Pain is an unpleasant sensation that can interfere with an individual's quality of life and requires effective management. Synthetic analgesics such as ketorolac, codeine, etoricoxib, and celecoxib are commonly used to relieve pain, but long-term use can cause adverse side effects, including gastrointestinal disturbances, dependence, and cardiovascular risks. Therefore, it is important to find safer, natural alternatives. One promising alternative is the extract of the dragon’s tail leaf (Rhaphidophora pinnata), a plant traditionally used in Indonesian medicine as a pain reliever. This study aims to evaluate and compare the analgesic effects of Rhaphidophora pinnata leaf extract with several synthetic drugs using the writhing test on male mice (Mus musculus). The mice were induced with pain by injecting a 0.5% acetic acid solution. The treatments included ketorolac, codeine, etoricoxib, celecoxib, and Rhaphidophora pinnata leaf extract. The results showed that all treatments, including both synthetic drugs and Rhaphidophora pinnata leaf extract, produced significant analgesic effects compared to the negative control (Na CMC 0.5%). Ketorolac showed the highest effectiveness, followed by codeine, etoricoxib, and celecoxib. Although the Rhaphidophora pinnata leaf extract showed potential as a natural analgesic, its effectiveness varied among individual test animals. Some mice showed a better response to the leaf extract, but overall, the analgesic effect was still lower than that of synthetic drugs. These findings support the use of medicinal plants as a safer alternative to synthetic analgesic drugs. Moreover, this study provides a foundation for further research aimed at isolating active compounds from Rhaphidophora pinnata leaf extract to develop more effective and safer pain-relieving medications.

Plants contain several active ingredients beneficial for health, one of which is the persimmon plant, known for its tannins and phenolic compounds. This research aims to determine the effect of Black Persimmon Leaf Extract (Diospyros nigra) as an alternative for lowering glucose levels in White Rats (Rattus norvegicus). This is a True Experimental research, which controls all external variables that could affect the experimental results. There are four control groups: a negative control using glucose and positive controls using extracts with concentrations of 15%, 30%, and 60%. The research data were measured using a UV-Vis Spectrophotometer and analyzed using One Way ANOVA with SPSS. The results of the Shapiro-Wilk normality test showed a significance value >0.05; Levene's Test for homogeneity showed a significance value >0.05; the One Way ANOVA test showed a significance value <0.05; and the Tukey HSD test showed a significance value >0.05. The objective of this research is to test the effectiveness of black persimmon leaves (Diospyros nigra) as an alternative to lowering blood glucose levels in white rats (Rattus norvegicus). Sample absorbance data were obtained using UV-VIS Spectrophotometry and analyzed using One Way ANOVA in SPSS.

Jamu is an Indonesian traditional medicine that has been used from generation to generation with the properties contained empirically proven. Herbs that will be marketed must meet safety and quality requirements and must not contain medicinal chemicals (BKO). However, the increasingly fierce competition in the herbal medicine industry has resulted in some herbal medicine makers adding medicinal chemicals (BKO) to the herbs they produce. Medicinal raw materials (BKO) commonly contained in herbal medicine, namely paracetamol. The research conducted analyzed the content of paracetamol in herbal medicine using the Uv-Vis spectrophotometric method with a wavelength of 240 nm to detect medicinal raw materials (BKO) and paracetamol levels contained in Fung seh cu tong wan herbal medicine. The analysis results from 3 replicates showed that the samples of jamu Fung seh cu tong wan were positive for paracetamol with levels of 0.0064%, 0.0104%, and 0.0184% and an average of 0.0117% was obtained.

<p><em><span lang="EN-US">The purpose of this research is to design and implement an automatic acrylic carving and cutting tool using a microcontroller-based 3-axis CNC machine. Computer Numerical Control (CNC) is a machine technology that is operated automatically to support the demand for a product that has a complex shape and high accuracy. In general, the construction of a 3-axis CNC machine and its working system is synchronization between the computer and its mechanics. This tool has a work process by utilizing the G-Code method as a command on the machine to carry out engraving and cutting automatically. The G-code was obtained from a previously designed image and then converted using the Aspire 9.0 software. Engraving and cutting is done by sending the G-code file to the microcontroller via the Universal G-code Sender software, then the microcontroller sends a signal to drive the motor driver which then drives the stepper motor so that the actuator movement is generated according to the image in the G-code file. Simultaneously the spindle motor will be active to engrave or cut acrylic. In this study, a trial scenario was carried out to determine the precision and accuracy of the tool, namely by engraving and cutting flat shapes such as squares, circles, triangles and segments. The percentage of success generated from this tool is 97,08% to 100%. Furthermore, testing is carried out to make products in the form of engraving writing, logos and calligraphy. Apart from that, we also tested cutting letters and key chains. When the test is carried out, the accuracy level is 1mm.  </span></em></p>

The purpose of this research is to design and realize a pipe leak detection system that can be monitored with an Android application. Water flow sensor is used to determine the flow of water, with water flow rate analysis it is possible to know the leakage area, pipe leakage rate, and the number of leaks in pipe area if there is a water flow rate decrease. The data is processed with an NodeMCU microcontroller based on Internet of Things (IoT). The accuracy of all water flow sensors after calibration is 97,53%. When testing with the determination of each leaked area the results are appropriate. The reading of each water flow rate decrease that occurs has an accuracy of determiningthe leakage rate of 90,2% while the suitability of reading the number of leakage areas depends on the detection of the leakage area and the classification of the leakage rate. The greater the water flow reads on the sensors, the precission level to determine the level of water pipe leakage is getting better, and vice versa, this is due to sensors having variable variations in flow rate readings.