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Media Jurnal Informatika
Published by Universitas Suryakancana
ISSN : 20882114     EISSN : 24772542     DOI : https://doi.org/10.35194/mji.v12i2
Core Subject : Science,
Computer Science & IT
Media Jurnal Informatika merupakan oleh jurnal yang diterbitkan oleh Program Studi Teknik Informatika Universitas Suryakancana Cianjur yang terbit setiap 6 Bulan pada Juni dan Desember. Media Jurnal Informatika mulai terbit dengan versi cetak pada tahun 2009 dan terbit satu kali dalam satu tahun, namun kemudian frekuensi terbit dinaikan menjadi dua kali dalam satu tahun. Fokus dan lingkup bidang Media Jurnal Informatika meliputi Geography Information System Security Network Big Data Information System Enterprise Resource Planning Internet of Things, Cloud Computing Artificial Intelligent Soft Computing Multimedia dan Game Human Computer Interaction
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 206 Documents
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Classification of Banana Ripeness Using a VGG16-Based Convolutional Neural Network (CNN) Maulana, Fikri
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5930

Abstract

The ripeness level of bananas is a crucial factor that affects the quality, taste, and selling value of the commodity, but the manual sorting process that is commonly carried out is still subjective, inconsistent, and time-consuming. This study aims to implement and evaluate the performance of a VGG16-based Convolutional Neural Network (CNN) architecture in automatically classifying the ripeness level of bananas. The research dataset consists of 5,616 digital images obtained from the Roboflow Universe platform and grouped into six specific classes: freshripe, freshunripe, overripe, ripe, rotten, and unripe. The system development methodology includes data division using stratified splitting techniques, image pre-processing with data augmentation strategies to prevent overfitting, and the application of transfer learning. The model was trained using the Stochastic Gradient Descent (SGD) optimization algorithm with a learning rate of 0.001 for 25 epochs on GPU-based hardware. Performance evaluation was conducted in depth using a confusion matrix, F1-Score metrics, and Precision-Recall curve analysis. The experimental results showed that the VGG16 model achieved an overall accuracy of 97.13%. Class-by-class analysis shows perfect performance in the freshunripe category, although there is a slight decrease in precision in the ripe class due to the similarity of visual characteristics with the overripe class. The stability of the training and validation accuracy curves also indicates that the model has good generalization capabilities. This study concludes that the VGG16 architecture is a reliable and accurate solution to support the efficiency of smart farming systems.
Evaluation of Deflate Algorithm in Lossless Compression of Digital Document Formats Nawawi, Muhammad Irwan; Nurpandi, Finsa
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5746

Abstract

As the volume of digital data continues to escalate across sectors such as education, business, and government, the demand for efficient data storage and transmission methods grows increasingly critical. Data compression algorithms offer a prevalent solution to this challenge. This study undertakes an evaluation of the Deflate algorithm's performance in compressing digital document files, specifically examining its efficacy in reducing file size and its efficiency in processing time. Employing a comparative analysis methodology, the research involves measuring file sizes before and after compression, recording compression and decompression durations on a machine with an Intel Core i5 CPU, 8 GB RAM, running Windows 10 64-bit, and calculating compression ratios. The implementation utilizes Python and the Zlib library, which directly supports the Deflate algorithm. Tests were conducted on diverse document types, including plain text files, mixed-content files, and files rich in visual elements like images. The findings indicate that the Deflate algorithm achieves a significant compression ratio, reducing file sizes by over 90% and reaching a maximum ratio of 99.60% for text files. Compression and decompression operations were most rapid for text files, averaging 0.01 seconds. However, for documents containing images, the compression ratio was considerably lower and less impactful. Notwithstanding this, the compression and decompression times remained relatively swift and consistent across all document types. These results underscore the importance of aligning compression algorithm selection with the specific content characteristics of a document to attain optimal efficiency.
Analysis Of Information System For Scientific Work Title Submission Selection At Digitech University Merliana, Elsa
Media Jurnal Informatika Vol 17, No 1a (2025): Special Issue Information System Media Jurnal Informatika (On Progress)
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i1a.5482

Abstract

This study aims to analyze the information system for scientific worktitle submission selection in the D3 Management Informatics Study Program at Digitech University. Based on observations, the title submission process is still carried out conventionally using Google Form and Microsoft Excel, which causes several problems such as title duplication, verification delays, and data tracking difficulties. This study uses observation, interview, and library research methods. SWOT analysis is conducted to identify strengths, weaknesses, opportunities, and threats of the current system. The results show that the conventional system is less efficient and needs to be immediately replaced by an integrated digital system that can improve the effectiveness of academic processes. The system recommendations provided are expected to support the institution's vision as a digital technology-based campus.
Emotion Detection in Indonesian Text Using the Logistic Regression Method Junianto, Erfian; Puspitasari, Mila; Zakaria, Salman Ilyas; Arifin, Toni; Agung, Ignatius Wiseto Prasetyo
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5927

Abstract

Emotion detection in Indonesian text has become a crucial topic in the advancement of human–computer interaction and sentiment analysis on digital platforms. Despite its importance, challenges arise from the linguistic complexity and frequent use of slang in Indonesian text. This study aims to evaluate the performance of three classification models—Logistic Regression, K-Nearest Neighbors (KNN), and Naive Bayes—in detecting emotions from Indonesian text. The dataset comprises 1,000 texts categorized into four emotions: happy, sad, angry, and fear. Preprocessing steps included slang normalization, text cleaning, tokenization, stopword removal, and stemming, followed by TF-IDF weighting. Each model was trained and further optimized using ensemble bagging to improve classification performance. The optimized Logistic Regression model achieved the best performance, with an accuracy of 89%, precision of 0.90, recall of 0.89, F1-score of 0.89, and an average ROC-AUC score of 0.98. Both KNN and Naive Bayes models reached 81% accuracy after optimization, but their overall performance remained lower than Logistic Regression. The findings demonstrate that Logistic Regression is the most effective method for detecting emotions in Indonesian text, as it can effectively handle simple grammatical structures and slang variations. This study contributes to the development of emotion analysis models for Indonesian text, supporting applications in social computing and affective computing.
Designing a Web-Based Cake Ordering System to Increase Accessibility and Efficiency Using the Extreme Programming Method at Dapur Bolu Ibu Kokom Miranda, Grasela Asta; Darmanto, Tedjo; Hermanto, Moch. Irwan
Media Jurnal Informatika Vol 17, No 1a (2025): Special Issue Information System Media Jurnal Informatika (On Progress)
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

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

Abstract

Digital transformation is driving the development of the culinary industry, including the cake and bread business. However, Dapur Bolu Ibu Kokom still uses a conventional ordering system that often leads to errors in recording orders and inefficiency of available stock. This research aims to design a web-based sponge cake ordering system to improve operational efficiency and customer satisfaction. Extreme Programming (XP) method is used to ensure the flexibility and responsiveness of the system by using javascript programming language and Unified Modeling Language system architecture design and blacbox testing system testing. The system features registration, login, ordering, stock checking, and delivery status. The results show that this system improves recording accuracy, speeds up ordering, and provides transparent stock and delivery information. Thus, the implementation of this system supports the digitalization of the culinary business and improves operational efficiency.
Autism Classification Using MobileNetV3 Feature Extraction and K-Nearest Neighbor Algorithm Husaini, Rahayun Amrullah; Pratama, Gede Yogi; Latif, Kurniadin Abd.; Zulfikri, Muhammad; Augustin, Kartarina
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5934

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behaviors. Early detection of ASD is crucial; however, conventional diagnostic methods rely heavily on clinical observation and expert assessment, which can be time-consuming and resource-intensive. Along with the rapid development of artificial intelligence, especially in computer vision and machine learning, automated image-based approaches have gained attention as alternative tools for ASD screening. This study proposes a hybrid classification approach that integrates MobileNetV3 as a feature extraction model with the K-Nearest Neighbor (KNN) algorithm for autism classification using facial image data. Unlike previous CNN–KNN approaches, this study specifically explores the use of MobileNetV3’s lightweight architecture to generate compact and discriminative facial features, which are then classified using KNN to evaluate its effectiveness in low-complexity and resource-efficient settings. This design highlights the novelty of combining an optimized lightweight CNN with a distance-based classifier for autism detection from facial images. The dataset used in this research was obtained from Kaggle and consists of 2,940 labeled facial images of children categorized into Autism and non-Autism classes. This study proposes a hybrid classification approach that combines MobileNetV3 as a lightweight feature extraction model with the K-Nearest Neighbor (KNN) algorithm for autism classification. Experimental evaluations were conducted over multiple independent runs to improve statistical reliability, and model performance was assessed using accuracy, precision, recall, and F1-score. The results indicate that the proposed hybrid model achieves satisfactory and consistent performance while maintaining computational efficiency. These findings suggest that integrating lightweight deep learning models with classical machine learning algorithms can provide an effective and resource-efficient approach for autism classification, with potential applicability as a supportive tool for early ASD screening rather than a definitive clinical diagnosis.

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