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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 229 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.
DEVELOPMENT OF A SALES FORECASTING APPLICATION USING THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD WITH EXTERNAL INPUT (ARIMAX) Fauziyyah, Aulia Aziizah; Brahmana, Jonanda Pantas Agitha; Simatupang, Paulina Lestari; Soewono, Eddy Bambang; Hayati, Hashri
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.v17i2.5693

Abstract

that also operates in the culinary industry through the Pempek Duo brand. In the operational business of the culinary sector, PT Selada has developed the Mireta Point of Sale (POS) system as a transactional and reporting tool. However, the existing system has not been equipped with a transaction history data analysis feature to predict sales trends. This condition makes it difficult for the company to identify which products are best-selling and which ones are less popular. This development aims to create a sales forecasting feature based on the Autoregressive Integrated Moving Average with Exogenous Input (ARIMAX) method in the Mireta POS system. The ARIMAX model was chosen because it can incorporate external variables into the prediction calculations, in this case, holiday factors. The development was carried out using a waterfall approach which includes the stages of requirements analysis, system design, model implementation, and accuracy testing. The data used consists of the sales transaction history of Pempek Duo products from January 2022 to February 2023, which has been grouped by week, as well as holiday data as an external variable. The model evaluation results show that the best parameter combination is ARIMAX(1,0,2) with a Mean Absolute Error (MAE) value of 4.3333. This value indicates an average prediction error of 4 sales packages per week. With this feature, Mireta POS can provide more accurate sales predictions, making it easier for the company to identify the best-selling and least popular products.
Application of Named Entity Recognition (NER) in Job Vacancy Matching Using an Ontology-Based Approach (Case Study: Information Technology Sector) Hodijah, ade
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.v17i2.5675

Abstract

The dissemination of job vacancies through online platforms still faces limitations in understanding the semantic relationships between the skills possessed by job seekers and the qualifications required by a job position. This mismatch results in an inefficient search process and longer search times. This study aims to develop a semantic-based job vacancy recommendation application (talent matching) using a skill ontology approach. One of the main challenges in developing the ontology is the lack of standardized data structures in job vacancy postings, particularly in the job description section. To address this issue, Named Entity Recognition (NER) techniques are applied to automatically extract skill entities from job description texts. The extracted results are then classified into a taxonomy structure using SkillsGPT, thereby forming a hierarchical skill concept model semantically represented within the ontology using Protégé. The matching process between user skills and job qualifications is conducted through semantic similarity calculations employing the Sánchez Similarity method. Job vacancy data are collected via web scraping, while system development follows the Rational Unified Process (RUP) methodology and is evaluated using Black Box testing. Evaluation results demonstrate that the developed system is capable of providing semantically relevant job vacancy recommendations according to the user's skill profile. Therefore, this study contributes both theoretically and practically to the development of ontology-based recommendation systems, particularly in the automated modeling of skill taxonomies from unstructured data.
Decision Support System For Oil Palm Seed Selection Using The Simple Additive Weighting Method Situngkir, Silvia Wulandari; Siallagan , Sanri Yuliana; Ridho, Muhammad
Media Jurnal Informatika Vol 17 No 1a (2025): Special Issue Information System Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i1a.5367

Abstract

The selection of superior oil palm seedlings is a crucial factor in improving plantation productivity. However, the selection process, which often relies on farmers’ subjective experience, frequently results in inaccuracies in determining ready-to-plant seedlings. This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist farmers in selecting high-quality oil palm seedlings based on four main criteria: number of fronds, seedling age, seedling height, and stem diameter. The SAW method was applied to calculate the preference value of each seedling alternative through normalization and weighting of all criteria. The system was developed as a web-based application using the Laravel framework and tested using the Black-Box Testing method to ensure functionality and accuracy. The testing results showed that the system produced recommendations identical to manual calculations (100% accuracy) and completed data processing in less than 2 seconds for 24 seedling alternatives. This decision support system has proven to be efficient, accurate, and stable in supporting the oil palm seedling selection process. The system’s main advantage lies in its ability to automate the evaluation process quickly and objectively, reducing human error and accelerating decision-making for farmers and plantation managers.
The Analysis of the Student Payment Information System at Madrasah Ibtidaiyah Mafaatikhul Huda Penarukan Raya, Mohamad Bintang Jagad; Krishantoro, Wahyu
Media Jurnal Informatika Vol 17 No 1a (2025): Special Issue Information System Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i1a.5425

Abstract

The student payment information system is an essential component in managing educational administration, particularly in primary-level institutions such as Madrasah Ibtidaiyah (MI) Mafaatikhul Huda Penarukan, located in Penarukan Village, Adiwerna District, Tegal Regency, Central Java Province. The purpose of this study to examine the current payment information system and provide an overview of its strengths, weaknesses, opportunities, and threats through a SWOT analysis of the manual system currently in use. This research employs a qualitative method, with data collected through observation, interviews, and library research. The analysis follows a four-stage method: surveying the existing system, analyzing or evaluating the survey results, identifying the current system’s needs, and specifying system requirements to support appropriate and relevant development. The finding indicate of the payment process is still manually, which is considered less effective due to a high risk of data loss and delays in recordkeeping. Therefore, the development of an integrated digital payment information system is needed to improve speed, accuracy, and transparency in administrative processes. This study is expected to serve as a foundation for recommending the modernization of financial administration systems at MI Mafaatikhul Huda Penarukan.

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