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This Author published in this journals
All Journal CommIT (Communication & Information Technology) Indonesian Journal of Artificial Intelligence and Data Mining Engineering, Mathematics and Computer Science Journal (EMACS)
Muhamad Keenan Ario
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Designing IoT-Based Smarthome System With Chatbot Muhamad Keenan Ario; David Leon; Muhammad Rizki Pratama; Gentrya Wirya Pamungkas
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 3 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i3.8787

Abstract

Smart home system aim to maximize surveillance, monitoring, and security. This system is integrated with telecommunications and control systems from the microcontroller to create the Internet of Things (IoT). Nowadays, home appliances are integrated with the Smart System that connected to the internet. On the other hand, messenger applications now integrated with chat bot with Artificial Intelligence to make user easier to communicate. This trend made a possibility to implement a system where a home appliance can be operated by only using a messenger application. In this research, a Smart home system designed with a client-server system based on Raspberry Pi as microcontroller and Telegram Messenger as interface that perform the control communication. The process separated into three stages: design, implementation, and result. The design consists of designing the server, interface, and Smart Home control system. To test the performance, the Messenger Bot are compared with other direct controller application. The result show that the Telegram Messenger application is suitable and more convinient for being the IoT controller.
Classification Between Suicidal Ideation and Depression Through Natural Language Processing Using Recurrent Neural Network Rhenaldy Rhenaldy; Ladysa Stella Karenza; Hidayaturrahman Hidayaturrahman; Muhamad Keenan Ario
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 2 (2022): September 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i2.17485

Abstract

The use of machine learning has been implemented in various ways, including to detect depression in individuals. However, there is hardly any research done regarding classification between suicidal ideations and depression among individuals through text analysis. Differentiating between depression and suicidal ideation is crucial, considering the difference in treatment between the two mental illness. In this paper, we propose a detection model using Recurrent Neural Network (RNN) in the hopes to improve previous models made by other researchers. By comparing the proposed model with the previous works as the baseline model, we discovered that the proposed model (RNN) performed better than the baseline models, with the accuracy of 86.81%, precision of 97.13%, recall score of 94.69%, f1 score of 95.90%, and area under the curve (AUC) score of 92.84%.
Fish Classification System Using YOLOv3-ResNet18 Model for Mobile Phones Suryadiputra Liawatimena; Edi Abdurachman; Agung Trisetyarso; Antoni Wibowo; Muhamad Keenan Ario; Ivan Sebastian Edbert
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8107

Abstract

Every country in the world needs to report its fish production to the Food and Agriculture Organization of the United Nations (FAO) every year. In 2018, Indonesia ranked top five countries in fish production, with 8 million tons globally. Although it ranks top five, the fisheries in Indonesia are mostly dominated by traditional and small industries. Hence, a solution based on computer vision is needed to help detect and classify the fish caught every year. The research presents a method to detect and classify fish on mobile devices using the YOLOv3 model combined with ResNet18 as a backbone. For the experiment, the dataset used is four types of fish gathered from scraping across the Internet and taken from local markets and harbors with a total of 4,000 images. In comparison, two models are used: SSD-VGG and autogenerated model Huawei ExeML. The results show that the YOLOv3-ResNet18 model produces 98.45% accuracy in training and 98.15% in evaluation. The model is also tested on mobile devices and produces a speed of 2,115 ms on Huawei P40 and 3,571 ms on Realme 7. It can be concluded that the research presents a smaller-size model which is suitable for mobile devices while maintaining good accuracy and precision.
Co-Authors Agung Trisetyarso Antoni Wibowo David Leon Edi Abdurachman Gentrya Wirya Pamungkas Hidayaturrahman Hidayaturrahman Ivan Sebastian Edbert Ladysa Stella Karenza Muhammad Rizki Pratama Rhenaldy Rhenaldy Suryadiputra Liawatimena