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PERBANDINGAN PENERAPAN ALGORITMA TEORI RESPON BUTIR DAN FUZZY TSUKAMOTO PADA COMPUTERIZED ADAPTIVE TEST Wahyuni - Wahyuni; Muhammad Fahmi
Just TI (Jurnal Sains Terapan Teknologi Informasi) Vol 12, No 2 (2020): JULI 2020
Publisher : Politeknik Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46964/justti.v12i2.347

Abstract

Pada dasarnya sistem CAT (Computerized Adaptive Test)  hampir sama dengan sistem CBT (Computerized Based Testing) dimana test dilakukan melalui komputer, namun sistem CAT dapat merandom dan memberikan soal sesuai dengan kemampuan pesertanya. Jika peserta tes tersebut memiliki kemampuan rendah, maka sistem akan memberikan soal yang cenderung mudah. Sebaliknya, jika peserta tes memiliki kemampuan tinggi, maka sistem akan memberikan soal yang cenderung sulit.Penelitian ini bertujuan untuk mengetahui metode manakah yang paling tepat untuk diterapkan pada sistem Computerized Adaptive Test dalam pemberian butir soal kepada peserta tes sehingga menghasilkan hasil yang maksimal. Pemilihan butir soal dimulai dari  butir soal kedua sampai soal yang terakhir. Metode Teori Respon Butir atau Item Response Theory (IRT)pada penelitian ini menggunakan tiga parameter logistik (3PL). Metode analisis yang dilakukan berupa pengujian kepada responden secara manual, menggunakan sistem CAT dengan metode IRT dan menggunakan sistem CAT dengan metode Fuzzy Tsukamoto. Setelah dilakukan pengujian, maka dibandingkan hasil yang telah didapat dari masing-masing pengujian tersebut untuk mengetahui metode manakah yang paling baik untuk diterapkan pada sistem CAT ini. Hasil yang di dapat adalah metode IRT dan Fuzzy  sama-sama dapat diterapkan dalam sistem CAT, namun metode IRT lah yang lebih baik untuk diterapkan dibandingkan dengan metode Fuzzy Tsukamoto.
Android Based Heart Rate Detection Tools with Arduino Nano Hidayatul Muttaqin; Ita Arfyanti; Wahyuni
TEPIAN Vol 2 No 1 (2021): March 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (430.106 KB) | DOI: 10.51967/tepian.v2i1.337

Abstract

Android-based Heart Rate Detector Using an Android-Based Fingerprint Using Arduino Nano at Midwife Dwi Inggrini's Maternity Clinic with the hope of helping and simplifying the medical team in checking the heart rate of pregnant women without having to carry devices that are not portable, improving services and errors due to blackouts PLN electricity. The software development method used is the prototype method which includes data collection, design, prototyping, the testing phase by conducting Black Box and White Box testing. To access this tool the user must first connect the bluetooth android device with bluetooth HC-05 on the Arduino device, after the two Bluetooth devices are connected.
Perbandingan Algoritma Machine Learning Dalam Mendeteksi Serangan DDOS Wahyuni; Pitrasacha Adytia
TEMATIK Vol 9 No 2 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i2.1070

Abstract

Ddos is an attack method by sending a lot of packets into a network that causes the device not to run according to its function. This attack will result in machine or network resources cannot be accessed or used by the user. Various methods are used to detect DDOS attacks on SDN [4] , namely statistical methods, machine learning, SDN architecture, blockchain, Network Function Virtualization, honeynets, network slicing, and moving target defense. Because so many people use machine learning to detect DDoS attacks, it is necessary to do further research to find out which one is the best and has high accuracy. Therefore, a research entitled “Comparison of Machine Learning Algorithms in Detecting DDoS Attacks was made. In this study, three machine learning algorithms will be compared, namely XGBoost, Decision Tree and ANN. The methods used are data acquisition, data understanding, data preparation, modeling, performance evaluation, and conclusions. In this study it can be said that for accuracy, the highest model is XGBoost in determining attacks, but to execute it requires the longest time among other models tested. While Decision tree also has high accuracy, slightly below XGBoost, but the time required to execute is fast or short. Therefore, in this study it can be said that the Decision Tree is the best model in detecting and classifying DDoS attacks.Keywords: Ddos Attack, Machine Learning, Decision Tree, XGBoost, ANN.