Sinkron : jurnal dan penelitian teknik informatika
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Usability Evaluation of GetContact Application Using Post-Study System Usability Questionnaire and Retrospective Think Aloud
No full textGetContact, aplikasi manajemen dan proteksi panggilan spam dengan lebih dari 700 juta unduhan di Google Play Store, masih memiliki ruang untuk peningkatan kegunaan. Studi ini bertujuan untuk memanfaatkan kegunaan GetContact berdasarkan ulasan Quora, komentar Google Play Store, dan wawancara dengan pengguna di area Palembang. Metode yang digunakan adalah Post-Study System Usability Questionnaire (PSSUQ) dan Retrospective Think Aloud (RTA). Hasil PSSUQ dari 190 responden menunjukkan tingkat kegunaan keseluruhan yang baik dengan skor 2,73. Skala Kegunaan Sistem mencatat nilai 2,60, dan Kualitas Informasi mencapai 2,80, menunjukkan kegunaan yang memuaskan. Namun, kualitas antarmuka dengan skor 2,89 masih perlu ditingkatkan. Temuan dari metode RTA juga mengidentifikasi kendala dalam fitur dan antarmuka aplikasi. Studi ini menyimpulkan bahwa meskipun tingkat kegunaan GetContact secara keseluruhan baik dan diterima oleh pengguna, pengembangan lebih dari antarmuka dan fitur masih diperlukan untuk meningkatkan kegunaan secara keseluruhan dan menciptakan pengalaman pengguna yang lebih baik
Sentiment Analysis of Tokopedia App Reviews using Machine Learning and Word Embeddings
No full textTokopedia, a prominent e-commerce platform in Indonesia, generates vast amounts of user feedback, offering valuable insights into customer satisfaction through sentiment analysis. However, sentiment analysis of app reviews specifically on Tokopedia reviews remains underexplored. Sentiment analysis, also known as opinion mining, categorizes user sentiments into positive or negative, offering insights into user preferences and service shortcomings. While traditional text representation techniques like TF-IDF are widely used for sentiment analysis, they lack the semantic richness provided by advanced word embeddings such as Word2Vec and FastText, which excel at capturing contextual relationships between words. These methods have shown potential to enhance the performance of machine learning models in sentiment analysis tasks. This study evaluates the performance of three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (NB)—combined with Word2Vec and FastText feature extraction. A dataset of 59,811 Tokopedia app reviews was scraped from the Google Play Store, preprocessed, and subjected to feature extraction using Word2Vec and FastText. SVM achieved the best performance, with an accuracy of 89.06% using FastText and 89.02% using Word2Vec. RF ranked second with accuracies of 88.07% for FastText and 88.14% for Word2Vec. NB showed the lowest performance, achieving 84.26% with Word2Vec and 83.73% with FastText. Differences in performance between Word2Vec and FastText embeddings were minimal across all algorithms, highlighting their comparable effectiveness. These results underscore SVM’s consistent superiority across various configurations for sentiment analysis
Comparison of RNN and LSTM Algorithms Based on Fasttext Embeddings in Sentiment Analysis on the Merdeka Mengajar Platform
No full textAs of 2024, the Merdeka Mengajar Platform has been used by more than 3.5 million teachers across Indonesia. This number represents an increase of more than 3.85% compared to the previous academic year, which was 3.37 million. However, the utilization of this application has not yet reached the expected target number of users, so an analysis is needed to identify the factors causing this. This research uses Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to perform sentiment analysis on reviews of the Merdeka Mengajar platform. RNN and LSTM are chosen for their advantages in handling sequential data, particularly in text processing for sentiment analysis. This research aims to address the challenges in understanding the positive or negative sentiments of users on the platform. The research methodology includes important stages such as data cleaning, preprocessing, and transforming text into numerical vectors using FastText embedding. Next, RNN and LSTM models are applied to predict sentiment based on patterns in the text data. The research results show that the LSTM model is capable of capturing long-term relationships in sequential data with an expected accuracy of 93.58%. Meanwhile, the RNN model yields a lower accuracy of 91.70%. The LSTM model is more effective in classifying sentiment with high accuracy, especially in text data with complex temporal contexts. This research contributes to understanding user perceptions and feedback regarding the Merdeka Mengajar platform, which is expected to provide insights for platform developers to enhance service quality
Maturity Level Analysis of SPBE Service Domain Using Capability Maturity Model Integration at the Kominfo Palembang City
No full textThis journal aims to analyze the maturity level of the implementation of Electronic-Based Government Systems (EBS) in the service domain at the Communication and Information Technology Office of Palembang City. This research uses the Capability Maturity Model Integration (CMMI) approach to evaluate processes, identify weaknesses, and provide recommendations for improvement. CMMI was chosen because it can measure process effectiveness and help organizations achieve optimal performance. The research was conducted using survey and interview methods to collect data related to SPBE implementation. The collected data was analyzed using the CMMI framework to determine the maturity level from level 1 (Initial) to level 5 (Optimizing). The results of the analysis show that the maturity level of the SPBE service domain at the Communication and Information Technology Office of Palembang City is at level 3 (Defined) with a maturity level value of 3.66 from a recapitulation of a value mapped to each process area: OPF, OPD, MA, CAR and PPQA. Some areas need to be improved, especially related to lack of clearly defined and consistently applied standard operating procedures (SOPs) leads to variations in service delivery and hampers the overall effectiveness of SPBE implementation, performance monitoring mechanisms such as tracking and evaluation of service delivery outcomes are inadequate which makes it difficult to assess the effectiveness of SPBE services, existing systems are not fully compatible or lack the necessary features to support technology integration within the SPBE framework leading to inefficiencies and failure to leverage technology to improve public services. This research contributes by providing strategic recommendations to improve the maturity of SPBE implementation at the Communication and Informatics Office of Palembang City. The recommendations given are increasing the capacity of human resources, consistent application of standard operating procedures (SOPs), and the use of more integrated technology to support more effective and efficient services. The results of this study are expected to serve as a guide for the Communication and Information Technology Office of Palembang City in implementing SPBE more optimally
Digital Transformation of Electricity Bill Collection: Predicting Delays Using Machine Learning
No full textDelays in electricity bill payments pose a significant challenge for PLN in maintaining financial stability and delivering equitable service quality to the public. This study aims to develop a payment delay prediction system to assist PLN UP3 Makassar Utara in prioritizing invoice distribution to customers with a high likelihood of late payments. The Random Forest algorithm was chosen for its ability to handle complex data and produce reliable predictions. This research analyses historical electricity customer data from 2018 to 2023, encompassing 227,163 entries. The data is processed using validation techniques such as K-Fold Validation and Rolling Window Validation to ensure the accuracy and generalizability of the model. The study's findings demonstrate that an accurate payment delay prediction model can be developed using the Random Forest method, incorporating historical features such as lag features, moving averages, and seasonal variables. Additionally, the system prioritizes invoice delivery to high-risk customers based on risk scores derived from historical delay patterns. This system reduces payment arrears at PLN UP3 Makassar Utara through proactive measures such as early notifications, personalized reminders, or payment incentives to encourage timely payments. As a result, the study indicates that the system effectively enhances the efficiency of payment management and supports the company's financial stability. However, the research is limited by the use of data from a single region, the absence of external factors in the model, and the high computational requirements. For broader implementation, further research should include data from other regions, consider external factors, and optimize computational resource usage
A Comparative Study of Ensemble Learning and Neural Networks for the Heart Disease Prediction
No full textHeart disease continues to be a leading global cause of death, making the development of predictive models for early diagnosis a critical task. This study investigates the performance of various machine learning and deep learning models for heart disease prediction using a structured dataset of 918 observations and 11 features. The analysis includes ensemble methods like Random Forest, Gradient Boosting, and XGBoost, as well as neural networks such as Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). Traditional classifiers, including Support Vector Machines (SVM) and Logistic Regression, are also considered for benchmarking. The dataset was preprocessed using label encoding, standardization, and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance and ensure data consistency. Model evaluation was conducted using key metrics such as precision, recall, F1-score, and ROC-AUC. The results demonstrated that ensemble methods, particularly Random Forest (ROC-AUC: 0.9313) and Gradient Boosting (ROC-AUC: 0.9279), consistently delivered superior performance. Among neural networks, MLPs showed promising results (ROC-AUC: 0.9232), outperforming CNNs, which were less effective in handling tabular data. Meanwhile, TabNet was found to be unsuitable for this dataset, as it significantly underperformed across all metrics. This research highlights the effectiveness of ensemble methods and MLPs in heart disease prediction and the importance of proper preprocessing techniques. Future work could focus on integrating hybrid models or advanced optimization techniques to further enhance predictive accuracy in clinical settings
Predicting Prospective Student Interests Using the C4.5 Algorithm and Naive Bayes
No full textStudents are individuals pursuing higher education at a university with the goal of enhancing their knowledge, skills, and character to succeed in the professional world and contribute to society. The purpose of this study is to analyze the factors that influence prospective students' interest in continuing their education using the C4.5 Algorithm and the Naïve Bayes Method. The importance of understanding prospective students' interest patterns is expected to help universities formulate more effective strategies. The purpose of this study is to determine how well the two methods classify data and understand the factors that most influence prospective students' decisions. The C4.5 Algorithm is known to be effective in building decision trees that are easy to interpret, while the Naïve Bayes Method has the advantage of handling datasets with independent attributes. This study uses the stages of data selection, data pre-processing, algorithm application, and model evaluation. The classification results obtained from the C4.5 Algorithm show that 132 data are included in the interest category and 8 data are not interested, while the Naïve Bayes Method produces 131 data of interest and 9 data are not interested. In conclusion, both methods have good accuracy levels, but the Naïve Bayes Method shows superiority in Recall value, while the C4.5 Algorithm excels in interpretation of results and clarity of classification patterns
Implementing TOGAF Enterprise Architec-ture in Indonesia’s Merchant Acquiring In-dustry: A Framework for Digital Trans-formation
No full textThe digital transformation of Indonesia's merchant-acquiring industry, accelerated by regulatory initiatives, fintech innovations, and changing consumer behavior, has created significant technological and organizational challenges. Fragmented legacy systems and complex regulatory requirements hinder seamless digital payment adoption. This study investigates the strategic implementation of The Open Group Architecture Framework (TOGAF) to systematically manage these challenges. Through an extensive literature review and case studies of major industry players—including BRI, BCA, Mandiri, BNI, and GoPay—this research uniquely explores TOGAF's specific applicability to merchant acquiring in Indonesia. The proposed TOGAF-based framework aligns closely with Bank Indonesia's Payment System Blueprint 2025, emphasizing enhanced interoperability, regulatory compliance, and sustainable growth. Findings suggest that enterprise architecture can unify fragmented technologies, bridge gaps in merchant activation, and strengthen cybersecurity, ultimately driving innovation in digital payment services. By providing a structured implementation roadmap tailored to Indonesia's regulatory environment, this research not only addresses current industry needs but also sets a foundation for future technological advancement and financial inclusion in Indonesia's merchant acquiring landscape
Enhancing EEG-Based Stress Detection Using ICA, Relative Difference, and Convolutional Neural Networks
No full text: EEG-based stress detection is crucial for early mental health monitoring, but signal quality is often degraded by artifacts and baseline variability. This study proposes an optimized preprocessing method combining Independent Component Analysis (ICA) for artifact removal and Relative Difference for baseline reduction. Using the SAM-40 EEG dataset, features were extracted with Differential Entropy and structured into a 3D EEG cube to preserve spatial-frequency information. A Convolutional Neural Network (CNN) classified stress levels into low and high categories. The proposed approach achieved 94.44% accuracy, with 100% precision for the high stress class and 81.82% recall. These results highlight the effectiveness of combining ICA and baseline reduction to enhance deep learning-based EEG signal processing for stress detection
ECG-Based Arrhythmia Classification in Students Using Random Forest: A Case Study with Class Imbalance Analysis
No full textArrhythmia is a heart rhythm disorder that can indicate a student’s heart health status. This research aims to develop a Random Forest model to classify arrhythmia in students based on ECG signals. ECG data was collected from 100 students at SMK Swasta Teladan Sumatera Utara 2 after learning activities. The extracted signal features include RR interval, PR interval, QRS duration, QT interval, ST segment, beats per minute (BPM) and R/S ratio. Data labeling was carried out manually by the researchers based on the range of ECG feature values that had been determined by the doctor for each class: Normal, Abnormal, Potential Arrhythmia and Very Potential Arrhythmia. The dataset is divided into 70% for training and 30% for testing. SMOTE is applied to address class imbalance. The model achieved 80% accuracy with the best performance in normal class with precision, recall and f1-score of 94%. However, no samples were identified for Potential Arrhythmia class, as there were no extracted feature values that met the criteria set by the doctor, so model could neither learn nor make predictions for this category, even after applying balancing methods such as SMOTE. For further research, based on these findings, it highlights the need for balanced class representation and expert-guided labeling to improve the performance of ECG -based arrhythmia classification