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All Journal Dinamik Sinkron : Jurnal dan Penelitian Teknik Informatika Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) Jurnal Kreativitas PKM JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) TEPIAN Journal of Electrical Engineering and Computer (JEECOM) Buletin Poltanesa Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Electronic Journal of Education, Social Economics and Technology The Indonesian Journal of Computer Science
Achmad Ridwan
Universitas Prima Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Mechanical Engineering Medicine & Pharmacology 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.
Portable ECG Prototype based on Arduino and Random Forest Classification for Home Heart-Rate Monitoring R. Ferdy Akbar Nugraha; Novendy Alberto Will Tindaon; Arya Susena; Alfonso Duandes; Achmad Ridwan
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3144

Abstract

Electrocardiogram (ECG) examination is essential for detecting heart rhythm disorders, yet limited access and high costs often prevent routine medical check-ups for many people. This study addresses these obstacles by designing and developing a portable ECG prototype capable of independent home-based heart monitoring. The system integrates an AD8232 sensor for signal acquisition, an Arduino Uno microcontroller as the main processor, and a simplified Random Forest classification algorithm to distinguish between normal, bradycardia, and tachycardia conditions. Measurement results are saved in CSV format on an SD card, then visualized and analyzed using Jupyter Notebook. The prototype was tested on 100 samples in a static and relaxed state to ensure signal stability. Its heartbeat classification achieved an accuracy of 99.0%, slightly higher than the PTB-XL reference dataset’s 98.0%, and consistent with results reported by recent TinyML- and Random Forest-based ECG studies. Unlike prior IoT-based frameworks, this work combines cost-effective microcontroller hardware with simplified offline on-device classification for practical daily monitoring without continuous cloud access. These findings confirm that the proposed system can produce reliable readings approaching clinical standards while remaining simple, affordable with a component cost under USD 31, and accessible for routine public heart health screening.
An IoT-Based Drinking Water Quality Monitoring System Using Random Forest for Potability Classification Debby Mutiara Br Sembiring; Achmad Ridwan; Yudi Aditia; Ayu Sentia Br Sembiring
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3805

Abstract

Contaminated drinking water remains a critical public health threat, particularly in peri-urban communities where laboratory-based quality assessment is inaccessible due to high cost and infrastructure requirements. This study developed an embedded, multi-parameter water quality detection system built on the ESP32 microcontroller, incorporating pH, Total Dissolved Solids (TDS), turbidity, and temperature sensors within an IP54-rated weatherproof enclosure. A Random Forest classifier, optimised via Grid Search Cross-Validation, was trained on a nine-parameter physicochemical dataset comprising 3,276 labelled water samples to perform binary potability determination. Feature importance analysis identified pH and TDS as the two dominant predictors, enabling a computationally efficient dual-parameter decision rule for on-device real-time inference. Sensor accuracy was validated against certified reference instruments: the pH sensor achieved 97.67% mean accuracy (error: 2.33%; SD: 0.017) and the TDS sensor achieved 97.55% (error: 2.45%; SD: 1.06 ppm). Seven real-world water specimens of diverse physicochemical composition were correctly classified as safe or unsafe, consistent with World Health Organization guidelines and Indonesian national standard SNI 01-3553-2006, requiring pH between 6.5 and 8.5 and TDS below 500 ppm. The Random Forest model outperformed rule-based single-threshold approaches by 6 to 13 percentage points, recording an F1-score of 84.6% on the held-out test set. Results are delivered instantly via an LCD 20×4 I2C display and colour-coded LED indicators, eliminating laboratory dependency and supporting practical deployment in resource-constrained North Sumatran communities.
IoT-Based K-Nearest Neighbor Classification of Beverage Sugar Levels for Early Diabetes Prevention Nurul Safira; Rizka Azizah; Sri Hayyu Fajhalika; Achmad Ridwan
TEPIAN Vol. 7 No. 2 (2026): June 2026
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v7i2.3729

Abstract

This study is motivated by the increasing consumption of sugar-sweetened beverages, which significantly contributes to the risk of diabetes mellitus. Therefore, a practical, accurate, and efficient system is required to detect sugar levels in beverages. This study aims to design and implement an Internet of Things (IoT)-based system for classifying sugar levels using the K-Nearest Neighbor (KNN) algorithm. The system is developed using an ESP32 microcontroller integrated with an ultrasonic sensor, a photodiode, and an infrared light source to capture the physical and optical characteristics of liquids. The research focuses on several commonly consumed beverages, namely sweet tea, coffee, milk, syrup, and lemon water, with varying sugar levels ranging from 10 to 60 grams. The collected data are processed through normalization using the StandardScaler method and classified based on Euclidean distance with a k value of 5. The classification results are grouped into three categories: low, medium, and high sugar levels. Experimental results show that the system achieves an accuracy of 85% under testing conditions. These results indicate that the proposed system can perform reliable classification in practical scenarios. In addition, the system provides a low-cost and real-time solution, making it suitable for practical applications in monitoring daily sugar intake and supporting the early prevention of diabetes mellitus.
Sistem Monitoring Kualitas Udara Ruangan Berbasis IoT Dengan Algoritma Fuzzy Logic Andreas Tandana; Achmad Ridwan; Tan Della Angelica; Caryn Evelyn Tannesia; Rivanco Winson
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9796

Abstract

Indoor air quality is often not monitored directly, although exposure to pollutant gases, fine particles, unsuitable temperature, and humidity may increase the potential for respiratory health problems. The main problem is that air monitoring devices are commonly expensive, difficult to access, and present technical data that is not easy for users to understand. This study aims to design an Internet of Things (IoT)-based indoor air quality monitoring system that processes sensor data using Fuzzy Logic to produce air quality risk categories rather than disease diagnosis. The system uses an ESP32 DevKit v1, DHT22, MQ-135, and GP2Y1010AU0F sensors, then sends data in real-time through the MQTT protocol to a Mosquitto broker and Python Bridge Server for visualization on a web dashboard. The testing results show that the DHT22 sensor has a maximum error of 0.90% for temperature and 2.17% for humidity, the MQ-135 can distinguish normal to dangerous air conditions, the GP2Y1010AU0F detects PM2.5 up to 89.3 ug/m3, and MQTT communication remains stable with latency below 200 ms. Fuzzy Logic testing produces scores of 15-91 that map clean, moderate, unhealthy, and dangerous air scenarios. The contribution of this study is a low-cost air monitoring prototype that presents real-time air quality information in an understandable form and can support early prevention actions against poor air exposure.
Pendampingan Penggunaan IoT untuk Budidaya Bayam dan Kangkung di Desa Serapuh Abdi Dharma; Christin Erniati Panjaitan; Achmad Ridwan; Agung Prabowo; Yennimar Yennimar; Meyga Fitri Handayani Nasution; Togar Timoteus Gultom; Dhanny Rukmana Manday; Dewi Sholeha; Saut Dohot Siregar; Putri Puspa Sari
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 9, No 7 (2026): Volume 9 Nomor 7 (2026)
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v9i7.26470

Abstract

ABSTRAK Dalam meningkatkan efisiensi dan produktivitas di sector pertanian Desa Serapuh, Kabupaten Simalungun maka potensi Intenert of Things( IoT) dihadirkan dalam berbagai aplikasi. Banyak petani di Desa Serapuh bertani sayur bayam dan kangkung. Hanya saja, masalah yang mereka hadapi masalah air pada tanaman sehingga hasil panen kurang maksimal. Dua masalah utama yang dihadapi dari para petani yakni pertama, cuaca yang panas menjadikan tanah kering dan pertumbuhan tanaman terhambat. Kedua, penyiraman tanaman yang tidak terkontrol sehingga air yang berlebih dapat menyebabkan kerusakan akar tanaman. Denga kedua masalah tersebut, maka pendampingan ini bertujuan untuk memberikan wawasan kepada petani dalam penggunaan IoT. Metode pendampingan ini berupa ceramah, diskusi, dan praktik langsung di lapangan. Dari kegiatan ini terlihat adanya peningkatan kemampuan petani dalam aspek penguasaan alat dan implementasi ke tanaman Bayam dan Kangkung. Kata Kunci: Pertanian, Iot, Petani, Irigasi. ABSTRACT For agricultural efficiency and productivity in Serapuh Village, Simalungun Regency, the Internet of Things (IoT) has several applications. Many Serapuh farmers grow spinach and kale. However, plant water issues reduce harvests. Farmers face two basic issues: hot weather dries soil and stunts plant growth. Second, excessive plant watering can damage roots. This coaching intends to help farmers use IoT for these two issues. Mentoring involves lectures, debates, and fieldwork. This activity shows farmers' tool and spinach/kale plant implementation skills have increased. Keywords: Agriculture, IoT, Farmer, Irrigation
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 Abdi Dharma Agung Prabowo Agung Prabowo Agung Prabowo Ahmad Arif Alfonso Duandes Amir Saleh Andreas Tandana Arya Susena Ayu Sentia Br Sembiring Caryn Evelyn Tannesia Christin Erniati Panjaitan Debby Mutiara Br Sembiring Delima Sitanggang, Delima Dewi Sholeha Dhanny Rukmana Manday Fadhillah Azmi Fadhillah Azmi Fadhillah Azmi, Fadhillah Gibran, M Khalil Ginting, Riski Titian HS, Christnatalis Hutagalung, Elia Yose Mayal Lenny, Lenny Lewis Francolim M Khalil Gibran M. Khalil Gibran Manday, Dhanny Rukmana Meyga Fitri Handayani Nasution Mhd. Raihan M. Manurung Natasya Aulia Angkat Novendy Alberto Will Tindaon Nurul Safira Othman, M.A. Perangin Angin, Despaleri Putri Puspa Sari R. Ferdy Akbar Nugraha Ridho Maulana Siregar Rivanco Winson Rizka Azizah Rosa Englina Silaban Sau Dohot Siregar Saut Dohot Siregar Seftia Angelina Segono, Yohandres Sihombing, Yehezkiel Simangunsong, Putra Torang Simbolon, Harly Gumanti Siregar, Saut Dohot Sri Hayyu Fajhalika Tan Della Angelica Togar Timoteus Gultom Togar Timoteus Gultom Us, Nurul Iman Violyn, Violyn Yennimar Yennimar Yennimar, Yennimar Yoga Tri Nugraha Yudi Aditia