Currently, the IEEE 802.11 standard protocol is prone to interference. Therefore, a strategy is needed to reduce or avoid intervention. Using the Nv2 Protocol at the 5 GHz frequency with bridge mode on the Point to Multipoint Network (PTMP) can be used as a solution to avoid interference. The method in this screening begins with a location survey then continues with needs analysis, topology design, network installation, network configuration and at the final stage testing is carried out. The results of the study showed that the connection between the two clients resulted in ping with a 0% loss package and the results were very good. The signal strength from the AP Bridge side to client 1 is -63 Db to client 2 is -54 Db. AP Client 1 with a Signal Strength of -63/-62dBm, Tx/Rx CCQ 9/18%. AP Client 2 with Signal Strength -54/-56 dBm, Tx/Rx CCQ 40/35%.

Abstract: This research aims to identify the local wisdom values ??of Sanghyang Siksakandang Karesian relevant to history learning. To achieve the stated objective, the investigation included using qualitative methods with a single case study, and data were collected using observation, interviews, and document reviews. Furthermore, the obtained data was analyzed using an interactive model consisting of phases: data condensation, data presentation, and conclusion. The results showed that (1) the local wisdom values ??of Sanghyang Siksakandang Karesian included dasakerta, Dasaperbakti, people's behavior (hulun), and complementary actions. Dasakerta is a guideline for human sensory behavior in daily life. Dasaperbakti describes the social hierarchy that requires legitimacy. While Panimbuh ning twah shows complementary rules in society, (2) History learning based on local wisdom values of Sanghyang Siksakandang Karesian is carried out through contextual learning by introducing and providing an understanding of Kagaluhan values to students to be applied in everyday life. This research can improve the understanding of local history and the character of students who are more rooted and culturally responsive, and strengthen the character of students. However, limited curriculum, learning resources, and teacher understanding are challenges. This study contributes to the literature gap by offering a local wisdom-based history learning model. The implication of this study emphasizes the importance of policy support to accommodate local history in the high school education curriculum for character building.
Abstrak: Penelitian ini bertujuan untuk mengidentifikasi nilai-nila kearifan lokal Sanghyang Siksakandang Karesian dan nilai-nilai yang relevan untuk pembelajaran sejarah. Penelitian ini menggunakan metode kualitatif dengan studi kasus tunggal. Teknik pengumpulan data terdiri dari observasi, wawancara, dan studi dokumen. Analisis data yang digunakan adalah analisis data model interaktif yang melalui tahapan kondensasi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa (1) nilai-nilai kearifan lokal Sanghyang Siksakandang Karesian meliputi dasakerta; Dasaperbakti; perilaku rakyat (hulun); dan pelengkap perbuatan (Panimbuh ning twah). Dasakerta merupakan pedoman perilaku indra manusia dalam kehidupan sehari-hari. Dasaperbakti menggambarkan hirarki sosial dalam masyarakat yang membutuhkan legitimasi. Sedangkan Panimbuh ning twah  menunjukkan aturan pelengkap dalam masyarakat (2) Pembelajaran Sejarah yang berbasis nilai-nilai kearifan lokal Sanghyang Siksakandang Karesian dilakukan melalui pembelajaran kontekstual dengan mengenalkan dan memberikan pemahaman nilai-nilai Kagaluhan pada peserta didik untuk diterapkan dalam kehidupan sehari-hari. Penelitian ini dapat meningkatkan pemahaman sejarah lokal dan karakter peserta didik yang lebih mengakar dan tanggap budaya, serta memperkuat karakter peserta didik. Namun, keterbatasan kurikulum, sumber belajar, dan pemahaman guru menjadi tantangan. Studi ini memberikan kontribusi terhadap kesenjangan literatur dengan menawarkan model pembelajaran sejarah berbasis kearifan local. Implikasi penelitian ini menegaskan pentingnya dukungan kebijakan untuk mengakomodasi sejarah lokal dalam kurikulum Pendidikan sekolah menengah atas untuk pembentukan karakter.

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.

This research aims to develop a training model based on Artificial Intelligence (AI) and blended learning to improve the professional competence of elementary school (SD) teachers in the digital era. This research uses the Research and Development (R&D) method with the Borg & Gall approach which involves ten systematic stages. The results of the study show that AI-based training models and blended learning are able to provide adaptive, personalized, and based on the real needs of teachers in the field. The evaluation of the effectiveness of this training model shows an increase in teachers' professional competence in integrating technology in learning. This research makes an important contribution to the development of technology-based teacher training policies in Indonesia.

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.

This study aims to determine: (1) The effect of interpersonal communication on employee work productivity at the Kolaka Regency Tourism Office, (2) The effect of motivation on employee work productivity at the Kolaka Regency Tourism Office. The sample in this study amounted to 55 respondents using a sampling technique, namely the census method. Testing research instruments using validity and reliability tests with SPSS 23.0. The research data analysis technique used the Structural Equation Modeling (SEM-PLS) method with SmartPLS 4.0. Based on the results of this study it can be seen that the interpersonal communication variable has a positive and significant effect on work productivity as indicated by a T-Statistics value of 1.694 and a P-Values ??of 0.047 and the motivational variable has a positive and significant effect on work productivity as indicated by a T-Statistical value of 2.191 and P-Values ??of 0.015.

The company's performance can be seen from the profitability and sustainability in maintaining its business, especially retail companies. The purpose of this study was to examine the influence of factors that affect the performance of retail companies, such as DER, NPM and TATO. The population of this research is 25 companies and the research sample is 8 companies on the Indonesia Stock Exchange (IDX).The research method uses Normality Test, t test, Anova test, Coefficient of Determinants, Correlation and Multiple Linear Regression using SPSS software version 22.
The results of the research Net Profit Margin (NPM) and Total Asset Turnover (TATO) have a significant effect on Return on Assets (ROA), while the Debt Equity Ratio (DER) has no significant effect.
However, simultaneously the three independent variables (DER, NPM and TATO) have a significant effect on Return on Assets with the equation Y = -0.015 - 0.001X1 + 1.549X2 + 0.011X3.

This paper aims at tracing the negotiation process between self-identity and institutional image of the church among Javanese Christian women, and makes use of a short biography as a narrative. It based on such a biography explored that in order to meet family need sufficiently, a rural pastor should not implement an entrepreneurial project, either personally or organizationally. If an entrepreneurial project inevitably is to do, the Javanese Christian women emphasizing that pastors’ entrepreneurial should go around the ministry they are doing, for instance, making books or teaching in a Christian school or seminary. This paper also argues that the pastors’ family that are in a harmony and self-sufficient is a social gate to show the institutional image of church in society. Due to all Javanese Christian women are having jobs and source of economic income, all of them are brave to negotiate with the church to sustain keep the job that they had been pursued, and involve partly in the church activity. This paper concludes that a simple life that the women showing on their life, and it carried out voluntarily is a kind of personal commitment and calling to support the husband ministry as a pastor.