Sinkron : jurnal dan penelitian teknik informatika
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Smart Contract Architecture for a Blockchain-Driven Multi Criteria DSS in Forest Fire Monitoring and Response
No full textThe current centralized system is vulnerable to data manipulation due to the absence of independent verification mechanisms, thereby compromising the reliability of information. In addition, the inconsistency of formats and data silos across agencies exacerbates information fragmentation. Delays in data distribution hamper rapid response in emergency situations, while uneven communication infrastructure—especially in remote areas—reduces real-time monitoring capabilities. Lack of coordination among stakeholders—such as BNPB, forestry agencies, local communities, and the private sector—adds to the complexity of disaster management and often leads to overlapping tasks. The decision-making process is further complicated by competing criteria, such as priority areas, resource availability, dynamic weather conditions, and limited IoT sensor coverage. Additionally, high operational costs for system maintenance and limited audit trails make it difficult to track data history and ensure accountability. Therefore, the Multi-Criteria Decision Making (MCDM) method is necessary to handle uncertainty, combine different geospatial factors in an organized way, and make sure the decision-making process is reliable and clear. This research fills the technological gap by introducing a decentralized audit trail while facilitating cross-sector collaboration in fire mitigation decision-making and minimizing the risk of evidence-based data errors
Real-Time Web-Based Ship Collision Risk Detection Using AIS Data and Collision Risk Index (CRI)
No full textThe high density of maritime traffic in Indonesian waters, particularly in the Lombok Strait and Nusa Penida region, increases the risk of ship collisions, especially among vessels lacking adequate navigation systems. This study presents the development of a web-based system for real-time ship monitoring and collision risk assessment using Automatic Identification System (AIS) data. The system integrates a backend powered by FastAPI and MongoDB with a frontend built using React JS. AIS data is collected from a base station and processed to detect ship encounters using the DBSCAN clustering algorithm combined with Haversine distance to identify encounter detection. The risk assessment applies the Collision Risk Index (CRI) method by calculating DCPA (Distance to Closest Point of Approach) and TCPA (Time to Closest Point of Approach), allowing for graded risk categorization. Real-time risk notifications are delivered via WebSocket, and the interface includes interactive maps, ship detail views, and maritime weather information from the BMKG API. The system achieved high responsiveness, with an average detection time of 0.0075 seconds per ship and an end-to-end response time of approximately 61 milliseconds. Functional and usability tests show that the system effectively supports early detection of collision risks and improves maritime situational awareness. The proposed solution is scalable and applicable for maritime safety monitoring in busy sea routes, contributing to safer navigation and proactive decision-making
Enterprise Architecture for the Cruise Industry: A TOGAF-ADM and ArchiMate-Based Approach
No full textDespite its growth and resilience over the last decades, the cruise industry faces significant challenges in its strategic, operational, and technology domains. The unique complexity of the industry requires cruise companies to adopt a structured approach to enterprise transformation. To address this problem, this aims to provide an Enterprise Architecture (EA) blueprint for the cruise industry. Using a case study of a leading cruise line, CruiseX, this study analyzes the operational model of the cruise line and apply two industry-leading standards: The Open Group Architecture Framework (TOGAF) and the ArchiMate modelling language. This study applies the four core phases of TOGAF Architecture Development Method (ADM) from the initial phase of Architecture Vision (Phase A), through the definition of Business Architecture, Information System Architecture, and Technology Architecture (Phase B to D). The ArchiMate language is utilized to visualize the core business processes, information systems, and technology architecture. By using TOGAF ADM as the technical guidelines and ArchiMate as the modeling language, the result of this study is a blueprint of core business processes, application and data that support each business processes, and the underlying technology infrastructure, that provides a structured framework and serves as an actionable tool for implementing enterprise architecture in cruise industry. This research also extends the application of TOGAF and ArchiMate to the under-research cruise industry domain. The study’s limitations include the reliance on publicly available data, the limited scope of business processes, and the lacks of practitioner validation, suggesting clear directions for future research
A Hybrid PULTS–SWARA–ELECTRE-I Model for Multi-Criteria Political Sentiment Classification on Indonesian Twitter Data
No full textSocial media platforms such as Twitter have become crucial for analyzing political sentiment, particularly in contexts where public opinion shifts rapidly. This study proposes a hybrid classification model that combines Probabilistic Uncertain Linguistic Term Set (PULTS), Stepwise Weight Assessment Ratio Analysis (SWARA), and ELimination Et Choice Translating REality (ELECTRE-I). Using a dataset of 7,800 tweets collected between January and July 2024 covering five major political parties in Indonesia, the model classifies tweets into positive, negative, and neutral sentiments. To address class imbalance, Easy Data Augmentation (EDA) was applied, while Term Frequency–Inverse Document Frequency (TF-IDF) was used for feature extraction. The results show that the proposed model achieves 90% accuracy and an F1-score of 85%, outperforming baseline methods such as SVM (86.7%), Naïve Bayes (83.3%), Decision Tree (88%), and K-Means (76.7%). These improvements demonstrate that the integration of linguistic uncertainty with expert-driven feature weighting provides measurable advantages in political sentiment classification. Beyond performance, the study contributes theoretically by extending multi-criteria decision-making methods into sentiment analysis and by offering a more interpretable alternative to opaque machine learning models. Together, these findings highlight the practical value of explainable decision frameworks for political communication while advancing methodological approaches for analyzing sentiment under uncertainty
Hybrid Artificial Intelligence–Blockchain Approach for Landslide Risk Classification and Recommendation
No full textIncreased rainfall intensity, steep topography, and changes in land use in Indonesia, particularly in Java, such as Garut Regency, have increased the risk of landslides that have a widespread impact on public safety and environmental stability. This study proposes a Hybrid Artificial Intelligence and Blockchain approach to develop an accurate, secure, and transparent landslide risk classification and recommendation system. The model integrates three Multi-Criteria Decision Making (MCDM) methods, namely Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). These three methods are used sequentially to determine criterion weights, calculate ideal solutions, and produce optimal compromise decisions based on geospatial factors. The dataset used consists of 766 geospatial observation data covering stability, rainfall, vegetation, river distance, slope, prediction, and ground truth parameters, obtained from satellite data and open geospatial repositories in the Java Island region. The research process included pre-processing, normalization, weighting analysis using AHP–TOPSIS–VIKOR, and integration of the results into the Ethereum Blockchain Smart Contract system with a Proof of Authority (PoA) consensus mechanism. The test results showed a 17.8% increase in classification accuracy and a 21.4% increase in data storage efficiency compared to conventional methods. This approach is expected to improve the reliability, security, and transparency of the analysis system and mitigate the risk of landslides based on smart technology in Indonesia
Usability Evaluation of Lecturer Information System in ITB STIKOM Bali
No full textThis research aims to provide a deeper understanding of the usability aspects of information systems, as well as help create more effective and efficient solutions in supporting academic activities in higher education. The ITB STIKOM Bali, Lecturer Information System (Sistem Informasi Dosen/ SID) is a system developed to assist lecturers in carrying out their academic responsibilities. This system has become a very vital tool in supporting various academic activities of lecturers. This research will be conducted using the Concurrent Think Aloud (CTA) method, Performance Measurement, and System Usability Scale (SUS) to assess effectiveness, efficiency, and user satisfaction of the system. The system was found effective, with a success rate exceeding 78%. Advanced users achieved a 95% success rate, while beginner users achieved 86%, with errors primarily in navigation-related tasks. User satisfaction analysis via SUS showed skilled users rated the system at 84.75 (Grade A, Acceptable), whilst beginner respondents scored 52.5 (Grades D, Marginal Low), reflecting usability challenges for beginners. Performance Measurement highlighted issues with small font sizes and unclear navigation, while CTA identified difficulties with the logout button, lack of search functionality, unreadable interface text, and unclear functional position menus. Recommendations include increasing font size to Arial 14, redesigning the logout button, adding search bars, and enhancing functional menus to include research and community service options. These improvements aim to enhance system usability and user experience across all proficiency levels
Effectiveness of Bi-GRU and FastText in Sentiment Analysis of Shopee App Reviews
No full textE-commerce is proof of evolution in the economic field due to its flexibility to shop for various necessities of life anytime and anywhere. Shopee is one of the e-commerce platforms in demand by people from varied circles in Indonesia. Multiple reviews are shed publicly by Shopee users on the Google Play Store regarding shopping experiences, which can be positive or negative. This condition affects the decision of other users to shop at Shopee, thus impacting the increase or decrease in profits from Shopee itself. Therefore, user sentiment analysis is needed as a form of effort to maintain user trust in Shopee. This research aims to build a system to classify the sentiment of Shopee application users through reviews in the Google Play Store by utilizing the Bidirectional Gated Recurrent Unit (Bi-GRU) deep learning model. The dataset contains 9,716 reviews, including 3,937 positive and 5,779 negative sentiments. Several test scenarios were conducted to achieve the highest peak of performance, utilizing TF-IDF feature extraction, FastText feature expansion, and optimization using the Cuckoo Search Algorithm. Additionally, SMOTE resampling was utilized to correct the dataset’s uneven distribution. The combined test scenarios mentioned significantly improved the accuracy by 1.03% and F1-Score by 1.04% from the baseline, with the highest accuracy reaching 90.48% and the highest F1-Score of 90.16%
A Comparative Study of Data Mining Algorithms for Fraud Detection in Financial Transactions
No full textDeteksi penipuan dalam transaksi keuangan merupakan tantangan penting bagi industri perbankan dan e-commerce. Seiring dengan semakin canggihnya aktivitas penipuan, kebutuhan akan metode deteksi tingkat lanjut menggunakan teknik penambangan data pun meningkat. Studi ini melakukan analisis komparatif terhadap berbagai algoritma machine learning, termasuk Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, dan model Deep Learning, untuk mendeteksi transaksi keuangan yang curang. Penelitian ini menggunakan kumpulan data yang terdiri dari transaksi yang curang dan sah serta menerapkan beberapa metrik evaluasi seperti akurasi, presisi, recall, F1-score, dan AUC-ROC untuk mengukur kinerja algoritma. Hasilnya menunjukkan bahwa model pembelajaran ensemble, khususnya Random Forest dan XGBoost, mengungguli metode klasifikasi tradisional dalam hal akurasi, efisiensi, dan ketahanan. Model Deep Learning juga menunjukkan hasil yang menjanjikan tetapi memerlukan sumber daya komputasi yang besar, kumpulan data yang besar, dan penyempurnaan untuk mencapai kinerja yang optimal. Selain itu, teknik praproses data seperti pemilihan fitur, pengurangan dimensionalitas, dan penyeimbangan kelas berdampak signifikan terhadap efektivitas deteksi. Temuan studi ini memberikan wawasan berharga bagi lembaga keuangan dalam memilih algoritma deteksi penipuan yang paling efisien, yang pada akhirnya meningkatkan keamanan transaksi dan mengurangi kerugian finansial. Penelitian di masa mendatang dapat mengeksplorasi pendekatan hibrida yang memadukan berbagai teknik, serta metode pemrosesan waktu nyata, untuk lebih meningkatkan akurasi deteksi penipuan dan meminimalkan kesalahan positif dalam sistem keuangan berskala besar
Implementation of the Dual Channel Convolution Neural Network Method for Detecting Rice Plant Diseases
No full textRice is a strategic and important food crop for the economy in Indonesia. Rice can be infected with diseases caused by fungi, bacteria and viruses. The disease that attacks rice plants goes unnoticed by farmers and farmers often do not understand the diseases that attack rice plants so that it is too late in treating them to diagnose the symptoms, causing rice production to decrease. To solve this problem, it is necessary to carry out a disease detection process in rice plants. In this research, the Dual-Channel Convolutional Neural Network (DCCNN) method will be used. This DCCNN method consists of two channels, namely deep channel and shallow channel. The process of detecting grape plant diseases using the DCCNN method will start from the process of extracting leaf parts from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to carry out the process of extracting characteristics, color and texture from the extracted leaf parts. Finally, the DCCNN method will be applied to carry out the process of classifying and detecting types of grape plant diseases. The results of this research are that the DCCNN method can be used to detect types of leaf diseases in rice plants. The accuracy of disease detection results using the DCCNN method depends on the number of datasets contained in the system with an accuracy level of up to 85%. However, more datasets will cause the execution process to take longer.
 
Comparative Analysis of SVM and BERT for Sentiment and Sarcasm Detection in the Boycott of Israeli Products on Platform X
No full textThe Israel-Palestine conflict has triggered a global consumer movement, including a widespread boycott of Israeli-affiliated products in Indonesia. As this campaign gains momentum on digital platforms like X (formerly Twitter), understanding public sentiment becomes crucial—not only for gauging public opinion but also for anticipating potential socio-economic impacts. This study evaluates the effectiveness of two sentiment analysis models—Support Vector Machine (SVM) and Bidirectional Encoder Representations from Transformers (BERT)—in classifying sentiment and detecting sarcasm related to the boycott campaign. A total of 5,637 Indonesian-language tweets were manually labeled into positive, neutral, and negative categories, with sarcasm detection performed using a fine-tuned IndoBERT, model which classified tweets into two categories: sarcastic
and non-sarcastic. The models were assessed using accuracy, precision, recall, F1-score, and computational efficiency. Results show that BERT outperforms SVM in both sentiment classification (accuracy: 69.26% vs. 64.58%; F1-score: 69.47% vs. 62.40%) and sarcasm detection (accuracy: 92.20% vs. 86.15%; F1-score: 92.38% vs. 85.27%). However, BERT requires significantly longer processing times 194.76 seconds for sentiment classification and 191.92 seconds for sarcasm detection, while SVM required only 18.81 seconds and 10.99 seconds. These findings highlight a trade-off between contextual comprehension and real-time efficiency. Future research may explore ensemble methods or threshold-tuning to optimize this balance. The practical implications of this research lie in its application for real-time public discourse monitoring and data-driven policy development. By improving the detection of nuanced expressions such as sarcasm, this study contributes to more accurate sentiment interpretation in polarized digital environments