This study compares three personal finance apps—Money Pocket, Catatan Keuangan, and Money Lover—used by Indonesian Generation Z students. An online purposive survey yielded 88 valid responses (Money Pocket n = 28; Catatan Keuangan n = 42; Money Lover n = 18). Reliability was acceptable (Cronbach’s α = 0.87–0.94). Mean scores (1–5) ranged from 3.90 to 4.20. One-way ANOVA identified a significant difference only in feature completeness (F = 4.12, p = 0.020); Tukey post-hoc tests showed Money Pocket > Catatan Keuangan (mean diff. = 0.33, p = 0.018). No significant inter-app differences were found for ease of use, perceived usefulness, satisfaction, or continuance usage intention. Overall satisfaction (3.96–4.12) and continuance usage intention (3.92–4.09) indicate moderate to high user approval. Developers should prioritize feature integration—bank synchronization, automated budgeting, bill reminders, and spending analytics—to enhance completeness and retention among student users.

This study presents the implementation of IndoRoBERTa, a pre-trained Indonesian language model, to improve the contextual clarity of homograph words in Text-to-Speech (TTS) systems, particularly for virtual chatbot applications addressing early marriage education in Lombok. The proposed system integrates IndoRoBERTa into the TTS pipeline to classify the context of homographs prior to grapheme-to-phoneme (G2P) conversion, ensuring accurate pronunciation based on meaning. The research was conducted in two fine-tuning phases: the first utilized 500 manually labeled conversational samples, achieving 96% test accuracy, while the second expanded the dataset with 2,000 auto-labeled samples and yielded 88% accuracy. Evaluation metrics including precision, recall, and F1-score demonstrated the model’s effectiveness across 20 homograph categories. Despite strong results, the study acknowledges limitations in data authenticity and challenges in underrepresented classes. Future work is recommended to incorporate real-world dialogue data and enhance the system’s generalization in more complex linguistic settings. This research contributes to the advancement of Indonesian NLP in TTS systems, particularly in socially impactful educational contexts.

This study investigates the optimization of Gravity Forward Modeling (GFM) by implementing an OpenMP parallel computing approach, focusing on the synthetic model and topography of Bawean Island. The research addresses computational limitations in traditional GFM by utilizing parallel processing techniques to enhance efficiency and resolution. By modeling subterranean layers with rectangular prisms and using the Okabe equation for calculations, the study significantly improves computational speed and resource management. The practical application on Bawean Island includes a strategic distribution of observation stations to facilitate Bouguer Anomaly (BA) computations, providing a clearer understanding of subsurface density anomalies. Notably, the study demonstrates a 600% increase in efficiency for the synthetic case when OpenMP with 8 threads is applied to the utilized architecture. For the field model, computations spanning 400 square kilometers on Bawean Island, encompassing over 1.3 billion GFM calculations, are completed in just 143 seconds. Additionally, the gravity terrain values on Bawean Island exhibit a range of up to 65 mGal, which closely correlates with the high-resolution topographic map. These findings highlight the considerable advantages of parallel computing in enhancing the efficiency and feasibility of complex geophysical modeling tasks, offering substantial improvements for large-scale geophysical exploration.

Stefan problem is a problem involving phase transition from solid to liquid or vice versa where boundary between solid and liquid regions moves as function of time. This paper presents numerical solution of one-dimensional two-phase Stefan problem by using finite element method. The governing equations involved in Stefan problem consist of heat conduction equation for solid and liquid regions, and also transition equation in interface position (moving boundary). The equations are difficult to solve by ordinary numerical method because of the presence of moving boundary. As consequence, the equations is reformulated into the form of internal energy (enthalpy). By the enthalpy formulation, solution of the heat conduction equations is no longer concerning the phase state of material. The advantage of the enthalpy formulation is that, finite element method can be easily implemented to solve Stefan problem. Numerical simulation of interface position, temperature profile, and temperature history has good agreement with the exact solution. The approximation of interface position using finite element method was found that it is more accurate than the approximation by using Godunov method. The simulation results also reveal that the finite element method for solving Stefan problem have smaller mean absolute error than the Godunov method.

Gold is a one of high selling value items in the market, and it can be used as an investment item. The price of gold in the market tends to be stable and not undergoing too significant changes which makes gold be a very valuable item. The aim of this research is to predict gold price using AR (1) and ARCH (1) model which are the part of time series methods. The data of gold price is obtained from ANTAM's daily historical website from 2007 - 2017. Here, the basic information about data is given by using descriptive statistic and the estimation of parameters in each model is condacted by using <em>Maximum Likelihood Estimation</em> (MLE). To evaluate the model, <em>Mean Absolute Error</em> (MAE) and <em>Root Mean Square Error</em> (RMSE) are used. In this research, the estimated model of AR (1) and ARCH (1) given as X_t = -0.012X_{t-1}+epsion_t and X_t = epsilon_t sqrt{0.000053+0.011958X^2_{t-1}} respectively. Moreover, the result of MAE and RMSE using AR (1) model are 0.0261 and 0.0342 respectively, meanwhile for ARCH (1) model are 0.0170 and 0.0251 respectively.

<div>Pada naskah ini simulasi persamaan <em>Shallow Water - </em>Exner dilakukan. Naskah ini menjabarkan bahwa penggabungan persaamaan antara <em>Shallow Water</em> dan Exner dapat dilakukan menggunakan teknik <em>splitting</em>, yaitu setelah ditemukannya nilai dari <em>SWE</em> menggunakan fluks numerik, persamaan Exner dicari secara <em>semi-implicit</em>. Fluks numerik <em>Harten-Lax-van Leer and Einfeld</em> digunakan. Naskah ini menjelaskan langkah-langkah dalam pembentukkan simulasi <em>Shallow Water Equation</em> - Exner: pencarian <em>SWE</em>, Exner, nilai fluks, dan juga penggunaan friksi pada simulasi. Setelah itu, diselesaikan beberapa kasus untuk membuktikan hasil simulasi. Dari hasil simulasi dapat dilihat bahwa simulasi menghasilkan bentuk yang sesuai dengan naskah yang dibandingkan.</div>

Harga suatu saham berubah secara cepat dari waktu ke waktu. Pergerakan indeks harga saham menjadi tolak ukur para pemilik saham untuk membuat keputusan kapan sebaiknya saham dibeli, dijual atau dipertahankan. Untuk itu diperlukan suatu model yang dapat memprediksi indeks harga saham untuk memantau pergerakan tersebut dan membantu para pemilikk saham dalam mengambil keputusan. Penelitian ini mengusulkan metode untuk memprediksi pergerakan harga saham dengan menggunakan metode gabungan Support Vector Regression (SVR) pada tahap dan Jaringan Syaraf Tiruan (JST) pada tahap kedua. Pada penelitian ini, Algoritma Genetika atau Genetic Algorithm (GA) akan digunakan untuk melakukukan optimasi parameter SVR. Prediksi dibuat untuk 1, 3, 5, 7, 10, 15, dan 30 hari kedepan. Dari serangkaian uji coba yang dilakukan, SVR-JST (SVR dioptimasi GA) memberikan tingkat kesalahan lebih kecil dibandingkan dengan metode JST.

Penggunaan <em>website</em> pada bidang pendidikan, khususnya sebuah universitas, bertujuan untuk menyimpan berbagai informasi yang ada pada lingkungan universitas tersebut. Untuk itu, perlu dilakukan perbaikan struktur untuk memelihara kualitas dari web. Salah satu teknik yang dapat digunakan adalah dengan menggunakan <em>web usage mining. Web usage mining</em> merupakan salah satu cabang dari <em>web mining</em> yang digunakan untuk menemukan informasi atau pengetahuan yang bermanfaat dari pola navigasi <em>user </em>pada sebuah<em> website</em>. Pada penelitian ini menggunakan metode berbasis graph untuk <em>frequent sequential access patterns</em> dan menggunakan Igracias Universitas Telkom sebagai studi kasusnya. Karena Igracias selalu digunakan oleh seluruh entitas yang ada pada Universitas Telkom. Metode ini memiliki kelebihan untuk menemukan <em>behavior</em> pola pengaksesan <em>user</em>. Dari implementasi metoda ini didapat pola akses group user secara berurutan.

<p>Time series analysis has been developed in concepts and theories to accommodate the behavior of the collected data by involving time. The unique feature of time series analysis is the time dependency. In this research, we observed a number of seasonal pets, fire caterpillars, on an oil palm plantation at Block Afdeling-D in Kalimantan. The number of Fire Caterpillars is dependent on time and spatial (location). Fire Caterpillars are seasonal pests on oil palm plantation. In addition, Pearson correlation indicates that the number of Fire Caterpillars is not influenced by the distance among the blocks. We suggests that the disinfection should be done simultaneously to avoid the migration of fire caterpillars. The spreading of fire caterpillars at Block Afdeling-D in Kalimantan is modeled with time series seasonal model, spesifically with ARIMA homoscedastic model. Kriging interpolation was conducted to identify behavior and determine the location Fire Caterpillars involving ARIMA model.</p><strong>Keywords: </strong>ARIMA, dependency, Kriging, Fire Caterpillars, variogram