Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JUSIFOR : Jurnal Sistem Informasi dan Informatika
Enggar Novianto
Universitas Teknologi Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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

Found 3 Documents
Search

 1 
Auditing Artificial Intelegence Menggunakan COBIT 2019 Muhammad Rizki; Enggar Novianto
Jurnal Sistem Informasi dan Informatika (JUSIFOR) Vol 2 No 1 (2023): JUSIFOR - JUNI 2023
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/jusifor.v2i1.1847

Abstract

Artificial Inteligence (AI) adalah salah satu cabang ilmu komputer. AI melibatkan matematika dan juga pemrograman komputer untuk menyelesaikan tugas – tugas yang biasanya membutuhkan manusia. AI telah menarik banyak perhatian dan telah menjadi bidang terkemuka di bidang informatika, AI memiliki dampak yang luar biasa di berbagai bidang industri, AI memiliki 2  turunan dari konsep dasar  yaitu Machine Learning dan Deep Learning, konsep – konsep tersebut menerapkan perhitungan algoritma berbasiskan Pendekatan Integral ataupun Probabilitas, sehingga hasil akhir yang diberikan jarang sekali mencapai akurasi 100%, kemungkinan ketidak pastian karena hal tersebut memberikan tantangan tersendiri yang akan dihadapi oleh Auditor dalam melakukan Audit pada suatu sitem yang mengadopsi AI didalamnya. COBIT 2019 memiliki 40 inti yang ada dialamnya, salah satu inti yang dimilki yaitu DSS06. DSS06 memberikan 5 tahapan untuk auditor agar dapat melakukan audit, diantaranya adalah aktif dalam  mencari informasi tentang design dan Arsitektur AI untuk menetapkan cakupan yang tepat, melibatkan semua pemangku kepentingan, menjelaskan serta berkomunikasi secara proaktif dengan pemangku kepentingan dengan cara melakukan penjabaran menggunakan bahasa yang dapat dimengerti oleh pemangku kepentingan, mengadopsi serta mempelajari framework yang digunakan dalam mengembangkan AI dan yang terakhir adalah fokus pada transparasi proses yang dilakukan secara berulang ulang.
KEAMANAN INFORMASI (INFORMATION SECURITY) PADA APLIKASI SISTEM INFORMASI MANAJEMEN SUMBER DAYA MANUSIA Enggar Novianto; Erik Herman Heri Ujianto; Rianto Rianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 1 (2023): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i1.2966

Abstract

The development of information technology in the current era is growing rapidly, which is indicated by the emergence of many innovative programs in personal information services. One of the contenders for such information is SIMPEG, an application from Sebelas Maret University. The purpose of this study is to determine the application of information security to address the aspects of confidentiality, integrity and availability of information. This application ensures the security of user's personal information and employee data. The results of this study are that employees are required to register an account using an email that has been registered with SSO. Users of this application are lecturers and education staff including general users and SIMPEG operators. The SIMPEG application also guarantees the confidentiality of each employee's personal data and can only be seen by Sebelas Maret University internal employees. The purpose of registering an account through NIP/NIK and employee staff email is with the aim of preventing actions that can harm the institution. One of the rules for using this application is that it is forbidden to update, copy and delete data unless the SIMPEG operator is responsible for updating the data.
KLASIFIKASI ALGORITMA K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA S1: COMPARATION OF K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE TO PREDICT UNDERGRADUATE STUDENTS TO GRADUATE ON TIME Enggar Novianto; Arief Hermawan; Donny Avianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3434

Abstract

Students are a crucial factor that must be considered in seriously evaluating study programs. The indicator of the success of the study program is the length of time it takes to complete the study. The study period is the time when students complete their studies. In addition, student study time reflects the level of student learning performance. In a broader perspective, the average student study time affects the quality of study programs and therefore student study time is used as one of the criteria in determining the assessment by the National Accreditation Board for Higher Education (BAN PT). The purpose of this study was to understand how well the K-Nearest Neighbor, Naive Bayes, Decision Tree performed to predict undergraduate students of the Law Study Program, Faculty of Law, Sebelas Maret University, graduating on time using the RapidMiner application. From the results of the testing and prediction process with the RapidMiner application using the three methods that have been carried out. The K-Nerest Neighbor (KNN) method obtained an accuracy of 96.67%, in the prediction test using the Naïve Bayes method it obtained an accuracy of 77.33%, while the Decision Tree method obtained an accuracy of 94.00%. So that the K-NN method is the best method in comparative classification in predicting student graduation on time with a predicted accuracy value of 96.67%.
Co-Authors Donny Avianto Erik Herman Heri Ujianto Muhammad Rizki Rianto Rianto