Language is closely related to social and cultural aspects, including in the process of learning foreign languages such as Mandarin. Effective Chinese language learning does not only focus on linguistic aspects, but also requires an understanding of the culture of origin of the language. The purpose of this paper is to analyse the application of culture-based learning in increasing motivation to learn Chinese. The method used is literature study or library research, with documentation technique as data collection method and content analysis technique as data analysis method. Based on the analysis of twelve research articles, it was found that the integration of cultural elements such as traditional games, visual media, and Chinese pop culture can create a learning experience that is fun, interesting, and encourages students' curiosity towards Chinese. In conclusion, culture-based learning is proven to have a positive influence on motivation to learn Chinese and can be used as an alternative teaching strategy that is more contextual and fun.

The research aims to determine the positive influence of video learning media on the learning motivation of Christian Religious Education and Character Education for class VIII students at SMP Negeri 1 Harian Samosir Regency for the 2023/2024 Academic Year. The research hypothesis is that there is a positive influence between video learning media on the learning motivation of Christian Religious Education and Characteristics of class VIII students at SMP Negeri 1 Harian Samosir Regency for the 2023/2024 Academic Year. Descriptive quantitative research method. The population of all class VIII students at SMP Negeri 1 Harian Samosir Regency for the 2023/2024 academic year who are Protestant Christians is 40 people and are also research respondents, therefore this research is a population research. The research instrument is a closed questionnaire. The results of data analysis obtained: a) The value of rcount=0.567>rtable=0.312 shows that there is a positive relationship between video learning media and student motivation to learn Christian Religious Education and Character. b) Regression equation. c) The determination test shows that the magnitude of the effect is 32.15%. d) Hypothesis testing obtained Fcount=17.907>Ftable=3.23 then H0 is rejected and Ha is accepted. The research concluded that there was a positive influence of video learning media on the learning motivation for Christian Religious Education and Character Education of class VIII students at SMP Negeri 1 Harian Samosir Regency for the 2023/2024 Academic Year.

This study aims to analyze defaults in cooperation agreements and examine the rights and obligations of the parties in dispute resolution. The urgency of this research arises from the increasing occurrence of defaults in cooperation agreements, which have significant legal and economic consequences for business relationships and economic stability. Using a normative juridical approach, the research examines provisions of the Indonesian Civil Code and relevant legal doctrines, focusing on dispute resolution mechanisms through litigation and alternative methods. Primary legal sources include the Civil Code (KUHPerdata), Law No. 30/1999 on Arbitration, and Supreme Court regulations, while secondary sources comprise legal journals and court decisions. The findings show that default in cooperation agreements results in legal consequences such as compensation, agreement termination, and performance enforcement. Several procedural conditions must be met, including the seriousness of the breach and timely legal action. The study provides a novel contribution by combining normative legal analysis with practical considerations in resolving contractual disputes within the Indonesian legal framework. It concludes that cooperation agreements must clearly define mutual obligations and dispute resolution procedures to ensure legal certainty and fairness. The research recommends that parties include specific clauses addressing default and adopt alternative dispute resolution mechanisms to reduce litigation and maintain business relationships.

Keamanan data transaksi merupakan aspek krusial dalam sistem e-commerce, khususnya bagi pelaku usaha kecil menengah (UMKM). Penelitian ini bertujuan untuk mengimplementasikan algoritma Blowfish dengan mode Cipher Block Chaining (CBC) dan panjang kunci 160-bit guna meningkatkan keamanan data transaksi penjualan. Algoritma Blowfish dipilih karena efisien dan mendukung penggunaan Initialization Vector (IV) acak yang memperkuat proteksi terhadap serangan pattern dan dictionary. Data transaksi pelanggan dan pembayaran disimpan dalam bentuk ciphertext menggunakan OpenSSL. Pengujian dilakukan terhadap 20 data transaksi menggunakan serangan dictionary berbasis OpenSSL, dan hasil menunjukkan bahwa data tidak dapat didekripsi, membuktikan efektivitas pendekatan ini. Hasil penelitian ini menunjukkan bahwa penggunaan Blowfish CBC 160-bit dengan IV acak mampu memberikan perlindungan kuat dan efisien dalam konteks aplikasi e-commerce untuk UMKM.
Kata kunci: Keamanan Data Transaksi, Blowfish CBC, Initialization Vector, E-Commerce, Dictionary Attack

This study examines the application of Islamic criminal law (fiqih jinayah) principles, specifically in the case of qadzaf—accusation of adultery without valid proof—when conveyed through kinayah (indirect or allusive expressions). The focus is on how legal maxims such as al-?ud?d tudra’ bi al-syubuh?t and al-a?l bar?’at al-dzimmah are employed to assess the legal validity of ambiguous accusations. This research adopts a qualitative approach through library research, analyzing both classical and contemporary Islamic legal texts. The findings reveal that qadzaf in the form of kinayah does not meet the criteria for ?add punishment due to its interpretive uncertainty and lack of clear evidence. Therefore, ta'zir—a discretionary punishment—should be applied instead, based on public interest and judicial discretion. This study is significant in addressing modern social communication dynamics, especially in the digital age, and contributes to a fair, contextual, and cautious implementation of Islamic criminal law.

This study aims to explore the strategy of literacy-based learning in Islamic Religious Education (IRE) to enhance students' understanding of Islam. Through a literature review approach, this research analyzes various sources related to the implementation of literacy in IRE teaching and its impact on students' comprehension of Islamic teachings. The findings indicate that literacy-based learning enhances students' critical skills in understanding religious texts and enriches their understanding of Islam in a practical context. Moreover, this strategy plays a key role in character building, fostering moral values, raising social awareness, and improving communication skills. The teacher's role as a facilitator guiding students in developing religious literacy skills is also crucial for the success of this approach. This study aims to contribute to the development of a more relevant and effective IRE teaching model in the digital era.

Abstract. This study explores the complex aspects of stock valuation and decision-making on the Jakarta Stock Exchange (JKSE), focusing on the Energy Sector, particularly within the Sub-Industry of Oil, Gas, & Coal Equipment & Services. The primary objective is to ascertain "Buy" and "Sell" recommendations grounded in anticipated returns and stock performance. This classification stems from a meticulous examination of each stock's alignment with both its intrinsic value and the overarching market value of the JKSE. Seven stocks (DEWA, DOID, MYOH, PKPK, PTRO, UNIQ, and WINS) merit "Buy" endorsements, as their projected returns surpass their present market valuations, potentially indicating undervaluation. Conversely, "Sell" recommendations are assigned to four companies (ITMA, RUIS, SICO, and TAMU) in cases where projected returns fall short, suggesting either underperformance or overvaluation. Incorporating Python and the Capital Asset Pricing Model (CAPM), this analysis yields a noteworthy insight. Companies like WINS and DOID display a substantial association with the JKSE market value, as evidenced by their notably low P-values of 0.014 and 0.038, respectively. In contrast, the P-values for other companies fall below the 0.05 threshold, implying that the market value of JKSE lacks a substantial impact. This research highlights the complex relationships involving market valuations, stock performance, and investor choices. By discerning the various degrees of correlation and comprehending the factors that influence "Buy" and "Sell" recommendations, investors can adeptly navigate the intricacies of the market and optimize their investment returns.
Keywords: Stock Selection, CAPM, Python, Energy Sector

In today's digital era, introducing the concept of computational thinking ( CT ) from an early age is very important. Binekas School, as an educational partner at the Playgroup, Kindergarten (TK), and Elementary School (SD) levels, is committed to introducing CT to children starting at the age of 4. Currently, Binekas School only offers optional extracurricular coding activities at the elementary school level and uses a hard coding approach, which may be too challenging for most students. This school wants to prepare students with an introduction to the basics of coding from kindergarten using a more child-friendly approach, namely plugged-in with tangible robotics. The proposed solution includes an introduction to CT knowledge with a focus on algorithm development and CT training through tangible plugged-in techniques using robots for children aged 4 years and above. This training will not only improve children's understanding of the CT concept in an interactive and fun way, but will also prepare them for future educational challenges. The tools that will be used in this training are the Robotic Gigo Smart Brick, a robotic system designed for children so that they can learn basic programming concepts and computational thinking through interactive games. This outreach activity uses a combination of unplugged CT through card media and physical maps to train problem-solving mindsets accompanied by the opportunity to test proposed solutions with plugged-in CT using the Robotic Gigo Smart Brick. From the results of the activity evaluation through a questionnaire, it was found that 92% of students agreed that the application of the combination of unplugged CT and plugged-in CT was fun, the material was easy to understand and they were interested in getting further material.

This study aims to optimize the implementation of the K-Nearest Neighbors (K-NN) algorithm for medical image classification by focusing on selecting the optimal KKK parameter and applying dimensionality reduction techniques to improve accuracy and efficiency. The data used was sourced from public medical image repositories such as The Cancer Imaging Archive (TCIA) and Medical Image Analysis datasets, covering various diseases, including brain tumors, lung cancer, and kidney lesions. The research process involves data collection, data preprocessing, dimensionality reduction using Principal Component Analysis (PCA), applying the K-NN algorithm with Euclidean, Minkowski, and Cosine distance metrics, and performance evaluation using accuracy, precision, recall, and F1-score. Experimental results demonstrate that K=5with the Euclidean distance metric provides the best performance, achieving an accuracy of 90%. Additionally, PCA effectively reduces computational time by 30% without significantly compromising accuracy. This study proves that K-NN is an effective method for medical image classification. However, further research is needed to integrate K-NN with deep learning models to enhance performance and feature extraction capabilities.

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.