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All Journal Journal of Applied Information, Communication and Technology (JAICT) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Techno Nusa Mandiri : Journal of Computing and Information Technology JURTEKSI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Mantik INTI Nusa Mandiri Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Sisfotek Global Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat INOVTEK Polbeng - Seri Informatika Global Advances in Science, Engineering & Technology (GASET)
Reyhan Achmad Rizal
Universitas Prima Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Classification Of Hypertension Using K-Nearest Neighbor Based On Photoplethysmograph Data And Blood Pressure Estimator Jasmin William Natanael Sinaga; Tasya Rouli Christy Tampubolon; Ester Farida Simanjuntak; Delima Sitanggang; Reyhan Achmad Rizal
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zswzf122

Abstract

Hypertension is a persistent cardiovascular condition, often termed the “silent killer” because it typically presents no symptoms in its early stages. To address the shortcomings of traditional blood pressure monitoring methods, this study develops a classification system that leverages photoplethysmography (PPG) signals in combination with the K-Nearest Neighbor (KNN) algorithm. PPG provides a promising non-invasive solution that is readily adaptable to portable devices. The classification process employs the Euclidean Distance method to determine the similarity between new data samples and previously labeled instances. Data were collected from 276 individuals spanning various age groups using PPG sensors connected to the MR-IAT Robot Covid platform. The system categorizes individuals into normotensive, prehypertensive, stage 1, and stage 2 hypertension groups. The study evaluates the performance of the KNN algorithm based on its ability to predict blood pressure categories from morphological features extracted from the PPG signals. Ultimately, the outcomes of this research are expected to advance the development of efficient, real-time, continuous blood pressure monitoring systems through user-friendly machine learning approaches.
Otomatisasi Pengawasan Penggunaan Masker Pada Siswa Yayasan Pendidikan Shafiyyatul Amaliyyah (YPSA) Reyhan Achmad Rizal; Marlince Novita Karoseri Nababan; Despaleri Perangin-Angin
Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Vol. 1 No. 2 (2021): Desember 2021
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jpstm.v1i2.721

Abstract

In preparing for face-to-face meetings, the Shafiyyatul Amaliyah Education Foundation (YPSA) requires supervision in applying the use of masks in the school envi-ronment. There are two main problems that exist at YPSA, namely supervising students at YPSA it is difficult for one supervisor to do and the size of the pages at YPSA is difficult to reach every student because students at YPSA vary from kindergarten, elementary, junior high and high school. The solution offered by the PKMS team at Universitas Prima Indonesia is to build an automation system with a door opening and closing model and a warning alarm at the entrance to the school environment. The initial implementation of this activity was carried out on August 16, 2021 to conduct interviews with YPSA part-ners in order to obtain a model of the automation tool for monitoring the use of masks in accordance with the YPSA environment. Every student is protected from the transmission of the Covid-19 virus.
Implementation of a Database Security System to Prevent SQL Injection in CRUD Applications Using Laravel T. Sukma Achriadi Sukiman; Reyhan Achmad Rizal; Cut Lika Mestika Sandy; Anni Zulfia; Annisa karima
Global Advances in Science, Engineering & Technology (GASET) Vol. 2 No. 1 (2026): Global Advances in Science, Engineering & Technology (GASET), Article Research
Publisher : Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/gaset.v2i1.322

Abstract

The development of information technology has driven the use of web-based applications in various sectors, but this growth has also been accompanied by an increase in cyber security threats, one of which is SQL Injection. SQL Injection is an attack technique that exploits input validation weaknesses in applications to insert malicious SQL commands into the system. This study aims to implement and evaluate a security system against SQL Injection attacks using the Laravel framework. The research method used is an experimental approach, involving the development of a simple Laravel-based CRUD application that is tested before and after the implementation of security features. The security features implemented include Eloquent ORM, parameter binding, input validation using Form Request, authentication, role-based access control, and user activity audit logs. The test results show that all SQL Injection attack attempts, both manual and automated using SQLMap, were successfully blocked by the system. In addition, the application functions optimally without performance issues, proving that the implementation of security does not hinder system operations. In conclusion, Laravel is capable of providing effective protection against SQL Injection when its security features are implemented correctly. This study also recommends strengthening against other types of attacks such as XSS and CSRF, as well as the use of data encryption to enhance overall system security.
PERBANDINGAN ALGORITMA YOLOV3 DAN YOLOV4 DALAM PENGELOMPOKAN UKURAN TELUR AYAM SECARA REAL TIME Lysheeba Abbygail Sembiring; Brian Fernanda Manik; Jovi Jonathan; Steven Giovano; Reyhan Achmad Rizal
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5699

Abstract

The common problem currently faced by MSMEs producing chicken eggs is the difficulty in calculating the number of eggs and grouping egg sizes where everything is still done manually so that errors often occur and many entrepreneurs often experience losses. To improve and strengthen productivity, management, and marketing in this business, technological innovation is needed. This study aims to detect the number of eggs and group egg sizes based on their type using the Yolov3 and Yolov4 algorithms. Based on the results of the tests carried out, it shows that the Yolov3 and Yolov4 algorithms are able to detect chicken eggs in real time with the best accuracy value obtained by the Yolov3 algorithm. The comparison was carried out using 10 epoch tests with an F1-Score value of 0.89 where the F1-Score value approaching 1 indicates that the system performance has been running well. The results of this classification can be used to create a real time egg calculation application that can help calculate the number of eggs every day by each MSME.
EEG SIGNAL CLASSIFICATION FOR STRESS LEVEL DETECTION USING THE C4.5 METHOD Christian Dwigo; Feri Herlangga; Michael Joan; Wilbert Nyoman; Delima Sitanggang; Reyhan Achmad Rizal
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 2 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i2.7193

Abstract

Stress is a common response to life's pressures that, if left untreated, can negatively impact physical and mental health. Accurately detecting and classifying stress levels is a significant challenge. Electroencephalography (EEG), as a non-invasive method, is capable of recording brain activity and representing a person's emotional state, including stress levels. However, the complexity of EEG data requires effective classification techniques. This study aims to develop a stress level classification system based on EEG signals using the Decision Tree C4.5 method. The EEG dataset was taken from Binjai Prison, with inputs in the form of brain waves (Delta, Theta, Alpha, Beta1, Beta2) and values from various electrodes. The output is a stress classification into three categories: stressed, relaxed, and neutral. The results show that the C4.5 method is able to classify stress levels with 98.68% accuracy, an average precision of 99.19%, and an average recall of 96.29%. The beta2 feature is the most dominant attribute, followed by theta and beta1. Thus, the C4.5 method shows good performance and provides clear interpretation in classifying stress levels based on EEG signals.
Classification of Hypertension Using Support Vector Machine Based on Data Photoplethysmography and Blood Pressure Estimator Glen Virdho Sirait; Anand Roshan; Delima Sitanggang; Reyhan Achmad Rizal
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 8 No. 2 (2025): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v8i2.7658

Abstract

This study formulates a Support Vector Machine (SVM)-based hypertension classification model using physiological features from Photoplethysmograph (PPG) signals and systolicdiastolic blood pressure estimates. PPG data were collected from 276 participants in three locations using a non-invasive device integrated into the MR-IAT Robot Covid web platform. Feature extraction and preprocessing were performed in MATLAB, while SVM (scikit-learn) model training and testing were conducted in Google Colab. Comparison of three kernel variants—linear, quadratic, and cubic—showed that the cubic kernel was the most superior with an accuracy of 96.4%, followed by quadratic at 94.9% and linear at 91.3%. Overall, the model achieved 93.9% accuracy in distinguishing six blood pressure categories (Hypotension, Normal, Prehypertension, Stage 1 Hypertension, Stage 2 Hypertension, Crisis). Visualization of the results (scatter plot, confusion matrix, parallel coordinates) revealed that systolic pressure, diastolic pressure, age, weight, and respiration rate were the most influential parameters. These findings underscore the potential application of SVM in portable devices for early detection and real-time monitoring of hypertension.
Co-Authors Abdi Zulfikri Ajulio Padly Sembiring Amsal Yusuf Gultom Anand Roshan Ananda Faridhatul Ulva Anni Zulfia Annisa Karima Ardi Bernandustahi Miduk Napitupulu Brian Fernanda Manik Christian Dwigo Cut Lika Mestika Sandy Dedy Ongky Wijaya Delima Sitanggang, Delima Despaleri Perangin-angin Ester Farida Simanjuntak Fadlisyah Fadlisyah Feri Herlangga Glen Virdho Sirait Jasmin William Natanael Sinaga Jovi Jonathan Kristina Sinaga Lidya Aprilla Siregar Lysheeba Abbygail Sembiring Marlince NK Nababan Michael Joan Muhathir, Muhathir Mutammimul Ula Nur Azizah Nurlela Octavia Purba Octavriana Della C. Sihombing Ona Rizal Fahmi Prabhu Warren RICO WIJAYA DEWANTORO Rinaldi Fauzan Sebastian Suhandinata Sonni Yudha Nugraha Arfan Srinjiwi Srinjiwi Steven Giovano Suardin Gulo T. Sukma Achriadi Sukiman Taripar Jonibet Siagian Tasya Rouli Christy Tampubolon Taufik Ismail Simanjuntak Wilbert Nyoman