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Gedung Fakultas Ilmu Komputer 2, 2nd Fl. Faculty of Computer Science, UPN "Veteran" Jawa Timur Jl. Rungkut Madya No.1, Gn. Anyar, Kec. Gn. Anyar, Surabaya, Jawa Timur 60294, Indonesia
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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 5 Documents
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Search results for , issue "Vol. 2 No. 2 (2024): Information Technology International Journal" : 5 Documents clear
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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