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All Journal bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) JAMAIKA: Jurnal Abdi Masyarakat Abdi Jurnal Publikasi Journal of Research and Publication Innovation Ranah Research : Journal of Multidisciplinary Research and Development International Journal of Enterprise Modelling OKTAL : Jurnal Ilmu Komputer dan Sains JRIIN :Jurnal Riset Informatika dan Inovasi TEKNOBIS : Jurnal Teknologi, Bisnis dan Pendidikan AI dan SPK : Jurnal Artificial Intelligent dan Sistem Penunjang Keputusan Riau Jurnal Teknik Informatika
Dede Sunandar
Universitas Pamulang
Aerospace Engineering Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Transportation Other

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Thesis Title Similarity Detection System Using Levenshtein Distance and Cosine Similarity Dede Sunandar; Adam Muiz
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2864

Abstract

The manual verification process of thesis titles in higher education institutions is often time-consuming and prone to oversight, making it difficult to ensure the uniqueness of each student’s work. This poses serious academic risks, as undetected similarities in thesis titles can lead to unintended plagiarism, compromise academic integrity, and undermine the credibility of educational institutions. In a broader sense, repeated or overlapping research topics also reflect a lack of innovation and weaken the scientific contribution of academic programs. To address this issue, an automated detection system is needed to efficiently identify similarities between thesis titles. This study aims to develop a web-based thesis title similarity detection system that integrates Levenshtein Distance and Cosine Similarity algorithms. The system was developed using the Waterfall model, involving stages of requirements analysis, design, implementation, and evaluation. Functional features such as login, title data management, old spelling normalization, and real-time similarity detection were implemented. The results show that the combination of both algorithms effectively detects similarities in character and semantic aspects. The inclusion of an old spelling normalization feature significantly improves detection accuracy by aligning historical and modern word forms prior to analysis. In conclusion, the developed system not only supports a faster and more objective title verification process but also contributes to the prevention of academic plagiarism and promotes integrity in higher education environments.
Web-Based Citrus Fruit Disease Detection Application Using MobileNet V2 for Agricultural Quality Assurance Nasrul Hidayah; Adam Muiz; Dede Sunandar
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3338

Abstract

Precision disease detection in citrus commodities has become increasingly essential within the framework of Agriculture 4.0, particularly for small-scale vendors who still rely on manual visual inspection that is often inconsistent and error-prone. This study develops and evaluates a web-based citrus fruit disease detection system using the MobileNet V2 Convolutional Neural Network architecture. The methodological novelty of this work lies in the integration of an optimized MobileNet V2 model enhanced through targeted data augmentation and lightweight fine-tuning into an end-to-end web ecosystem that supports two inference modes: static image upload and real-time webcam-based detection, tailored to the operational needs of small vendors. The system classifies citrus fruit images into four categories: Black-Spot, Citrus Canker, Greening (Huanglongbing), and Fresh, using more than 1,000 augmented images standardized to 224×224 pixels with an 80:20 train–test split. Experimental results show that the model achieves an accuracy of 96.21%, with consistently high precision and recall across disease classes, while the Fresh class exhibits relatively higher misclassification due to visual similarity with early-stage symptoms. The Flask-based web application demonstrates stable performance under black-box testing and delivers rapid, high-confidence predictions. These findings affirm the effectiveness of lightweight CNN approaches in improving fruit quality inspection accuracy, reducing sorting errors, and supporting more efficient workflows for local vendors.
Identification of Dengue Hemorrhagic Fever (DHF) Using the Naïve Bayes Classifier Method Erdy Sutriyatna; Dede Sunandar; Adam Muiz
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3378

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health burden in tropical and subtropical countries, with Indonesia consistently reporting the highest incidence in Southeast Asia since 1968. Early diagnosis traditionally depends on clinical evaluation and laboratory confirmation, processes that may require several days and often delay intervention during the critical plasma leakage phase. Addressing this gap, the present study introduces an intelligent early identification system for DHF based on the Naïve Bayes Classifier, a probabilistic data-mining method recognized for its computational efficiency and strong performance in handling categorical medical attributes. The model was trained using 100 anonymized patient records and DHF screening forms collected from Puskesmas Pasir Buah, Curug, Bitung, spanning 2020–2023, incorporating twelve clinically relevant predictors consisting of symptom-based indicators and basic hematological parameters. Following preprocessing and 10-fold cross-validation, the system achieved an average accuracy of 94.67%, precision of 95.2%, recall of 93.8%, and an F1-score of 94.5%, demonstrating its reliability for preliminary DHF assessment. The resulting web-based prototype allows health workers to input patient symptoms and receive immediate probabilistic classifications (Positive/Negative) accompanied by recommendations for confirmatory laboratory testing. By providing rapid and interpretable diagnostic support, this system has the potential to reduce diagnostic delays at the primary healthcare level and enhance decision-making in resource-limited environments.
Web-Based Waste Management Information System Using the Waterfall Method Noviadih, Muhamad; Muiz, Adam; Sunandar, Dede
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.v8i3.3804

Abstract

The rapid growth of urbanization and industrial activities has significantly increased waste generation, posing complex environmental and operational challenges when information management remains fragmented across institutions. This study aims to design and implement a web-based waste management information system to support integrated waste handling processes at the Environmental Agency of Tangerang Selatan. The system was developed using the Waterfall method, encompassing requirement analysis, system design, implementation, testing, and maintenance, with data collected through observation, stakeholder interviews, and literature review. UML modeling was employed to formalize system architecture, and the application was implemented using Laravel (PHP) and MySQL. The proposed system introduces a unified platform that integrates citizen reporting, institutional workflow management, and public information dissemination, addressing limitations of prior web-based systems that are typically function-specific and lack cross-stakeholder integration. The system supports waste reporting, processing, and monitoring within a closed-loop workflow, enhancing traceability and coordination. Quantitative evaluation through functional testing achieved a 100% success rate across all core modules, while preliminary operational observations indicate reduced reporting delays and improved data accessibility compared to prior manual processes. The system also enhances public engagement through structured reporting and educational content delivery. These findings demonstrate that the system provides measurable improvements in operational efficiency and stakeholder coordination, offering a scalable and cost-effective digital governance solution for municipal waste management in resource-constrained environments.
Web-Based Expert System for Common Disease Diagnosis Using Forward Chaining Dede Sunandar; Muiz, Adam; Mutaqim, Zainul
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.v8i3.3814

Abstract

The increasing number of patients seeking medical consultation at primary healthcare facilities often leads to prolonged waiting times and delayed preliminary diagnosis, particularly for common diseases with overlapping symptoms. At Puskesmas Karanganyar, diagnostic procedures remain largely dependent on direct consultation, limiting service efficiency under high patient volumes. This study aims to design and implement a web-based expert system to support early diagnosis by simulating clinical reasoning. The system employs a rule-based inference mechanism using Forward Chaining to identify potential diseases from patient-selected symptoms, while the Certainty Factor method is integrated to quantify diagnostic confidence. Unlike prior rule-based diagnostic systems, this study contributes an integrated multi-disease diagnostic framework tailored to primary healthcare workflows, combining transparent rule traceability with graded confidence representation to enhance interpretability and practical usability. Knowledge acquisition was conducted through expert interviews and literature review, resulting in a knowledge base of ten diseases and forty symptoms. The system was implemented using PHP and MySQL and is accessible across devices. Empirical evaluation through structured functional testing and user-oriented validation indicates that the system achieves consistent diagnostic outputs with 100% functional success across tested scenarios, average response time below 2 seconds, and positive usability feedback from healthcare staff, demonstrating operational reliability in real-world settings. The findings suggest that the proposed system provides fast, consistent, and informative preliminary diagnoses, supporting more efficient decision-making in primary healthcare services.
Co-Authors Adam Muiz Adam Muiz Adam Muiz Adam Muiz Akhdan Navin Naeem Badriah Nursakinah Baren Maulana Wibowo EKO WAHYUDI Erdy Sutriyatna Haydar Kurni Ilham Padhilah Intan Kumala Kumalasari Ja’far Abdullah Mesak Hutagaol Muiz, Adam Mutaqim, Zainul Nasrul Hidayah Noviadih, Muhamad Nur Alif, Wildan Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Nurhayati Putra Pamungkas, Adhitya Putri, Sri Rama Raditia Vindua Raditia Vindua Raditia Vindua Rinaldy, Ferry Sobari, Dhika Supriyadin Hulu Yatatema Zai