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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 473 Documents
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Mengontrol Pengumpulan Data Stok dan Penentuan Harga di Sistem Inventaris Rizal Hafizh; Irma Handayani
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.v15i4.1055

Abstract

Abstract — Data collection regarding the activities and transactions of products entering and leaving a company is intimately linked to inventory management in businesses. CV. Amalia Buku, is an individual company located on Jl. Juminahan, Purwokinanti, Pakualaman, which operates in the field of book sales. Recording incoming and outgoing goods inventory on CV. Amalia Books are still done by writing them down in notebooks and this causes the notes to be lost or damaged. Based on these problems, this research designs and builds a mobile web-based book inventory system. This system was built using the RAD (Rapid Application Development) method and uses Laravel framework, while mobile with Android Studio whose programming language is Kotlin. The data processed in this system is incoming book data, outgoing book data, book stock data, and user data , who can collect data on stock reports, and incoming book reports and outgoing book reports. The results of this research produce a mobile web-based book inventory system that can be used by CVs. Amalia Books.
Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy Azka Khoirunnisa; Nur Ghaniaviyanto Ramadhan
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.v15i4.1056

Abstract

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.
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%.
Feature Extraction vs Fine-tuning for Cyber Intrusion Detection Model Sanmorino, Ahmad; Suryati, Suryati; Gustriansyah, Rendra; Puspasari, Shinta; Ariati, Nining
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.v16i2.996

Abstract

This study investigates the effectiveness of feature extraction and fine-tuning approaches in developing robust cyber intrusion detection models using the Network-based Security Lab - KDD dataset (NSL-KDD). The role of cyber intrusion detection is pivotal in securing computer networks from unauthorized access and malicious activities. Feature extraction, involving methods such as PCA, LDA, and Autoencoders, aims to transform raw data into informative representations, while fine-tuning leverages pre-trained models for task-specific adaptation. The study follows a comprehensive research method encompassing data collection, preprocessing, model development, and experimental evaluation. Results indicate that LDA and Autoencoders excel in the feature extraction phase, demonstrating precision, high accuracy, F1-Score, and recall. However, fine-tuning a pre-trained Multilayer Perceptron model surpasses individual feature extraction methods, achieving superior performance across all metrics. The discussion emphasizes the complexity and flexibility of these approaches, with fine-tuned models showcasing higher adaptability. In conclusion, this study provides valuable insights into the comparative effectiveness of feature extraction and fine-tuning for cyber intrusion detection. The findings underscore the importance of leveraging pre-trained knowledge and adapting models to specific tasks, offering a foundation for further advancements in enhancing network security through advanced machine learning techniques.
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.
A Semantic Segmentation of Nucleus and Cytoplasm in Pap-smear Images using Modified U-Net Architecture Arhami, Muhammad; Rudi F, Fachri Yanuar; Hendrawaty, Hendrawaty; Adriana, Adriana
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.v16i2.1006

Abstract

Pap-smear images can help early detection of cervical cancer, but the manual interpretation by a pathologist can be time-consuming and prone to human error. Semantic segmentation of the cell nucleus and cytoplasm plays an essential role in Pap smear image analysis for the detection of cervical cancer automatically. This study proposes a modified U-Net architecture by adding batch normalization to each convolution layer. Batch normalization aims to stabilize and accelerate the convergence of the model during training, thus overcoming the vanishing gradient problem. The modified U-Net model achieves high accuracy and low loss during the training process, indicating its ability to learn and recognize patterns in the data. The performance evaluation of the model resulted in 91.4 % accuracy, 79.9 % sensitivity, 87.7 % specificity, 81.7 % F1-score, and 83.7 % precision. The results show that the proposed modification of U-Net architecture with batch normalization improves the segmentation performance for cervical cancer cells in Pap smear images. However, improvement in architecture is still required to increase the ability to overcome overlapping areas between the nucleus, cytoplasm, and background.

Page 39 of 48 | Total Record : 473

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