Pendidikan adalah investasi sumber daya manusia jangka panjang yang mempunyai nilai strategis bagi kelangsungan peradaban manusia di dunia. Oleh sebab itu, hampir semua negara menempatkan variabel pendidikan sebagai sesuatu yang penting dan utama dalam konteks pembangunan bangsa dan negara. Salah satu komponen penting dalam pendidikan adalah guru. Guru dalam konteks pendidikan mempunyai peranan yang besar dan strategis. Gurulah yang langsung berhadapan dengan peserta didik untuk mentransfer ilmu pengetahuan dan teknologi sekaligus mendidik dengan nilai-nilai positif melalui bimbingan dan keteladanan.Oleh karena itu, sudah selayaknya guru mempunyai berbagai kompetensi yang berkaitan dengan tugas dan tanggung jawabnya. Salah satunya ialah mengenai metodologi pengajaran pembelajaran. Sebagai seorang pendidik muslim sudah seharusnya mengetahui tentang metodologi pengajaran pendidikan dalam Islam. Pendidikan ke-islaman atau pendidikan agama Islam, yakni upaya mengajarkan ajaran agama Islam dan nilai-nilainya menjadi way of life (pandangan dan sikap hidup) seseorang. Untuk itu, hendaknya diterapkan metodologi pengajaran Pendidikan Agama Islam yang tepat dan baik dalam proses pembelajaran agar tercapai tujuan pendidikan Islam sebagaimana apa yang diharapkan.

This comprehensive review delves into the realm of green corrosion inhibitors for iron alloys, focusing on a thorough exploration guided by data-driven investigation, density functional theory (DFT) simulations, and experimental validation. Harnessing the potential of plant extracts, this study scrutinizes their effectiveness in mitigating corrosion in iron alloys through a multi-faceted approach. By integrating computational modeling with empirical experimentation, a deeper understanding of the inhibitive mechanisms is achieved, offering insights into their practical application. The review synthesizes findings from diverse studies, elucidating the pivotal role of DFT in predicting inhibitor behavior and optimizing their performance. Furthermore, experimental validation provides crucial validation of theoretical predictions, highlighting the synergistic relationship between simulation and real-world application. Through this journey of exploration, the review underscores the promise of green corrosion inhibitors derived from natural sources, paving the way for sustainable corrosion control practices in the realm of iron alloys.

This study investigates machine learning-based quantitative structure-property relationship (QSPR) models for predicting the thermal stability of zinc metal-organic frameworks (Zn-MOF). Utilizing a dataset comprising 151 Zn-MOF compounds with relevant molecular descriptors, ridge (R) and kernel ridge (KR) regression models were developed and evaluated. The results demonstrate that the R model outperforms the KR model in terms of prediction accuracy, with the R model exhibiting exceptional performance (R² = 0.999, RMSE = 0.0022). While achieving high accuracy, opportunities for further improvement exist through hyperparameter optimization and exploration of polynomial functions. This research underscores the potential of ML-based QSPR models in predicting the thermal stability of Zn-MOF compounds and highlights avenues for future investigation to enhance model accuracy and applicability in materials science.

The purpose of this study is to use quantitative structure-property relationship (QSPR)-based machine learning (ML) to examine the corrosion inhibition capabilities of benzimidazole compounds. The primary difficulty in ML development is creating a model with a high degree of precision so that the predictions are correct and pertinent to the material's actual attributes. We assess the comparison between the extra trees regressor (EXT) as an ensemble model and the decision tree regressor (DT) as a basic model. It was discovered that the EXT model had better predictive performance in predicting the corrosion inhibition performance of benzimidazole compounds based on the coefficient of determination (R2) and root mean square error (RMSE) metrics compared DT model. This method provides a fresh viewpoint on the capacity of ML models to forecast potent corrosion inhibitors.

Empirical studies of possible compound corrosion inhibitors require a lot of money, time, and resources. Therefore, we used a machine learning (ML) paradigm based on quantitative structure-property relationship (QSPR) models to evaluate ensemble algorithms as predictors of corrosion inhibition efficiency (CIE) values. Our investigation reveals that the gradient boosting (GB) regressor model outperforms other ensemble-based models. This advantage is evaluated objectively using the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In summary, our research provides a new perspective on how well machine learning algorithms in particular ensembles work to identify organic molecules such as pyridazine that have the potential to prevent corrosion on the surfaces of metals such as iron and its alloys.

Corrosion in materials is a significant concern for the industrial and academic fields because corrosion causes enormous losses in various fields such as the economy, environment, society, industry, security, safety, and others. Currently, material damage control using organic compounds has become a popular field of study. Pyridine and quinoline stand out as corrosion inhibitors among a myriad of organic compounds because they are non-toxic, inexpensive, and effective in a variety of corrosive environments. Experimental investigations in developing various candidate potential inhibitor compounds are time and resource-intensive. In this work, we use a quantitative structure-property relationship (QSPR)-based machine learning (ML) approach to investigate support vector machine (SVR), random forest (RF), and k-nearest neighbors (KNN) algorithms as predictive models of inhibition performance. (Inhibition efficiency) corrosion of pyridine-quinoline derivative compounds as corrosion inhibitors on iron. We found that the RF model showed the best predictive ability based on the coefficient of determination (R2) and root mean squared error (RMSE) metrics. Overall, our study provides new insights regarding the ML model in predicting corrosion inhibition on iron surfaces.

Investigating potential corrosion inhibitors via empirical research is a labor- and resource-intensive process. In this work, we evaluated various linear and non-linear algorithms as predictive models for corrosion inhibition efficiency (CIE) values using a machine learning (ML) paradigm based on the quantitative structure-property relationship (QSPR) model. In the quinoxaline compound dataset, our analysis showed that the XGBoost model performed the best predictor of other ensemble-based models. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean squared error (RMSE) metrics were used to objectively assess this superiority. To sum up, our study offers a fresh viewpoint on the effectiveness of machine learning algorithms in determining the ability of organic compounds like quinoxaline to suppress corrosion on iron surfaces.

Penelitian ini bertujuan untuk mengetahui tentang hukum rujuk dalam pernikahan pada adat batak toba menurut persfektif hukum islam. Penelitian ini menggunakan metode studi pustaka yang dianalisis secara mendalam sehingga mendapatkan hasil tentang hukum rujuk adat batak toba. Hasil penelitian menunjukkan bahwa tata cara rujuk yang dipakai di adat Batak Toba menurut perspektif hukum Islam boleh dilakukan dengan berbagai ketentuan yang tidak melangar hukum islam. Dapat disimpulkan bahwa suatu adat boleh dilakukan  apabila dia tidak bertentangan dengan syariat dan dia masuk pada kaidah Ushul fiqih yaitu Al urf atau kebiasaan yang mana kaidah ini menjadi salah satu sumber hukum menurut ilmu Ushul fiqih sedangkan alasan lain kebiasaan ini atau al urf tidak bertentangan dengan Alquran dan hadist.

The Covid-19 pandemic has had various impacts on regional and urban developments. This phenomenon affects the planning and development process, forcing a correction in development planning in Indonesia. Development planning can be started within the scope of small area given that the concept of development in Indonesia refers to development from below and from above. RW I Kelurahan Kramas is one of the areas affected by the Covid-19 pandemic so it requires mitigation of development planning within the scope of an area oriented towards social disaster mitigation. This community development activity aims to produce planning documents that are oriented towards social disaster mitigation but still consider the sustainability aspects of an area and can serve as a guide for policy makers in formulating development programs in RW I Kelurahan Kramas. This planning document formulating uses a Rational Comprehensive Planning (RCP) analysis method by conducting a comprehensive analysis of the conditions of an area. The data collection method used in this service was through interviews and secondary data search. The results obtained in this analysis are 6 groups of development directions, namely improving the quality of infrastructure, improving health and safety, building layout, improving environmental quality, increasing the economy and improving the aesthetics of the area.

Penelitian ini bertujuan untuk membahas pelaksanaan Merdeka Belajar dalam Perspektif Pendidikan Agama Islam. Peneltian ini menggunakan pendekatan deskriptif kualitatif. Adapun pengumpulan data dengan menggunakan metode studi pustaka. Di mana penulis mengkajinya dengan menelusuri dan mencari bahan serta mengumpulkan informasi yang yang bersumber dari buku, jurnal, tulisan dan laporan peneltian sebelumnya yang menjelaskan pelaksanaan merdeka belajar. Referensi utama dalam penelitian ini ialah : Rangkaian kebijakan Pendidikan terbaru Kemendimbud RI, tentang Kurikulum Merdeka. Selain itu, peneltian ini juga memanfaatkan sumber data berupa peraturan perundang-undangan negara dan sumber-sumber lain yang relevan. Hasil peneltian menunjukkan bahwa gagasan merdeka belajar oleh Kemendikbud adalah bentuk reformasi di bidang pendidikan, yang arahnya untuk memberikan kebebasan bagi siswa, guru dan satuan pendidikan dalam berinovasi dan pengembangan kualitas pendidikan. Sebagai mata pelajaran, Pendidikan Agama Islam dapat diimplementasikan dan sejalan dengan merdeka belajar, yang memberikan ruang serta keleluasaan pada peserta didik agar dapat berkeksplorasi sesuai dengan bakat dan minatnya. Karena itu diperlukan pembentukan peserta didik yang produktif, kreatif dan inovatif serta mempunyai keterampilan yang menunjang terhadap bidang yang diminati. Meskipun demikian kebebasan dalam arti harus konsisten menjaga agar tidak melanggar etika, norma serta nilai-nilai yang telah ditentukan oleh ajaran Islam.