This research aims to analyze the portrait of the Indonesian public economy with a focus on the secondary crops, plantations, forestry, livestock and fisheries sectors. This study uses a descriptive analytical approach to identify the contribution of each sector to the national economy, the challenges faced, and the potential for future development. Data was obtained from various national statistical sources, government reports, and relevant academic literature. The results of the analysis show that these sectors have an important role in providing employment opportunities, increasing regional income, and contributing to national GDP. Secondary crops and plantations, for example, show high potential for economic diversification and food security. The forestry sector, despite facing the challenges of deforestation and climate change, remains significant in providing industrial raw materials and environmental conservation. Livestock and fisheries also play an important role in meeting people's protein needs and supporting food security. However, these various sectors face challenges such as lack of infrastructure, limited market access, and policies that are not yet optimal. This study suggests the need for a more integrated and sustainable development strategy to maximize the contribution of these sectors to the Indonesian economy.

Perguruan Tinggi merupakan sebuah lembaga pendidikan dimana didalamnya mempunyai tugas dalam pelayanan akademik. Kepuasan mahasiswa dalam memperoleh pelayanan akademik  merupakan hal yang sangat penting dalam menilai sebuah Perguruan Tinggi. Tujuan dari penelitian ini adalah agar dapat mengetahui bagaimana tingkat kepuasan mahasiswa program studi Teknik Informatika dalam hal memperoleh pengajaran oleh dosen, mengenai sarana dan prasarananya. Metode klasifikasi dan prediksi yang digunakan pada penelitian ini diambil dari salah satu model Decision Tree yaitu algoritma C4.5. Algoritma C4.5 berfungsi untuk mengekspolari data, menemukan hubungan tersembunyi antara sejumlah calon variabel input dengan sebuah variabel target. Hasil pengukuran yang didapat adalah nilai akurasi sebesar 94,23%. Nilai recall dari setiap kelas sebesar 94,12% untuk kelas Ya dan 100% untuk kelas Tidak. Sedangkan nilai presisi setiap kelas adalah sebesar 100% untuk kelas Ya dan 25% untuk kelas Tidak.

Handling rape cases requires effective forensic evidence collection to ensure justice for victims and truth disclosure in court. This study aims to evaluate protocols for collecting forensic evidence in rape cases, focusing on enhancing evidence quality and perpetrator identification accuracy. The evaluation method involves analyzing the implementation of existing protocols, including procedures for collecting and analyzing relevant physical, biological, and digital evidence in the context of sexual crimes. The study's findings indicate that increasing training and awareness of updated protocols can significantly improve the quality of collected evidence and the accuracy of perpetrator identification. These findings highlight the importance of collaboration among investigators, forensic experts, and other authorities in improving and implementing effective protocols for handling rape cases. This research provides a foundation for ongoing improvements in forensic procedures to enhance justice for victims of sexual crimes and overall community safety.

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.

Steel is part of raw material for manufacturing process making a product. Ferro Casting Ductile is part of manufacturing process from iron ore to be metal part using casting process. Die casting is a part of manufacturing process especially in automotive industries for engine of vehicle production process. this research will be focus on casting process, Ferro Casting Ductile (FCD). In this research using material steel HDF 12 Material, in this process production Magnesium, Pig Iron, Scrap, Silicon, Carburizer, Inoculant and Slagtol.  Manufacturing process is melting process of all raw material using temperature (melting point) at 1100 - 1400 ºC. When Specimen finish to process production will be analyze using spicrometer and SM machine for check of characteristic material and structure for crack issue. The result show that after casting process, the HDF12 have composition rate iron (Fe) content, namely 93.37% and also carbon (C) 3.82% and silicon (Si) 2.31% after check using. As visual check after die casting, lower arm is there no any crack, flash, flow mark finding by visual check inspection result. SEM Measurement check result until 300?m the result show that no any finding crack issue and result of Tensile strength of material Lower Arm upper limit is 433Kgf/cm2 and lower limit is 420 Kgf/cm and Elongation is 32%.