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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech)
Rizki, Yoze
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management

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Knowledge Management System For Forest and Land Fire Mitigation in Indonesia: A Web-Based Application Development Unik, Mitra; Rizki, Yoze; Sukaesih Sitanggang, Imas; Syaufina, Lailan
Jurnal Manajemen Hutan Tropika Vol. 30 No. 1 (2024)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.30.1.12

Abstract

Forest and land fires in Indonesia have serious impacts on many aspects, including the environment, health, economy, politics, and international relations. They cause haze pollution that extends to neighboring countries and peatland degradation. Despite extensive research and mitigation efforts, forest and land fires continue to occur and cost lives. Therefore, effective management and mitigation strategies are required. This research developed a web-based knowledge management system (KMS) using the Laravel framework as an effective forest and land fire mitigation platform. The KMS aims to support decision-making, facilitate knowledge exchange, improve coordination between stakeholders, and expand access to relevant information, while maintaining the sustainability of forest and land resources in Indonesia. The KMS evaluation results cover two important aspects: blackbox evaluation and performance evaluation. The blackbox evaluation showed that KMS provides knowledge retrieval features based on expert knowledge. The performance evaluation revealed that the KMS provides easy and quick access to information on forest and land fire prevention and management. Thus, this research has great potential to help overcome the problem of forest and land fires in Indonesia and protect the environment and society from their adverse effects.
Expert System Diagnosa Gangguan Autisme Secara Dini Pada Anak dengan Metode Forward Chaining Fuad, Evans; Aminullah, Rabiah; Soni, Soni; Rizki, Yoze
Building of Informatics, Technology and Science (BITS) Vol 3 No 4 (2022): March 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.099 KB) | DOI: 10.47065/bits.v3i4.1413

Abstract

Autism is a developmental and behavioral disorder of children where social relationships are disrupted, namely children with autism cannot interact with other people, including their parents. Autistic children experience disorders such as communication disorders, social relationship disorders, and behavioral disorders, resulting in a child. Parents often do not realize the differences and abnormalities that appear in their children until they are three years old. They just realized that their child is different from other children. There are several basics of treating autism and there are some teachers who can help cure autism in children. The teacher treats autistic children with an approach that is adapted to the problem. Types of autism basically this child is divided into four categories, including social interaction disorders, behavioral generalized disorders, communication disorders, and self-stimulation disorders. This research designs and builds an expert system (expert system) for diagnosing Autism disorders in early childhood based on the web Method of Forward Chaining. This Forward Chaining is very good to use if the work starts with recording the initial information and wants to reach the completion or final goal. This expert system is considered capable of providing information and solutions for parents, about the types of autism disorders in early childhood, based on the symptoms entered and can provide solutions
Analisis Digital Forensik Keaslian Video Rekaman CCTV Menggunakan Metode Localization Tampering Mualfah, Desti; Rizki, Yoze; Gea, Meiriladiwis
Computer Science and Information Technology Vol 3 No 1 (2022): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v3i1.3697

Abstract

Video is one the valid evidence if the handling process is in accordance with digital forensic procedures. Closed circuit television is a video sourse that is often used as authentic evidence in court. The authenticity of the video is something that is often doubted by certain parties. For this reason, this discusses how to detect the authenticity of video as digital evidence by comparing the original video files and tampering video files resulting from attack frame addition and attack frame delection. The media info tool is used to analyze the metadata and the localization tampering method is used to detect the frame where manipulation occurs. Localization tampering analyzes frame by frame, calculates as histogram and displays a histogram graph. Based on the results of the metadata analysis of the original video file and the video file tampering, it displays different information, which means that the video has been manipulated. Next, analyze the video with the localization tampering method to display the location on the video frame where manipulation has occurred. From the analysis results provide different information both from the calculation of the RGB value and the histogram graph.
Klasifikasi Kebakaran Hutan Dan Lahan Dengan Algoritma You Only Learn One Representation Rizki, Yoze; Yogi Alfinaldo; Soni; Sy, Yandiko Saputra; Rahmad Firdaus
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6434

Abstract

Forest areas have a function of storing carbon dioxide and producing oxygen from trees and plants. The function of forests is very important for life, so forests are highly protected. One solution that can be taken is to take preventive measures, namely monitoring fire hotspots in forest and land areas by air. This research was tested using the same dataset as the YOLO (You Only Look Once) algorithm against the You Only Learn One Representation (YOLOR) algorithm with a train data division model of 1188 image data and test data of 75 image data with mAP results of 66.36%. . So it can be confirmed that the YOLOR algorithm is better than the YOLO algorithm which gets an mAP value of 50.65%.
Implementasi Algoritma Random Forest Untuk Klasifikasi Pencemaran Udara di Wilayah Jakarta Berdasarkan Jakarta Open Data Firdaus, Rahmad; Habibie, Husnul; Rizki, Yoze
JURNAL FASILKOM Vol. 14 No. 2 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i2.7669

Abstract

Pencemaran udara merupakan masalah dunia yang cukup memprihatinkan di beberapa negara, dan termasuk salah satunya di Jakarta. DKI Jakarta merupakan salah satu kota dengan peringkat tertinggi dalam kualitas udara yang terburuk di dunia. Algoritma Random Forest adalah pengembangan dari metode Classification and Regression Tree (CART) yang dapat meningkatkan hasil akurasi dalam membangkitkan atribut untuk setiap node yang dilakukan secara acak. Pada penelitian ini bertujuan untuk mengetahui Performa Algoritma Random Forest terhadap klasifikasi dalam data pencemaran udara wilayah Jakarta tahun 2016- 2021 dan untuk mendapatkan hasil klasifikasi dari Algorima Random Forest dalam klasifikasi pencemaran udara wilayah Jakarta tahun 2016-2021. Sehingga penelitian ini semoga dapat menjadi rujukan atau acuan bagi peneliti tentang algoritma Random Forest, dalam klasifikasi data Pencemaran Udara. Hasil performa model dari algoritma Random Forest, pada data train mendapatkan nilai precision, recall, dan F1-score yang sempurna yaitu 100% disemua kelas dan AUC juga sebesar 100%, lalu pada data test pada nilai precision untuk setiap kelas juga sangat tinggi yaitu 99%, dan AUC sebesar 99,96%. Hasil klasifikasi dari algoritma Random Forest mendapatkan akurasi pada data train sebesar 100% dan untuk data test mendapatkan akurasi pada data train sebesar 99,95%.
KLASIFIKASI CITRA COVID-19 MENGGUNAKAN METODE DEEP LEARNING: LITERATUR REVIEW Galih Anggoro, Hendar; Fatma, Yulia; Rizki, Yoze; Firdaus, Rahmad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 5 (2024): JATI Vol. 8 No. 5
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i5.11990

Abstract

Pandemi COVID-19, yang pertama kali muncul di Wuhan, Cina pada tahun 2019, telah menyebabkan krisis kesehatan global yang signifikan. Virus ini dikenal sebagai Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) dan menyebabkan pneumonia parah yang dapat berakibat fatal. Metode diagnostik utama untuk COVID-19 adalah tes Polymerase Chain Reaction (PCR), namun metode ini memiliki keterbatasan sensitivitas. Sebagai alternatif, citra radiologi dada seperti Computed Tomography (CT) dan sinar-X telah digunakan untuk diagnosis dini. Dengan keterbatasan alat tes yang optimal pada awal pandemi, klinisi di Cina mengandalkan hasil klinis dari citra X-ray dan CT dada. Teknologi deep learning, khususnya Convolutional Neural Network (CNN), telah menunjukkan keunggulan dalam klasifikasi citra medis, termasuk deteksi pneumonia COVID-19. Penelitian ini menggunakan metode kajian literatur untuk mengumpulkan dan menganalisis 15 artikel dari jurnal terakreditasi SINTA 1 hingga SINTA 4 yang diterbitkan antara tahun 2020 hingga 2024. Analisis literatur menunjukkan berbagai arsitektur CNN yang diterapkan dalam deteksi pneumonia dari citra X-ray dada, dengan akurasi yang bervariasi. Model CNN seperti AlexNet, VGG-16, ResNet-152, dan InceptionResNet-V2 menunjukkan performa yang menjanjikan dengan akurasi hingga 99%. Literatur Review ini menyimpulkan bahwa CNN dapat menjadi alat yang efektif dalam mendukung diagnosis pneumonia COVID-19, terutama di situasi dengan keterbatasan tenaga medis. Melalui sintesis informasi dari berbagai sumber literatur, penelitian ini memberikan panduan untuk pengembangan lebih lanjut dalam deteksi pneumonia menggunakan teknologi deep learning
Co-Authors Aminullah, Rabiah Evans Fuad Fatma, Yulia Galih Anggoro, Hendar Gea, Meiriladiwis Habibie, Husnul Imas Sukaesih Sitanggang Lailan Syaufina Mualfah, Desti Rahmad Firdaus Soni Sy, Yandiko Saputra Unik, Mitra Yogi Alfinaldo