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All Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer
Ahmad Maulid Ridwan
Universitas Nusa Mandiri
Computer Science & IT

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PERBANDINGAN BERBAGAI MODEL MACHINE LEARNING UNTUK MENDETEKSI DIABETES Ahmad Maulid Ridwan; Gilang Dwi Setyawan
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.152

Abstract

  Diabetes mellitus, often known as diabetes, is a significant metabolic illness that has a negative impact on living organisms. It causes high blood sugar levels by either creating inadequate insulin or using it inefficiently. Diabetes that is not effectively treated raises the risk of heart attacks, retinopathy, vision loss, skin disorders, and other ailments. Early detection is critical for guiding essential actions. In this setting, machine learning (ML) has emerged as a potent tool. We used Python data manipulation tools to develop ML techniques for discovering patterns and risk factors in the Pima Indian diabetes dataset in our study. We correctly identified patients as diabetes or non-diabetic using K-Nearest Neighbors (KNN), AdaBoost, Logistic Regression (LR), Light Gradient Boosting, Random Forest (RF),  dan Support Vector Machine (SVM). Notably, we used the Synthetic Minority Over-sampling Technique (SMOTE) to solve class imbalance, which enhanced model performance. By efficiently utilizing ML and SMOTE in diabetes categorization, our work greatly adds to the scientific area. We suggest studying cutting-edge technology and undertaking external validation and clinical studies to assure trustworthy and generalized models for diabetic patient care in the future. With diabetes's increasing prevalence, such improvements have enormous promise for improving early identification and management, eventually leading to better health outcomes.
IMPLEMENTASI METODE ADASYN DALAM DETEKSI URL BERBAHAYA MENGGUNAKAN MACHINE LEARNING: DEMI MENINGKATKAN KEAMANAN SIBER DI ERA DIGITAL Gilang Dwi Setyawan; Andrie Yuswanto; Ahmad Maulid Ridwan; Budi Wibowo; Maman Firmansyah
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.153

Abstract

Cybercriminals exploit malicious URLs as a distribution channel to spread harmful software across the internet. They take advantage of vulnerabilities in browsers to install malicious software with the aim of gaining remote access to the victims' computers. Typically, this malicious software aims to gain access to networks, steal sensitive information, and silently monitor targeted computer systems. In this research, a data mining approach known as Classification Based on Association (CBA) is employed to detect malicious URLs using both the URL itself and the features of the presented web pages. The CBA algorithm utilizes a training dataset of URLs as historical data to discover association rules that can be used to create an accurate classifier. By detecting dangerous URLs and malicious software, this contribution can assist organizations and individual users in enhancing the security of their computer systems and networks, thereby protecting sensitive data and reducing the risk of security incidents. The experimental results demonstrate that CBA achieves performance on par with tested classification algorithms, achieving an accuracy of 99% and low rates of false positives and false negatives. Future research could expand its focus to detect malicious URLs and software on mobile devices and embedded systems, as they have become significant targets for cybercriminals.
ANALISIS SENTIMEN PRODUCT TOOLS & HOME MENGGUNAKAN METODE CNN DAN LSTM Safrizal Ahmad; Ahmad Maulid Ridwan; Gilang Dwi Setyawan
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.154

Abstract

Sentiment analysis is gaining popularity as the number of internet users increases. Internet users often express their opinions through reviews on websites. The customer opinions expressed have a huge impact on sellers and customer numbers, as many consumers rely on online reviews as a reference when purchasing products. In order to quickly understand sentimental views and tendencies towards a product or event, a text sentiment analysis is performed on the opinions expressed by users. Sentiment analysis focuses on understanding the sentiments contained in the text. One common approach in sentiment analysis is to use Deep Learning (DL) models. This study aims to analyze product sentiment in the Tools & Home category from Amazon using models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The CNN model is used to extract features from words that reflect short-term sentiment dependencies, while LSTM is used to establish long-term sentiment relationships between words. CNN and LSTM are sophisticated DL models, capable of efficiently processing text data and recognizing relationships and patterns that exist at various levels of abstraction. The purpose of this study is to understand the differences in the performance of the DL model in conducting sentiment analysis, it is hoped that it can also be a reference for those who plan to apply other DL models.
Co-Authors Andrie Yuswanto Gilang Dwi Setyawan Maman Firmansyah Safrizal Ahmad