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Ahmad Azhari
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Magister Teknik Informatika Jl. Prof. Dr. Soepomo SH, Janturan, Warungboto, Yogyakarta 55164
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Mobile and Forensics
Published by Universitas Ahmad Dahlan
ISSN : 26566257     EISSN : 27146685     DOI : https://doi.org/10.12928/mf
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science Neuroscience
Mobile and Forensics (MF) adalah Jurnal Nasional berbasis online dan open access untuk penelitian terapan pada bidang Mobile Technology dan Digital Forensics. Jurnal ini mengundang seluruh ilmuan dan peneliti dari seluruh dunia untuk bertukar dan menyebarluaskan topik-topik teoritis dan praktik yang berorientasi pada kemajuan teknologi mobile dan digital forensics.
Arjuna Subject : Teknik (semua kategori) - Teknologi Media
Articles 109 Documents
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Enhancing Early Diabetes Detection Using Tree-Based Machine Learning Algorithms with SMOTEENN Balancing Syahrani Lonang; Ahmad Fatoni Dwi Putra; Asno Azzawagama Firdaus; Fahmi Syuhada; Yuan Sa'adati
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.14495

Abstract

Diabetes continues to be a critical global health issue, demanding accurate predictive systems to enable preventive interventions. Traditional diagnostic tests lack efficiency for large-scale early screening, which has led to growing interest in artificial intelligence solutions. This research proposed an effective methodology for diabetes classification based on tree-based algorithms enhanced with SMOTEENN balancing. The study employed the Kaggle Diabetes Prediction Dataset with 100,000 instances and eight medical and demographic features. Preprocessing steps included handling missing and duplicate values, encoding categorical variables, and scaling numerical attributes with Min-Max normalization. To address severe class imbalance, SMOTEENN was adopted, producing a cleaner and more balanced dataset. Model evaluation was performed using Stratified 5-Fold cross-validation on six classifiers: Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, and CatBoost. Experimental results indicated significant gains after balancing, with ensemble methods outperforming single-tree baselines. Random Forest delivered the best overall performance (98.93% accuracy, 98.96% F1-score, 99.16% recall, 99.94% AUC), followed by CatBoost and XGBoost with comparable results above 99% AUC. While Decision Tree benefited most from SMOTEENN in relative terms, it remained less competitive. Analysis of the importance of the analysis revealed HbA1c level and blood glucose level as dominant predictors, validating clinically meaningful learning. These findings suggest that integrating hybrid resampling with ensemble tree classifiers provides reliable and general predictions for diabetes risk. The approach holds promise for deployment in healthcare decision support systems.
Enhancing Offline Shopping Experiences With Real-Time Mobile Apps, Specifically in Batam City Herman; Hernando; Fredian Simanjuntak
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.14748

Abstract

Information asymmetry in the offline retail market imposes substantial "search costs" on purchasing professionals, who frequently lack visibility into real-time product availability across physical stores. Existing solutions, such as generic store locators, fail to provide inventory context, while traditional e-commerce platforms are unable to meet immediate, same-day procurement needs due to logistical delays. This research addresses this gap by developing a Real-Time Location-Aware mobile artifact aimed at optimizing offline procurement efficiency in Batam City. Grounded in Design Science Research (DSR), the system employs a short-polling architecture implemented via Expo (React Native), Express.js, and PostgreSQL to ensure data freshness. Technical performance testing validated the system's "Near Real-Time" capabilities, achieving an average API response time of 180 ms and a stable synchronization interval of 5 seconds under 4G network conditions. Furthermore, a usability evaluation involving 40 purchasing professionals yielded an average System Usability Scale (SUS) score of 71.81, categorizing the application as "Good." These results empirically demonstrate that lightweight polling architectures can effectively mitigate cognitive load and search latency, offering a scalable software engineering solution for the "Offline-to-Offline" (O2O) retail sector.
Forensics of Low-Quality Facial Images from CCTV Using The Generative Adversarial Network (GAN) Method Muhammad Adil Kustian; Ahmad Luthfi
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.14805

Abstract

CCTV facial images often suffer from poor quality due to low resolution, motion blur, and poor illumination, complicating forensic investigation and identification. This situation necessitates more modern image restoration methods. This study proposes a Generative Adversarial Network (GAN)-based pipeline that combines two architectures, Real- ESRGAN for resolution enhancement and GFPGAN for more natural facial feature recovery. Experimental results show significant improvements in perceptual quality with a decrease in NIQE values from 12.56 to 7.81 and BRISQUE from 70.81 to 44.23, with an 82% image recovery success rate. Additional evaluations using texture entropy and gradient histograms demonstrate consistency in facial structure and edge sharpness. This study contributes by demonstrating an integrated two- stage GAN approach as an effective solution for face recovery in low-quality CCTV images, while highlighting the need for standardized forensic protocols and facial identity validation in real-world applications. Thus, this pipeline can serve as a pre-processing stage to improve image readability and has the potential to be used as a tool for forensic investigations.
Design and Expert Validation of AI-Supported Collaborative Digital Learning Model for Introductory Multimedia Course SPADA Indonesia Muh. Al Amin; Ahmad Fatoni Dwi Putra
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.15407

Abstract

This study develops and conceptually validates an AI-Supported Collaborative Digital Learning (AI-CDL) model for an Introduction to Multimedia course delivered through the national LMS, SPADA Indonesia. Using a Design and Development Research approach aligned with early-stage Design-Based Research, the study followed four phases: (1) contextual and needs analysis of course outcomes,  commonly referred to as CPL (Capaian Pembelajaran Lulusan) and CPMK (Capaian Pembelajaran Mata Kuliah), existing learning activities, and available LMS affordances; (2) conceptual model design grounded in collaborative learning theory and multimedia learning principles; (3) development of project-based collaborative scenarios and supporting artefacts (learning paths, assessment rubrics, and responsible AI-use guidelines); and (4) conceptual validation through expert review and alignment with recent evidence syntheses on AI-supported collaboration in higher education. The resulting AI-CDL model operationalizes AI support across three layers intelligent content support, AI-supported collaboration, and AI-augmented production workflows mapped to key multimedia topics and implemented through SPADA activities. Expert feedback informed iterative refinements, particularly in task orchestration, assessment transparency, and ethical safeguards. This study contributes a validated design blueprint and transferable design principles for integrating AI into collaborative multimedia learning within a national-scale LMS. Future work will empirically evaluate learning processes and outcomes through classroom implementation and learning analytics.
Development of Web Application for Certificate Automation using Cloudflare DNS API and ZeroSSL Fredian Simanjuntak; Gary Happydinata`; Herman
Mobile and Forensics Vol. 8 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i2.15016

Abstract

Existing SSL/TLS automation tools were largely server-centric and required host-level configuration. These limitations increased complexity in distributed and multi-domain environments. This study proposed a centralized web-based SSL certificate automation system. The system integrated the Cloudflare DNS API and ZeroSSL REST API for certificate lifecycle management. The research adopted the Agile Scrum methodology during system development. An asynchronous queue-based architecture was implemented to support concurrent certificate issuance. The architecture reduced API rate-limit constraints. Automated Domain Validation (DV) was successfully performed through DNS integration. The system centralized certificate storage and monitoring. The interface simplified administrator operations. Configuration errors were reduced during certificate deployment. Operational efficiency was improved for distributed infrastructures. A usability evaluation was conducted with 25 technical practitioners. The evaluation produced a System Usability Scale mean score of 83.5 with a standard deviation of 6.7. The findings indicated excellent user acceptance and system usability. In conclusion, the proposed system effectively automated SSL certificate management through a centralized and usability-oriented approach. The system also minimized operational overhead and dependency on host-level configuration.
Resource-Efficient Optimization for Multi-Class Hematological Diagnosis: A Hybrid BPSO-Extra Trees Approach with Data Imbalance Handling Dimas Chaerul Ekty Saputra; Zahid Abdullah Nur Mukhlishin; Affifah Mutiara Pertiwi; Mochammad Zulfikar Alfany; Irianna Futri; Raksmey Phann
Mobile and Forensics Vol. 8 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i1.15703

Abstract

Complete Blood Count (CBC) remains the cornerstone for initial screening of hematological disorders, yet manual interpretation is often challenged by overlapping biological patterns and substantial inter-patient variability. Although machine learning approaches have demonstrated promise for automated diagnosis, many existing studies prioritize classification accuracy while neglecting computational efficiency and the persistent class imbalance inherent in medical datasets. This study develops a lightweight yet effective diagnostic framework for classifying nine hematological conditions using routine CBC parameters. Evaluated on a public dataset of 1,281 records from Kaggle, the proposed model is benchmarked against standard Random Forest, XGBoost, and Support Vector Machine (SVM) classifiers. The approach integrates the Synthetic Minority Oversampling Technique (SMOTE) to mitigate class imbalance, and Binary Particle Swarm Optimization (BPSO) to identify a compact and clinically informative feature subset of exactly 6 parameters, referred to as a clinical fingerprint, optimized for the Extra Trees classifier. Evaluated using ten-fold cross-validation, the BPSO-Extra Trees model achieved an average accuracy of 87.43 percent and an F1 score of 82.75 percent, while demonstrating superior resource efficiency with peak memory consumption of only 0.249 MB, corresponding to a 46.6 percent reduction compared with the standard Random Forest baseline. These findings confirm that swarm intelligence optimized models can effectively balance diagnostic performance with extreme computational frugality, enabling the potential deployment of accurate hematology-based decision support systems on portable devices and in resource-limited laboratory environments.
Artefact-to-Legal Element Mapping in iOS-Based Web Gambling Cases: A Cross-Source Correlation Framework under KUHP and ITE Law Sudirman Sudirman; Yudi Prayudi
Mobile and Forensics Vol. 8 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i2.15926

Abstract

The widespread use of web-based online gambling through mobile browsers and instant messaging applications has made digital traces increasingly difficult to interpret as legally admissible evidence. Previous forensic research primarily concentrated on artefact recovery from dedicated gambling applications, leaving limited attention to systematic approaches for associating web-based traces with legal elements. This study examined the identification, classification, and legal mapping of web-based gambling artefacts using the proposed Artefact-to-Legal Element Mapping Framework (ALMF). A real-case evidence set acquired from an Apple iPhone XR was analysed with Magnet AXIOM Process v9.4.0.44917, generating Quick Image and Decrypted evidence for extraction and correlation. The analysis identified four major artefact categories, including web browsing traces, credential and persistence artefacts, WhatsApp communication records, and visual media files. Browser artefacts reflected intentional discovery and access to gambling websites, whereas credential artefacts indicated recurring or sustained access. WhatsApp conversations provided the strongest evidence of promotional activities through shared links, invitations, and dissemination patterns. Visual artefacts further strengthened the gambling context by revealing website interfaces and financial indicators. Cross-source correlation differentiated player activities from promoter or distributor behaviour. The mapped evidence supported KUHP Article 303 and UU ITE Article 27(2). The findings demonstrated that contextual interpretation across multiple artefact sources produced stronger evidential value than analysing individual artefacts separately.
Machine Learning-Based Lifestyle Analysis for Health Risk Detection in Coffee Drinkers Sri Winiarti; Ahmad Azhari; Nathaniela Isya Nur Rofiah
Mobile and Forensics Vol. 8 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i2.16102

Abstract

This study aims to develop a machine learning-based intelligent system to detect health risks in coffee drinkers through a lifestyle analysis approach. The background of this study is based on the increasing consumption of coffee as part of a modern lifestyle, which has the potential to cause various health risks if not balanced with a healthy lifestyle. The dataset was collected through a survey covering several important variables, such as coffee consumption frequency, sugar intake, sleep duration, physical activity level, body mass index (BMI), and blood pressure. The research stages included data preprocessing, normalization, and classification using three algorithms: Random Forest and Gradient Boosting. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics to ensure the system’s reliability. Test results showed that the Random Forest algorithm performed best with an accuracy of 0.91, followed by Gradient Boosting with an accuracy of 0.90. Further analysis revealed that the variables most influential on health risks are coffee consumption frequency, sleep duration, and sugar intake. The developed system proved effective in detecting health risks early and has the potential to serve as a data-driven educational tool to raise public awareness of the importance of a healthy lifestyle.
A Hybrid Machine Learning and Digital Forensics Framework for Detecting Coordinated Suspicious Accounts on X Riko Iman Decamarta; Yudi Prayudi
Mobile and Forensics Vol. 8 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i2.16449

Abstract

Coordinated Inauthentic Behavior (CIB) on X threatens public discourse, yet single-method approaches fail to detect implicit coordination. This study proposes a conditional hybrid framework spanning four forensic stages: (1) automated classification using multilingual BERT text embeddings concatenated with 21 numerical features, (2) sockpuppet candidate generation via Sentence-BERT stylometric fingerprinting and cosine similarity, (3) graph-based coordination analysis using the Louvain algorithm, and (4) manual forensic confirmation with OSINT investigation. Trained on the Cresci-2017 benchmark which exhibits substantial temporal and linguistic domain mismatch from our 2025 Indonesian target domain, the BERT model achieved an accuracy, precision, recall, and F1-score of 1.00 on its held-out test partition prior to deployment. The framework was applied to 1,309 Indonesian Military Law (UU TNI) tweets from 720 accounts, which resulted in the identification of 114 suspicious accounts (15.8%) and the extraction of 28 candidate accounts from 6 groups. Manual validation by the authors through side-by-side timeline inspection confirmed one pair of accounts with synchronized and verbatim identical content. The absence of direct network interactions despite high stylometric similarity is interpreted as consistent with implicit CIB evasion strategies, but attribution to organized groups cannot be definitively established on the basis of platform data alone.

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