Deterministic Economic Resilience Through Gross Regional Domestic Product Using Nonparametric Geographically Weighted Regression Spline Truncated

Megatrends are large-scale global movements with huge impacts, influenced by socio-economic, political, ecological and technological factors. As a developing country, Indonesia faces challenges such as political instability and limited infrastructure, so strengthening economic resilience through increasing Gross Regional Domestic Product (GRDP) is important. The aim of this research is to analyze Indonesia\u27s GRDP data in 2022, which shows significant spatial variability between provinces to see the resilience of the Indonesian economy. The method used is Nonparametric Geographically Weighted Regression - Spline Truncated (NGWR-ST). The NGWR-ST approach is well suited because it allows location-specific parameter variations, captures complex nonlinear relationships through spline functions, and minimizes the influence of extreme values ​​using truncation. The results indicate that an optimal model is achieved with two knot points (GCV = 0.293) and a fixed kernel bi-square weighting function with a 19.174 bandwidth (CV = 974.621), providing optimal spatial weighting. Among the factors analyzed, the Human Development Index (HDI) and the Rate of Return (ROR) are identified as having a significant influence on GRDP, contributing insights for strengthening Indonesia’s economic resilience. Thus, this study will contribute to formulating appropriate regional policy strategies to strengthen the economy in facing the World Megatrend in 2045  Nonparametric Geographically Weighted Truncated Spline Regression (NGWTSR) merupakan salah satu model pengembangan regresi spasial dan nonparametrik sebagai pendekatan yang digunakan untuk menyelesaikan permasalahan analisis spasial yang tidak diketahui kurva regresinya. Fungsi spline digunakan untuk memodelkan hubungan yang kompleks dan nonlinier antar variabel prediktor, serta akan memperhitungkan adanya variasi regional antar regional dalam bentuk dan kekuatan hubungan. Fungsi terpotong dalam metode ini mengacu pada penyesuaian yang digunakan untuk mengurangi pengaruh data outlier. Data yang digunakan dalam penelitian ini adalah data Produk Domestik Regional Bruto (PDRB) di Indonesia tahun 2022 beserta faktor-faktornya yaitu Penanaman Modal Asing (PMA), Penanaman Modal Dalam Negeri (PMDN), Tingkat Partisipasi Angkatan Kerja (TPAK), Proporsi Nilai Tambah Manufaktur (PNTM ), Indeks Pembangunan Manusia (IPM), dan Pendapatan Asli Daerah (PAD). Temuan penelitian menunjukkan bahwa model NGWTSR dengan kernel bisquare tetap sebagai fungsi pembobotan dan dua titik simpul merupakan model NGWTSR terbaik dalam menjelaskan PDRB di Indonesia pada tahun 2022. Secara umum, faktor yang mempunyai pengaruh signifikan terhadap PDRB di Indonesia adalah IPM dan PAD

The Mitigating Overfitting in Sentiment Analysis Insights from CNN-LSTM Hybrid Models

This study aims to improve sentiment analysis accuracy and address overfitting challenges in deep learning models by developing a hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks. The research methodology involved multiple stages, starting with preprocessing a dataset of 5,456 rows. This process included removing duplicate data, empty entries, and neutral sentiments, resulting in 2,685 usable rows. To overcome data quantity limitations, data augmentation expanded the training dataset from 2,148 to 10,740 samples. Data transformation was carried out using tokenization, padding, and embedding techniques, leveraging Word2Vec and GloVe to produce numerical representations of textual data. The hybrid model demonstrated strong performance, achieving a training accuracy of 99.51%, validation accuracy of 99.25%, and testing accuracy of 87.34%, with a loss value of 0.56. Evaluation metrics showed precision, recall, and F1-Score values of 86%, 87%, and 86%, respectively. The hybrid model outperformed individual models, including Convolutional Neural Networks (70% accuracy) and Long Short-Term Memory Networks (81% accuracy). It also surpassed other hybrid models, such as the multiscale Convolutional Neural Network-Long Short-Term Memory Network, which achieved a maximum accuracy of 89.25%. The implications of this study demonstrate that the hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks effectively improves sentiment analysis accuracy while reducing the risk of overfitting, particularly in small or imbalanced datasets. Future research is recommended to enhance data quality, adopt more advanced embedding techniques, and optimize model configurations to achieve better performance

Implementation of Conversational Artificial Intelligence in a3-Dimensional Game onWaste Impact

The escalating volume of waste in Indonesia presents significant environmental challenges, primarilydue to insufficient public awareness and engagement. This study aimed to develop a dynamic threedimensionalsimulation game to enhance young people’s understanding of the environmental impactsof waste. The game integrates conversational artificial intelligence technology to create non-playablecharacters that engage users in natural text and voice dialogues. The research employed a research anddevelopment approach following the Software Development Life Cycle waterfall method, encompassingstages of analysis, design, implementation, testing, and maintenance. The game design adopted theMechanical, Dynamic, and Aesthetic framework method. It implemented a first-person perspective tocreate an immersive learning experience: evaluation involved functionality tests, expert reviews, anduser trials. The functionality testing achieved a perfect score of 100 percent, while evaluations by educationaltechnology experts yielded an average score of 94 percent for content quality and interfacedesign. User trials, conducted with individuals aged 10 to 18, indicated a high level of satisfactionwith an average score of 86 percent. These results conclude and demonstrate that integrating conversationalartificial intelligence into a simulation game provides an engaging and effective educationaltool to raise environmental awareness. Nonetheless, the study highlights the need for ongoing supportfrom parents and educators to cultivate sustainable waste management practices among young people.Future research should focus on expanding the game’s scope and evaluating its long-term impact onusers’ environmental literacy

Comparison of Random Forest Support Vector Machine and Passive Aggressive Models on E-nose-Based Aromatic Rice Classification

Accurate classification of aromatic rice types is crucial for maintaining quality and meeting consumer preferences. The purpose of this study is to classify MentikWangi, PandanWangi, and C4 rice based on their volatile content using e-nose. C4 rice, as a popular non-aromatic variety, was included as a comparison for sensor response analysis. The research method involved preprocessing the e-nose gas sensor readings, including feature extraction, baseline manipulation, and missing value checking, to ensure data quality. The classification was performed using Random Forest, Support Vector Machine, and Passive-Aggressive methods. The results showed that the Random Forest model achieved the highest accuracy of 97%, followed by the Support Vector Machine at 95% and Passive Aggressive at 90%. The model evaluation utilized a Confusion Matrix and Receiver Operating Characteristics, which confirmed that Random Forest was the best-performing model. This study concludes that e-nose-based classification effectively differentiates between aromatic rice types, providing significant potential for objective and efficient quality assessment and offering valuable insights for further research in areas such as food technology, agricultural science, and chemical analysis

Evaluation Analysis of the Necessity of Stemming and Lemmatization in Text Classification

Stemming and lemmatization are text preprocessing methods that aim to convert words into their root and to the canonical or dictionary form. Some previous studies state that using stemming and lemmatization worsens the performance of text classification models. However, some other studies report the positive impact of using stemming and lemmatization in supporting the performance of text classification models. This study aims to analyze the impact of stemming and lemmatization in text classification work using the support vector machine method, in this case, devoted to English text datasets and Indonesian text datasets, and analyze when this method should be used. The analysis of the experimental results shows that the use of stemming will generally degrade the performance of the text classification model, especially on large and unbalanced datasets. The research process consisted of several stages: text preprocessing using stemming and lemmatization, feature extraction with Term Frequency-Inverse Document Frequency (TF-IDF), classification using SVM, and model evaluation with 4 experiment scenarios. Stemming performed the best computation time, completing in 4 hours, 51 minutes, and 41.3 seconds on the largest dataset. While lemmatization positively impacts classification performance on small datasets, achieving 91.075% accuracy results in the worst computation time, especially for large datasets, which take 5 hours, 10 minutes, and 25.2 seconds. The Experimental results also show that stemming from the Indonesian balanced dataset yields a better text classification model performance, reaching 82.080% accuracy

Parental Income and Financial Attitudes towards Financial Management Behaviour Moderated by Financial Knowledge

Financial management behavior can be implemented to attain a satisfactory standard of living. A person\u27s perspective on saving is influenced by their financial attitude and parental income. Financial knowledge has the potential to influence the relationship between parental income and financial attitude toward financial management behavior. This study aims to examine the influence of parental income and financial attitude on financial management behavior moderated by financial knowledge. The study concentrated on 250 respondents from Generation Z in Mataram City, who were selected through purposive sampling techniques with a data collection method using a questionnaire. The data analysis is conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the assistance of SmartPLS 3.0 software. The findings indicated that parental income and financial attitude had a positive and significant effect on financial management behavior. However, financial knowledge has failed to mitigate the relationship between parental income and financial management behavior. Financial knowledge was unable to moderate the relationship between financial attitude and financial management behavior. Thus, the findings highlighted the significance of parental income and financial attitude in influencing financial management behavior in Generation Z. Therefore, the findings enhance the comprehension that parental income and financial attitude significantly influence an individual\u27s future financial status.Financial management behavior can be implemented to attain a satisfactory standard of living. A person\u27s perspective on saving is influenced by their financial attitude and parental income. Financial knowledge has the potential to influence the relationship between parental income and financial attitude toward financial management behavior. This study aims to examine the influence of parental income and financial attitude on financial management behavior moderated by financial knowledge. The study concentrated on 250 respondents from Generation Z in Mataram City, who were selected through purposive sampling techniques with a data collection method using a questionnaire. The data analysis is conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the assistance of SmartPLS 3.0 software. The findings indicated that parental income and financial attitude had a positive and significant effect on financial management behavior. However, financial knowledge has failed to mitigate the relationship between parental income and financial management behavior. Financial knowledge was unable to moderate the relationship between financial attitude and financial management behavior. Thus, the findings highlighted the significance of parental income and financial attitude in influencing financial management behavior in Generation Z. Therefore, the findings enhance the comprehension that parental income and financial attitude significantly influence an individual\u27s future financial status

Analysis of Gold Price Forecasts Using Automatic Clustering Method and Fuzzy Logic Relationship

Gold is often chosen as an investment due to its lucrative potential. To maximize profits and avoid losses, investors need to understand the volatile price movements of gold. This research aims to forecast the price of gold in the next period. In this research, the forecasting method used is Automatic Clustering and Fuzzy Logical Relationship (ACFLR). ACFLR is a method that uses the concept of fuzzy logic for modeling time series data. The forecasting process includes data sorting, cluster formation, interval determination, fuzzification, FLR and FLRG formation, and calculation of forecasting values. Based on this method, the result of the gold price forecast in Padang City for the next period, namely January 2024 using the ACFLR method is IDR 978,796.9. with a MAPE value of 0.9%, which means this method is very good. For further researchers, it is hoped that the Fuzzy Time Series method can use other forecasting models in order to obtain the most optimal method for forecasting gold prices

Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series

This study addresses the challenge of generating high-quality synthetic financial time series data, acritical issue in financial forecasting due to limited access to complete and reliable historical datasets.The aim of this research was to compare the performance of the standard Variational Autoencoder andthe Vector Quantized Variational Autoencoder (VQ-VAE) in generating synthetic multivariate time seriesdata using the Adaro Energy Indonesia stock dataset. The VQ-VAE incorporates a discrete latentspace to improve the structure and control of the data generation process, whereas the standard VAEutilizes a continuous latent space. This research method was based on the implementation of bothmodels, followed by a quantitative evaluation using statistical metrics, including mean absolute error(MAE), mean squared error (MSE), root mean squared error (RMSE), and R² score. This researchshowed that the VQ-VAE outperformed the standard VAE in replicating the statistical characteristicsof stock prices, as shown by lower error values and higher R² scores across all tested features. The discretelatent space of the VQ-VAE led to the generation of more structured and statistically consistentsynthetic data. The implications of these findings suggest that the VQ-VAE model is highly suitablefor financial forecasting applications and indicate the potential for future enhancements throughintegration with hybrid models, such as attention mechanisms or generative adversarial networks

Integrasi Basis Data Properti Menggunakan Metode Schema Matching Dengan Pendekatan Linguistic dan Constraint

Background: The rapid development of technology has driven progress across various sectors, including the property industry in Indonesia. However, property data integration on Lombok Island still faces challenges due to the diversity of attribute naming, which hinders efficient information retrieval.Objective: This study aims to integrate four property databases (Saduthama, Salva, SJP, and Garden View) using a schema matching method based on linguistic and constraint approaches.Methods: The linguistic approach is used to identify similarities between attributes, even when their names differ, using the Bigram technique, which proved effective in identifying attribute similarities with a threshold of 0.7. Meanwhile, the constraint approach evaluates the compatibility of attributes based on additional criteria such as data type, attribute length, null values, and uniqueness, ensuring that the integrated attributes work compatibly. The integration process includes preprocessing, generalization, and attribute matching.Result: The evaluation results show precision (P), recall (R), and F-measure of 90%, with an average accuracy of 84%.Conclusion: This result outperforms previous studies that achieved 100% precision, 60% recall, and 75% F-measure.Perkembangan teknologi yang pesat telah mendorong kemajuan di berbagai sektor, termasuk industri properti di Indonesia. Namun, integrasi data properti di Pulau Lombok masih menghadapi tantangan akibat keragaman penamaan atribut, yang menghambat efisiensi dalam pencarian informasi. Penelitian ini bertujuan untuk mengintegrasikan empat basis data properti (Saduthama, Salva, SJP, dan Garden View) menggunakan metode schema matching berbasis pendekatan linguistik dan constraint. Pendekatan linguistik digunakan untuk mengidentifikasi kesamaan antar atribut meskipun memiliki nama yang berbeda, dengan menerapkan teknik Bigram yang terbukti efektif dalam mengenali kemiripan atribut dengan ambang batas 0,7. Sementara itu, pendekatan constraint mengevaluasi kesesuaian atribut berdasarkan kriteria tambahan seperti tipe data, panjang atribut, nilai null, dan keunikan, sehingga memastikan bahwa atribut yang terintegrasi dapat bekerja secara kompatibel. Proses integrasi mencakup tahap preprocessing, generalisasi, dan pencocokan atribut. Hasil evaluasi menunjukkan nilai precision (P), recall (R), dan F-measure sebesar 90%, dengan akurasi rata-rata 84%. Hasil ini melampaui penelitian sebelumnya yang mencapai precision 100%, recall 60%, dan F-measure 75%

Revolusi Sistem Transportasi Cerdas: AODV Berbasis Learning Automata untuk Peningkatan Komunikasi V2V di Jalan Bebas Hambatan

Backgroud: Vehicle-to-vehicle communication has become a crucial element in the development of intelligent transportation systems. However, conventional routing protocols face limitations in coping with dense and dynamic traffic conditions. Objective: The objective of this study is to improve communication efficiency between vehicles by modifying an on-demand routing protocol using a learning automata approach. Method: This study employed a simulation method with traffic modeling using traffic modeling software and network simulation tools, based on data from highways in the Soekarno-Hatta International Airport area. Result: The results of this study show that the developed protocol increases the packet delivery ratio to 87.7% and reduces latency by 6.5%. Conclusion: The conclusion of this study is that the application of learning automata in vehicle routing enhances communication reliability and supports the implementation of a more adaptive and efficient transportation system.  Komunikasi antar kendaraan merupakan elemen penting dalam Jaringan Kendaraan Bersifat Sementara dan Sistem Transportasi Cerdas di jalan bebas hambatan. Namun, protokol pengiriman data yang ada, seperti Protokol Pengiriman Data Jarak Jauh berdasarkan Permintaan, perlu disesuaikan dengan kondisi lalu lintas yang dinamis dan padat. Tantangan ini menyebabkan penurunan dalam kinerja, termasuk rasio pengiriman paket yang lebih rendah, penurunan kecepatan pengiriman data, dan peningkatan latensi, yang memengaruhi keandalan aplikasi Sistem Transportasi Cerdas seperti penghindaran tabrakan dan sistem peringatan darurat. Penelitian ini mengusulkan modifikasi pada Protokol Pengiriman Data Jarak Jauh berdasarkan Permintaan dengan menggunakan Pembelajaran Otomata untuk mengoptimalkan komunikasi antar kendaraan dalam kondisi lalu lintas jalan bebas hambatan. Simulasi dengan data lalu lintas di area Bandara Internasional Soekarno-Hatta menunjukkan bahwa modifikasi ini meningkatkan rasio pengiriman paket hingga 87,7%, lebih tinggi dibandingkan dengan Protokol Pengiriman Data Jarak Jauh berdasarkan Permintaan yang hanya mencapai 79%. Penurunan latensi hingga 6,5% juga teramati dalam kondisi pada