Sinkron : jurnal dan penelitian teknik informatika
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Efficient CNN-Based Classification of SARS-CoV-2 Spike Gene Sequences Using Alignment-Free Encoding
No full textThe COVID-19 pandemic caused by SARS-CoV-2 continues to challenge the global health system through the emergence of various variants with genetic characteristics that affect vaccine transmission and effectiveness. Conventional identification methods such as Whole-Genome Sequencing (WGS) have high accuracy but are constrained by significant cost and time. Most classification studies today still rely on complex hybrid architectures such as CNN-LSTM or image-based representations that increase computational load. This study aims to develop an efficient and lightweight pure Convolutional Neural Network model based on alignment-free encoding to classify five Variant of Concern (VOC) variants of SARS-CoV-2 (Alpha, Beta, Delta, Gamma, and Omicron) with an exclusive focus on the Spike gene sequence. The dataset consists of 5,000 Spike gene sequences that are represented using integer encoding and standardized with zero-padding. CNN proposed Lightweight architecture consists of four 1D convolution layers with a total of approximately 1.6 million parameters. The test results show that the model achieves excellent performance with an overall accuracy of 98.93%. The precision, recall, and F1-score values averaged 0.99, while the analysis of the ROC curve showed AUC values above 0.99 for all variants. This approach has proven to be efficient and effective, offering a fast, scalable, and resource-efficient solution to support real-time genomic surveillance systems in future pandemic mitigation
Performance Evaluation and Optimization of an IoT-Based Fish Smoking Monitoring System for Ensuring Product Quality
No full textFish smoking is a widely used preservation method; however, the quality of smoked fish is highly dependent on the stability of temperature, humidity, and smoking duration. Manual control of these parameters has limitations and may reduce product quality. Existing studies on fish smoking monitoring systems primarily focus on temperature control without providing quantitative evaluation of how multi-parameter process stability affects product quality and shelf life. This study aims to design and implement an Internet of Things (IoT)-based monitoring system for fish smoking equipment to ensure the quality of smoked fish. The research method used is Research and Development (R&D), which includes needs analysis, system design, development, testing, and evaluation stages. The system integrates temperature and humidity sensors, a microcontroller, and an IoT platform for real-time monitoring. The test results show that the system is capable of monitoring the smoking chamber temperature within a range of 60–80 °C with an average error of ±1.5 °C compared to a standard measuring instrument, and maintaining an optimal temperature of 70 °C during the smoking process. Quality testing of the smoked fish indicates uniform doneness, a golden-brown color, firm texture, and an average moisture content reduction of 35%. Shelf-life testing shows that the smoked fish can last up to 7–10 days at room temperature and up to 21 days under cold storage without significant changes in aroma and texture. Unlike previous works, this study provides quantitative evidence that improved stability of multiple smoking parameters through IoT-based monitoring significantly enhances product quality consistency and extends the shelf life of smoked fish
Music-Structure Segmentation in Balinese Gamelan (Tabuh Lelambatan) with SSM, Checkerboard Novelty, and HMM
No full textThis study aims to automatically segment the musical structure of Balinese gamelan by combining the Self-Similarity Matrix (SSM) method, the Checkerboard Novelty kernel, and Hidden Markov Models (HMM). Balinese gamelan has a complex musical structure that is cyclical and based on a colotomik system, requiring an adaptive analytical approach to repetitive patterns and transitions between musical sections. The research data consists of 30 Tabuh Lelambatan gamelan audio recordings obtained from public digital sources and validated through expert annotation to produce ground truth. The segmentation process was carried out through feature extraction using Constant-Q Transform (CQT), SSM formation to detect acoustic similarity patterns, application of the checkerboard kernel to mark transitions between segments, and temporal sequence modeling using HMM to refine boundary detection. System performance evaluation was carried out by comparing the segmentation results with ground truth using precision, recall, and F1-score metrics. The test results showed an average macro precision value of 0.998, a recall of 0.705, and an F1-score of 0.818, indicating that this method is capable of detecting the main boundaries of musical structures with high accuracy and consistent stability. However, the model still tends to miss gradual micro transitions. This research contributes to the field of Music Information Retrieval (MIR) and supports efforts to preserve traditional Balinese music through data-based analysis and the development of music computing technology
Integrating Agile Development and Content-Based Filtering for Personalized Digital Cultural Heritage Applications: A Case Study of Sri Ranggah Rajasa Sang Amurwabhumi
No full textThe preservation of Indonesia’s cultural heritage increasingly requires digital innovation that not only archives historical material but also engages users through adaptive interaction. However, existing digital cultural platforms seldom provide personalized learning experiences and often lack iterative user-centered development, creating a clear gap in adaptive digital cultural heritage applications. This study aims to design and develop a cultural application titled Sri Ranggah Rajasa Sang Amurwabhumi using a hybrid framework that integrates the Agile Development Method with a Content-Based Filtering (CBF) approach. Agile was applied through iterative cycles of design, development, implementation, integration, and testing, enabling continuous enhancement based on user feedback. Meanwhile, the CBF algorithm was used to generate personalized cultural content recommendations by analyzing semantic similarities among historical items. The novelty of this research lies in the unified hybridization of Agile and CBF to support adaptive, personalized digital cultural learning centered on a specific Indonesian cultural figure. Data were gathered from 30 respondents, including students and cultural practitioners, through usability testing and structured questionnaires. Results indicate high performance across key aspects: functionality (91%), usability (90%), recommendation accuracy (88%), and user satisfaction (93%). These findings demonstrate that combining Agile and CBF strengthens technical reliability while improving engagement through adaptive content delivery. Agile supports iterative refinement of user interfaces and system responsiveness, whereas CBF enables intelligent personalization in cultural learning environments. Nevertheless, this study is limited by its modest sample size and its focus on a single cultural topic, which may reduce generalizability. Future work will expand the dataset, incorporate multimodal cultural content, and validate the hybrid framework across broader Indonesian cultural domains.
Line-of-Sight Dominance Over Vegetation: Simulation-Based LoRa Performance in Tropical Forest Terrain
No full textLow-Power Wide-Area Network (LPWAN) technologies, especially LoRa, are receiving considerable interest for applications involving environmental monitoring in difficult terrain conditions. However, existing research predominantly examines vegetation attenuation or terrain elevation effects separately, leaving a critical research gap in understanding their combined and interactive impacts on LoRa connectivity in tropical forest environments. Furthermore, most studies rely on simplified propagation models that inadequately represent the complex radio environment of tropical forests, and few investigations systematically compare the relative importance of vegetation density, elevation, and line-of-sight conditions. This work addresses these gaps through an in-depth simulation-based investigation of LoRa network behavior in the University of Brawijaya (UB) Forest, which serves as a typical tropical forest setting in Indonesia. We performed detailed simulations using Python and LoRaSim, employing fine-resolution elevation datasets and precise vegetation classification to examine how dense vegetation, medium vegetation, and elevation parameters influence LoRa communication performance. Our findings indicate that, in contrast to traditional propagation models, nodes located in dense vegetation zones reached a 90.0% success rate, as opposed to 65.0% in zones without vegetation. Additional investigation shows that line-of-sight presence (28.6% versus 0.0% success rate) and relative elevation relative to the gateway (11.1% versus 27.3% success rate for nodes positioned above and below the gateway, respectively) represent more crucial factors for connectivity compared to vegetation attenuation by itself. These outcomes offer important guidance for enhancing LoRa-based environmental monitoring systems in tropical forest settings through strategic node positioning that considers elevation characteristics and line-of-sight availability
Security Evaluation of Indonesian LLMs for Digital Business Using STAR Prompt Injection
No full textThe adoption of Large Language Models (LLMs) in digital business systems in Indonesia is rapidly increasing; however, systematic security evaluation against Indonesian language prompt injection remains limited. This study introduces the Indonesian Prompt Injection Dataset, consisting of 50 attack scenarios constructed using the STAR framework, which combines structured instruction variations with sociotechnical context to expose potential model vulnerabilities. The dataset was used to evaluate three commercial LLM platforms ChatGPT using a GPT-4 class lightweight variant (OpenAI), Gemini 2.5 Flash (Google), and Claude Sonnet 4.5 (Anthropic) through controlled experiments targeting instruction manipulation in Indonesian. The results reveal distinct robustness profiles across models. Gemini 2.5 Flash exhibits moderate observed resilience, with 76% of scenarios classified as medium risk and 12% as high risk. ChatGPT demonstrates higher observed robustness under the tested scenarios, with 88% of cases classified as low risk and no high-risk outcomes. Claude Sonnet 4.5 shows intermediate observed resilience, with 72% low-risk and 28% medium-risk scenarios. High-risk cases primarily involve direct role override, urgency- or emotion-based prompts, and anti-censorship instructions, while structural ambiguities and multi-intent manipulations tend to result in medium risk, and mildly persuasive prompts fall under low risk. These findings suggest that while contemporary LLM defense mechanisms are effective against explicit attacks, contextual and emotionally framed manipulations continue to pose residual security challenges. This study contributes the first Indonesian-language prompt injection dataset and demonstrates the STAR framework as a practical and standardized approach for evaluating LLM security in digital business applications
An OWL-Based Ontology Model of Food Production and Distribution in Indonesian
No full textFood security in Indonesia is influenced by the dynamics of production, distribution, and availability between regions. However, many existing information systems still rely on conventional data structures without semantic integration, which limits interoperability and hinders interregional analysis. To address this gap, this study developed an ontology model based on the Web Ontology Language (OWL) that formally represents the relationships between food production, commodity characteristics, distribution flows, food insecurity conditions, and geographical context. The ontology was built using Protégé through stages of literature review, official data collection from BPS, FAO, and the Ministry of Agriculture, conceptual model design, implementation, and evaluation. Conceptual validation was conducted through Focus Group Discussions (FGD) with food supply chain experts to ensure the suitability of the ontology structure and the actual conditions of the national food system. The technical evaluation involved consistency testing using the Pellet reasoner and Competency Question (CQ) testing through SPARQL queries to assess the ontology's ability to respond to essential information needs. The resulting ontology consists of five core classes (FoodProduction, FoodItem, FoodDistribution, FoodSecurityStatus, and GeographicRegion) which collectively represent the semantic structure of Indonesia's food supply chain. The evaluation results show that the ontology is structurally consistent and capable of producing outputs that are in line with CQ, including the retrieval of production-distribution information and the initial identification of commodity surpluses and deficits based on instance data. These findings indicate that the developed ontology provides a coherent semantic foundation for modeling food systems and has strong potential to support the development of knowledge-based food security management applications
Digital Transformation of Toddler Posyandu Services via an Android-Based Application
No full textAbstract: Posyandu Balita as a community-based health service holds an essential role in improving maternal and child health in Indonesia. Nevertheless, the dependency on manual documentation frequently causes delays in reporting immunization, incomplete records, and limited access for parents to monitor child growth. This study sought to design and assess an Android-based Posyandu Balita application by applying a Research and Development (R&D) model combined with the System Development Life Cycle (SDLC) approach. The development process covered several phases: needs analysis, system design, application construction, pilot implementation, and evaluation through the Technology Acceptance Model (TAM).
The pilot, which involved 10 health cadres and 10 parents, revealed that the application reduced data loss, facilitated more accurate immunization tracking, and encouraged stronger parental involvement. Functional testing indicated that the main features—digital medical records, reminder notifications, and growth chart visualization—worked consistently as intended. Based on TAM analysis, perceived usefulness (PU) and perceived ease of use (PEOU) significantly shaped users’ behavioral intention to utilize the system (PU = 62%, PEOU = 58%). Moreover, the level of parental compliance in child health monitoring increased, where 85% of parents actively accessed the digital platform compared to only 40% before the trial.
Overall, the results demonstrate that mobile health applications developed with user-centered approaches can improve the effectiveness and efficiency of community-based services. The Posyandu Balita application is a promising innovation to support Indonesia’s digital health transformation. Further research is required to examine large-scale implementation, integration with national health information systems, and strategies for long-term sustainability.
Keywords: Community Health, Toddler Posyandu, Android-based Application, Mobile Health, Technology Acceptance Model, Digital Innovatio
Multi-Disease Retinal Classification Using EfficientNet-B3 and Targeted Albumentations: A Benchmark on Kaggle Retinal Fundus Images Dataset
No full textRetinal diseases remain one of the leading causes of blindness worldwide. This study develops a deep learning pipeline for multiclass retinal disease classification using EfficientNet-B3 combined with Albumentations to improve generalization. We target four classes: cataract, diabetic retinopathy, glaucoma, and normal. We use the Kaggle Retinal Disease dataset (4,217 fundus images) divided into 70% training, 10% validation, and 20% testing. Images are resized to 224×224 and augmented with horizontal flip, random brightness contrast, CLAHE, shiftscale rotate, crop, gamma correction, and elastic transformation. The EfficientNet-B3 backbone is refined after head training with warm-up and learning rate regularization (batch normalization, dropout). After 50 epochs, the best validation performance reaches 0.9526, and on the hold-out test set, the model achieves 95.38% overall accuracy. The F1 scores per class were 1.0000 (diabetic retinopathy), 0.9685 (cataract), 0.9255 (normal), and 0.9184 (glaucoma). Confusion analysis showed that most errors involved glaucoma being misclassified as normal, likely due to optic disc similarities. These results demonstrate that EfficientNet-B3 with targeted augmentation provides accurate and reliable multi-disease screening of fundus images, with the potential to support faster and more consistent triage in clinical workflows. Future research should expand clinical validation and explore attention mechanisms or multimodal input to reduce glaucoma-normal ambiguity
Comparative Analysis of Four Machine Learning Algorithms for Smoke Detection Using SMOTE-Rebalanced Sensor Data
No full textSmoke detection plays a critical role in preventing fire-related hazards, particularly in intelligent monitoring and early warning systems. Conventional smoke sensors often exhibit limited responsiveness in dynamic environmental conditions, prompting the adoption of IoT-based sensor data combined with machine learning techniques. This study presents a comparative evaluation of four supervised classification algorithms, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Gradient Boosting, using the Smoke Detection Dataset from Kaggle. The methodology integrates SMOTE to address class imbalance and Z-score normalization for feature standardization. Hyperparameter tuning was performed using GridSearchCV with 5-fold cross-validation, and model performance was assessed based on accuracy and execution time. Experimental results show that KNN achieved the highest accuracy (98.33%) with the lowest execution time (0.0327 s), whereas Decision Tree recorded the lowest accuracy (84.17%) but remained computationally fast (0.0406 s). Random Forest and Gradient Boosting demonstrated strong predictive capability (97.22% and 96.94%, respectively), but at higher computational costs (1.4338 s and 8.3819 s, respectively). Almost all models achieved perfect scores (1.00) for precision, recall, and F1-score following SMOTE-based balancing, except KNN which obtained slightly lower values (0.99). The findings indicate a trade-off between predictive performance and computational efficiency, suggesting that lightweight models such as KNN are better suited for real-time IoT-based smoke detection. In contrast, ensemble models may be more appropriate for backend analysis. This research contributes an integrated evaluation framework that combines data rebalancing, multi-model benchmarking, and time-based performance analysis, providing practical insights for the development of responsive and scalable early smoke detection systems