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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 260 Documents
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The Navigating the Data Labyrinth: A Bibliometrix of Data Governance Challenges in Implementing Digital Twins for Disaster Management in Developing Countries Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara; Irawan; Estiko Rijanto; Irfan Dwiguna
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6295

Abstract

Indonesia faces significant disaster risks due to its location in the Ring of Fire, necessitating advanced mitigation technologies like Digital Twins (DT). However, the effectiveness of DT relies heavily on real-time data integration, which is often hindered by governance issues rather than technological capability. This study aims to identify specific data governance challenges in adopting DT for the public sector specifically in disaster management and proposes a conceptual framework suitable for developing countries, using Indonesia as the primary representative case. A Bibliometric analysis was conducted using the PRISMA protocol. Data was collected from Scopus (n=107) and Google Scholar/PoP, covering the period 2018–2026, focusing on the intersection of Digital Twin, Disaster Management, and Data Governance. Additionally, a qualitative case study approach was employed, utilizing Indonesia as the primary representation of developing countries to validate the proposed framework. The study identifies three key challenge dimensions: (1) Organizational (data silos and ownership ambiguity), (2) Technical (semantic interoperability and legacy systems), and (3) Legal-Ethical (data privacy and sovereignty). The paper proposes the "Integrated Disaster Data Governance for Digital Twin (IDDG-DT)" framework, which aligns with the Satu Data Indonesia policy, emphasizing that robust data governance is a prerequisite for successful Digital Twin implementation.
Development of an AI-Assisted Mobile Point of Sales and E-Commerce System with Real-Time Inventory Synchronization Maulana Irfan; Miftakhul Ushbah Nastaftian; Hermawan Sentyaki Sarjito; Jonathan; Irham Maulana Johani; Muhammad Nasir; Aditya Wicaksono
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6354

Abstract

Indonesian micro, small, and medium enterprises (UMKMs) face significant operational bottlenecks due to their reliance on manual and fragmented sales and inventory management systems. To address these challenges, this research designs, develops, and evaluates PasPOS, an integrated mobile Point of Sales (POS) and E-Commerce system tailored for Indonesian micro-retailers, utilizing Toko Mpok Nani as an implementation case study. Developed chronologically using the Agile Scrum framework across eight sprints, the platform implements a decoupled microservices architecture. This infrastructure consists of a centralized Laravel 12 backend API and Flutter-based Admin and Member applications, all synchronized through an 11-table relational database. Furthermore, the Admin App integrates an edge computer vision-based barcode scanning feature to accelerate cashier transactions using standard smartphone cameras. System validation via User Acceptance Testing (UAT) using a structured Likert-scale questionnaire yielded an overall average score of 4.44 out of 5.00 for the Admin App and 4.43 for the Member App, classifying both as "Very Good". These empirical findings demonstrate that PasPOS successfully eliminates cross-channel stock inconsistencies while offering a replicable architectural blueprint for broader UMKM digitalization initiatives.
A Stock Demand Forecasting for MSME E-Commerce Using LSTM and Facebook Prophet: A Comparative Study Fatimatuzzahra Fatimatuzzahra; Akmal Amilunizar; Khen Dedes; Helyatin Nisyak; Nadzirotul Fitriyah
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6365

Abstract

Manual sales processes and stock management in micro, small, and medium enterprise (MSME) settings often lead to limited market reach and inefficient inventory management. The purpose of this research is to solve these operational problems by designing and developing a web-based e-commerce system equipped with an integrated monthly stock demand forecasting module to enhance the competitiveness of MSMEs in the roster industry. The system was developed following the waterfall methodology and it has a decoupled architecture to separate the artificial intelligence computational workloads from the core application. Two time series forecasting models Long Short-Term Memory (LSTM) and Facebook Prophet were applied and compared for forecasting stock requirements from intermittent, zero-inflated demand patterns of historical sales data. System functionality was validated using User Acceptance Testing and the forecasting accuracy was measured using Root Mean Square Error (RMSE) and Weighted Mean Absolute Percentage Error (WMAPE). The performance evaluation showed that the unscaled LSTM model outperformed the linear additive regression method of Facebook Prophet in terms of lower physical volume deviation and consistent operational error in the course of the evaluation period. The developed platform provides a reliable data-driven decision support for inventory management. The incorporation of forecasting using neural networks in the e-commerce system has reduced the risk of stockouts, expanded the market, and proved the increase in the business competitiveness of artisan MSMEs.
IoT-Based Egg Incubator with MobileNetV2 CNN for Candling Image Classification and Fertility Detection Irfan Rifqy Widya Syahbani; Fadila Azahra; Della Arviyanti; Christiano Nicoma Boseke; Ariel Pasha Ramaditya; Adinda Octhavia Indriyani Indriyani; Fathir Gunadireja; Setya Mega Bagaskara; Muhammad Nasir; Gema Parasti Mindara
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6368

Abstract

Egg incubation and fertility detection are critical processes in poultry production, requiring stable environmental conditions and accurate embryo monitoring. Conventional incubation and candling methods often rely on manual observation, which may reduce efficiency and increase the risk of human error. This study aims to develop an IoT-based automatic egg incubator integrated with a MobileNetV2-based Convolutional Neural Network (CNN) for candling image classification and fertility detection. The proposed system combines an ESP32-based incubation monitoring platform, ESP32-CAM image acquisition module, ESP-NOW wireless communication, and a web-based monitoring interface. Candling images were collected and classified into three categories: live fertile, dead fertile, and infertile eggs. Transfer learning using the MobileNetV2 architecture was employed to train the classification model. The developed CNN model achieved an accuracy of 91.26% on the testing dataset and 82.22% during validation under real-world conditions. Performance evaluation showed precision values up to 0.965, recall values up to 0.967, and F1-scores above 0.89 across the three classes. Furthermore, the integrated system successfully enabled real-time monitoring of incubation conditions, automated image acquisition, and web-based fertility classification. System testing using 45 egg samples demonstrated that the proposed solution could effectively identify embryo development and fertility status. The integration of IoT technology, ESP32-CAM-based candling, and MobileNetV2 CNN provides an effective solution for automated incubation monitoring and egg fertility detection. The developed system improves monitoring efficiency, reduces manual intervention, and supports decision-making during the incubation process.
Hybrid IndoBERT and Support Vector Machine for Multi-class Emotion Classification of Indonesian Tourism Reviews Firas Atqiya; Afrida Helen; Muhammad Rizqi Sholahuddin
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6377

Abstract

Online reviews hold emotional nuances that binary sentiment analysis cannot adequately capture for targeted tourism management. Indonesian reviews pose additional computational challenges due to informal language, Sundanese vernacular, and severe class imbalance. Objective:   This study develops a hybrid classification framework using IndoBERT as a frozen feature extractor and a Support Vector Machine (SVM) across five emotional classes. It investigates integrating Principal Component Analysis (PCA) and SMOTE within a strict cross-validation pipeline to mitigate extreme minority class scarcity while preventing data leakage. The duplicate-free dataset comprises 446 manually annotated reviews from agro-tourism destinations in Rancakalong. Annotations followed Ekman’s emotions plus a neutral category, cross-validated by a Large Language Model (Cohen's Kappa = 0.7475). To satisfy oversampling constraints, three extreme minority classes (fear, surprise, disgust) were consolidated into an 'OTHER' class. Three configurations were evaluated via 5-Fold Stratified Cross-Validation: TF-IDF + SVM (M1 baseline), IndoBERT + SVM (M2), and IndoBERT + PCA + SMOTE + SVM (M3), utilizing Macro F1 as the primary metric. Results:  The M1 baseline yielded a Macro F1 of 0.3920. By capturing contextual semantics, M2 improved accuracy to 0.7131 and Macro F1 to 0.4133. The proposed M3 architecture achieved the highest Macro F1 (0.4321), demonstrating that combining dimensionality reduction and oversampling strengthens minority class decision boundaries. However, erratic performance on the synthetic 'OTHER' class confirms that merging distinct emotions disrupts cohesive semantic signatures. Integrating frozen IndoBERT embeddings with PCA and SMOTE within a cross-validated SVM architecture significantly outperforms traditional baseline models on highly imbalanced, low-resource Indonesian text data. This study contributes an empirically validated emotion corpus and establishes a foundational, data-driven behavioral modeling framework to guide targeted managerial interventions in local agro-tourism.
AI-Assisted Web-Based Steganography for Assessment Document Embedding Using Binarized Neural Networks Muhammad Nasir; Edwar Julistina Ramdon
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6380

Abstract

The administration of assessment documents in The National Narcotics Board requires a mechanism that can conceal confidential information while preserving the visual quality of the carrier image. This study aims to develop an AI-assisted web-based steganography system for embedding assessment documents using a Binarized Neural Network (BNN)-based embedding region selection model. The proposed method extends conventional Least Significant Bit (LSB) steganography by incorporating BNN-based patch classification before data insertion. Assessment data submitted through a web form were automatically converted into PDF files and embedded into residency cover images using an adaptive LSB technique guided by the BNN model. Due to confidentiality restrictions, this study used nine valid residency images, which were segmented into 32×32-pixel patches, producing 900 patches categorized into suitable and unsuitable embedding regions. The BNN model achieved an accuracy of 91.7%, precision of 91.2%, recall of 92.2%, and F1-score of 91.7%. Image quality evaluation showed an average MSE of 2.722, PSNR of 43.79 dB, and SSIM of 0.981, indicating high visual similarity between cover and stego images. Functional testing using black-box testing was conducted on 12 system scenarios, including login validation, role-based access, PDF generation, image upload, embedding, extraction, usage history, and user management access. All 12 scenarios were successfully passed, resulting in a functional success rate of 100%. Implementation observation also indicated that direct conventional LSB embedding tended to produce larger stego-image file sizes than the proposed BNN-assisted adaptive LSB approach. The findings suggest that BNN-assisted region selection can support imperceptible document embedding in web-based steganography applications. However, the limited number of real images, the absence of formal steganalysis, and the lack of full quantitative baseline comparison remain limitations that should be addressed in future work.
Dynamic Scoring for Quran Memorization Assessment in Journey of Ayat Educational Game Using the Fuzzy Tsukamoto Algorithm Aysyah Noor Shobah; Rio Andriyat Krisdiawan; Rio Priantama
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6401

Abstract

Conventional Qur’an memorization assessment in Madrasah Diniyah Takmiliyah (MDT) often relies on direct teacher evaluation and simple correct-or-wrong scoring, which may not fully represent students’ memorization performance. At MDT An-Nidzom, preliminary assessment showed that students experienced difficulties in completing verse fragments of selected short surahs. Objective: This study aimed to develop Journey of Ayat, an Android-based educational game, and implement the Fuzzy Tsukamoto algorithm as a dynamic scoring mechanism for Qur’an memorization assessment. Methods: The study employed a Research and Development approach using the Game Development Life Cycle. The scoring model used three input variables, namely correct answers, completion time, and remaining lives, to generate a final score. The system was evaluated through black-box testing, white-box testing, Fuzzy Tsukamoto calculation validation, User Acceptance Testing, and pretest-posttest analysis involving 40 students and one teacher at MDT An-Nidzom. Results: The developed system provided memorization practice, gameplay interaction, score calculation, and teacher monitoring. The Fuzzy Tsukamoto calculation was consistent with manual calculation, with an error value of 0.00 in the validation scenario. Black-box testing showed that the main features operated as expected, while white-box testing produced a cyclomatic complexity value of 2. The UAT results indicated very feasible ratings of 92.00% from the teacher and 89.06% from students. The mean memorization score increased from 62.75 to 70.00, and the Wilcoxon signed-rank test showed a statistically significant difference in the observed sample (p = 0.0237). Conclusion: Journey of Ayat is feasible as a supporting medium for Qur’an memorization practice and preliminary assessment. However, further studies involving broader samples, control-group comparison, and oral recitation assessment are needed to strengthen evidence of learning effectiveness.
Gamified English Vocabulary Puzzle Game with FSM-Based Emotional Feedback and Fisher–Yates Shuffle Muhammad Fachrul Reihan Fauzian; Rio Andriyat Krisdiawan; Nida Amalia Asikin
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6402

Abstract

English vocabulary learning among elementary school students often faces challenges related to low engagement, repetitive practice activities, and limited interactive feedback in conventional learning media. Educational games with gamification elements can provide a more engaging learning environment, while rule-based character feedback and randomized puzzle arrangements may improve gameplay variation and user interaction. Objective: This study aimed to develop an Android-based English vocabulary puzzle game that integrates gamification elements, an FSM-based emotional feedback mechanism, and the Fisher–Yates Shuffle algorithm for sixth-grade elementary school students. Methods: The game was developed using the Game Development Life Cycle (GDLC) method through initiation, pre-production, production, testing, beta, and release stages. The application was implemented using Unity and C#, with Firebase Realtime Database used for data management. System evaluation was conducted through Black Box Testing, White Box Testing, and User Acceptance Testing involving 35 sixth-grade students of SD Negeri 2 Cikeusal. Results: The developed game successfully implemented gamification elements, including scores, levels, time limits, life indicators, and visual character feedback. The FSM-based mechanism generated predefined emotional responses through thirteen states and seventeen events, while the Fisher–Yates Shuffle algorithm produced varied puzzle arrangements. Black Box Testing confirmed that all main features functioned as expected, and White Box Testing verified the control flow of the FSM and Fisher–Yates Shuffle implementations. User Acceptance Testing produced an overall acceptance score of 94.3%, indicating that the game was well accepted by users. Conclusion: The integration of gamification, FSM-based emotional feedback, and Fisher–Yates Shuffle  indicates its potential to provide an engaging and interactive medium for English vocabulary practice. However, this study evaluated user acceptance and system functionality, not direct vocabulary learning improvement.
Mitigating WireGuard VPN Latency Overhead on Sequential ORM Queries Using Redis Caching Juri Pebrianto; Ridhwan Dery Iradat
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6441

Abstract

The transition to distributed cloud architectures relies heavily on Virtual Private Networks (VPNs) like WireGuard for secure communication, and Object-Relational Mapping (ORM) frameworks for rapid application development. However, the sequential query generation (N+1 query problem) inherent in ORMs creates a severe latency bottleneck when traversing encrypted network tunnels. Objective: This study aims to quantitatively evaluate the performance degradation caused by WireGuard VPN latency on ORM-driven relational databases and measure the effectiveness of Redis in-memory caching as an architectural mitigation strategy. Methods: A quantitative experimental approach was conducted using a containerized multi-VM topology to isolate environment variables. We compared the execution latency of PostgreSQL and Redis under a local baseline scenario against a remote WireGuard VPN environment, utilizing mathematical modeling to analyze the bottleneck shift from disk I/O to network Round Trip Time (RTT). Results: Experimental results reveal that PostgreSQL latency spiked exponentially by 52,400% (from 0.04 ms to 21.00 ms) when forced through the VPN due to accumulated RTT. Conversely, implementing Redis caching bypassed the synchronous relational overhead, restricting the latency spike to 5,860% (13.13 ms) and yielding a 1.43x system speedup. In a simulated extreme N+1 scenario (500 sequential queries), Redis caching saved approximately 4 seconds of execution time. Conclusion: Shifting the computational load from relational disk to asynchronous memory is not merely an optional performance enhancement but an architectural necessity for ORM-based applications deployed over encrypted networks. Future research should explore AI-driven automated caching strategies to address dynamic workloads.
AI Persona-Based Student Counseling Chatbot Using Large Language Model, RAG, and Prompt Engineering Vina Zahrotun Nazah; Rully Pramudita
Media Jurnal Informatika Vol 18 No 1 (2026): Media Jurnal Informatika
Publisher : Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v18i1.6449

Abstract

Chatbots are increasingly used in student counseling services because they offer easy access, fast responses, and flexible availability. However, conventional chatbots often produce generic responses, have limited contextual understanding, and provide insufficient emotional support. This study aims to develop an AI Persona-based student counseling chatbot using a Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and prompt engineering to generate relevant, contextual, and empathetic responses. The study uses a Research and Development (R&D) approach with the CRISP-DM framework. The system uses Gemini 2.5 Flash as the generative model, multilingual-e5-small as the embedding model, and FAISS as the vector index. Four institutional documents and campus service data are processed through chunking, embedding, and semantic retrieval. Evaluation is conducted using LLM-as-a-Judge on 45 scenarios and User Acceptance Testing (UAT) with 20 students. The LLM-as-a-Judge evaluation produces an average score of 4.47 out of 5, with the highest score in Context Relevance at 4.70. UAT achieves 91% user acceptance in the very good category, with naturalness and empathy as the highest indicator at 95%. The results show that integrating LLM, RAG, and prompt engineering can improve chatbot response quality without fine-tuning, although further development is needed in multimodal document support, local model deployment, and retrieval mechanism improvement.

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