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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal technoscientia Prosiding SNATIF PROtek : Jurnal Ilmiah Teknik Elektro Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu Jurnal RESISTOR (Rekayasa Sistem Komputer) Building of Informatics, Technology and Science JURNAL TEKNOLOGI TECHNOSCIENTIA
Irawan Hadi
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Image Identification System for Beef and Pork Using a Convolutional Neural Network Fauzi, Nadiyah Salsabila; Salamah, Irma; Hadi, Irawan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the modern era, assurance of the halalness of meat products has become a fundamental need for Indonesian Muslims, as awareness and sensitivity towards the consumption of halal products increases. This has led to the development of innovative solutions to ensure the authenticity of beef and distinguish it from pork. This research presents an Android-based meat image identification tool that relies on the Convolutional Neural Network (CNN) algorithm to process and analyze images. The research includes hardware design, deep learning model with CNN algorithm, and Android application for real-time integration of detection results. This tool is equipped with an LCD screen and speaker to display identification results. The results show the accuracy of the CNN model reaches 99% in distinguishing beef and pork on the test dataset. In real-time testing of the tool using fresh beef and pork samples, the system achieved 92% accuracy, demonstrating good performance under practical conditions. The system provides a reliable and practical solution for consumers to verify the type of meat, while contributing to efforts to ensure the halalness of food products in society.
Penerapan Algoritma Random Forest untuk Memprediksi Curah Hujan pada Masa Mendatang di Daerah Berpotensi Banjir Aswarisman, Novie Rahmadani; Handayani, Ade Silvia; Hadi, Irawan
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5593

Abstract

Palembang, as one of the largest cities in Indonesia, regularly experiences severe flooding problems every year. Flooding not only disrupts the daily activities of residents, but also causes significant economic losses and social impacts. To solve this problem, it is crucial to have an in-depth understanding of flooding patterns and some of the factors that influence them. The purpose of this research is to apply highly efficient Machine Learning (ML) technology for the prediction analysis of future flood-prone areas. The integration of ML can help in identifying patterns, predicting risks, and making more accurate decisions in flood mitigation. In an effort to achieve this goal, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology will be applied to ensure the research is conducted systematically and comprehensively. Therefore, research on the analysis of mapping flood-prone areas in Palembang using ML is essential to provide a fairly effective and efficient solution to the long-standing flooding problem. With the CRISP-DM approach, it is expected that this research can produce an accurate and reliable prediction model by integrating the Random Forest algorithm as a regression model, and provide long-term benefits for flood risk management in Palembang and several other cities in Indonesia that experience similar problems.
Sistem Monitoring Kesehatan Dalam Penentuan Kondisi Tubuh Dengan Metode Fuzzy Mamdani Plowerita, Sanyyah; Handayani, Ade Silvia; Hadi, Irawan; Husni, Nyayu Latifah
PROtek : Jurnal Ilmiah Teknik Elektro Vol 8, No 2 (2021): Protek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v8i2.3341

Abstract

In this study, designing a health monitoring system with an Android-based Application of Health Detector (AHD) application. The data displayed is an input for multi-sensor readings from the detection of body health. From the detected health, it will provide a determination of the body's health condition, using the fuzzy mandani algorithm. The variables calculated were age, gender, heart rate, body temperature, systolic blood pressure, diastolic blood pressure, and blood oxygen levels. The stages of the fuzzy mamdani method in determining body health conditions include the formation of fuzzy sets, application of implications functions, and composition of rules. From the results of this study, it was found that the age factor affects health conditions. Older people tend to have indications of health conditions, only some of them have indicated health conditions, and almost all of them have healthy health conditions. The level of accuracy of the fuzzy mamdani method in this study was 85.18%. This is because in this study many variables are used which causes many rules to be made so that they are prone to errors.
Co-Authors Ade Silvia Handayani Adewasti Adewasti Alpharisy, Kevin Farid Aswarisman, Novie Rahmadani Ciksadan Ciksadan Ciksadan Ciksadan Emilia Hesti Fauzi, Nadiyah Salsabila Irma Salamah Kinasih, Ayu Antika Sekar Martinus Mujur Rose Nyayu Latifah Husni, Nyayu Latifah Plowerita, Sanyyah Reza Umami, Reza Satria, Muhammad Diffa Siti Chodijah Sopian Soim, Sopian Suroso Suroso Susanzepi Susanzepi Syaniah, Yunita