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Journal of Computer Science and Informatics Engineering
Published by Ali Institute of Research and Publication
ISSN : -     EISSN : 28278356     DOI : -
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Machine Learning Natural Language Processing Computer Vision Text Speech Text Mining Data mining Cryptography Data visualization Expert System Deep Learning Fuzzy Logic IoT and smart environments Neural Networks Pattern Recognition Image Processing Optimization Digital Signal Processing Networking Technology Web intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol 4 No 2 (2025): April" : 6 Documents clear
Implementasi sistem informasi berbasis web menggunakan model RAD (CV indo karyatama perkasa somagede) Nardi Riana, Septi; Anggoro, Tri
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1070

Abstract

The current strategy of several companies from small to large scale is to utilize web-based technology for promotional media so that it is not limited by space and time, therefore the author plans to implement a web-based information system using the RAD method for CV Indo Karyatama Perkasa because This CV is a start-up in the advertising sector that does not yet have a website for promotional media and services so that it can capture a wider market share. Flowcharts as a design model and black box testing are used as a medium for testing the work results of the system. This research will produce an information system regarding CV Indo Karyatama Perkasa.
Deteksi Kecurangan Ujian Pada Ruangan Tertutup Menggunakan Algoritma YOLOv8 Nur Aziz Thohari, Afandi; Fathul Lathief, Muttabik; Triyono, Liliek; Santoso, Kuwat
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1100

Abstract

One of the challenges in monitoring exams in the classroom or closed room is the limited eye of the tire supervisor if he/she continues to monitor for a long time. Therefore, many behaviors of students cheating over the escape. One solution to overcome this problem is to implement an smart monitoring system that capable of detecting student exam cheating. A number of studies on smart monitoring systems have been conducted. However, the studies have not archieved optimal accuracy in identifying exam cheating. Therefore, this study provides a method to detect exam cheating in a closed room. The method used to detect the object is YOLO version 8 (Yolov8). Before training using the YOLOv8 method, hyperparameter tuning was made to generate best model performance. The test results have shown that the Yolov8s model has created the best performance with the precision, recall, IoU-Score and mAP50 values ​​of 0.952, 0.966, 0.8977 and 0.984. Testing in the working environment shows that the Yolov8s model can detect exam cheating in real time at a frame rate of 28 fps. Although it has achieved quite optimal performance. However, the performance of this exam cheating monitoring system can still be improved. Furthermore, this study has limitations, specifically that it can only detect cheating in the place where the dataset was collected.
Optimization of Diabetes Disease Classification Using Learning Vector Quantization Algorithm(LVQ) Sianipar, Eska Avelina; Yasin S, Muhammad
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1121

Abstract

Diabetes is a chronic disease that attacks humans. Diabetes is caused by high levels of sugar in the human body. Diabetes attacks the metabolic function of the body where the body cannot digest or use high levels of sugar in the human body. Diabetes is classified as a dangerous chronic disease because it has a very fatal impact on humans, especially if complications of the disease have occurred. This study aims to develop a method for classifying diabetes using the Learning Vector Quantization (LVQ) method. Clinical data from patients diagnosed with diabetes and patients who are not diagnosed with diabetes will be analyzed to identify patterns related to this disease. Attribute data such as pregnancies, glucose, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function and age will be used as variables in this analysis. The calculation results show that the Winning Class (Lowest value from the weighted results). So the classification results show an assessment to class 2 because the closest value is 70.002265497656 while the furthest value is 182.52975490778 in class 1
E-Commerce Consumer Data Clustering Using K-Means Algorithm And Kaggle Dataset Sabrina, Ade Eka; Yasin, Muhammad
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1123

Abstract

Online shopping has become a part of people's lifestyle. This is because of the many conveniences obtained to meet primary to tertiary needs. The current condition, consumer purchase transaction history data has not been utilized optimally so that it is less effective. This makes the company feel that it has not been optimal in meeting customer expectations in increasing consumer loyalty. In addition, the current marketing strategy is also considered ineffective because the offers made by the company to each consumer are still general, the company has not offered products or promotions that are really needed by consumers. Through this study, the author tries to provide solutions to companies to increase the efficiency of marketing strategies that greatly influence increasing consumer loyalty by conducting clustering analysis of e-commerce consumer data. The purpose of this study is to design and create an application for clustering e-commerce consumer data using the k-means algorithm and the kaggle dataset. The data used in this study is e-commerce consumer data. The output results of solving the problem of clustering e-commerce consumer data. It can be seen based on the results of the payment data grouping for Cluster 1 (Electronic Payment): Total Payment: 72,000,000.00, Cluster 2 (Shoe Payment): Total Payment: 6,600,000.00, Cluster 3 (Clothing Payment): Total Payment: 2,940,000.00 and Cluster 4 (Cosmetic Payment): Total Payment: 105,000,000.00
Smart Farming System Design Based on Long Range and Internet of Things Rifki Fauzan Anshori; Muhammad Saleh; Abqori Aula
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1128

Abstract

Mustard greens require an efficient irrigation system to support optimal growth and increase productivity. One irrigation system that can be used is a drip irrigation system. This study designs a smart farming system based on the Internet of Things (IoT) and Long Range (LoRa) technology to automatically manage drip irrigation in mustard greens. This smart farming system uses a capacitive soil moisture sensor to monitor soil moisture, a soil pH sensor to monitor soil pH, and an HC-SR04 ultrasonic sensor to monitor the water level in the water tank. The microcontroller used in this smart farming system is ESP32 and the communication module used is LoRa SX1278. This smart farming system uses a drip irrigation system that will be displayed on the Blynk application. This system allows remote monitoring of plant conditions and automatic drip irrigation based on soil moisture levels. The results of testing the smart farming system using a drip irrigation system showed the accuracy of the ultrasonic sensor to measure water levels of 96.71%, the soil moisture sensor to measure soil moisture levels of 95.65%, and the soil pH sensor to measure soil pH of 97.46%. Communication using LoRa in areas with minimal interference can reach a distance of up to 321.66 meters. While in areas with a lot of interference it can reach a distance of up to 100 meters. The average LoRa data transmission time is 3.185 seconds
Smart Shopping System: Integration of Web Technology and IoT for Digital Transformation of Small and Medium-Scale Retail Stores Banjarnahor, Wiwin Sry Adinda; Sinuraya, Junus; Prayudani, Santi; Tumanggor, Orli
Journal of Computer Science and Informatics Engineering Vol 4 No 2 (2025): April
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v4i2.1217

Abstract

The modern retail sector faces challenges in meeting consumer expectations for efficient and convenient shopping experiences. This research develops a web-based Si-Smart Shop system integrated with Internet of Things (IoT)-based smart shopping cart to optimize retail store operations. The research problem formulation includes reducing customer queue density, providing real-time price information independently, and automating the price label creation process that was previously done manually. The research methodology uses a system development approach with the waterfall model. Implementation results show that the system successfully optimizes operational workflow by reducing consumer waiting time and minimizing price calculation errors. Blackbox testing on 42 test cases demonstrates that all system functionalities meet operational criteria. The main contribution of this research is providing accessible technology solutions for various business scales with affordable investment, addressing operational problems in transaction processes and product information management, as well as improving operational efficiency and service quality in retail environments.

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