Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi CESS (Journal of Computer Engineering, System and Science)
Gatot Budi Santoso
Trisakti University
Computer Science & IT Library & Information Science

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
THE APPLICATION OF GRAPHOLOGY AND ENNEAGRAM TECHNIQUES IN DETERMINING PERSONALITY TYPE BASED ON HANDWRITING FEATURES Dian Pratiwi; Gatot Budi Santoso; Fiqih Hana Saputri
Jurnal Ilmu Komputer dan Informasi Vol 10, No 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.139 KB) | DOI: 10.21609/jiki.v10i1.372

Abstract

This research was conducted with the aim of developing previous studies that have successfully applied the science of graphology to analyze digital handwriting and characteristics of his personality through shape based feature extraction, which in the present study will be applied one method of psychological tests commonly used by psychologists to recognize human’s personality that is Enneagram. The Enneagram method in principle will classify the personality traits of a person into nine types through a series of questions, which then calculated the amount of the overall weight of the answer. Thickness is what will provide direction personality type, which will then be matched with the personality type of the result of the graphology analysis of the handwriting. Personality type of handwritten analysis results is processed based on the personality traits that are the result of the identification of a combination of four dominant form of handwriting through the software output of previous studies, that Slant (tilt writing), Size (font size), Baseline, and Breaks (respite each word). From the results of this research can be found there is a correlation between personality analysis based on the psychology science to the graphology science, which results matching personality types by 81.6% of 49  respondents data who successfully tested.
AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH Dian Pratiwi; Gatot Budi Santoso; Leni Muslimah; Raden Davin Rizki
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (997.358 KB) | DOI: 10.21609/jiki.v12i2.642

Abstract

Dengue hemorrhagic fever is one of the most dangerous diseases which often leads to death for the sufferer due to delays or improper handling of the severity that has occurred. In determining that severity level, a specialist analyzes it from the symptoms and blood testing results. This research was developed to produce a system by applying Deep Neural Network approach that is able to give the same analytical ability as a doctor, so that it can give fast and precise decision of dengue handling. The research stages consisted of normalizing data to 0 – 1 intervals by Min-Max method, training data into multilayer networks with fully connected and partially connected schemes to produce the best weights, validating data and final testing. From the use of network parameters as much as 10 input units, 1 bias, 2 hidden layers, 2 output units, learning rate of 0.3, epoch 1000, tolerance rate 0.02, threshold 0.5, the system succeeded in generating a maximum accuracy of 95% in data learning (60 data), 87.5% on data learning and non-learning (40 data), 85% on non-learning data (20 data).
Analysis of DDoS Attack Detection Using Neural Network Backpropagation Approach Ahmad Fajri Khumara; Agung Sediyono; Gatot Budi Santoso
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 1 (2022): January 2022
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (499.659 KB) | DOI: 10.24114/cess.v7i1.27090

Abstract

Distributed Denial of Service (DDoS) adalah suatu serangan yang dimana memiliki volume, intensitas, serta biaya mitigasi yang akan terus meningkat sejalan dengan pertumbuhannya skala dari suatu instansi. Pada penelitian ini, peneliti mempunyai tujuan untuk menerapkan suatu konfigurasi terbaik dalam sebuah arsitektur jaringan syaraf tiruan guna meningkatkan tingkat akurasi yang sangat tinggi pada pendeteksian serangan DDoS menggunakan algoritma Backpropagation. Data yang digunakan dalam penelitian ini adalah data trafic jaringan dimana sudah ditandai keterangan DDoS tidaknya dari masing – masing data. Penelitian ini dilaksanakan menggunakan aplikasi Matlab berserta fiturnya yaitu NNToolBox. Dengan menguji 12 jenis Training Function berserta arsitektur Hidden Layer. Berdasarkan dari uji coba tersebut, nilai Error (MSE) paling minimal didapatkan sebesar 0,0585 dengan menggunakan Training Function trainbr serta arsitektur Hidden Layer berbentuk 3 lapisan dengan tiap lapisan terdapat masing – masing 4 neuron.
Co-Authors Agung Sediyono Ahmad Fajri Khumara Dian Pratiwi Fiqih Hana Saputri Leni Muslimah Raden Davin Rizki