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Jusikom : Jurnal Sistem Komputer Musirawas
Published by Universitas Bina Insan Lubuklinggau
ISSN : 25411896     EISSN : 26148714     DOI : https://doi.org/10.32767/jusikom.v9i1
Core Subject : Science,
Computer Science & IT
JUSIKOM is a place of information in the form of research results, literature studies, ideas, application of theory and critical analysis studies in the fields of research in the fields of Computer Systems, Computer Science, and Electronics. Focus and Scope: Embedded system, Intelligent control system, Software engineering, Computer network, Mobile computing, Artificial Intelligent, Internet of Things, and Information system.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 237 Documents
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SISTEM INFORMASI PERAWATAN LUKA DIABETES BERBASIS WEB MENGGUNAKAN LARAVEL PADA KLINIK KITAMURA nurmalasari nurmalasari; Esa Junita Pratiwi; Muhammad Rafli Pasha; Agung Sasongko
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3182

Abstract

Diabetes is a chronic disease that can cause complications in the form of diabetic wounds if not treated properly. The process of monitoring diabetic wound care in many healthcare facilities is still carried out manually, which can lead to recording errors, data loss, and difficulties in monitoring the progress of patients' wound conditions. Kitamura Clinic, as one of the healthcare providers specializing in diabetic wound care, requires a system that can assist medical personnel in managing patient data and monitoring wound progress effectively. This study aims to implement a web-based Diabetic Wound Care Monitoring Information System using the Laravel framework. The system development method used is the Waterfall model, which includes requirements analysis, system design, implementation, testing, and maintenance. The developed system provides features for patient data management, wound care recording, automatic wound area calculation, Ankle Brachial Index (ABI) calculation, wound exudate volume recording, and clinical wound photo documentation. The results indicate that the system can support diabetic wound care monitoring in a more structured, effective, and accessible manner for healthcare professionals, thereby improving the quality of healthcare services at Kitamura Clinic
RANCANG BANGUN SISTEM SMART PARKING BERBASIS IoT DENGAN MONITORING REAL-TIME MENGGUNAKAN ESP32 DAN APLIKASI WEB Erlan Syahputra; Muh Subhan; Yogi Perdana
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3197

Abstract

Limited parking availability and manual parking management often cause various problems, such as inefficient land use, difficulties in finding parking spaces, and increased risks of vehicle loss due to the absence of proper access control. This condition also forces users to spend more time searching for parking, which can increase fuel consumption and carbon emissions. To address these problems, this research designs and develops an Internet of Things (IoT)-based Smart Parking system using ESP32, ultrasonic sensors, RFID, and a web application. The system was developed using the Prototype method, which consists of requirements analysis, system design, implementation, and system testing. The results show that the system is able to detect vehicle presence, manage user access, identify the use of more than one parking slot by the same user, and display real-time parking information through the web application. Based on the testing results, all system functions operate properly, with an average response time of 1.8 seconds and a reliability level of 99.44%. The developed system successfully supports parking management by making it more organized, secure, and efficient
IMPLEMENTASI FACE RECOGNITION DENGAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN LIVENESS DETECTION PADA SISTEM ABSENSI PT. XYZ: IMPLEMENTASI FACE RECOGNITION DENGAN CONVOLUTIONAL NEURAL NETWORK (CNN) DAN LIVENESS DETECTION PADA SISTEM ABSENSI PT. XYZ Nolan Efranda; Berta Erwin SLAM; Feri Irawan; Rifaldi Herikson; Yustida Bellini
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3202

Abstract

Employee attendance management is a crucial aspect in improving organizational efficiency and productivity. However, in practice, fraudulent activities such as buddy punching are still frequently encountered. Therefore, PT.XYZ requires an efficient and secure attendance system to address this issue. This study aims to implement a face recognition-based attendance system using the Convolutional Neural Network (CNN) method combined with liveness detection. The system is developed on both mobile and desktop platforms using Python, TensorFlow, and Firebase technologies. The research process includes collecting a dataset of 320 facial images from 32 employees, image preprocessing, CNN model training, and the integration of liveness detection based on facial movement analysis to verify user authenticity. System evaluation is conducted based on accuracy, response time, and robustness under varying conditions such as lighting and facial positions. The results show that the system is capable of recognizing faces in real-time with an accuracy rate of 97.81% and a response time ranging from 2 to 5 seconds. The system also demonstrates stability under various lighting conditions and shows good scalability. Therefore, the CNN and liveness detection-based attendance system is effective in improving accuracy, security, and supporting a more efficient, professional, and transparent employee attendance management system.
PERBANDINGAN ALGORITMA RANDOM FOREST DAN KNN UNTUK DETEKSI SERANGAN DDOS SECARA REAL-TIME PADA JARINGAN LOKAL YUSRIDA JELIANTI SIHITE SIHITE; Dedy Kiswanto; Muhammad Rois Lukman Damanik
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3207

Abstract

Distributed Denial of Service (DDoS) attacks are a cyber security threat capable of massively disrupting network services within a short period of time. Static rule-based detection methods have proven inadequate in the face of constantly evolving attack patterns, making machine learning approaches a more adaptive alternative. This study compares the performance of the Random Forest (RF) and K-Nearest Neighbour (KNN) algorithms in detecting DDoS attacks in real-time on a local network. The local network topology was physically constructed using a Router 1941, a Switch, an Attacker PC, and a Normal PC, whilst model training utilised the CICIDS2017 dataset comprising 225,711 samples with 78 features via Google Colab. The system was built entirely using Python, with Scapy as the packet sniffing engine and Streamlit as the interactive web dashboard framework, allowing detection results to be monitored simultaneously via both the command-line interface (CLI) and a browser-based visual display. Experimental results show that RF achieved an accuracy of 99.99% with a prediction time of 0.74 seconds and only 2 misclassifications, whilst KNN achieved an accuracy of 99.96% with a prediction time of 87.33 seconds and 14 misclassifications. In real-time latency testing, RF recorded 40.027 ms and KNN 34.678 ms. RF is recommended as the primary algorithm for DDoS detection systems on local networks.
SISTEM MONITORING JARINGAN SMALL OFFICE DAN DETEKSI ANOMALI TRAFIK BERBASIS MACHINE LEARNING DENGAN VISUALISASI Yohanes Gerardus Haga Zai; Dedy Kiswanto; Bicanro Gebriyan Panjaitan; Zahira Putri Julia Daulay
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3208

Abstract

Jaringan small office umumnya dikelola dengan sumber daya terbatas sehingga rentan terhadap ancaman siber yang tidak terdeteksi secara dini. Penelitian ini merancang dan mengimplementasikan sistem monitoring infrastruktur jaringan small office yang terintegrasi dengan mekanisme deteksi anomali trafik berbasis deep learning. Sistem dibangun menggunakan tiga komponen utama: pengumpulan data melalui protokol SNMP dan packet sniffing berbasis Scapy, pemrosesan dan klasifikasi trafik menggunakan model Fully Connected Neural Network dengan fungsi aktivasi Softmax, serta dashboard visualisasi. Topologi jaringan disimulasikan menggunakan GNS3 dengan MikroTik Cloud Hosted Router (CHR) sebagai gateway utama dan Kali Linux sebagai agen penyerang untuk pengujian skenario serangan. Dataset dibangun dari empat kelas trafik — Normal, Denial of Service (DoS), Port Scanning, dan SSH Brute Force — yang diekstraksi menggunakan metode Time-Windowing 3 detik. Model deep learning yang dilatih menggunakan algoritma class weighting, Early Stopping, dan ReduceLROnPlateau berhasil mencapai akurasi pelatihan 99,28% dan akurasi validasi 99,48% tanpa indikasi overfitting. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi seluruh kelas serangan secara real-time dan menampilkan peringatan pada dashboard secara otomatis, menjadikannya solusi monitoring yang komprehensif, ringan, dan adaptif untuk infrastruktur jaringan small office.
PENGEMBANGAN DASHBOARD MONITORING JARINGAN BERBASIS WEB Sevta Triwana Simamora; Farizky Aulia Lubis; Dedi Kiswanto; M. Tsabat Muhyiyuddin
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3213

Abstract

The increasing complexity of network infrastructure requires monitoring systems capable of performing effective and real-time monitoring. Delayed fault detection, difficulties in monitoring multiple devices, and the lack of automated anomaly detection remain significant challenges for network administrators. This study aims to develop a web-based network monitoring dashboard capable of real-time device monitoring, network anomaly detection, and automatic notification delivery. The research employed a Research and Development (R&D) method with a prototyping approach. The system was developed using React, Node.js, Express, and MySQL, supported by ICMP Ping, TCP Port Check, HTTP Check, and API Check monitoring methods. Anomaly detection was implemented using the Z-Score method based on historical data, while real-time notifications were delivered through the Telegram Bot API. The results show that the system successfully displays network metrics, including latency, jitter, packet loss, success rate, and uptime in real time. Testing produced a Z-Score value of 4.78, exceeding the anomaly threshold (Z > 3), indicating successful latency anomaly detection. The system also calculates outage risk as an indicator of potential disruptions and sends notifications for offline conditions, high latency, and network anomalies. Therefore, the developed system improves monitoring effectiveness and accelerates network fault handling.
RANCANG BANGUN PENDETEKSI KEBOCORAN GAS LPG DENGAN SENSOR MQ-5 BERBASIS ARDUINO UNO Muawan Bisri; Lukman Hakim; Okto Kurnia
Jusikom : Jurnal Sistem Komputer Musirawas Vol. 11 No. 1 (2026): Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v11i1.3294

Abstract

Liquefied Petroleum Gas (LPG) leakage is one of the leading causes of fires and explosions in residential and commercial environments. The inability to detect gas leaks quickly and accurately can increase the risk of accidents, property damage, and loss of life. This study aims to design and implement an LPG gas leakage detection system using an MQ-5 gas sensor based on an Arduino Uno microcontroller. The research employed the Prototype method, which consists of requirements analysis, prototype design, prototype testing, program coding, system testing, and device implementation. The MQ-5 sensor was utilized to detect LPG gas concentration and classify leakage levels into three categories: safe at concentrations below 300 ppm, warning at concentrations ranging from 300 to 700 ppm, and danger at concentrations above 700 ppm, while the Arduino Uno served as the main controller for processing sensor data. The system was equipped with an LCD to display gas concentration, a buzzer as an audible warning device, and a GSM SIM800L module to automatically send SMS notifications when a gas leak was detected. The testing results demonstrated that the proposed system was able to accurately detect LPG gas leakage and provide early warnings through both audible alarms and SMS notifications. Therefore, the developed system can enhance safety and minimize the risk of fires caused by LPG gas leakage by providing real-time monitoring and multi-level risk classification.

Page 24 of 24 | Total Record : 237

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