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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) Jurnal Teknoinfo JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Virra Retnowati A’izzah
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Computer Science & IT Control & Systems Engineering Industrial & Manufacturing Engineering

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PENERAPAN DATA MINING UNTUK PEMETAAN DAERAH RAWAN BENCANA SEBAGAI UPAYA KESIAPSIAGAAN TERHADAP BENCANA Vega Purwayoga; Ali Astra Mikail; Salma Dewi Nur Faridah; Virra Retnowati A’izzah
Jurnal Teknoinfo Vol 17, No 1 (2023): Vol 17, No 1 (2023): JANUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v17i1.2381

Abstract

Disasters have a major impact on several sectors, such as infrastructure, manufacturing, tourism and transportation. One way to prepare for or improve disaster preparedness is to implement preventive measures. Preventive actions can be taken by identifying disasters in each area from past data. This study aims to map areas affected by disasters to facilitate disaster preparedness programs. The data used in this research are areas of West Java that will be affected by the disaster in 2022 from January to October. The disaster data used in this study are floods, landslides, abrasion, tornadoes, droughts, fires, earthquakes and tsunamis. Research to use data mining techniques, namely grouping techniques. The clustering algorithm used in this study is the K-means cluster. The clustering process was carried out several times to find out the comparison of the quality of the grouping results which in this study used the Within Cluster Sum of Squares (WSS). The best WSS value is when the number of k or the number of clusters is 5, which is 89.8%. This research is expected to be a reference for disaster preparedness. This research also produced disaster grouping maps, where each cluster has different characteristics or types of disaster.
CryptMAIL: Keamanan Ganda Email Menggunakan Algoritma Kriptografi Virra Retnowati A’izzah; Dwi Ramti Asih; Anggi Putri Meriani; Alam Rahmatulloh
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.4962

Abstract

The development of technology and information has changed the way humans communicate. Email is an online correspondence service that makes it easier for users to exchange information and communicate with other parties. The convenience offered attracts many people to switch to using email as a medium for exchanging information, and this creates new opportunities for cybercriminals to take action. Problems with email such as data or information leaks, file misuse or message theft due to negligence or others can occur. One way to anticipate this is to implement Cryptographic Techniques. Cryptography is an encryption technique to hide confidential messages from plaintext messages into ciphertext messages that are difficult to understand. In this study, we will discuss the implementation of cryptography with the AES-128 and RC4 algorithms for encryption and decryption of messages and file attachments sent via email. The result of this research is a web-based application 'CryptMAIL' which can encrypt and decrypt messages using the AES-128 and RC4 cryptographic algorithms. The 'CryptMAIL' application is expected to provide double security to anticipate security problems in email.
Twitter Sentiment Analysis of Recession 2023: A Comparative Study of Machine Learning Approaches Virra Retnowati A’izzah; Vega Purwayoga
Jurnal Rekayasa Sistem & Industri Vol 11 No 01 (2024): Jurnal Rekayasa Sistem & Industri
Publisher : School of Industrial and System Engineering, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jrsi.v11i01.612

Abstract

Sentiment Analysis helps understand public opinion on a particular topic. One recent topic that has attractedattention is the potential for a global recession in 2023. In this study, five different algorithms - BernoulliNaive Bayes (BNB), Support Vector Machine (SVM), Linear Regression, K-Nearest Neighbors (KNN), andDecision Tree - were compared to determine which algorithm provided the most accurate sentiment analysisof Twitter data related to this topic. The results showed that the SVM algorithm had the highest accuracy,and most Twitter users had negative sentiments towards topics related to a potential recession in 2023, witha prediction rate of 81.7% compared to 16.3% for positive sentiments. The results of this study are expectedto be used to understand the general public's viewpoints regarding the predicted recession in 2023 and toprovide insights for developing policies and strategies to mitigate the economic downturn.
Co-Authors Alam Rahmatulloh Ali Astra Mikail Anggi Putri Meriani Dwi Ramti Asih Purwayoga, Vega Salma Dewi Nur Faridah Vega Purwayoga