Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JURNAL INSTEK (Informatika Sains dan Teknologi) Patria Artha Technological Journal Ceddi Journal of Education
Tahir, Tamus Bin
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Mathematics Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Rancang Bangun Sistem Penyiraman Tanaman Otomatis Dan Pendeteksi Kondisi Tanah Menggunakan Soil Moisture Berbasis Arduino Syarif, Irwan; Tahir, Tamus Bin; L, Nurlia; Yunendar, Wakhid
Patria Artha Technological Journal Vol 5, No 1 (2021): Patria Artha Technological Journal
Publisher : Department of Electrical Engineering, University of Patria Artha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33857/patj.v5i1.417

Abstract

Design Automatic Watering System and Soil Condition Detection Using Arduino Based Soil Moisture. Watering plants is closely related to the needs of water with different levels. This will of course affect the frequency and quantity of its watering. If planted in pots, the same plants (their type and age) will require a more frequent frequency of watering than plants that grow directly from the soil. This research aims to design and implement automatic watering system and soil condition detection using Arduino-based soil moisture.  The type of research that is implemented when testing on this system is done by means of direct testing or simulation of tools that work automatically in accordance with the working principles of each tool
SISTEM PENGAMBILAN KEPUTUSAN PENERIMA BEASISWA DI SMKN 4 JENEPONTO DENGAN MENGGUNAKAN METODE WEIGHTED PRODUCT Taha, Syamsumarlin; Tahir, Tamus Bin
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 2 No 2 (2017): Volume 2, Nomor 2, oktober 2017
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (906.867 KB) | DOI: 10.24252/instek.v2i2.4018

Abstract

Penelitian ini bertujuan untuk merancang aplikasi sistem pengambilan keputusan penerima beasiswa di SMKN 4 Jeneponto dengan menngunakan metode Weighted Product untuk menentukan penerima beasiswa dulunya masih menggunakan cara manual menjadi terkomputerisasi. Hasil uji coba pembuatan aplikasi yang dilakukan peneliti dengan menggunakan metode uji kelayakan aplikasi ISO 9126 dengan hasil dan fungsi-fungsi dalam aplikasi sistem pengambilan keputusan penerima beasiswa  telah berfungsi dengan benar dan sesuai yang diharapkan, sedangkan hasil uji coba diperoleh kesimpulan bahwa sikap tiap responden terhadap kualitas aplikasi sistem pengambilan keputusan penerima beasiswa adalah baik, ini dapat dilihat dari validasi yang telah dilakukan dan menyimpulkan bahwa kualitas sistem ini baik dan layak digunakan.  Kata Kunci- Sistem Pengambilan Keputusan, Weighted Product. 
Student Expression Detection Based on Facial Image Using Convolutional Neural Network (CNN) Muh. Riyaldi Pratama; Amiruddin, Erwin Gatot; Kamaruddin, Kamaruddin; Tahir, Tamus Bin; Qadri, Muhammad; Vivek, Kumar
Ceddi Journal of Education Vol. 4 No. 1 (2025): June
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/cje.v4i1.126

Abstract

The widespread adoption of electronic learning (e-learning) in higher education has brought significant changes to how knowledge is delivered. Despite its advantages, many implementations remain focused solely on content dissemination, often neglecting learners’ emotional engagement. Emotional states, particularly in academic contexts, influence concentration, motivation, and comprehension. One of the most effective and intuitive indicators of emotion is facial expression. This research investigates the use of Convolutional Neural Networks (CNN), a deep learning approach, to automatically detect student emotions through facial image analysis. A dataset of facial expressions was constructed and divided into training and testing sets, each containing five distinct emotional categories: anger, happiness, fear, neutrality, and surprise. The CNN model was trained for 100 epochs, resulting in a training accuracy of 89% and a testing accuracy of 88%. These results demonstrate that CNN-based emotion recognition has strong potential to enhance e-learning platforms by providing instructors with real-time emotional insights. By integrating emotional feedback, educators can adapt instructional strategies more effectively to improve student engagement and learning outcomes. This study contributes to the growing field of affective computing and emphasizes the importance of emotional awareness in digital learning environments.
Co-Authors Amiruddin, Erwin Gatot Kamaruddin Kamaruddin L, Nurlia Muh. Riyaldi Pratama Qadri, Muhammad Syarif, Irwan TAHA, SYAMSUMARLIN Vivek, Kumar Yunendar, Wakhid