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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Techno Nusa Mandiri : Journal of Computing and Information Technology JurTI (JURNAL TEKNOLOGI INFORMASI) Computer Science (CO-SCIENCE) JUSTIAN - Jurnal Sistem Informasi Akuntansi Jurnal Insan
Muhammad Rezki
Universitas BSI
Computer Science & IT

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Journal : Computer Science (CO-SCIENCE)

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Animasi Interaktif Klasifikasi Jangkauan Dan Topologi Jaringan Komputer Berbasis Android Sebagai Media Belajar Muhammad Rezki; Muhammad Ifan Rifani Ihsan; Diah Ayu Ambarsari
Computer Science (CO-SCIENCE) Vol. 1 No. 2 (2021): Juli 2021
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v1i2.466

Abstract

Classification of range and topology has its own types and characteristics. There are many types of outreach classifications and topologies, as well as process methods in learning in schools that are less interactive so that students are bored in the learning process, especially for students of SMK Computer and Network Engineering Department. In practice, making LAN cables requires equipment that is quite expensive, making it difficult to carry out these practical activities outside of school. So that students find it difficult to develop their abilities, especially in the vocational field. With current technological developments, of course, it can be used in the world of education, one of the uses is by making educational applications that can provide a new atmosphere in learning. Interactive animation can be a solution in overcoming the practice of making LAN cables and an explanation of Network Topology. This interactive animation was made using Construct 2 software, developed using multimedia development methods and data collection techniques in the form of observations, interviews and literature studies. The purpose of making interactive applications is so that students can understand the range classification and topology well, and can practice installing LAN cables through interactive animation simulation software as a substitute for school facilities.
Segmentasi Api dan Asap Pada Kebakaran Dengan Metode K-Means Clustering Muhammad Rezki; Siti Nurdiani; Rizky Ade Safitri; Muhammad Ifan Rifani Ihsan; Muhammad Iqbal
Computer Science (CO-SCIENCE) Vol. 2 No. 1 (2022): Januari 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v2i1.849

Abstract

Fire is a frequent disaster, especially in tropical climates. Factors causing fires that often occur are intentional and human negligence. When fires occur, fires often get out of control and spread in the direction of global warming, climate change, landslides, and floods, causing harm to the environment. Because it is difficult to analyze the size of the fire when a fire occurs, therefore with the development of technology and sophistication, it is now possible to know the position of the fire and the thickness of the smoke in a fire using digital images. Image is another term for image as a multimedia component that plays a very important role as a form of visual information. This study aims to segment the fire image by separating 2 images, namely fire and smoke to determine the position of the fire and the thickness of the smoke in the fire. The fire and smoke segmentation research uses the K-means clustering method using the Matlab application. The stages carried out in this research include the Image Input Process, Converted Original Image to Binary, Threshold Conversion Segmentation, Image Segmentation Process. The image segmentation process for the fire and smoke fire images succeeded in separating the fire and smoke objects with a value of T3 = 61. The smoke and fire objects were in the matrix coordinates [50:100,300:200].
Pendekatan Algoritma Klasifikasi Machine Learning untuk Deteksi Penyakit Demensia Muhammad Iqbal; Hendri Mahmud Nawawi; Muhammad Rezki; Abdul Hamid; Sri Rahayu
Computer Science (CO-SCIENCE) Vol. 3 No. 2 (2023): Juli 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v3i2.1987

Abstract

Early detection of dementia through the use of machine learning classification algorithms is important for providing appropriate interventions to patients. In this context, this study aims to compare the performance of several machine learning classification algorithms in detecting dementia using the attribute selection method. In the early stages, patient data including medical history, cognitive test results, and other attributes were collected as input, an attribute selection algorithm was used to select the most informative attribute subset in detecting dementia. The subset of attributes used as input for training machine learning classification models, several classification algorithms such as Extra Trees (ET), Linear Discriminant Analysis (LDA), Random Forest (RF) and Ridge. In this study, accuracy is used as the main metric to compare algorithm performance. The evaluation results show that the Random Forest (RF) algorithm produces the best performance with an accuracy of 91.56%. The Extra Trees (ET) algorithm has an almost comparable accuracy of 91.44%, while Ridge and Linear Discriminant Analysis (LDA) have an accuracy of 90.44% respectively. In the context of dementia detection, the performance of the Random Forest algorithm with the attribute selection method proved to be the best with an accuracy of 91.56%. These results indicate that the developed model is capable of recognizing complex patterns and relationships between features that are relevant to dementia status. The use of the attribute selection method also contributes to increasing the accuracy and efficiency of the classification algorithm.
Co-Authors Abdul Hamid Abdul Hamid Ahmad Hafidzul Kahfi Ahmad Rifqy Alfiyan Ali Mustopa, Ali Ari Abdillah Badariatul Lailiah Badaritul Lailiah Diah Ayu Ambarsari Doni Purnama Alamsyah Dwiza Riyana Elbiansyah Elbiansyah Hendri Mahmud Nawawi Laksono Trisnantoro Linda Sukmawati Mochammad Abdul Azis Mochammad Rizky Kusumayudha Muhammad Faisal Muhammad Ifan Rifani Ihsan Muhammad Ifan Rifani Ihsan Muhammad Iqbal Muhammad Iqbal Nuraeni Herlinawati Rabiatus Sa'adah Rabiatus Sa’adah Rizky Ade Safitri Safitri Linawati Siti Nurdiani Sri Rahayu winanda winanda