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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Informatika dan Teknik Elektro Terapan INTEGER: Journal of Information Technology JUTIM (Jurnal Teknik Informatika Musirawas) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) ILKOMNIKA: Journal of Computer Science and Applied Informatics CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Jurnal Abdi Insani Jurnal Teknik Informatika (JUTIF) Journal of Information System and Technology (JOINT) International Journal Of Computer, Network Security and Information System (IJCONSIST) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Algoritme Jurnal Mahasiswa Teknik Informatika Biner : Jurnal Ilmiah Informatika dan Komputer Ranah Research : Journal of Multidisciplinary Research and Development
Nurlaili, Afina Lina
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Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Transportation Other

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Journal : bit-Tech

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Desain dan Pengembangan Aplikasi Pengelolaan Properti Mode Offline Menggunakan Sinkronisasi Otomatis dan CQRS Event Sourcing Adiputra, Muhammad Ariq Hawari; Swari, Made Hanindia Prami; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3332

Abstract

The advancement of information technology has accelerated the digitization of project management, particularly in the supervision and monitoring of construction progress previously handled manually through paper-based documents and Excel spreadsheets. Such manual processes have led to delays in reporting, data duplication, and reduced data accuracy. This study aims to design and implement a web- and mobile-based project management and property sales system featuring Offline-First Synchronization, Command Query Responsibility Segregation (CQRS), and Event Sourcing to maintain the integrity of progress data and empower field supervisors to operate without an internet connection. The research method follows the waterfall model, comprising needs analysis, system design with a clear separation of command and query, and the implementation of event log storage as the single source of truth for every data change, using Laravel as the backend and React Native with MMKV for local storage. Testing results demonstrate that the system ensures data consistency through automatic synchronization once network connectivity is available and can reconstruct project development progress using stored event data. Performance benchmarking showed that CQRS bulk operations reduced processing time to 0.053 seconds, outperforming traditional CRUD bulk operations at 0.073 seconds, while query latency in event sourcing read models averaged 0.101 seconds, only slightly higher than 0.089 seconds in direct database queries. The system also achieves reliable auditability and supports efficient task update and historical recalculation via event replay. The findings confirm that applying CQRS and Event Sourcing within an offline-first architecture improves reliability, auditability, and efficiency in field project monitoring.
Implementation of MobileNetV3-Large in Rhizome Classification Nurdiansyah N.A, M. Ryan; Via, Yisti Vita; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3365

Abstract

Rhizomes are widely used in Indonesia as cooking spices and herbal ingredients, yet their visual similarity often causes misidentification when recognition relies on manual inspection, leading to inconsistent product quality and economic losses. This study presents an automatic rhizome image classification system based on the MobileNetV3-Large architecture to distinguish eight Indonesian rhizome types (bangle, ginger, kencur, kunci, turmeric, galangal, temu ireng, and temulawak) from RGB images. The dataset is organised by class and processed with a pipeline that includes resizing to 224×224 pixels, image flipping and rotation, brightness adjustment, zoom, and normalisation, before being split into training, validation, and testing subsets with an 80:10:10 ratio. MobileNetV3-Large pretrained on ImageNet is used as a feature extractor with a three layer dense classification head and dropout regularisation, trained using the Adam optimiser with a learning rate of 0.0001 and a checkpoint mechanism to store the best validation model. The proposed model achieves 97.50% accuracy, 97.65% precision, 97.50% recall, and 97.51% f1-score on the test set, indicating stable and balanced performance across all rhizome classes despite their similarity. Compared with earlier rhizome classification approaches based on handcrafted features, which typically report lower accuracies on fewer classes, and with heavier VGG or ResNet backbones, this work provides, to the best of the authors’ knowledge, the first evaluation of MobileNetV3-Large for multi class rhizome classification and shows that it offers a practical and computationally efficient baseline for image based rhizome identification on resource constrained mobile or embedded devices.
Expert System Implementation Using Certainty Factor Method for Early Pregnancy Disease Detection Hariyanti, Nanda Syarla; Nurlaili, Afina Lina; Aditiawan, Firza Prima
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3366

Abstract

Pregnancy requires continuous and accurate monitoring to prevent complications that may endanger both the mother and the fetus. Data from the 2024 Maternal Perinatal Death Notification (MPDN) system recorded an increase in maternal mortality, largely driven by delays in early diagnosis and late referral to appropriate healthcare facilities. These conditions highlight the need for decision-support technologies capable of providing timely and consistent early risk detection. This study develops HerBump, a web-based expert system designed to support the early identification of common pregnancy-related diseases by integrating the Certainty Factor (CF) method with expert medical knowledge. The novelty of this work lies in the use of CF to represent the degree of confidence from both experts and users, which helps improve diagnostic accuracy compared with conventional rule-based systems, especially in cases where symptoms are overlapping, incomplete, or vary between individuals. Evaluation results show that HerBump can generate early diagnostic outputs accurately and efficiently, supported by a System Usability Scale (SUS) score of 98.3 (Excellent) and Blackbox Testing that confirms all features function correctly across different scenarios. More broadly, the system has meaningful implications for maternal health, as it can support earlier interventions, enhance the consistency of risk assessments, and potentially help reduce maternal and infant mortality through faster and more reliable early detection. Its simple and scalable design also enables potential use in resource-limited areas, including regions with shortages of healthcare workers, with future development opportunities through expanded disease coverage and more diverse datasets to strengthen diagnostic reliability.
A Web-Based Online Reservation System with Personalized Tourism Recommendations Using Content-Based Filtering Amelia, Rizky; Nurlaili, Afina Lina; Aditiawan, Firza Prima
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3382

Abstract

The rapid growth of digital technologies has transformed the tourism industry and increased the need for personalized recommendation systems to enhance user experience and business competitiveness. However, many small- and medium-scale travel agencies still rely on manual reservation processes and social media–based promotions, which limit service efficiency and personalization. This study designs and implements a web-based reservation and tourism recommendation system for Sumovacation Tour using a Content-Based Filtering approach enhanced with feature weighting and cosine similarity. The main novelty of this study lies in the feature weighting mechanism, which assigns different importance levels to package attributes such as activities, travel duration, package type, and budget, improving recommendation relevance compared to standard content-based methods. Data were collected from Google Maps reviews in 2025, resulting in approximately 300 rating and review entries. The recommendation engine computes weighted relevance scores from user preference tags and package metadata to generate personalized recommendations. System functionality was validated using Black Box Testing, where all core workflows successfully passed, while usability evaluation using the USE Questionnaire showed high user acceptance, with usefulness, satisfaction, and ease of use each scoring 94.4%, and ease of learning reaching 95.2%. During testing, challenges related to data consistency and user input variation were addressed through input validation. The results show that the proposed system improves recommendation relevance while enhancing operational efficiency by reducing manual booking handling and supporting digital reservation management.
Analisis Perbandingan Deteksi Penyakit Daun Jagung Menggunakan YOLO dan CNN Rifqi, Mohammad Habim Hazidan; Haromainy, Muhammad Muharrom Al; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3392

Abstract

This study compares the performance of two deep learning methods, You Only Look Once version 8 (YOLOv8) and the Convolutional Neural Network (CNN) EfficientNetB0, in detecting and classifying maize leaf diseases. The background of this research stems from the importance of early plant disease identification to support food security, as well as the limitations of manual inspection methods, which are slow, subjective, and inefficient. The study combines primary and secondary data, totaling 2,000 images that underwent undersampling, augmentation, resizing, and bounding box annotation for YOLO training needs. Both models were trained on the same dataset with an 80% training and 20% testing split. YOLOv8n was trained using a transfer learning approach for 30 epochs, while the CNN was trained using EfficientNetB0 with similar training parameters. The results show that YOLOv8 achieved high detection performance with an mAP@0.5 of 0.985 and the highest class accuracy in the Healthy category (0.99). Meanwhile, the CNN demonstrated more stable classification performance, achieving the highest accuracy in the Grey Leaf Spot class (0.99) and a validation accuracy of 0.96. The comparison indicates that YOLO excels in object detection and disease localization in field images, whereas the CNN is more consistent in classifying segmented leaf images. These findings provide practical implications for real world deployment: YOLOv8 is suitable for real time detection in field conditions, including potential integration into mobile based early warning systems for farmers, while EfficientNetB0 is more appropriate for offline or laboratory based classification of static leaf samples.
Sensitivity Test Of Simple Additive Weighting And Weighted Product Methods For Toddler Nutritional Status Salsabila, Nadia Dita; Rahajoe , Ani Dijah; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3713

Abstract

Toddler nutritional status is an important indicator of child health and development and requires accurate assessment. In Posyandu, nutritional evaluation is often performed manually, which may lead to inefficiencies and inconsistencies when processing large amounts of data. Decision Support Systems (DSS) can assist health workers in conducting more systematic and objective assessments. Previous studies have applied multicriteria decision-making methods such as Simple Additive Weighting (SAW) and Weighted Product (WP) in various decision-making contexts. However, most studies mainly focus on producing ranking results and rarely examine how sensitive these methods are when criteria weights change. In addition, only limited research evaluates these methods using real anthropometric data collected from community health services such as Posyandu. Therefore, this study aims to analyze and compare the sensitivity of the SAW and WP methods in determining toddler nutritional status using empirical anthropometric data. The dataset consists of 412 toddlers collected from Posyandu activities, including gender, age, weight, height, and body mass index, which were converted into nutritional indicators. Sensitivity was assessed by modifying each criterion weight under two scenarios (0.5 and 1) and measuring the percentage change in the resulting preference values. The results show that the SAW method produced a change of 4%, whereas the WP method showed a change of 0.0028%. These findings indicate that SAW is more responsive to weight variations, while WP produces more stable preference values. The results provide empirical insight into the behavior of different multicriteria decision-making methods when applied to real nutritional monitoring data.
Web-Based Woven Fabric Recommendation System Integrated Fuzzy AHP and MOORA Based on User Preferences Bhakti, Mulyani Satya; Mumpuni, Retno; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3768

Abstract

This study proposes a web-based decision support system for woven fabric selection by integrating the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA). The system addresses limitations in conventional selection processes that rely on subjective judgment and lack structured multicriteria evaluation. The proposed approach combines uncertainty-based weighting using Fuzzy AHP with objective ranking using MOORA, enabling a transparent and systematic decision-making process. Unlike previous hybrid MCDM-based recommender systems, this study integrates user preference modeling within a web-based framework and incorporates consistency validation and sensitivity analysis to ensure reliable results. The experimental results show that fabric type is the most influential criterion, with a weight of 0.33, and that alternative A4 consistently ranks as the best option, with an optimization value of 0.392. Sensitivity analysis shows that the ranking results remain stable across a 20% weight variation, and comparison with the SAW method confirms consistent rankings. In addition, User Acceptance Testing (UAT) involving 20 respondents achieves a score of 86.4%, indicating high usability and user satisfaction. However, the system is evaluated within a limited dataset and does not incorporate adaptive learning mechanisms. Therefore, future work is directed toward expanding the dataset and integrating machine learning-based approaches to enhance adaptability and scalability. Overall, the proposed system provides a structured, transparent, and empirically validated solution for multicriteria decision-making.
Design of Web-Based Decision Support System Using AHP and bcrypt Security Subagio, Moh. Mario; Mumpuni, Retno; Nurlaili, Afina Lina
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3769

Abstract

This study presents the development of a web-based decision-support system to prioritize village administrative services using a structured, data-driven approach. This research addresses a gap in existing systems, which generally lack integration of systematic decision-making methods and robust security mechanisms, leading to inefficiency, low transparency, and subjective decision-making in village administrative processes. To overcome these limitations, the system integrates the Analytical Hierarchy Process (AHP) to evaluate multiple criteria, including submission time, document completeness, urgency level, service type, and request frequency, thereby enabling qualitative assessments to be transformed into measurable, comparable priority values. In addition, the bcrypt algorithm enhances system security by protecting user authentication data through password hashing, thereby mitigating risks such as unauthorized access, brute-force attacks, and rainbow-table attacks. The system is developed as a web-based application to ensure accessibility, scalability, and centralized data management. Evaluation results indicate that the system produces consistent and reliable priority rankings, as evidenced by a Consistency Ratio (CR) within the acceptable threshold, and demonstrates improved decision accuracy and operational efficiency compared to conventional manual approaches. Document completeness is identified as the most influential criterion in determining service priority. Furthermore, the proposed system offers broader applicability beyond village administration, particularly in other public service domains requiring transparent, efficient, and secure decision-making processes. Overall, this study contributes by integrating AHP and bcrypt within a unified system to enhance both decision quality and data security in digital administrative services.
Co-Authors Abadi, Luthfiyana Mahrurin Achmad Junaidi Adiputra, Muhammad Ariq Hawari Akbar, Fawwaz Ali Andreas Nugroho Sihananto Ani Dijah Rahajoe Arfiqi, Fawwaz Az-zahra, Firlie Aurellia Azira, Volem Alvaro Azira Bhakti, Mulyani Satya Chaurina, Agfanadita Rezkia Christianty, Theressa Marry Dhian Satria Yudha Kartika Eka Prakarsa Mandyartha Fathoni, Fuad Mahrus Fathuddin, Muhammad Abdul Hafizh Firza Prima Aditiawan Gusti Eka Yuliastuti Hariyanti, Nanda Syarla Henni Endah Wahanani Ismiati, Suci Made Hanindia Prami Swari Mardhavi, Arif Maulana, Dimas Octa Maulana, Hendra Maulana, Vieri Arief Muhammad Muharrom Al Haromainy Muhsin Muhsin Nurdiansyah N.A, M. Ryan Pratama, Novandi Kevin Purnomo, Ryan Putra, Chrystia Aji Putri, Della Atika Rahajoe , Ani Dijah Retno Mumpuni Rifan, Triyono Rifqi, Mohammad Habim Hazidan Riski, Agung Mustika Rizky Amelia Rizky, Agung Mustika Sadewa, Rahma Danu Saharani, Aulia Salsabila, Nadia Dita Siregar, Talitha Aurora Nadenggan Subagio, Moh. Mario Waskito, Muhammad Rizal Yana, Baktiar Yudha Yisti Vita Via