Maraknya tawuran antar gangster di malam hari membuat masyarakat menjadi resah untuk beraktivitas dengan bebas. Hingga di era saat ini, perempuan masih menjadi objek atau target utama tindak kriminalitas akibat stigma bahwa perempuan adalah kaum yang lemah.  Desa Batursari memiliki populasi sebesar 35.229 jiwa dengan sebaran jumlah penduduk perempuan sebanyak 17.625 jiwa sehingga memiliki kerentanan menjadi korban kriminalitas yang tinggi. Oleh karenanya, Tim Pengabdi berkolaborasi bersama Dinas Perempuan dan Anak (DP3AP2KB) Provinsi Jawa Tengah dan perangkat Desa Batursari untuk mengadakan kegiatan edukasi dan praktik dasar Muay Thai sebagai bentuk antisipasi terhadap terjadinya kriminalitas. Hasilnya, terjadi 67,7% peningkatan pengetahuan dan kapabilitas kaum perempuan masyarakat Desa Batursari dalam bidang perlindungan diri.

For some, the role of housewives is often underestimated because it is called an unproductive role. In fact, housewives have many skills and capabilities in self-development if they find the right place. Desa Batursari has a population demographic with a high population of 35,229 people. Of the total population, 17,625 are women. This is also a high potential for carrying out various aspects of empowerment and increasing competence for women, especially housewives. Housewives in Desa Batursari have many skills in producing creative goods and processed food products but are constrained in marketing which is only by word of mouth. Therefore, the Community Service Team collaborated with the Kampus UMKM Shopee Semarang to provide appropriate digital marketing education and training for housewives in Desa Batursari. The activity was held with a presentation of the education followed by the practice of optimizing digital marketing in the marketplace. It is hoped that with this community service activity, housewives in Desa Batursari can increase the economic potential of themselves and their families.

Golden Age is an era for children which is most crucial in building character in the future. However, nowadays many children are exposed to gadgets from an early age. The role of parents is very important to facilitate creative and positive activities to divert children's addiction to gadgets. Ecoprint is one of the activities that can be done with children. So, the service team held a service in the form of ecoprint training which was attended by women from Desa Batursari who are members of the Batursari Creative Women's School. As a result, participants took part in the training enthusiastically and produced good creations. The hope is that this activity can be an idea for playing with children at home.

In the clinical treatment of skin diseases and cancer, cryotherapy and immunotherapy offer effective and minimally invasive alternatives. However, the complexity of patient response demands more sophisticated analytical strategies for accurate outcome prediction. This research focuses on analyzing the effect of preprocessing in various machine learning models on the prediction performance of cryotherapy and immunotherapy. The preprocessing techniques analyzed are advanced feature engineering and Synthetic Minority Over-sampling Technique (SMOTE) and Tomek links as resampling techniques and their combination. Various classifiers, including support vector machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), XGBoost, and Bidirectional Gated Recurrent Unit (BiGRU), were tested. The findings of this study show that preprocessing methods can significantly improve model performance, especially in the XGBoost model. Random Forest also gets the same results as XGBoost, but it can also work better without significant preprocessing. The best results were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790, respectively, for accuracy, recall, specificity, precision, and f1 on the Immunotherapy dataset, while on the Cryotherapy dataset, respectively, they were 0.8889, 0.8889, 0.6000, 0.9037, and 0.8790. This study confirms the potential of customized preprocessing and machine learning models to provide deep insights into treatment dynamics, ultimately improving the quality of diagnosis.

CT is the ability to think to solve problems whose solution is computing. CT abilities cannot possibly grow in an instant, CT knowledge and skills need time to grow and develop so that they produce as expected. CT training for kindergarten and elementary school teachers at Gaussian Kamil School will be carried out in several stages, starting with an introduction to the CT concept, practice questions, practicing CT using digital methods, and Unplugged. It is hoped that this training can improve teachers' CT abilities, so that teachers will infuse it with students. Students who receive CT from an early age are expected to be able to be independent and behave better because they are used to solving problems in the correct, fast and efficient way.

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.

Pandemi covid-19 telah membangun budaya baru yaitu jual beli online. Hal ini juga terjadi di Kampung Batik Rejomulyo yang berpenduduk dengan mata pencaharian umumnya adalah pengusaha batik. Pergeseran penjualan konvensional menjadi online, menimbulkan masalah baru yaitu kesulitan mendapatkan foto produk yang baik dan memuaskan konsumen. Para pemilik toko batik memiliki pola pikir bahwa melakukan foto produk tidak bisa dengan smartphone yang dimiliki karena kualitasnya kurang baik. Padahal faktanya dengan pengetahuan tentang teknik dan pengalaman menggunakan smartphone untuk mengambil foto produk, kita bisa mendapatkan hasil foto yang mengagumkan. Kegiatan pengabdian ini dilakukan untuk berbagi pengetahuan dan pengalaman tentang teknik pengambilan foto produk dari smartphone termasuk teknik angle dan pemanfaatan cahaya.

Kampung Batik Rejomulyo, sebuah kampung yang memiliki warisan seni budaya dan kerajinan batik yang kaya. Dalam konteks globalisasi dan persaingan ketat, pengembangan dan promosi kampung batik ini diperlukan untuk menjaga relevansi dan daya saingnya. Salah satu caranya melalui pembuatan desain logo yang akan dijadikan ikon baru kampung tersebut. Logo ini bertujuan untuk meningkatkan pengenalan produk batik semarangan, serta dapat menjadi aset berharga untuk strategi branding produk batik semarangan di Kampung Batik Rejomulyo. Harapan dari penggunaan logo baru ini dapat memberikan identitas yang kuat bagi Kampung Batik Rejomulyo, tidak hanya di tingkat lokal tetapi juga secara nasional maupun internasional.

Penelitian ini merupakan studi eksperimen untuk melakukan penyelidikan inhibitor korosi oleh senyawa Benzimidazole dengan melakukan pendekatan machine learning (ML). Karena korosi menyebabkan banyak kerugian yang timbul karena kehilangan material konstruksi, keselamatan kerja dan pencemaran lingkungan akibat produk korosi dalam bentuk senyawa yang mencemarkan lingkungan. Melakukan pendekatan ML adalah untuk mendapatkan model akurasi yang terbaik sehingga dapat digunakan untuk memprediksi dengan relevan dan akurat terhadap suatu material. Dalam penelitian ini, kami mengevaluasi algoritma ML dengan metode linear dan nonlinear dengan menggunakan metode k-fold cross-validation untuk membantu dalam mengukur performa model ML. Mengacu pada metrik coefficient of determination (R2) dan root mean square error (RMSE), kami menyimpulkan bahwa model AdaBoost regressor (ADA) merupakan model dengan performa prediksi terbaik dari eksperimen yang kami lakukan dari literatur untuk dataset senyawa benzimidazole. Keberhasilan model penelitian ini menawarkan perspektif baru tentang kemampuan model ML untuk memprediksi penghambat korosi yang efektif.