image Security Purnamasari, Dewi; Herlinudinkhaji, Didin; Dewi, Astrie Kusuma; Mauludin, Muhammad Zairon
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1029

Abstract

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.
Strategi Resampling dan Pengaruhnya terhadap Fitur Variabilitas Denyut Jantung pada Data Elektrokardiogram Berfrekuensi Sampling Rendah Zakariyah, Muhammad; Zaky, Umar; Nurjaman, Muhammad; Istikmal, Agil Ghani; Widianto, Hafizh Athallah
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1049

Abstract

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.
Memahami Persepsi Pelanggan tentang Produk Fashion Lokal di Pasar Online melalui Analisis Konten Nata, Imam Adi; Maarif, Muhammad Rifqi
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1070

Abstract

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.
Sentimen Topik Menggunakan Regresi Logistik dan Alokasi Dirichlet Laten sebagai Model Analisis Kepuasan Pelanggan Cahyo, Puji Winar; Aesyi, Ulfi Saidata; Santosa, Bagas Dwi
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1081

Abstract

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.
Combination of Binary Particle Swarm Optimization (BPSO) and Multilayer Perceptron (MLP) for Survival Prediction of Heart Failure Patients Sutikno, Sutikno
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.974

Abstract

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.
Imbalance Dataset in Aspect-Based Sentiment Analysis on Game Genshin Impact Review Perwira, Prabowo Adi; Widiastuti, Nelly Indriani
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.984

Abstract

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%.
Pembelajaran Ensemble Voting Tertimbang dari Arsitektur CNN untuk Klasifikasi Retinopati Diabetik Desiani, Anita; Primartha, Rifkie; Hanum, Herlina; Dewi, Siti Rusdiana Puspa; Suprihatin, Bambang; Al-Filambany, Muhammad Gibran; Suedarmin, Muhammad
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.999

Abstract

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.
Klasifikasi Citra Kupu-Kupu Menggunakan Convolutional Neural Network dengan Arsitektur AlexNet Maftukhah, Ainin; Fadlil, Abdul; Sunardi, Sunardi
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1004

Abstract

Kurangnya pengetahuan tentang kupu-kupu dapat menimbulkan masalah karena kupu-kupu berperan penting dalam ekosistem. 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. Tujuan dari penelitian ini adalah untuk mengklasifikasikan spesies kupu-kupu. Dataset yang digunakan adalah dataset citra kupu-kupu sebanyak 5.499 dengan total 50 spesies. Metode yang diterapkan adalah convolution neural network (CNN) dengan arsitektur AlexNet. Proses pelatihan menggunakan arsitektur AlexNet diawali dengan input dataset citra, dataset akan diproses terlebih dahulu seperti resizing dan RGB to grayscale.Kemudian lakukan filter atau kernel. Output dari kernel digunakan untuk melakukan pooled convolution. Konvolusi dan pooling dilakukan sebanyak lima kali. Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. Setelah itu, terhubung sepenuhnya. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. Setelah itu, terhubung sepenuhnya. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Setiap hasil max pooling terakhir diratakan tiga kali untuk mengubah gambar berbentuk matriks menjadi tiga dimensi. Setelah itu, terhubung sepenuhnya. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Tahap terakhir adalah citra dapat diklasifikasikan. Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu. Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. Tahap terakhir adalah citra dapat diklasifikasikan.Proses pengujian menggunakan arsitektur AlexNet diawali dengan input dataset citra, dilakukan preprocessing dataset seperti resizing dan RGB to grayscale. Kemudian dataset diklasifikasikan dengan arsitektur AlexNet CNN. Setelah itu dilakukan evaluasi model, dan terakhir adalah hasil klasifikasi citra kupu-kupu. Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. dan hasil terakhir pengklasifikasian citra kupu-kupu. Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200. dan hasil terakhir pengklasifikasian citra kupu-kupu.Hasil klasifikasi diperoleh akurasi sebesar 80% dengan resize 100x100, 82% dengan resize 150x150, dan 82% dengan resize 200x200.
Temporal Sequential-Artificial Neural Network Enhancements for Improved Smart Lighting Control Putrada, Aji Gautama; Abdurohman, Maman; Perdana, Doan; Nuha, Hilal Hudan
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1025

Abstract

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.

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