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Infact: Jurnal Sains dan Komputer
Published by Universitas Kristen Immanuel
ISSN : 25278363     EISSN : 28290259     DOI : https://doi.org/10.61179/infact
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal sains dan komputer (INFACT) berisi artikel bidang informatika dengan scope:  Database Management,  Computer Networks,  Software Engineering,  Graphics and Multimedia,  Theory of Computation,  Web Technology,  Soft Computing,  Web Data Management,  Software Quality Testing,  Artificial Intelligence,  Robotics,  Augmented and Virtual Reality,  Mobile application development,  Cloud and Big Data,  Cyber security,  Data Mining,  Information Retrieval,  Multimedia Technology,  Mobile Computing,  Artificial Intelligence,  Computer Vision,  Image Processing, dan Internet of Things
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 73 Documents
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Design of Web-Based Information System for Jemaat Hidup Baru Salatiga Baptist Church Welli; Sitokdana, Melkior N. N.
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.661

Abstract

The use of the web as a medium for disseminating information has been utilized, both by individuals and institutions. Churches are one of the religious institutions that utilize the web as a medium for disseminating information. JHB Salatiga Baptist Church does not yet have a web as a means of delivering information to its congregation. Information about the church and service activities has not been documented and is only delivered directly after Sunday worship. Therefore, supporting information media is needed that can support the delivery of information as a whole and can be accessed at any time so that it is more effective and efficient. In this research, the method used is the waterfall method. And this research uses the bootstrap 4 framework and for the testing method using black box. The result of the research with the title "Design of Web-Based Information System of Jemaat Hidup Baru Salatiga Baptist Church", is a web design of JHB Salatiga Church for the delivery of information that makes it easy for congregants to access information effectively and efficiently.
Integration of Federated Learning in Big Data Analytics for IoT-based Intelligent Transportation System Budiman Wijaya; Irma Putri Rahayu; Heri Wijayanto
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.704

Abstract

This article examines the integration of Federated Learning (FL) into big data analytics for intelligent transportation systems based on the Internet of Things (IoT). FL enables distributed machine learning model training without transferring sensitive data to a central server, preserving privacy and reducing data breach risks. The literature review highlights three key studies. The first demonstrates how FL improves traffic prediction accuracy using data from various sources, including vehicles and environmental sensors. The second introduces a big data architecture that integrates FL for real-time analysis and decision-making. The third emphasizes FL's role in sustainable traffic management, reducing congestion and carbon emissions through data-driven solutions. This article identifies research gaps and offers recommendations for optimizing FL in big data analytics, aiming to enhance efficiency, safety, and sustainability in modern transportation systems
Aspect-Based Sentiment Analysis Using Latent Dirichlet Allocation (LDA) and DistilBERT on Threads App Reviews Kambayo, Andreas Noprianto Kambayo; Berutu, Sunneng Sandino; Jatmika, Jatmika; Nshimiyimana, Aristophane
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.707

Abstract

Threads is a social media application that offers news services and user interaction, integrated with Instagram. Unlike other platforms, Threads does not have features like direct messaging (DM), trending topics, or advertisements. To understand users' opinions about this app, a sentiment analysis based on aspects was conducted on Threads reviews. The steps involved include applying web scraping techniques to collect reviews data from the Play Store. Aspect categories were identified using the Latent Dirichlet Allocation (LDA) algorithm. Sentiment labeling was then performed for positive and negative categories using the DistilBERT method. The results show that the LDA algorithm identified three aspects: Usage and Integration (with 3.147 positive and 8.173 negative reviews), Features and Comparisons (with 1.108 positive and 1.709 negative reviews), and User Experience and Satisfaction (with 3.529 positive and 2.208 negative reviews). The sentiment analysis results indicated 7,784 positive reviews and 12,090 negative reviews. Model performance evaluation using the Confusion Matrix showed an accuracy of 96.71%, precision of 97.24%, recall of 94.48%, and F1-score of 95.84%. Evaluation was also conducted for each aspect, with an accuracy of 96.99%, precision of 96.60%, recall of 92.85%, and F1-score of 94.69% for the Usage and Integration aspect; accuracy of 95.74%, precision of 94.11%, recall of 95.23%, and F1-score of 94.67% for the Features and Comparisons aspect; and accuracy of 96.74%, precision of 95.83%, recall of 99.06%, and F1-score of 97.42% for the User Experience and Satisfaction aspect.
Enhanced Dermatological Diagnosis: Autoimmune and Non-Autoimmune Skin Disease Classification Using MobileNet and ResNet Tyara Regina Nadya Putri; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.711

Abstract

Autoimmune diseases arise when the immune system mistakenly attacks the body's healthy cells, causing a range of symptoms that can greatly affect a patient's quality of life. In Indonesia, these conditions present a significant public health concern. According to research by Ministry of Health Republic Indonesia in 2024, autoimmune lupus affects approximately 0.5% of the population, impacting over 1.3 million individuals. This study proposes a classification and detection model utilizing Convolutional Neural Networks (CNN) with transfer learning, incorporating MobileNetV2, MobileNetV3Small, MobileNetV3Large, ResNet50, ResNet101, and ResNet152 architectures. The model's performance is assessed using a confusion matrix, evaluating precision, recall, and F1-score, while computational efficiency is analyzed using a GPU T4. Experimental results demonstrate that ResNet152 achieved the highest accuracy at 92%. These findings emphasize the crucial role of selecting an optimal CNN architecture to enhance the accuracy of autoimmune and non-autoimmune skin disease classification and detection.
Implementation of Convolutional Neural Network for Detecting Cataract Disease Severity in Eye Images Fadlilatunnisa, Fanny; Widodo, Agung Mulyo
Infact: International Journal of Computers Vol. 9 No. 01 (2025): International Journal of Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v9i01.712

Abstract

Cataract is a condition that causes clouding of the lens of the eye, leading to blindness and poor vision. According to the WHO, around 18 million people suffer from cataract-related blindness, making it one of the leading causes of blindness globally. Prompt and accurate diagnosis is essential to prevent more serious outcomes. This research aims to develop a deep learning model that utilises Convolutional Neural Networks (CNN) in categorising cataract severity into four groups: hypermature, normal, immature and mature. This model is expected to provide a more efficient and accurate alternative to traditional methods in diagnosing cataracts. To achieve this, we implemented transfer learning using three popular CNN architectures: VGG16, VGG19, and ResNet50. Experiments were conducted using a dataset of pre-labelled eye images for training. Model performance was evaluated by calculating F1-score, recall, accuracy, and precision using a confusion matrix. The results showed that VGG19 produced 88% accuracy and F1-score of 0.87, while VGG16 had the best accuracy. On the other hand, ResNet50 showed the lowest accuracy with 63% and F1-score of 0.59. These findings highlight the importance of selecting the right CNN architecture for cataract diagnosis, while underlining the potential application of deep learning in ophthalmology.
Forensic Metadata Analysis in Detecting Digital Image Manipulation Mario Anugraha; Ryan Putranda Kristianto; Andre Hartanto
Infact: International Journal of Computers Vol. 9 No. 02 (2025): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of the digital era has brought significant changes to various aspects of life, including the field of photography. Digital photos offer both advantages and disadvantages, one of which is the ease with which they can be modified using image editing software. This makes it increasingly difficult to distinguish between original and manipulated images. Edited photos can spread widely through social media, causing public concern and doubts about the authenticity of information. Individuals often manipulate images for personal gain or interests. The easy access to image editing software further facilitates this manipulation process. In the field of research, digital image forensics has emerged as a scientific method to verify the authenticity of images through accountable evidence. This study aims to detect forgery in digital photos using that approach. The method employed in this research is metadata analysis with the assistance of an offline tool called JPEGsnoop and online tool Forensically Beta. The results show differences in metadata between the original and manipulated images, indicating that modifications have been made to the image.
A System Engineering Approach to Sustainability Decision Support System Based on the Global Reporting Invitiative Syaukani, Muhammad
Infact: International Journal of Computers Vol. 9 No. 02 (2025): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Study this to develop a Sustainability Decision Support System (SDSS) based on the Global Reporting Initiative (GRI) at Insitut Teknologi Bisnis dan Bahasa Dian Cipta Cendikia (ITBA DCC) using a systems engineering approach. SDSS is designed to support decision-making sustainability data through a staged engineering system that includes analysis requirements, design, implementation, testing, application, and maintenance. This system integrates GRI indicators with campus data and produces visualizations as well as multi-criteria analysis, using increased transparency and accountability within institutions. The results show that SDSS is capable of facilitating comprehensive reporting in dimensions of social, economic, environmental, and governance. Research also confirms the importance of stakeholder collaboration in designing a responsive system to challenge sustainability in college high. The developed model is expected to be replicated in other institutions to strengthen digital governance? sustainability and standardization.
Implementation of Latent Dirichlet Allocation Topic Modeling and VADER on Aspect-Based Sentiment Analysis Kevin, Kevin Miracle Satoko; Berutu, Sunneng Sandino; Jatmika; Palupi, Retno
Infact: International Journal of Computers Vol. 10 No. 01 (2026): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v10i01.708

Abstract

Aspect-Based Sentiment Analysis on a Product or Service is Crucial for Enhancing Customer Satisfaction. This Study Applies Latent Dirichlet Allocation (LDA) Topic Modeling to Identify Aspects. Then, the Valence Aware Dictionary and Sentiment Reasoner (VADER) Lexicon Method is Adopted to Determine Sentiment on These Aspects. The Data Source Comes from Customer Reviews of a Gelato Ice Cream Shop at Taman Siswa. Data was collected from Google Maps Using the Web Scraping Method via the Instant Data Scrapper Application. The Experimental Results Show that the LDA Method Identified 3 Aspects: Flavor, Place, and Service. Meanwhile, Sentiment Measurement Using VADER on the Flavor Aspect Revealed a Positive Sentiment of 213%, Negative Sentiment of 60%, and Neutral Sentiment of 218%. The Next Aspect, Place, Had a Positive Sentiment of 32%, Negative Sentiment of 4%, and Neutral Sentiment of 4%, while the Service Aspect Composed of 32% Positive Sentiment, 3% Negative Sentiment, and 3% Neutral Sentiment. Overall, the Positive Sentiment for the Flavor Aspect (Taste) Outweighed the Negative and Neutral Sentiments for the Place (Location) and Service (Service) Aspects.
Data-Driven Classification of Poverty Status in Indonesia using Machine Learning Techniques Syaila Fathia Azzahra; Yudi Ahmad Hambali; Ismail Marzuki Randos
Infact: International Journal of Computers Vol. 10 No. 01 (2026): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v10i01.753

Abstract

This study explores the use of the K-Nearest Neighbor (KNN) algorithm to classify poverty status in Indonesia using publicly available socio-economic indicators. Traditional poverty classification methods are often inefficient and lack nuance. By leveraging the Knowledge Discovery in Databases (KDD) process, including data preprocessing, normalization, and dimensionality reduction via PCA, the study builds a robust classification model. The dataset includes indicators such as education, health, and expenditure levels from 514 districts/cities. The optimal KNN model, determined through cross-validation, achieved a test accuracy of 95.15%, with strong precision, recall, and ROC AUC scores. Feature importance analysis via Random Forest on PCA-transformed data highlights the predictive influence of certain component combinations. The results demonstrate the potential of machine learning to support more accurate and data-driven policy targeting in poverty alleviation. Future enhancements may involve integrating time-series or satellite data to increase relevance and precision.
Literature Review: Utilization of Cloud Computing in Drip Irrigation System Putra, Ida Ayu Devian Branitasandhini; Ayoedya, Jasmine Nabila; Wijayanto, Heri
Infact: International Journal of Computers Vol. 10 No. 01 (2026): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v10i01.760

Abstract

Low levels of food production can be caused by some countries still using traditional farming methods, which can affect crop yields. Farmers who still use conventional farming methods often cause inefficient use of resources. This can be overcome by using smart farming methods by utilize technology. This technology is known as the Internet of Things. IoT technology in agriculture is utilized in irrigation systems to create smart irrigation systems. In addition to utilizing IoT in the use of smart irrigation systems in agriculture, irrigation systems also utilize Cloud Computing. The purpose of this literature review is to examine the use of Cloud Computing in IoT-based smart irrigation systems, as well as to identify the benefits and challenges associated with efficient water use and agricultural consumption. This study uses a narrative literature review method by collecting literature studies related to the use of IoT and Cloud Computing in drip irrigation systems. From the results of the analysis of five journals discussing the use of Cloud Computing in IoT-based smart irrigation systems, it can be concluded that the use of Cloud Computing technology in smart irrigation systems provides various significant benefits, especially in terms of water use efficiency and better irrigation management.

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