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International Journal Of Computer, Network Security and Information System (IJCONSIST)
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 26863480     DOI : https://doi.org/10.33005/ijconsist.v3i1
Core Subject : Science,
Computer Science & IT
Focus and Scope The Journal covers the whole spectrum of intelligent informatics, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Autonomous Agents and Multi-Agent Systems • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Cognitive systems • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, fault analysis and diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High Performance Computing • Information storage, security, integrity, privacy and trust • Image and Speech Signal Processing • Knowledge Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Memetic Computing • Multimedia and Applications • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Stochastic systems • Support Vector Machines • Ubiquitous, grid and high performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 91 Documents
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A Structured Approach to Organizational Website Development and Usability Measurement Using the Modified System Usability Scale Vinza Hedi, Satria; Hedi Amelia Bella, Cintya
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.156

Abstract

The increasing demand for accessible and up-to-date digital identity has encouraged organizations to adopt websites as a primary information platform. However, limited technical resources often hinder effective content management. This study proposes a structured approach to developing an organizational website using the Waterfall model and evaluates its usability through the System Usability Scale (SUS). The development process consists of five stages: communication, planning, modelling, construction, and deployment. Requirements were collected through semi-structured interviews with organizational stakeholders, resulting in the decision to implement a web-based system using a Content Management System (CMS) to ensure ease of maintenance. After deployment, usability testing was conducted using Modified SUS with a Likert scale of 1–5, involving five respondents instead. The evaluation produced an average score of 4.6, indicating that the system is highly acceptable and easy to use, although suggestions were made to improve dashboard terminology and add a search feature. The results demonstrate that a structured web development approach combined with CMS integration can effectively empower non-technical users in managing digital content. Future development may include interface personalization and multi-admin features to further enhance usability.
Stock Price Prediction Using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) Methods Riza Akhsani Setyo Prayoga; Ariansyah, Fery Almas; Daffa, Muhammad Falikhuddin; Laqma Dica Fitrani; Masti Fatchiyah Maharani; Angga Lisdiyanto; Angkawidjaja , Steven
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.158

Abstract

This research aims to improve the accuracy of stock price prediction through the application of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) methods, focusing on stocks from the Composite Stock Price Index (CSPI) referred to as the IDX Composite. The research process includes comprehensive steps, including data collection and preprocessing, dataset creation with emphasis on stock closing prices, and division of the dataset into training and test data. The LSTM and GRU models were designed with a recurrent layer and a Dense layer and then trained for 100 epochs with a batch size of 32. Model evaluation was performed by comparing key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) on the test set. The EPOCH-RMSE graph provides an overview of the changes in the RMSE value during training. The best result of the LSTM model was achieved at the 96th epoch with RMSE 40.36, MSE 1385.97, and MAE 30.09, while GRU achieved peak performance at the 92nd epoch with RMSE 37.33, MSE 908.29, and MAE 25.42. In conclusion, GRU can be considered as a more effective option in predicting JCI stock prices based on performance evaluation using various metrics such as RMSE, MSE, and MAE.
Sentiment Analysis of User Reviews for the LinkedIn Application Using Support Vector Machine and Naïve Bayes Algorithm Ulinnuha, Nurissaidah; Pertiwi, Aisyah; Basuki, Athiyah Fitriyani; Kristanti, Beni Tiyas; Haniefardy, Addien; Burhanudin, Muhamad Aris; Satria, Vinza Hedi
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.159

Abstract

Social Networking Sites (SNS) have become integral communication platforms for knowledge sharing and professional connections. LinkedIn, a leading professional network, is widely utilized in today's digital era, primarily by professionals and the business community. This research focuses on analyzing user sentiment on LinkedIn through the application of the Support Vector Machine (SVM) and Naive Bayes methods. Understanding user opinions and satisfaction is important, and sentiment analysis serves as a key tool for this purpose. This study is a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithm for classifying user reviews of the LinkedIn application. Drawing on data from Google Play reviews, this research explores a range of user sentiment towards the LinkedIn platform, including positive, negative and neutral reviews. The application of SVM and Naive Bayes algorithms successfully classifies reviews into relevant sentiment categories. Analyzing 2000 review datasets with an 80% training and 20% testing data split, Support Vector Machines demonstrate an 80% accuracy rate, while Naïve Bayes achieves a 70% accuracy rate. The Support Vector Machines (SVM) algorithm has better accuracy than the Naïve Bayes algorithm based on the test scenarios that have been carried out.
Classification of Eye Diseases Using the AlexNet Convolutional Neural Network Model Algorithm Pratama, Moch Deny; Sultoni, Royal Fajar; Wardhani, Adil Sandy; Sechuti, Maulana Hassan; Yerezqy Bagus; Dina Zatusiva Haq; Yoga Ari Tofan
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.160

Abstract

This study uses the Convolutional Neural Network (CNN) method with the AlexNet model to classify eye diseases based on medical images. The dataset includes labeled images of three types of eye diseases: cataract, glaucoma, and diabetic retinopathy. The experimental results show that the model achieved an accuracy of 75.18%, which indicates that CNN with the AlexNet architecture can classify eye diseases quite well. This research shows that deep learning can be used to help doctors or health professionals in diagnosing eye diseases through automatic image analysis. Although the accuracy still needs to be improved, this study can serve as a reference for developing an automated diagnostic system in the future. Further research is expected to increase accuracy, expand the dataset, and apply other deep learning techniques to improve the performance of eye disease detection.
Explainable Artificial Intelligence (xAI) for Reliable Financial Decision-Making in Credit Scoring System Azizah, Nabila Wafiqotul; Ajizah, Imroatul; Muhammad Reza Pahlawan; Mohammad Al Hafidz
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.161

Abstract

Finance plays a vital role as one of the key elements necessary for sustaining life. Since financial stability is closely linked to overall well-being, many individuals resort to borrowing from financial institutions. As a result, the increasing number of loan applications has led to a rise in financial burdens and fund congestion within these institutions. To mitigate such risks, credit scoring has become an essential predictive approach widely adopted in financial institutions to evaluate customer creditworthiness. Through credit scoring, institutions can determine whether a customer is eligible to receive a loan. This study employs an open-source dataset obtained from Kaggle and follows the CRISP-DM methodology, which consists of six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The research implements a classification approach by comparing two algorithms—Random Forest Regression and XGBoost. The results show that the Random Forest Regression model performs better, achieving the highest accuracy, recall, and precision, with an AUC value of 0.796 and a Coefficient of Variation (CV) of 0.712
Application of the Random Forest Classifier Method in Grouping Patients with Intellectual Disabilities Ainiyah, Nuchaila; Afifudin, Muhammad; Masyhuri, Reyhan Dela; Fardana, Muhamad Hakam; Wahyuningtyas, Sischa; R, Awang Putra Sembada; Pratama, Muhamad Liswansyah
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.162

Abstract

This research explores the effectiveness of the Random Forest Classifier method in grouping mental retardation patients based on their level of severity. Medical record data from mental hospitals is collected and processed to train a classification model. The preprocessing process is applied to ensure data quality before use. Model evaluation is carried out by measuring the accuracy of the scores. The research results showed that the Random Forest Classifier succeeded in classifying mental retardation patients with an accuracy of 84%. These findings show the potential of the Random Forest Classifier method as a clinical tool for doctors in determining appropriate interventions for mental retardation patients based on their level of severity.
Enhancing Guest Security in Smart Hospitality: Face Recognition-Based Hotel Room Verification Using Haar Cascade Algorithm Putra, Adzanil Rachmadhi; Prayoga, Aji; Gumiwang, Zacky Yaser Malik; Karim, Mohammad Daniel Sulthonul; Wicaksono, Muhammad Galang Satrio; Faishol, Olive Khoirul Lukluil Maknun Al; Prisyanti, Affifiana
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.163

Abstract

This study aims to design and implement a hotel room verification system based on facial recognition using the Haar Cascade algorithm. The research was motivated by the growing need to enhance both security and service efficiency in the modern hospitality industry. The study was conducted through several stages, including facial image data collection using a webcam, preprocessing (RGB to grayscale conversion, image resizing, and cropping), model training, and real-time face recognition testing. The Haar Cascade algorithm was employed to detect facial features by utilizing Haar-like features combined with the Adaboost method to accelerate classification. The experimental results showed a recognition accuracy of 55% under varying lighting conditions and viewing angles. These findings indicate that the Haar Cascade algorithm performs adequately in detecting faces under ideal conditions, although further optimization is required to handle lighting variations and facial stability. This research contributes to the application of artificial intelligence technology in hotel security systems, with potential future improvement through the integration of deep learning methods to enhance accuracy and reliability in face verification. Keywords: face recognition, Haar Cascade, hotel room verification, facial detection, digital security.
Optimizing Red Onion TSS (True Shallod Seed) Production in the Lowlands Based on Smart Sensors Moeljani, Ida Retno; Rahajoe, RR Ani Dijah; N, Pangesti
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.115

Abstract

Onion cultivation technology using seeds still needs to be developed and socialized at the farm level. to be socialized at the farmer level, considering that until now farmers still cultivate shallots with consumption seed bulbs because there are still not many TSS produced, especially in the lowlands.In principle, not all shallot varieties are capable of flowering, some shallot varieties are capable of flowering. flowering, some shallot varieties are only 30% capable of flowering. This problem can be solved by optimizing flowering with an automation system. The advancement of Internet of thing (IoT) technology can be applied to optimize flowering by using smart sensors on the onion. flowering by using smart sensors on several varieties of shallots. The lanchor blue variety had no flower bulbs that set fruit and produced TSS seeds. This is because all the flower bulbs of the lanchor blue variety were rotten/damaged by disease due to the use of high watering during the growth period that led to flowering, fertilization, and sprouting. There was no interaction between varieties and application of gibberellic acid + and packlobutrazol on seed yield of TSS (Table4). In Bauji and Maaserati varieties, the percentage of flower bulbs that bear fruit and seed (harvested) is still better than BiruLanchor, only about 59.68 to 70% of the total number of flower bulbs that grow (Table 4). This indicates that the process of fertilization and seed formation of shallots is not optimal.
Design of Industrial Practice and Thesis Monitoring Information System (SIPIKRI) In Informatics Engineering Study Program, State University of Surabaya Alit, Ronggo; Agus Prihanto; Aditya Prapanca
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.116

Abstract

Abstract— Industrial Practice Activities are a place for students to gain experience in the world of work and research experience in the form of a thesis by practicing the theories they have learned in lectures. In addition, it is also a place where students express their ideas, scientific ideas and produce research results and products before they become a Bachelor. The mechanism for appointing supervisors and examiners as well as scheduling Industrial Practice and thesis in the Informatics Engineering study program is carried out by the study program through the administration while the schedule and examination team are informed via messages to each examiner. Previously, there was a system that only solved the problem of industrial practice activities, namely the Web-based Industrial Practice Monitoring Information System, which is a system that can provide information about industrial practice programs online. This system has advantages in terms of the speed of presenting the information produced. In addition, this system is web-based so that it can be accessed at any time. This process takes a long time and good management, so that students get supervisors who are in accordance with their fields of expertise and the right exam schedule so that it does not interfere with the lecture schedule and lecturer schedule, but the same problem arises related to student thesis activities. then a system is needed that can solve problems that arise related to thesis activities. The conclusion that can be drawn in this study is the production of the Industrial Practice and Thesis Monitoring Information System (SIPIKRI) which can help the Informatics Engineering study program, Faculty of Engineering, Surabaya State University to run smoothly. In addition, it is hoped that this information system can assist the work of informatics engineering study program managers in carrying out the administration of these activities. Keywords— Information systems, Industrial Practice and Thesis, Monitoring
Internet Of Things Based Car Parking Monitoring Device Integrated with WEB Server Alvin Indra Pratama; Tunggadewi, Elsyea Adia; Winarno
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.119

Abstract

The rapid increase in the number of vehicles has intensified the need for an efficient and well-managed parking system. Conventional parking systems, which rely heavily on human attendants, often suffer from the absence of real-time monitoring, resulting in congestion and operational inefficiencies. This research proposes an Internet of Things (IoT)-based Smart Parking System employing the HC-SR04 ultrasonic sensor to detect vehicle presence in each parking slot. The system is integrated with a web server that enables real-time monitoring and provides recommendations for parking slot based on proximity to entrance. The implementation utilizes ESP32 microcontrollers to process sensor data and communicate with the web server. All sensor readings are stored in a MySQL database and accessed through a PHP-based web application. Experimental evaluation demonstrates a 100% accuracy rate in slot detection, response time, and recommendation performance. The proposed system significantly enhances parking management efficiency and minimizes fuel consumption by reducing the time required to locate available parking spaces.

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