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ITIJ
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 30253152     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Information Technology International Journal (ITIJ) is international referred journal with the objectives to explore, develop, and elucidate the knowledge of Information Technology, to keep practitioners and researchers informed on current issues and best practices, as well as serving as a platform for the exchange of ideas, knowledge, and expertise among technology researchers and practitioners.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 31 Documents
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Long Short Term Memory Method and Social Media Sentiment Analysis for Stock Price Prediction Mas Diyasa, I Gede Susrama; Mustika, Agung; Amanullah , Nurkholis
Information Technology International Journal Vol. 2 No. 1 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i1.13

Abstract

The stock market is a complex arena of interest yet uncertainty. Trading stocks, binaries, gold, and bitcoin is growing in popularity, but is prone to price fluctuations influenced by economic and political factors. Social media, particularly Twitter, is where views on companies are shared. Social media sentiment analysis can provide additional insights to evaluate potential future stock price movements, preventing unwanted speculation. The purpose of this research is to develop a Tesla stock price prediction model by integrating the Long Short-Term Memory (LSTM) method and social media sentiment analysis from Twitter to improve prediction accuracy. Stock price data is obtained from Kaggle and Twitter sentiment data is processed through pre-processing. Evaluation values such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) are lower in the model with sentiment indicating the ability of the model to more accurately model the dynamics of stock price movements. Lower MSE and RMSE indicate that the model's predictions are closer to the true values, and therefore, the model can be considered more reliable in projecting future stock price changes. These results provide support for the use of Twitter sentiment analysis as a useful source of additional information in improving the prediction accuracy of LSTM regression models in the context of stock market analysis
The Possibility Prediction of Inheriting Blood Types in Parents Based on The Child's Allele Combination Adi Nugroho, Afrizal; Salsa Billa, Aidha; Mochamad Fabian, Reyhan; Munawir, Munawir; Taufik Dwi Putra , Muhammad
Information Technology International Journal Vol. 2 No. 1 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i1.14

Abstract

The application of predicting the likelihood of blood group inheritance in children based on allele combinations from parents is becoming increasingly relevant. Understanding the combination of alleles that may appear in offspring can provide better insight for individuals and couples in planning families. The existence of an application to predict a child's blood type by using program development to form a family tree based on blood type can provide a clear visual picture of the inheritance of blood type from generation to generation. The purpose of this research is to identify allelic combinations of certain genes that affect a person's blood type and analyze how genetic inheritance occurs from parents to children. This study proves that we can see the likelihood of blood type inheritance of a child by looking at the allele combinations of both parents. This concept can be explained using the concept of a family tree (Tree). By using the Tree concept, we can easily visualize the inheritance of blood groups based on alleles, and facilitate the understanding of the formation of allele combinations that occur in the genetic inheritance process.
Enterprise Resource Planning (ERP) Design Using TOGAF ADM and ACMM (Case Study: PT XYZ) Triyanto, Triyanto; Supriyanto, Aji
Information Technology International Journal Vol. 2 No. 1 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i1.16

Abstract

Enterprise Resource Planning (ERP) is software used by companies to unite and manage information from various parts of the company, or a tool that integrates all aspects of business in a company. However, in order to support the operations of PT XYZ, it has not implemented an Information System in its business activities. In order to support the smooth running of business processes at PT XYZ, it is necessary to design a structured Information System with good planning using the TOGAF ADM framework. Before starting the design, the researcher evaluated the maturity level of PT XYZ by comparing the current condition with the desired one using the Architecture Capability Maturity Model (ACMM). The results of this evaluation were recommendations for an ERP architecture that was adapted to PT XYZ's business processes, including business architecture, data, applications, and technology that have reached their respective level 3 maturity levels.
The Accuracy of Supervised Learning Algorithm on Machine Learning Implementation: a Literature Review Tarangga, Bagas; Trafika, Evania
Information Technology International Journal Vol. 1 No. 2 (2023): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v1i2.17

Abstract

Machine Learning has become an integral element in technological development, having a significant impact on various sectors of life. This study explores the contribution of Machine Learning in big data processing, automated decision making, and predictive system development. The advantages of Machine Learning, especially in supervised learning, are emphasized by discussing algorithms such as regression, Support Vector Machines (SVM), and Neural Networks. Literature research includes five journals related to supervised learning applications, highlighting findings such as the effectiveness of the Random Forest algorithm in diagnosing pregnancy, the contribution of the SVM model in predicting student study periods, and the level of accuracy with the hybrid LSH and k-NN methods for weather prediction. The practical implementation of fruit detection using cameras shows real application in facilitating price checks and fruit recognition. In conclusion, the literature review confirms the potential and relevance of Machine Learning techniques, especially supervised learning, in providing solutions to various challenges in various sectors. It is recommended that further research explore different industrial sectors or specific case studies to gain a more comprehensive and relevant perspective on current trends in the development of Machine Learning techniques
Analysis of Factors Affecting Minimum Salary of Workers in Indonesia Using Binary Logistic Regression Hadi, Surjo; Renaldi, Sahat; Trimono, Trimono; Susrama Mas Diyasa , I Gede
Information Technology International Journal Vol. 2 No. 1 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i1.20

Abstract

Salary is an important indicator used to measure the compensation and recognition individuals receive for their contributions to the workforce. Investigating the factors that influence salary levels is an intriguing research area. This study uses a logistic regression approach to analyze the relationship and influence of job field, job level, company location, and tenure on workers' salaries in Indonesia. The research findings reveal that the variables of job level and company location have a significant relationship with the minimum salary level received by workers. Based on the logistic regression modeling results, the variables that influence the minimum salary level are the company location (foreign) and average tenure
Time Series Analysis for Electricity Demand Forecasting: A Comparative Study of ARIMA and Exponential Smoothing Models in Indonesia Ilman Nugraha, Rizky; Agussalim
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.23

Abstract

The increasing global demand for electricity, driven by rapid urbanization and industrialization, necessitates accurate forecasting models to ensure efficient energy management. This study investigates electricity consumption patterns in Indonesia from 1970 to 2022 and evaluates time series forecasting methods for predicting future demand. The models employed include AutoRegressive Integrated Moving Average (ARIMA) and Exponential Smoothing, both of which are commonly used for short-term and long-term forecasts. The dataset was collected from Indonesia's national energy statistics, and preprocessing steps were applied to ensure data quality and consistency. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). While ARIMA captured short-term trends, Exponential Smoothing demonstrated better long-term forecasting accuracy. The results highlight the effectiveness of these models in identifying electricity consumption trends and provide insights for policymakers and energy providers in optimizing energy distribution and production. Future work may incorporate advanced machine learning models and additional external factors for improved forecasting precision.
Design Of An Attendance Application System Using Face Recognition And Location Based On Android Ainun Rizkyani Fadillah; Jalil, Abdul
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.29

Abstract

This research presents the design of an attendance application using face recognition and location technology for CV. Waysolve. The aim is to improve the efficiency, accuracy, and security of employee attendance compared to traditional methods. The study begins with an analysis of the current attendance system to identify issues such as data inaccuracies and fraud potential. Utilizing advanced technologies like face recognition algorithms and GPS, the proposed system streamlines attendance processes. The design phase employs Use Case Diagrams and Class Diagrams to define functional requirements and database structure, while a client-server architecture ensures clear separation between user interface and business logic, enhancing security. Incorporating Object-Oriented Programming (OOP) and functional programming paradigms results in clean, maintainable code. The successful implementation of this system is expected to improve employee productivity, accountability, and data accuracy, providing a solid foundation for future developments in attendance management.
Exploring Opportunities and Challenges in Multi-Cloud and Hybrid Cloud Implementation Firdaus, Wigananda; Sukmaaji, Anjik
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.30

Abstract

This study reviews the opportunities and challenges of implementing Multi-Cloud and Hybrid Cloud models that focus on security and data management. Multi-Cloud implementation offers flexibility, but also brings challenges related to security and privacy. Data security in multi-cloud can be improved by implementing encryption such as Homomorphic Encryption and Hybrid Crypto which combines DES and RSA algorithms. The Hybrid Cloud model allows integration between public and private clouds. where the implementation of Zero Trus can improve the security of the cloud network. The results of this Literature Review emphasize the importance of security policies at every layer of the cloud from infrastructure to applications. this is done to protect sensitive data in the cloud environment. of course, the right strategy is needed in cloud data management so that the implementation of cloud computing is more effective
Detection of Abnormal Human Sperm Morphology Using Support Vector Machine (SVM) Classification Mas Diyasa, I Gede Susrama; Prasetya, Dwi Arman; Cahyani Kuswardhani, Hajjar Ayu; Halim, Christina
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.36

Abstract

Abnormal sperm morphology is a key indicator of male infertility, making its accurate detection crucial for reproductive health assessments. This study explores the application of Support Vector Machine (SVM) classification to automatically detect abnormalities in human sperm morphology. A dataset of microscopic sperm images was collected and labelled based on normal and abnormal morphological features, including head shape, midpiece defects, and tail irregularities. Feature extraction techniques were employed to quantify key morphological characteristics, which were then used to train the SVM model. The proposed SVM-based approach demonstrated high accuracy in classifying normal versus abnormal sperm morphology, significantly reducing the time and error associated with manual analysis. This method provides an efficient, automated solution for andrology laboratories and fertility clinics, enhancing diagnostic consistency and reliability. By incorporating machine learning techniques, this system holds promise for improving the precision of sperm morphology analysis, ultimately contributing to better fertility treatments and outcomes
A Tracer Study Design With Whatsapp Chatbot Integration Using Natural Language Processing Firdaus, Wigananda
Information Technology International Journal Vol. 2 No. 2 (2024): Information Technology International Journal
Publisher : Magister Teknologi Informasi UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/itij.v2i2.24

Abstract

Tracer study is a method used by educational institutions to track alumni and assess the effectiveness of the education provided. A key challenge in conducting these studies is the low participation rate of respondents, often due to lengthy surveys and a lack of interactive engagement. To address this issue, a WhatsApp chatbot system powered by Natural Language Processing (NLP) was developed. This system facilitates an interactive and user-friendly survey experience, allowing respondents to complete the survey directly through WhatsApp without needing to visit a website. Responses are automatically stored in Google Sheets via an API. By using a microservices architecture, the project efficiently separates crucial components such as WhatsApp API, NLP services, and Google Sheets API, leading to improved data collection efficiency and a more convenient survey process for respondents.

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