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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Soft Computing Exploration Journal of Student Research Exploration Scientific Journal of Informatics
Budi Prasetiyo
Computer Science Department, FMIPA, Universitas Negeri Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Penerapan Kriptografi Algoritma Blowfish pada Pengamanan Pesan Data Teks Budi Prasetiyo; Much Aziz Muslim; Hendi Susanto
Techno.Com Vol 16, No 4 (2017): November 2017
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.9 KB) | DOI: 10.33633/tc.v16i4.1452

Abstract

Kriptografi dibutuhkan untuk pengamanan data dalam jaringan komunikasi. Artikel ini membahas implementasi algoritma Blowfish menggunakan Microsoft Visual Basic. Permasalahan pada tulisan ini bagaimana penerapan algoritma blowfish pada pengamanan dayta teks dan bagaiaman performa algoritma dalam mengkesekusi proses enkripsi maupun deksripsi. Metode yang digunakan yaitu menggunakan SDLC dengan membangun perangkat lunak menggunakan Visual Basic. Algoritma kriptografi yang digunakan yaitu Blowfish. Data uji menggunakan beberapa file dengan ukuran berbeda mulai dari 64Kb, 128Kb, 256Kb, 512Kb, dan 1024Kb. Berdasar hasil implementasi algoritma Blowfish menggunakan MS Visual data berhasil dienkripsi maupun didekripsi dan dapat kembali seperti semula, sehingga dapat digunakan untuk melakukan pengamanan data. Hasil pengujian waktu eksekusi menunjukkan proses enkripsi membutuhkan waktu yang lebih lama daripada proses dekripsi. Data yang diperoleh menunjukkan proses deksripsi 33% lebih cepat daripada proses enkripsi.
Peningkatan Akurasi Klasifikasi Algoritma C 4.5 Menggunakan Teknik Bagging pada Diagnosis Penyakit Jantung Erwin Prasetyo; Budi Prasetiyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 5: Oktober 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020752379

Abstract

Perkembangan teknologi yang begitu pesat menjadikan kebutuhan akan suatu informasi semakin meningkat, sehingga keakuratan suatu informasi menjadi suatu hal yang sangat penting, Terutama keakuratan informasi yang dibutuhkan dalam memprediksi penyakit dalam bidang medis. Dalam proses pengumpulan suatu informasi dibutuhkan metode tertentu, sehingga informasi yang telah diproses menjadi sebuah pengetahuan menggunakan suatu metode tertentu disebut dengan penambangan data atau istilah lainnya adalah data mining. Umumnya data mining digunakan untuk memprediksi suatu penyakit yang bersumber dari data rekam medis pasien, khususnya penyakit jantung. Data penyakit jantung diambil dari dataset UCI Machine Learning Repository. Tujuan dari penulis melakukan penelitian ini yaitu untuk mengetahui penerapan teknik bagging pada algoritma C4.5, mengetahui hasil akurasi dalam algoritma C4.5, dan membandingkan tingkat akurasi dari penerapan teknik bagging pada algoritma C4.5. Dataset yang diklasifikasikan dengan algoritma C4.5 memperoleh akurasi sebesar 72,98%. Hasil akurasi ini dapat ditingkatkan dengan menerapkan teknik bagging menghasilkan akurasi sebesar 81,84%, sehingga terjadi peningkatan akurasi sebesar 8,86%  dari penerapan teknik bagging pada Algoritma C4.5. AbstractThe quick development of technology makes the need for information increase, so that the accuracy of the information becomes a very important thing, especially the accuracy of the information needed in predicting diseases in the medical field. In the process of gathering information certain methods are needed, so information that has been processed into knowledge using a certain method is called data mining or other terms is data mining. Data mining is generally used to predict a disease originating from patient medical record data, especially heart disease. Heart disease data is taken from the UCI Machine Learning Repository dataset. The purpose of the authors conducting this research is to determine the application of bagging techniques on the C4.5 algorithm, determine the accuracy of the results in the C4.5 algorithm, and compare the level of accuracy of the application of bagging techniques on the C4.5 algorithm. The dataset classified by the C4.5 algorithm obtained an accuracy of 72.98%. The results of this accuracy can be improved by applying bagging techniques resulting in an accuracy of 81.84%, resulting in an increase in accuracy of 8.86% from the application of bagging techniques in the C4.5 Algorithm.
Fuzzy Simple Additive Weighting Method in the Decision Making of Human Resource Recruitment Budi Prasetiyo; Niswah Baroroh
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 7, No. 3 Desember 2016
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.383 KB) | DOI: 10.24843/LKJITI.2016.v07.i03.p05

Abstract

The Company is one of the jobs that was founded to reduce unemployment. The progress of a company is determined by the human resources that exist within the company. So, the selection of workers will join the company need to be selected first. The hardest thing in making a selection factor is the effort to eliminate the subjectivity of the personnel manager so that every choice made is objective based on the criteria expected by the company. To help determine who is accepted as an employee in the company, we need a method that can provide a valid decision. Therefore, we use Fuzzy Multiple Attribute Decision Making with Simple Additive Weighting method (SAW) to decide to make in human resource recruitment. This method was chosen because it can provide the best alternative from several alternatives. In this case, the alternative is that the applicants or candidates. This research was conducted by finding the weight values for each attribute. Then do the ranking process that determines the optimal alternative to the best applicants who qualify as employees of the company. Based on calculations by the SAW obtained the two highest ranking results are A5 (alternative 5) and A1 (alternative 1), to obtain two candidates received.
Restricted boltzmann machine and softmax regression for acute respiratory infections disease identification Afrizal Rizqi Pranata; Alamsyah Alamsyah; Budi Prasetiyo; Hilda Vember
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.90

Abstract

Restricted boltzmann machines (RBM) have attracted much attention lately after being proposed as building blocks of deep learning blocks. RBM is an algorithm that belongs to the artificial neural network (ANN) algorithm. Deep learning models can be used in the health field to identify diseases using medical data records. Acute Respiratory Infection (ARI) is a disease that infects the respiratory tract. A patient infected by ARI diseases is high. To identify ARI can use the symptoms that the patient had experienced. Based on this background, this study aims to help identify ARI disease using its symptoms. The method used for identification is the deep learning model, which was built using the RBM and softmax regression. Three steps were used in this research, which are training, testing, and implementation. The trained deep learning model will be implemented to identify ARI disease. This research will use ARI data from Puskemas Warungasem, Indonesia. From the research result, the deep learning model can get an accuracy of 96%. The deep learning configuration used in this research has 4 RBM layers, 1 Softmax layer as the output layer, and a learning rate value of 0.01 and 1000 iterations. This research can be used as a reference so that the next researcher can add other algorithms to Deep learning to improve accuracy.
Application go-sport as a solution to search information on facilities, places, partners, and sports events for students Rofik Rofik; Tasya Fitria Anggraini; Budi Prasetiyo; Cecep Bagus Suryadinata KA
Journal of Student Research Exploration Vol. 1 No. 2: July 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i2.164

Abstract

Sport is a physical and mental activity that is beneficial for people to maintain the body and develop the quality of health. This makes exercise an activity that needs to be done for everyone to maintain their stamina. However, the lack of information about places, facilities, partners, and sports events is a strong reason in terms of reducing student motivation in carrying out sports activities themselves. The purpose of this research is none other than to design an application that can help students get all sports information. These things are none other than to foster a strong desire to do sports activities. Through technology smartphone which has been owned by the wider community, this research creates a solution by designing an application called "Go-Sport". This study uses the "Design Thinking" method, which focuses on finding and understanding user needs to obtain an optimal solution in the form of the results of the features to be made. From this research, a design or prototype of the "Go-Sport" application was produced which is ready to be implemented and tested on users.
Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization Yosza Dasril; Much Aziz Muslim; M. Faris Al Hakim; Jumanto Jumanto; Budi Prasetiyo
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.3060

Abstract

The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.
Optimizing Customer Segmentation in Online Retail Transactions through the Implementation of the K-Means Clustering Algorithm Desi Adrianti Awaliyah; Budi Prasetiyo; Rini Muzayanah; Apri Dwi Lestari
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.6137

Abstract

Purpose: The main objective of this research is optimal use of customer segmentation using the Recency, Frequency and Monetary (RFM) approach so that companies can better understand and comprehend the needs of each customer. By carrying out this segmentation, companies can communicate better and provide services tailored to each customer. Methods: The K-means algorithm is used as the main method for customer segmentation in this research. This research uses a dataset of online retail customers. Apart from that, this research also uses the elbow method to help determine the best number of clusters to be created by the model. Result: Based on the elbow method, the most optimal is to use 3 clusters for this case. Thus, in K-means modeling, forming 3 clusters is the best choice. Clusters produce groups of customers who have specific characteristics in each cluster. The analysis shows that quantity and unit price have a significant influence on online retail customer behavior. Novelty: This research strengthens the trend of using the K-means algorithm for customer segmentation in online retail datasets, which has proven popular in journals from 2018 to 2022. This research creates 3 new variables that will be used by the model to understand the characteristics of customer transaction behavior. This study also emphasizes the importance of exploratory data analysis in understanding data before clustering and the use of the elbow method to determine the most appropriate number of clusters, providing a significant contribution in analyzing customer segmentation.
Co-Authors Afrizal Rizqi Pranata Alamsyah Apri Dwi Lestari Cecep Bagus Suryadinata KA Desi Adrianti Awaliyah Erwin Prasetyo Hakim, M. Faris Al Hendi Susanto Hilda Vember Jumanto Unjung Much Aziz Muslim Much Aziz Muslim Niswah Baroroh Rini Muzayanah Rofik Rofik Tasya Fitria Anggraini Yosza Dasril