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All Journal ACTIVE: Journal of Physical Education, Sport, Health and Recreation The Indonesian Journal of Computer Science
Indroasyoko, Narwikant
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Public Health

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The Sport Co-Curricular as Social Skill Reinforcement for Students of Apprenticeship Program Indroasyoko, Narwikant; Muhammad, Achmad; Sabarini, Sri Santoso
ACTIVE: Journal of Physical Education, Sport, Health and Recreation Vol 9 No 3 (2020)
Publisher : Department of Physical Education, Sport, Health and Recreation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/active.v9i3.41607

Abstract

The aim of this study is to gain deep knowledge about the benefit of co-curricular program both team sport and individual sport and to find out the influencing factors in following co-curricular activity, as social skill reinforcement for intern students, so concept and strategy are acquired to create Human Resources who are professional, healthy physically and mentally to face the era of revolution 4.0. The study result shows that implementation of co-curricular in campus greatly affects student’s learning system when they do on -the- job training in apprentice location. Students can find new things that occur in social interaction such as relation among students, student with mentor, student with employee in apprentice place or inside campus so students have more ethics in learning and in daily social intercourse. The obstacles among others are students do not get mentor in campus who has guidance skill profession, mentor who has trouble to equalize problem with material and time limitation, infrastructure which is less conducive in the process of co-curricular program implementation.
PENERAPAN FUZZY LOGIC DALAM SISTEM PEMANTAUAN VITAL SIGN BERBASIS INTERNET OF THINGS Rahmatulloh, Muhammad Rafy; Indroasyoko, Narwikant; Khoirunnisa, Hilda
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4112

Abstract

The development of the Internet of Things (IoT) has brought innovations in healthcare, especially in vital sign monitoring, crucial for detecting physiological changes and supporting disease diagnosis. Outpatient vital sign monitoring is often neglected due to time and equipment constraints. Previous research, such as using Bluetooth technology, showed range limitations, while other solutions couldn't classify patient conditions. This study develops an IoT-based vital sign monitoring device with four parameters: blood pressure, body temperature, heart rate, and oxygen saturation, accessible online. The device uses fuzzy logic to classify patient status. Test results show accuracy rates of 96.4% and 91.3% for blood pressure, 98% for heart rate, 98% for oxygen saturation, and 98% for body temperature readings. Patient classification tests showed 9 out of 10 samples had the same risk output as the NEWS assessment.
Pemantauan dan Deteksi Penyakit Daun Tomat Berbasis IoT dan CNN dengan Aplikasi Android Pancono, Suharyadi; Indroasyoko, Narwikant; Asep Irfan Setiawan
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4083

Abstract

Tomatoes are a high-value commodity in agriculture, so farmers make various efforts to ensure the production of fresh and ready-to-consume tomatoes. However, farmers often face difficulties in monitoring tomato growth because they still use manual methods and have limited knowledge in detecting diseases on tomato leaves. This research offers a solution by utilizing transfer learning and fine-tuning Convolutional Neural Network (CNN) using DenseNet169 architecture, as well as Internet of Things (IoT) technology. The model is implemented in an Android application using TensorFlow on the Flutter platform after being converted to tflite format. The test results show that the accuracy of the model reaches 94%, while the accuracy of the application in detecting tomato leaf diseases reaches 92.80% and has a response time of about 1077.56 ms. In addition, the application can monitor plant conditions in realtime by having a delay of 1,998 ms.
Co-Authors Achmad Muhammad, Achmad Asep Irfan Setiawan Khoirunnisa, Hilda Pancono, Suharyadi Rahmatulloh, Muhammad Rafy Sabarini, Sri Santoso