Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal JURNAL MAHAJANA INFORMASI
Rizki Setyawan, Muhammad
Unknown Affiliation
Library & Information Science

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

Found 3 Documents
Search

 1 
Sistem Pakar Deteksi Penyakit Kambing Menggunakan Certainty Factor Berbasis Android Rizki Setyawan, Muhammad
Jurnal Mahajana Informasi Vol 8 No 1 (2023): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v8i1.4008

Abstract

Diseases pose a serious threat to the health and well-being of livestock in the livestock industry, especially in goat farming. In addressing this challenge, the use of intelligent technologies such as expert systems is becoming increasingly important. Expert systems are computer programs that can mimic the knowledge and skills of human experts in specific fields. One of the methods used in expert systems is the Certainty Factor method. Certainty Factor is an inference method used in expert systems to measure the level of confidence in a conclusion based on predefined rules. This method takes into account certainty or uncertainty factors in decision-making. In the detection of diseases in goats, the Certainty Factor method considers the symptoms found in goats and calculates the confidence weights for each disease that may be the cause of those symptoms. This research aims to develop an Android-based expert system application using the Certainty Factor method. The results obtained from this research show that the application can assist goat farmers in identifying diseases that affect their livestock, enabling early intervention and reducing losses due to animal deaths.
Perbandingan Tools Forensik Dalam Analisis Bukti Digital Pada Aplikasi Skype Menggunakan Framework NIST Rizki Setyawan, Muhammad
Jurnal Mahajana Informasi Vol 8 No 2 (2023): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v8i2.4580

Abstract

In the rapidly evolving digital era, human communication has undergone significant changes thanks to instant messaging. Instant messaging enables us to easily and quickly connect with people around the world through mobile devices or computers. However, the use of instant messaging applications can have negative impacts such as fraud, harassment, illegal transactions, and more. Therefore, proper handling of cybercrime cases is crucial, which is why mobile forensics has become significant. Mobile forensics is a branch of digital forensics that aims to collect, analyze, and interpret data from mobile devices. This research aims to evaluate the performance of three forensic tools: Oxygen Forensic Suite, Belkasoft Evidence Center, and MOBILedit Forensic Express. It focuses on finding digital evidence related to activity information in the Skype application using the framework provided by NIST. The research results indicate that Oxygen Forensic Suite achieved the highest index value (98%), followed by Belkasoft Evidence Center (88%) and MOBILedit Forensic Express (84%).
Implementasi Deep Learning Menggunakan Cnn Untuk Klasifikasi Tingkat Kematangan Buah Jeruk Berbasis Android Rahardika Bahari Putra, Fajar; Rizki Setyawan, Muhammad; Soekarta, Rendra; Nabila; Fakhri , La Jupriadi
Jurnal Mahajana Informasi Vol 9 No 2 (2024): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v9i2.5462

Abstract

Indonesia is a country that heavily relies on the agricultural sector, including various types of horticultural commodities, especially fruits. One example is oranges, which have many benefits and a sweet, refreshing taste. To obtain the best flavor and freshness, fully ripe oranges are the preferred choice. However, the process of recognizing the ripeness of oranges still faces many challenges. With advances in computer technology, particularly through smartphones, many human tasks can now be performed more efficiently and practically. One useful technology is computer vision, which can be used to automatically identify and determine the ripeness of oranges. This research aims to implement Convolutional Neural Networks (CNN) to measure the model's performance and ensure its capability in classifying the ripeness of oranges. The results of the research show that classification using CNN with the VGG-16 architecture achieved a training accuracy of 96% and a validation accuracy of 97%.
Co-Authors Fakhri , La Jupriadi Nabila Rahardika Bahari Putra, Fajar Soekarta, Rendra