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All Journal JURNAL MAHAJANA INFORMASI JITU : Journal Informatic Technology And Communication
Rizki Setyawan, Muhammad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science

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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%.
Perancangan Electronic Nose (E-Nose) untuk Analisis dan Klasifikasi Aroma Daging Menggunakan PCA dan LDA Rizki Setyawan, Muhammad; Fadlil, Abdul; Yudhana , Anton
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2254

Abstract

Meat is a vital food commodity prone to adulteration through species mixing or chemical contamination such as formalin and borax. This study aimed to design and test an Electronic Nose (E-Nose) system for aroma pattern analysis and meat classification using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Samples included pure meat (beef, chicken, pork), mixed meat, and chemically contaminated meat. Aroma data were captured using an array of gas sensors sensitive to Volatile Organic Compounds (VOCs) and standardized prior to analysis. PCA reduced eight sensor features into three principal components explaining a total variance of 79.63%. PC1, PC2, and PC3 accounted for 46.10%, 20.58%, and 12.96% of variance, respectively, showing clustering patterns among samples with minor overlap. LDA provided clearer class separation with three discriminant components LD1, LD2, and LD3 explaining 77.13%, 16.63%, and 4.59% of between-class variance, totaling 98.34%. LD1 separated pure, mixed, and contaminated meat, LD2 distinguished variations due to contaminant type and species, and LD3 refined separation of similar classes. Classification evaluation achieved an overall accuracy of 82%. Most classes were well classified, while classes 1 and 10 experienced misclassification due to similar aroma patterns. The findings confirm that E-Nose combined with PCA and LDA is a rapid, non-destructive, and efficient method for detecting meat authenticity and adulteration, showing strong potential for food quality monitoring in the field
Co-Authors Abdul Fadlil Fakhri , La Jupriadi Nabila Rahardika Bahari Putra, Fajar Soekarta, Rendra Yudhana , Anton