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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jiko (Jurnal Informatika dan komputer)
Adya Zizwan Putra
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Computer Science & IT

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ECG-Based Arrhythmia Classification in Students Using Random Forest: A Case Study with Class Imbalance Analysis Adya Zizwan Putra; Sitorus, Ariyanto; Simanjuntak, Paulus Anggiat Ruben; Mega Cristin Angelina. H.; Situmorang, Kevin Agustino
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14793

Abstract

Arrhythmia is a heart rhythm disorder that can indicate a student’s heart health status. This research aims to develop a Random Forest model to classify arrhythmia in students based on ECG signals. ECG data was collected from 100 students at SMK Swasta Teladan Sumatera Utara 2 after learning activities. The extracted signal features include RR interval, PR interval, QRS duration, QT interval, ST segment, beats per minute (BPM) and R/S ratio. Data labeling was carried out manually by the researchers based on the range of ECG feature values that had been determined by the doctor for each class: Normal, Abnormal, Potential Arrhythmia and Very Potential Arrhythmia. The dataset is divided into 70% for training and 30% for testing. SMOTE is applied to address class imbalance. The model achieved 80% accuracy with the best performance in normal class with precision, recall and f1-score  of  94%. However, no samples were identified for Potential Arrhythmia class, as there were no extracted feature values that met the criteria set by the doctor, so model could neither learn nor make predictions for this category, even after applying balancing methods such as SMOTE. For further research, based on these findings, it highlights the need for balanced class representation and expert-guided labeling to improve the performance of ECG -based arrhythmia classification.
IMPLEMENTING SHA-256 IN BLOCKCHAIN FOR SECURE AND TRUSTED ONLINE TRANSACTIONS OF MSMEs Matthew Luis; Rico Wijaya Dewantoro; Andrian Reinaldo Crispin; Adya Zizwan Putra; Abdi Dharma; Celine Chrysia; Cherry Piya Vagga
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10606

Abstract

The advancement of information technology has driven digital transformation in various sectors, including Micro, Small, and Medium Enterprises (MSMEs), which are vital to Indonesia's economy. However, local MSMEs still face challenges in online transactions, especially related to data security and low consumer trust. Issues like data manipulation, lack of transparency, and weak security systems hinder optimal digitalization. This study implements the SHA-256 cryptographic algorithm in a blockchain system to enhance security and trust in local MSMEs' online transactions. SHA-256 is chosen for its ability to produce unique, permanent, and tamper-resistant hashes. The system adopts a decentralized blockchain model, where transactions are recorded in encrypted, chronologically linked blocks. The testing results show that the SHA-256-based blockchain system functions effectively in maintaining data integrity and preventing manipulation. Black Box Testing confirmed that the system operates correctly from the user's perspective, including login validation, transaction recording, manipulation detection, and transaction history retrieval. White Box Testing validated the internal logic of the system, proving the correct implementation of SHA-256 hashing, block linking, Proof of Work (PoW), and transaction validation mechanisms. All test cases passed successfully, demonstrating that the system is stable, functional, and secure.
A DEEP LEARNING-BASED SMART MOBILE APPLICATION FOR AUTOMATED CITRUS FRUIT QUALITY CLASSIFICATION Armando Sitorus; Laskar Eltriman Gulo; Titien The Lawren Pasaribu; Josi Leonardo Davinsi Saragih; Adya Zizwan Putra
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11750

Abstract

This study aims to develop a digital image-based citrus fruit quality detection system using the Convolutional Neural Network (CNN) method with the MobilenetV2 architecture and implement it into a cross-platform mobile application. The dataset used is a combination of public datasets and local citrus fruit datasets with five quality classes, namely fresh, half-ripe, black spots, damaged, and rotten. The total data is 5001 data, with 80% as training data and 20% as testing data. The CNN model was trained for 10 epochs and evaluated using accuracy, precision, recall, and F1 score metrics. The test results show that the CNN model is able to classify citrus fruit quality with high and consistent performance, indicated by precision, recall, and F1 score values in the range of 0.96-0.97. The trained model is integrated into a Flutter-based mobile application through the Flask backend, enabling real-time citrus fruit quality detection through a smartphone camera. The results of the study prove that the integration of CNN and cross-platform mobile applications can be an effective and objective solution in automatically detecting citrus fruit quality.
Co-Authors Abdi Dharma Andrian Reinaldo Crispin Armando Sitorus Celine Chrysia Cherry Piya Vagga Josi Leonardo Davinsi Saragih Laskar Eltriman Gulo Matthew Luis Mega Cristin Angelina. H. Rico Wijaya Dewantoro Simanjuntak, Paulus Anggiat Ruben Sitorus, Ariyanto Situmorang, Kevin Agustino Titien The Lawren Pasaribu