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All Journal Jurnal Elektrosista The Indonesian Journal of Computer Science
Bambang Pilu Hartato
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science

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PENGEMBANGAN PROTOTIPE ALAT KEAMANAN BARANG ELEKTRONIK BERBASIS INTERNET OF THINGS (IOT) Nur Athif Oldika Gunawan; Nila Feby Puspitasari; Bambang Pilu Hartato
JURNAL ELEKTROSISTA Vol. 12 No. 1 (2024): DESEMBER 2024
Publisher : PPM Sdirjianbang Akademi Militer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63824/jtep.v12i1.242

Abstract

The increasing security risks today have driven the development of more sophisticated protection solutions, especially for personal electronic goods. This study aims to design and implement an innovative Internet of Things (IoT)-based security device, designed to protect electronic goods such as mobile phones, laptops, and other electronic devices. The technology used includes the integration of smart sensors, such as GPS, Ultrasonic, and Passive Infrared, which function to detect threats accurately and responsively. This prototype-based system is equipped with smart sensors designed to detect potential threats to personal goods. In addition, a user application was successfully developed to support efficient interaction between users and the system. The test results show that this system is portable and capable of providing real-time notifications to users regarding threats to electronic goods, as well as responding to these threats quickly and appropriately. The approach taken not only emphasizes physical security, but also integrates modern technology to provide an efficient, practical, and easily accessible monitoring experience. This study offers an innovative solution in protecting personal electronic goods in the digital era.
Analisis Perbandingan Performa Algoritma XGBoost dan LightGBM pada Klasifikasi Kanker Payudara Wijayanto, Danang; Bambang Pilu Hartato
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3901

Abstract

Breast cancer is one of the most common types of cancer and attacks women throughout the world. Judging from death cases, breast cancer is in second place in deaths caused by cancer. The fine needle aspiration method is one way to detect breast cancer early, but there are several disadvantages such as limited samples which affect the accuracy of the diagnosis or dependence on the skill and experience of the person carrying out the method. Machine learning is considered to be able to help overcome problems in the health sector, including being able to diagnose whether someone has cancer or not using the XGBoost and LightGBM algorithms. XGBoost and LightGBM are efficient algorithms for learning and have differences in learning strategies, namely level-wise and leaf-wise. This research will compare the accuracy, sensitivity and specificity performance of two algorithms, namely XBoost and LightGBM, to see which algorithm can perform better classification. From the experimental results, it was found that XGBoost had better performance by obtaining an average accuracy of 97.03%, an average sensitivity of 97.40% and an average specificity of 96.81%, while LightGBM obtained an average accuracy of 95.59%, average sensitivity 94.70% and average specificity 96.10%.
Co-Authors Danang Wijayanto Nila Feby Puspitasari Nur Athif Oldika Gunawan