DESIGN OF BAJAKAH ROOT CHOPPING MACHINE INTO TEA POWDER

Tea (camellia sinensis) is one type of plant that is often found as a processed beverage. There are several raw materials for making tea that are often used including using leaves, shoots, flowers, and roots. One of the teas made from roots is bajakah root tea where the manufacturing process is still done in the traditional way, namely chopped using a machete. Therefore, in this study, a bajakah root chopping machine was designed with the aim of helping the bajakah root production process into tea powder. This bajakah root chopping machine into tea powder is designed using the France method with the demands of a sturdy frame and easy to move. This bajakah root chopping machine has dimensions of 340 mm long, 240 mm wide and 600 mm high. The engine is driven by an electric motor with a power of 0.5 Hp, a rotation of 1400 rpm using a pulley drive system with a ratio of 1: 1.5 and the rotation of the chopper shaft is 933 rpm. From the test results taken from a sample of 500 grams of raw material with 3 times testing, the resulting average is perfectly chopped as much as 389 grams, not perfectly chopped as much as 54 grams, with a time of 167.3 seconds. The production capacity of the machine is 8.37 kg/hour.  The machine that has been made is able to chop bajakah roots with a machine production efficiency of 77.8%.Tea (camellia sinensis) is one type of plant that is often found as a processed beverage. There are several raw materials for making tea that are often used including using leaves, shoots, flowers, and roots. One of the teas made from roots is bajakah root tea where the manufacturing process is still done in the traditional way, namely chopped using a machete. Therefore, in this study, a bajakah root chopping machine was designed with the aim of helping the bajakah root production process into tea powder. This bajakah root chopping machine into tea powder is designed using the France method with the demands of a sturdy frame and easy to move. This bajakah root chopping machine has dimensions of 340 mm long, 240 mm wide and 600 mm high. The engine is driven by an electric motor with a power of 0.5 Hp, a rotation of 1400 rpm using a pulley drive system with a ratio of 1: 1.5 and the rotation of the chopper shaft is 933 rpm. From the test results taken from a sample of 500 grams of raw material with 3 times testing, the resulting average is perfectly chopped as much as 389 grams, not perfectly chopped as much as 54 grams, with a time of 167.3 seconds. The production capacity of the machine is 8.37 kg/hour.  The machine that has been made is able to chop bajakah roots with a machine production efficiency of 77.8%

Hate Speech Detection for Banjarese Languages on Instagram Using Machine Learning Methods

Hate speech refers to verbal expression or communication that aims to provoke or discriminate against individuals. The Ministry of Communication and Information of Indonesia has encountered and dealt with 3,640 cases of hate speech transmitted through digital channels between 2018 and 2021. Particularly in South Kalimantan, hate speech in the local language, Banjarese has become increasingly prevalent in recent years. Surprisingly, there is a lack of research on using machine learning to detect hate speech in the Banjarese language, specifically on Instagram. Therefore, this study aimed to address this gap by constructing a dataset of Banjarese language hate speech and comparing various feature extraction and machine learning models to detect Banjarese language hate speech effectively. Thisresearch used several feature extraction techniques and machine learning methods to detect Banjareselanguage hate speech. The feature extraction methods used were Word N-Gram, Term Frequency- Inverse Document Frequency (TF-IDF), a combination of Word N-Gram and TF-IDF, Word2Vec, and Glove, while the machine learning methods used were Support Vector Machine (SVM), Na¨ıve Bayes, and Decision Tree. The results of this study revealed that the combination of TF-IDF for feature extraction and SVM as the model achieves exceptional performance. The average Recall, Precision, Accuracy, and F1-Score score exceeded 90%, demonstrating the model’s ability to identify Banjarese hate speech accurately

Mapping rheumatoid arthritis susceptibility through integrative bioinformatics and genomics

Rheumatoid arthritis (RA) is an autoimmune disease that influences several organs and tissues, especially the synovial joints, and is associated with multiple genetic and environmental factors. Numerous databases provide information on the relationship between a specific gene and the disease pathogenesis. However, it is important to further prioritize biological risk genes for downstream development and validation.  This study aims to map RA-association genetic variation using genome-wide association study (GWAS) databases and prioritize influential genes in RA pathogenesis based on functional annotations. These functional annotations include missense/nonsense mutations, cis-expression quantitative trait locus (cis-eQTL), overlap knockout mouse phenotype (KMP), protein-protein interaction (PPI), molecular pathway analysis (MPA), and primary immunodeficiency (PID). 119 genetic variants mapped had a potential high risk for RA based on functional scoring. The top eight risk genes of RA are TYK2 and IFNGR2, followed by TNFRSF1A, IL12RB1 and CD40, C5, NCF2, and IL6R. These candidate genes are potential biomarkers for RA that can aid drug discovery and disease diagnosis

Mapping Rheumatoid Arthritis Susceptibility through Integrative Bioinformatics and Genomics

Rheumatoid arthritis (RA) is an autoimmune disease that influences several organs and tissues, especially the synovial joints, and is associated with multiple genetic and environmental factors. Numerous databases provide information on the relationship between a specific gene and the disease pathogenesis. However, it is important to further prioritize biological risk genes for downstream development and validation. This study aims to map RA-association genetic variation using genome-wide association study (GWAS) databases and prioritize influential genes in RA pathogenesis based on functional annotations. These functional annotations include missense/nonsense mutations, cis-expression quantitative trait locus (cis-eQTL), overlap knockout mouse phenotype (KMP), protein-protein interaction (PPI), molecular pathway analysis (MPA), and primary immunodeficiency (PID). 119 genetic variants mapped had a potential high risk for RA based on functional scoring. The top eight risk genes of RA are TYK2 and IFNGR2, followed by TNFRSF1A, IL12RB1 and CD40, C5, NCF2, and IL6R. These candidate genes are potential biomarkers for RA that can aid drug discovery and disease diagnosis

Transcriptomics-driven drug repositioning for the treatment of diabetic foot ulcer

Abstract Diabetic foot ulcers (DFUs) are a common complication of diabetes and can lead to severe disability and even amputation. Despite advances in treatment, there is currently no cure for DFUs and available drugs for treatment are limited. This study aimed to identify new candidate drugs and repurpose existing drugs to treat DFUs based on transcriptomics analysis. A total of 31 differentially expressed genes (DEGs) were identified and used to prioritize the biological risk genes for DFUs. Further investigation using the database DGIdb revealed 12 druggable target genes among 50 biological DFU risk genes, corresponding to 31 drugs. Interestingly, we highlighted that two drugs (urokinase and lidocaine) are under clinical investigation for DFU and 29 drugs are potential candidates to be repurposed for DFU therapy. The top 5 potential biomarkers for DFU from our findings are IL6ST, CXCL9, IL1R1, CXCR2, and IL10. This study highlights IL1R1 as a highly promising biomarker for DFU due to its high systemic score in functional annotations, that can be targeted with an existing drug, Anakinra. Our study proposed that the integration of transcriptomic and bioinformatic-based approaches has the potential to drive drug repurposing for DFUs. Further research will further examine the mechanisms by which targeting IL1R1 can be used to treat DFU