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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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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 (On Progress)
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.
Development of a Scientific Article Recommendation Web Application Using a Hybrid Recommender System: A Case Study in Computer Science 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.v17i2.5672

Abstract

The rapid increase in scientific publications has created significant challenges for researchers in finding relevant literature. Conventional citation-based recommender applications often have drawbacks, such as bias toward popular articles and vulnerability to manipulation through citation cartels, which reduce objectivity. To address these limitations, this development aimed to design and develop a web-based scientific article recommendation application using a hybrid recommender system approach. The development followed the waterfall methodology, covering requirements analysis, design, implementation, and testing stages. The hybrid approach combines Content-based filtering by analyzing content similarity and Collaborative filtering based on user interaction history. Scientific articles and user preferences were modeled in a graph database to map relationships, with the implementation of Graph Data Science Library using algorithms named K-Nearest Neighbor, Degree centrality, and PageRank. The resulting application provided multiple recommendation features by combining content analysis and user preferences. This application is expected to help researchers, students, and practitioners find relevant references more effectively.
STRIDE-Based Threat Analysis and AI-Driven Dataset Design for Securing Educational E-Payment Systems Ferdiansyah, Doddy; Lidya, Leony; Muttaqin, Miftahul Fadli
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.v17i2.5918

Abstract

The increasing adoption of electronic payment (e-payment) systems in educational settings introduces significant cybersecurity challenges. This study conducts a systematic security analysis of a web-based school canteen e-payment system using the STRIDE threat modeling framework. The methodology involves three stages: system modeling with a Data Flow Diagram (DFD), threat mapping across system components, and qualitative risk assessment based on potential impact and likelihood. The analysis identified six STRIDE threat categories, with high-risk findings in Tampering (balance and price manipulation), Spoofing (account takeover), and Denial of Service (flooding attacks). Recommended mitigation strategies include multi-factor authentication, strict server-side input validation, immutable logging, and secure session management. Beyond manual threat analysis, this research contributes by designing a structured threat dataset as a foundation for artificial intelligence (AI) integration. This dataset enables the development of AI models for automated threat classification, risk prediction, and adaptive mitigation recommendations. The findings highlight the importance of proactive and forward-looking security approaches while opening pathways for future research on data-driven security automation in educational digital infrastructures.
ANALYSIS OF THE INFORMATION SYSTEM FOR THE MANAGEMENT OF INCOMING AND OUTGOING LETTERS AT SMK YPE NUSANTARA SLAWI Ramadhina, Nofa Rizki; Widyatmojo, Galih
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.5505

Abstract

Penelitian ini membahas tentang permasalahan pengelolaan surat masuk dan surat keluar di SMK YPE Nusantara Slawi yang masih dilakukan secara manual. Proses tersebut dinilai kurang efisien, rawan terjadi kehilangan arsip, dan memperlambat penyaluran surat kepada pihak terkait. Penelitian ini menggunakan metode pengumpulan data berupa observasi, wawancara, dan studi pustaka untuk memperoleh informasi yang komprehensif terkait sistem administrasi surat yang sedang berjalan. Analisis dilakukan melalui survei terhadap sistem yang sedang berjalan, identifikasi kebutuhan informasi dari pihak terkait seperti admin, kepala sekolah, dan guru, serta analisis SWOT untuk menilai kekuatan, kelemahan, peluang, dan ancaman dari sistem manual yang digunakan. Hasil penelitian menunjukkan bahwa sistem manual menghambat efisiensi pencatatan dan penelusuran arsip, serta menimbulkan ketergantungan yang tinggi terhadap dokumen fisik. Berdasarkan temuan tersebut, penelitian ini merekomendasikan pengembangan sistem informasi berbasis digital untuk meningkatkan efisiensi, keakuratan, dan keamanan data surat. Sistem digital ini juga diharapkan mampu mempercepat proses disposisi dan mendukung koordinasi antar staf secara lebih optimal.
Rice Leaf Disease Classification Based on ResNet50 and MobileNetV3 Feature Extraction Using Random Forest Pratama, Gede Yogi; Husaini, Rahayun Amrullah; Nasri, Muhammad Haris; Hammad, Rifqi
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.v17i2.5939

Abstract

Diseases in rice plants are one of the main factors contributing to decreased agricultural productivity. Early and accurate disease identification is crucial to support effective decision-making in plant disease management. This study aims to compare the performance of deep learning models based on Convolutional Neural Networks (CNN), namely ResNet50 and MobileNetV3, as well as their integration with the Random Forest (RF) algorithm for rice leaf disease classification. The dataset used consists of rice leaf images categorized into several disease classes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with a macro-average approach. The results show that the standalone ResNet50 and MobileNetV3 models achieved accuracies of 62.5% and 65.7%, respectively, with macro F1-scores below 0.65, indicating moderate classification performance. However, combining CNN models with Random Forest significantly improved classification performance. The ResNet50 + RF model achieved an accuracy of 99.6%, while the MobileNetV3 + RF model attained the highest accuracy of 99.8%, along with equally high macro-averaged precision, recall, and F1-score values. These findings demonstrate that integrating CNN-extracted features with the Random Forest algorithm enhances the model’s ability to distinguish disease classes more accurately and consistently. Therefore, the hybrid CNN–Random Forest approach shows strong potential as an effective solution for image-based rice plant disease detection systems.
Development of Mobile-Based Attendance Application at SMPIT Ummul Qurro' Batam Using the Agile Software Development Method Hamdani, Dadan; Nur, Siti; 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 | DOI: 10.35194/mji.v17i1a.5769

Abstract

The digitalization of education encourages the need for an efficient and accurate attendance system. SMPIT Ummul Qurro' Batam still uses manual methods that are time-consuming and error-prone. This study designed a mobile-based attendance application using the Agile Software Development method. Data was obtained through interviews, observations, and needs analysis with teachers, staff, and school leaders. Development is carried out through six stages: planning, implementation, testing, documentation, deployment, and maintenance. The results of black-box testing showed that the application was able to support real-time attendance and exit, user data management, and automatic recap. This research proves that the Agile Software Development method is effective in producing a simple and reliable system, which increases efficiency, accuracy, and transparency while supporting the school's digital transformation.
Analysis Of Garbage Retribution Information System At Bumdes Kaligayam Talang Tegal Regency alfiyah, fuji
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.5450

Abstract

The garbage retribution payment system in Kaligayam Village was still conducted manually, causing delays in reporting, data loss risks, and lack of transparency. This study aimed to analyse the existing garbage retribution information system and propose an efficient digital solution. The research employed direct observation, interviews with the leader of Neighborhood Association (RT), the BUMDes treasurer, and village officials, as well as literature review. Analysis was conducted using SWOT methodology and workflow mapping through Flow of Document. The results revealed that while the manual system benefited from strong social structure, it suffered from administrative inefficiencies. Therefore, a web-based system was recommended to enable automatic payment recording, real-time reporting, and improved accuracy and citizen participation in garbage retribution management. 
Evaluating Machine Learning Models Across Feature Extraction and Data Balancing Scenarios for Coretax Sentiment Analysis Syah Putra, Subhan; Riminarsih, Desti
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.v17i2.5968

Abstract

The implementation of the Core Tax Administration System (Coretax) by the Indonesian Directorate General of Taxes has generated diverse public responses on social media, particularly on platform X, making sentiment analysis a relevant approach to assess public perception of this policy. This study aims to evaluate the performance of machine learning classifiers across different feature extraction and data balancing scenarios. Three machine learning classifiers, namely Multinomial Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression were evaluated under four experimental scenarios combining two feature extraction methods, namely Term Frequency–Inverse Document Frequency (TF-IDF) and Bag of Words (BoW), with original and balanced data distributions. A dataset of more than 50,000 Coretax-related posts collected from platform X was preprocessed and automatically labeled into positive, negative, and neutral sentiment classes using a pretrained IndoBERT sentiment model. A brief manual inspection of a random subset indicates moderate agreement between automatic and manual labels, highlighting potential noise while supporting the use of automatic labeling for comparative analysis. The results show that performance is shaped by the combined effects of representation and data distribution rather than algorithm choice alone. Logistic Regression consistently achieved the most stable and competitive performance across all scenarios, with accuracy values ranging from approximately 0.80 to 0.83 and macro F1-scores around 0.72–0.73. TF-IDF generally provided more stable performance, while data balancing improved prediction fairness for minority sentiment classes despite a slight decrease in overall accuracy. These findings demonstrate that Logistic Regression is the most robust model for Coretax sentiment analysis across varying feature extraction and data balancing conditions and provide practical insights into the influence of data representation and distribution on sentiment classification performance.
Implementation of Smoothing and Noise Reduction for Digital Image Quality Enhancement Using Neighborhood Operations Suheri, Agus; Widaningsih, Sri; Nazihah, Abiyyatun
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.v17i2.5893

Abstract

The rapid growth in digital imaging technology has brought profound changes to various sectors. However, the quality of digital images is often compromised by noise, such as gaussian noise, salt-and-pepper noise, and spackle noise. Noise not only reduces the aesthetics of an image, but can also hinder image analysis and interpretation. In addition, it can obscure important details in the image and reduce the clarity and accuracy of visual analysis. To improve the quality of digital images, effective smoothing and noise removal techniques are needed, one of which is the neighborhood operation. The main purpose of smoothing and noise removal is to improve visual quality so that images are clearer and easier to analyze. In this study, a mean filter is used for smoothing and a median filter is used for noise reduction. Combine mean and median filtering in this study is directly aligned with its emphasis on a pixel-domain, low-level analysis of convolution-based smoothing. The mask used has a size of 5, 9, 25, or 49 points as the kernel in the convolution mask operation to remove noise and smooth the image at the same time. The digital image processing application was created following the waterfall software development model stages.The BMP format was selected in this study primarily to ensure data integrity and experimental control. To measure the quality of the image produced after the smoothing process, the Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) standards are used. From the experimental results, the MSE values for mask sizes 5, 9, 25, and 49 are 12.96, 14.36, 14.72, and 16.80, respectively. Meanwhile, the PSNR values were 37, 36.56, 36.54, and 35.87, respectively. From the image quality results in the form of MSE and PSNR, it can be seen that the larger the mask size, the greater the MSE value, but the smaller the PSNR value. The smaller the PSNR value, the worse the image quality, and vice versa. This is supported by visual analysis, where more details of the original image are lost. However, the PSNR value is still in the range of 30-40 dB, which means the quality is still in the Good category. The quality is still acceptable with minimal distortion. The quality of the results is still very close to the original image. The highest image quality is found in mask 5.
Digital Transformation of Stock Management with a Web-Based Inventory System Using the Extreme Programming Method (Case Study: PT.Tata Makmur Sejahtera) Khaidar, Daffa Muhammad; 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 | DOI: 10.35194/mji.v17i1a.5596

Abstract

PT Tata Makmur Sejahtera is a textile company that since 2004 has grown rapidly in services and number of branches. Currently, stock management still uses Microsoft Excel and manual spreadsheets, which risks causing data input errors and is less efficient. To overcome these problems, a web-based inventory management system was developed using the Extreme Programming method to improve the efficiency and accuracy of stock management. The system is designed to be responsive, easy to use, and allows direct user testing for iterative UI/UX improvements. Through this system, the company has the ability to manage stock more systematically, prevent shortages or excess goods, and increase the effectiveness of warehouse operations.

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