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All Journal CommIT (Communication & Information Technology) Jurnal Informatika Universitas Pamulang ComTech: Computer, Mathematics and Engineering Applications
Agus Widodo
Bina Nusantara University
Computer Science & IT Engineering Mathematics

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General Cybersecurity Maturity Assessment Model: Best Practice to Achieve Payment Card Industry-Data Security Standard (PCI-DSS) Compliance Khairur Razikin; Agus Widodo
CommIT (Communication and Information Technology) Journal Vol. 15 No. 2 (2021): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v15i2.6931

Abstract

The use of technology in the era of the Industrial Revolution 4.0 is essential, marked by the use of technology in the economy and business. This situation makes many companies in the payment sector have to improve their information technology security systems. In Indonesia, Bank Indonesia and the Financial Services Authority (Otoritas Jasa Keuangan - OJK) are agencies that provide operational permits for companies by making Payment Card Industry-Data Security Standard (PCI-DSS) certification as one of the requirements for companies to obtain operating permits. However, not all companies can easily get PCI-DSS certification because many companies still do not meet the PCI-DSS requirements. The research offers a methodology for measuring the level of technology and information maturity using general cybersecurity requirements adopted from the cybersecurity frameworks of CIS, NIST, and Cobit. Then, the research also performs qualitative calculations based on interviews, observations, and data surveys conducted on switching companies that have been able to implement and obtain certification. PCI-DSS to produce practical cybersecurity measures, in general, can be used as a measure of the maturity of technology and information security. The results and discussion provide a model assessment tool on the procedures and requirements needed to obtain PCI-DSS certification. The maturity level value of PT XYZ is 4.0667 at maturity level 4, namely quantitatively managed, approaching level 5 as the highest level at maturity level.
Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS Rani Puspita; Agus Widodo
Jurnal Informatika Universitas Pamulang Vol 5, No 4 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v5i4.7622

Abstract

BPJS is really helpful because one of its goal is to provide good service for the member in terms of healthiness. But, when there’s many people using the service, then it will cause more pros and contras. Therefore, researcher will be doing sentiment analysis in the field of data mining towards bpjs users on social media Twitter as much as 1000 data that later will be filtered to be 903 data because there are some data that has been duplicated. Researchers used the KNN, Decision Tree, and Naïve Bayes methods to compare the accuracy of the three methods. Researchers used the RapidMiner version 9.7.2 tools. The results showed that the sentiment analysis of Twitter data on BPJS services using the KNN method reached an accuracy level of 95.58% with class precision for pred. negative is 45.00%, pred. positive is 0.00%, and pred. neutral is 96.83%. Then the Decision Tree method the accuracy rate reaches 96.13% with the precision class for pred. negative is 55.00%, pred. positive is 0.00%, and pred. neutral is 97.28%. And the last one is the Naïve Bayes method which achieves 89.14% accuracy with precision class for pred. negative is 16.67%, pred. positive was 1.64%, and pred. neutral is 98.40%.
Analisis Sentimen terhadap Layanan Indihome di Twitter dengan Metode Machine Learning Rani Puspita; Agus Widodo
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.13247

Abstract

Indihome is a digital service such as the internet that can be used at home, landlines and interactive TV. However, because it is so extensive, Indihome has received a lot of criticism because the internet connection is rarely stable. Therefore, a sentiment analysis in the field of was carried out data mining on customers Indihomeon Twitter social media which consisted of 1350 data and filtered into 1309 data because a lot of data indicated duplicates. In this study, researchers used the methods Random Forest and Gradient Boosted Trees (GBT). This research was conducted using tools Rapidminer version 9.8. Research shows that sentiment analysis on Indihome services using the method Random Forest achieves an accuracy of 99.54% with class precision for pred. negative is 99.92%, pred positive is 25.00%, and pred. neutral is 60.00%. Then the GBT method has an accuracy rate of 99.31% with a precision class ofn for pred. negative is 99.46%, pred. positive is 0.00%, and pred. neutral is 0.00%. So it can be concluded that the Random Forest method is a better method when compared to GBT.
Prediksi Pendapatan Sewa Dengan Data Mining Pada Perusahaan XYZ May Liana; Christine Sanjaya; Agus Widodo; Marshall Martinus
ComTech: Computer, Mathematics and Engineering Applications Vol. 1 No. 2 (2010): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v1i2.2344

Abstract

XYZ Company has a program to predict leasing income that only predict in constant condition where every tenant assumed for leasing renewal. This research is done to build accurate income prediction system that accommodate in making strategic decision towards the company. Premier data collecting is through direct interview with the company management. The analysis is through data training from the previous years to build neural network model. The analysis result shows that this model has produced error total value that is smaller than the previous error total value in years before. Therefore, it could be concluded that data mining with neural network technique that produced more accurate leasing income that could help the company making decision based on the hidden information in the database.
Co-Authors Christine Sanjaya Khairur Razikin Marshall Martinus May Liana Rani Puspita