Article Per Year (5 Year)
p-Index From 2020 - 2025
1.037
P-Index
This Author published in this journals
All Journal Jurnal Inovasi Teknologi Pendidikan Techne : Jurnal Ilmiah Elektroteknika JAIM (Jurnal Abdi Masyarakat) Journal of Informatics and Communication Technology (JICT) Journal of Informatics and Communication Technology (JICT)
Hidayaturrohman, Qisthi Alhazmi
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Public Health Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
PENERAPAN ENERGI MATAHARI DALAM MENDUKUNG SISTEM KOLAM PETERNAKAN IKAN MAS HIAS JAMAL SSFARM Hidayaturrohman, Qisthi Alhazmi; Lukmana, Muhammad Arifudin; Fahrudin, Fahrudin; Fauzi, Ade Fikri
Jurnal Abdi Masyarakat Vol 5, No 1 (2021): Jurnal Abdi Masyarakat November 2021
Publisher : Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/jaim.v5i1.2109

Abstract

Solar radiation could be used as alternative energy by using solar panel as a transducer to convert solar radiation energy to electricity energy. Jamal SSFarm, a family-owned business in decorative fish cultivation had problem in electricity energy limitation for their operational works. Researcher and team offered solutions to install and implement solar energy as an alternative energy by using solar panels. This community service works have done by two steps, the first step was surveying and buying stuffs and the second step was installment and testing at the partner location. In our activities, the alternative energy system works well. The testing device showed 1.25 ampere of its current, which shows that the device works well. That matter are in line with our partner expectation.
Teachers' belief and implementation of ICT in early childhood education classroom Sulistyaningtyas, Reza Edwin; Astuti, Febru Puji; Yuliantoro, Prasetyo; Hidayaturrohman, Qisthi Alhazmi
Jurnal Inovasi Teknologi Pendidikan Vol. 11 No. 1 (2024): March
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jitp.v11i1.67300

Abstract

Technology has a vital role in every aspect of life. Early childhood education also requires technology in its learning. This study aims to analyze teacher beliefs and explain the application of ICT in Early Childhood Education (ECE) classes. This research uses mixed methods, questionnaires, and interviews as data collection tools. Questionnaires were used to obtain data related to ICT used in ECE and teacher beliefs, while interviews were used to obtain data on the implementation of ICT in ECE classrooms. The sample was 132 ECE teachers in Magelang, Central Java, and Yogyakarta.  The sampling technique used is a simple random sampling technique. Regression analysis is used in this study's data analysis technique. The results obtained include 1) 40% of teachers use laptops in class, 2) 26.67% of teachers never involve children in using ICT, and 3) there is a relationship between educational background and ICT implementation beliefs in class. Implication This study aims to hold workshops for ECE teachers to apply ICT in learning activities involving children in their use.
Design of human heartbeat monitoring system based on wireless sensor networks Hidayaturrohman, Qisthi Alhazmi; Lukmana, M. Arifudin; Zaman, Akhmad Nidhomuz
Techné : Jurnal Ilmiah Elektroteknika Vol. 22 No. 2 (2023)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v22i2.354

Abstract

The IoT technology plays an important role in Industry 4.0 revolution. The IoT technology has potential to be implemented in the medical industry, especially for the development of telemedicine system. IoT able to send the medical sensor data wirelessly to the nearest medical facility like hospital. In this research, the author designed the heart beat monitoring system by using 802.11 communication protocol and simple web interface. The pulse sensor that used in this research was able to read the pulse rate of the human and convert it to BPM (beat per minute). It has 98.89% accuracy and 1.11% error compared to the smartwatch result. In the other hand, ESP-32 also implemented as the microcontroller as well as the sensor node of the system. It was able to send the data wirelessly from sensor node to the coordinator node. The coordinator node was also able to fetch the sensor data into the database using POST and GET method and then visualize the sensor data over web interface so the other users are able to see the visualization of the sensor data.
Komparasi Performansi Model Machine Learning untuk Prediksi Readmisi Pasien Gagal Jantung Aznur, Fatimah; Merdijaya, Paizal; Hidayaturrohman, Qisthi Alhazmi
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cardiovascular diseases are the leading cause of global mortality. By utilizing machine learning methods, patient data analysis can estimate risk and plan more effective interventions. The aim of this study is to evaluate the performance of algorithms such as Random Forest, Decision Tree, and Gradient Boosting in predicting cardiovascular diseases. The first step involves using the K-Nearest Neighbors (KNN) imputation method to address missing values in the dataset derived from cardiovascular disease patients. The data is split into training and testing sets with a ratio of 80:20. The three machine learning algorithms are tested on this data, with evaluations conducted using accuracy, precision, recall, and F1-score. The results show that Random Forest delivers the best performance with an accuracy of 65%, precision of 60%, recall of 31%, and F1-score of 41%. Although Decision Tree and Gradient Boosting demonstrate competitive results, they are slightly lower than those of Random Forest. The KNN imputation method is proven effective in handling missing values. In conclusion, Random Forest outperforms, followed by Gradient Boosting and Decision Tree, providing a foundation for the development of more accurate predictive models in diagnosing cardiovascular diseases.
Komparasi Performansi Model Machine Learning untuk Prediksi Readmisi Pasien Gagal Jantung Aznur, Fatimah; Merdijaya, Paizal; Hidayaturrohman, Qisthi Alhazmi
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cardiovascular diseases are the leading cause of global mortality. By utilizing machine learning methods, patient data analysis can estimate risk and plan more effective interventions. The aim of this study is to evaluate the performance of algorithms such as Random Forest, Decision Tree, and Gradient Boosting in predicting cardiovascular diseases. The first step involves using the K-Nearest Neighbors (KNN) imputation method to address missing values in the dataset derived from cardiovascular disease patients. The data is split into training and testing sets with a ratio of 80:20. The three machine learning algorithms are tested on this data, with evaluations conducted using accuracy, precision, recall, and F1-score. The results show that Random Forest delivers the best performance with an accuracy of 65%, precision of 60%, recall of 31%, and F1-score of 41%. Although Decision Tree and Gradient Boosting demonstrate competitive results, they are slightly lower than those of Random Forest. The KNN imputation method is proven effective in handling missing values. In conclusion, Random Forest outperforms, followed by Gradient Boosting and Decision Tree, providing a foundation for the development of more accurate predictive models in diagnosing cardiovascular diseases.
Co-Authors Astuti, Febru Puji Aznur, Fatimah Fahrudin Fahrudin Fauzi, Ade Fikri Lukmana, M. Arifudin Lukmana, Muhammad Arifudin Merdijaya, Paizal Prasetyo Yuliantoro Sulistyaningtyas, Reza Edwin Zaman, Akhmad Nidhomuz