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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Journal of Robotics and Control (JRC)
Furizal Furizal
Universitas Ahmad Dahlan
Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering

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Temperature and Humidity Control System with Air Conditioner Based on Fuzzy Logic and Internet of Things Furizal Furizal; Sunardi Sunardi; Anton Yudhana
Journal of Robotics and Control (JRC) Vol 4, No 3 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i3.18327

Abstract

Work is an activity that takes most of the day to earn a living and improve the standard of living. During work, many people have to work indoors, which can be a less comfortable and unhealthy place if the temperature and humidity are not well controlled. Unsuitable temperature and humidity conditions can negatively affect the health and comfort of workers, as well as interfere with productivity and work quality. However, the problem that often arises is the difficulty of controlling room temperature and humidity effectively, especially in rooms that are closed and do not get air circulation from outside. Therefore, an effective solution is needed to control the temperature and humidity of the room automatically and remotely via the internet. The contribution of this research is to develop an effective and efficient AC control system in controlling room temperature and humidity using Tsukamoto's Fuzzy Inference System (FIS) method and the Internet of Things (IoT). Tsukamoto's FIS is used to produce AC temperature values in room temperature and humidity control as measured by the DHT22 sensor directly integrated with the ESP32 microcontroller. This control system is monitored remotely using IoT concepts through a mobile application interface. The results of this study show that room temperature can be controlled under normal conditions, with an average change of -1.67°C and an overall average temperature of 25.95°C. While the average humidity is at a value of 80.16% which is included in the Wet set. This suggests that humidity cannot be controlled under normal conditions, so it still requires further development. In addition, it is also necessary to further investigate the effectiveness of the tool in various sizes and more complex layouts of rooms.
Impact of Fuzzy Tsukamoto in Controlling Room Temperature and Humidity Sunardi Sunardi; Anton Yudhana; Furizal Furizal
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 7 No 2 (2023): August 2023
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v7i2.19652

Abstract

Dry season is a season where the room temperature exceeds the needs of the body so that it is unpleasant, unhealthy and can interfere with human productivity. In addition, the efficiency of use and resource requirements are also a concern for some people. To overcome this problem, an automatic room temperature control device was created using the ESP32 microcontroller with Tsukamoto's fuzzy algorithm optimization as a data processing technique to produce optimal fan speeds in duty cycle units based on temperature and humidity conditions in realtime. Four tests by running a fan for 30 minutes on each showed that the average difference between the maximum and minimum temperatures in the room was 0.95°C, while the average difference between maximum and minimum humidity was 2.0%. In addition, the test graph shows that when the fan is rotated in a closed room without air circulation, the relative temperature change increases from the initial minute to the last minute of the test. Meanwhile, changes in relative humidity decrease, although fluctuations increase within 1-4 minutes. This study found that fans are not effective in lowering room temperature optimally. Therefore, it is recommended to replace with an exhaust fan in future research.
Role of Finite State Automata in Transliterating Latin Script into Javanese Script Suprihatin Suprihatin; Imam Riadi; Furizal Furizal; Izzan Julda D.E Purwadi Putra
Khazanah Informatika Vol. 9 No. 2 October 2023
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v9i2.22303

Abstract

Writing Javanese script is considered complicated and difficult for people who learn it. The process of transliterating Latin into Javanese script cannot be done directly, because each alphabet is not always represented by one Javanese script. Javanese script is not represented by one or more Latin letters, so if transliteration of Latin letters to Javanese letters is required, a parsing process is required. The rows of Javanese letters form a ligature with certain rules, so parsing is also needed to arrange the rows of Javanese letters correctly. This study aims to design a program to facilitate the transliteration of Latin script to Javanese script.  Finite State Automata (FSA) is used to describe writing rules. This study is limited to lowercase letters only, capital letters will be subtracted first, number symbols are not discussed in this study. The results of the study are in the form of a program design that can transliterate Latin writing into Javanese. Experiments were carried out as many as 4 structures of vowel consonant variations. All syllabic structures that include KV, KKV, KVK, KKVK have been tried. The transliteration results show conformity with a 100% accuracy rate in accordance with the rules of writing Javanese script. This research shows that the application of FSA can handle the transliteration of Latin letters into Javanese.
Application of Machine Learning in Healthcare and Medicine: A Review Furizal Furizal; Alfian Ma'arif; Dianda Rifaldi
Journal of Robotics and Control (JRC) Vol 4, No 5 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i5.19640

Abstract

This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.
Co-Authors Alfian Ma’arif Anton Yudhana Dianda Rifaldi Imam Riadi Izzan Julda D.E Purwadi Putra Sunardi, Sunardi Suprihatin Suprihatin