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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika Jiko (Jurnal Informatika dan komputer) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Journal of Electrical Engineering and Computer (JEECOM) Electronic Journal of Education, Social Economics and Technology The Indonesian Journal of Computer Science
Achmad Ridwan
Universitas Prima Indonesia
Religion Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Veterinary Other

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Tren Terkini dan Tantangan dalam Implementasi IoT untuk Layanan Kesehatan: A Systematic Literature Review Simangunsong, Putra Torang; Sihombing, Yehezkiel; Ridwan, Achmad
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10317

Abstract

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.
Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases Saleh, Amir; Ridwan, Achmad; Gibran, M Khalil
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): 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.v12i5.3379

Abstract

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.
INTEGRATION SYSTEM OF THREE SECURITY FEATURES IN SMART DUAL MCB WITH AUTOMATIC LOAD BALANCING AND FIRE DETECTION BASED ON ARDUINO UNO (SINTAKS) Achmad Ridwan; Agung Prabowo
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.363

Abstract

The increasing use of electronic devices in Indonesian households has significantly strained traditional electrical systems, with electricity consumption growing 4.5% annually and 78% of urban homes utilizing over 10 electronic devices. This situation poses substantial fire risks, as electrical short circuits cause 62.8% of urban fires, with MCB overloads accounting for 27% of incidents. This research introduces SINTAKS (Sistem Integrasi Tiga Keamanan Smart Dual MCB), an innovative integrated system combining three essential safety features: energy monitoring, automatic load balancing, and early fire detection. Unlike conventional systems requiring separate components, SINTAKS provides a comprehensive solution using Arduino Uno as the main controller, integrated with ACS712 current sensors, DS18B20 temperature sensors, MQ-2 smoke detectors, and relay modules. The system demonstrates remarkable performance with 97.8% current measurement accuracy, load balance improvement from 62.7% to 91.3%, and fire detection response time of 2.9-4.7 seconds. Field testing in real household installations confirmed system reliability with 94.8% success rate across various operational scenarios. SINTAKS achieves 4.2% energy savings while maintaining cost-effectiveness at IDR 875,000, making it accessible for widespread residential implementation. This autonomous system operates independently without IoT dependence, ensuring reliable protection even in offline environments. The research successfully addresses critical gaps in household electrical safety through practical, affordable, and integrated technology.
COMPARATIVE ANALYSIS OF ENSEMBLE CLASSIFICATION MODELS AND SUPPORT VECTOR MACHINES IN MEASURING STRESS LEVELS BASED ON EEG SIGNALS Seftia Angelina; Sau Dohot Siregar; Achmad Ridwan; Lewis Francolim
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 1 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i1.11151

Abstract

Stress is a physiological and psychological response that can develop into serious health issues when prolonged. EEG-based stress detection has become an important approach; however, many studies still lack validation for multilevel classification and real-world conditions. This study focuses on inmates at Binjai Correctional Facility and compares the performance of Support Vector Machine (SVM), Random Forest (RF), and a combined ensemble model of Random Forest and AdaBoost for classifying three stress levels: stressed, relaxed, and neutral, using EEG signals. Experimental results show that the SVM model achieved an accuracy of 81% with a Minimum Classification Error (MCE) of 0.16. The Random Forest model significantly improved performance, reaching 96% accuracy and an MCE of 0.04. The best performance was obtained by the ensemble model combining Random Forest and AdaBoost, which achieved an accuracy of 97% and reduced the MCE to 0.03, indicating a 1% improvement over Random Forest alone.
Application of the Decision Tree Algorithm for Early Detection of Heart Disease Based on IoT Rosa Englina Silaban; Ridho Maulana Siregar; Natasya Aulia Angkat; Mhd. Raihan M. Manurung; Achmad Ridwan
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.1048

Abstract

Heart disease is one of the leading causes of death worldwide, accounting for 32% of all global deaths. Technological developments, particularly in the Internet of Things (IoT), enable real-time monitoring of heart health and early warning alerts. This study aims to implement a Decision Tree algorithm to classify patient conditions based on vital parameters, including BPM, SpO₂, systolic and diastolic blood pressure, and body temperature. The model was trained using a vital parameter dataset and evaluated using a confusion matrix, ROC curve, and feature importance. Test results show that the Decision Tree model achieves an accuracy of 85% with a macro-AUC value of 0.448. These results prove that the Decision Tree algorithm can be used for patient condition classification with reasonably good performance, although the model still tends to make prediction errors in some minority classes.
Co-Authors Agung Prabowo Ahmad Arif Amir Saleh Duandes, Alfonso Fadhillah Azmi Fadhillah Azmi Fadhillah Azmi, Fadhillah Gibran, M Khalil Hutagalung, Elia Yose Mayal Lenny, Lenny Lewis Francolim M Khalil Gibran M. Khalil Gibran Mhd. Raihan M. Manurung Natasya Aulia Angkat Nugraha, R. Ferdy Akbar Othman, M.A. Ridho Maulana Siregar Rosa Englina Silaban Sau Dohot Siregar Seftia Angelina Segono, Yohandres Sihombing, Yehezkiel Simangunsong, Putra Torang Simbolon, Harly Gumanti Susena, Arya Tindaon, Novendy Alberto Will Us, Nurul Iman Violyn, Violyn Yennimar, Yennimar Yoga Tri Nugraha