Purwa Hasan Putra, Azanuddin Azanuddin, Bister Purba, Y. A. Dalimunthe
At this time in the era of cars that use renewable energy fuels such as electric cars which are highly supported by the government so that it has an impact on used cars based on these problems an analysis is needed. Determining whether or not the price of buying or selling a used car is appropriate is one of the obstacles faced by the community in making decisions when buying or selling a car or vehicle. Therefore, most people choose an alternative by buying a used car that is still good and usable. One way to make price predictions is to use the Machine Learning method. In this study the authors used random forest and decision tree methods to predict car prices. The results of the research on car price prediction analysis using the random forest and decision tree methods have different percentage results. Where using the random forest method there is an accuracy: 72.13% whereas with the analysis of the decision tree method accuracy: 67.21%. So it can be concluded that the Random Forest method has better analytical accuracy than the Decision Tree method.
{"title":"Random forest and decision tree algorithms for car price prediction","authors":"Purwa Hasan Putra, Azanuddin Azanuddin, Bister Purba, Y. A. Dalimunthe","doi":"10.54076/jumpa.v3i2.305","DOIUrl":"https://doi.org/10.54076/jumpa.v3i2.305","url":null,"abstract":"At this time in the era of cars that use renewable energy fuels such as electric cars which are highly supported by the government so that it has an impact on used cars based on these problems an analysis is needed. Determining whether or not the price of buying or selling a used car is appropriate is one of the obstacles faced by the community in making decisions when buying or selling a car or vehicle. Therefore, most people choose an alternative by buying a used car that is still good and usable. One way to make price predictions is to use the Machine Learning method. In this study the authors used random forest and decision tree methods to predict car prices. The results of the research on car price prediction analysis using the random forest and decision tree methods have different percentage results. Where using the random forest method there is an accuracy: 72.13% whereas with the analysis of the decision tree method accuracy: 67.21%. So it can be concluded that the Random Forest method has better analytical accuracy than the Decision Tree method.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89326770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Al-Hakim, Hexa Hidayah, Esa Putri, Karsam Karsam
Ethnobiology became a research trend of human-integrated biology-ethnicity to support their lives. Ethnobiology for ethnic and indigenous peoples continued to be passed down by belief in each generation—Indigenous peoples in Central Java-Indonesia, Kejawen esoteric, whose lives always coincide with ethnobiological science. A previous study reported the preliminary study of the interaction between Kejawen esoteric and wild macaques at Kalisalak Forest, Central Java-Indonesia, but it is unclear. This study is an extended version of the previous study that explains more about the human-primate interaction at Kalisalak Forest. The method used in-depth interviews with 83 householders that believed in Kejawen's esoteric faith. Ethnobiological studies discussed are in the form of ethnobotany and ethnozoology (ethnoprimatology). This study result shows that there is no visible relationship between Kejawen esotericism and the presence of wild macaques in the Kalisalak forest, as well as indigenous Kejawen esoteric really cares about the wild macaque’s population in other ways—the interaction related to supporting the biodiversity and SDGs 2030's goals.
{"title":"Preliminary study of the interaction between kejawen esoteric and wild macaques in kalisalak forest, central java-indonesia","authors":"R. Al-Hakim, Hexa Hidayah, Esa Putri, Karsam Karsam","doi":"10.54076/jumpa.v3i2.281","DOIUrl":"https://doi.org/10.54076/jumpa.v3i2.281","url":null,"abstract":"Ethnobiology became a research trend of human-integrated biology-ethnicity to support their lives. Ethnobiology for ethnic and indigenous peoples continued to be passed down by belief in each generation—Indigenous peoples in Central Java-Indonesia, Kejawen esoteric, whose lives always coincide with ethnobiological science. A previous study reported the preliminary study of the interaction between Kejawen esoteric and wild macaques at Kalisalak Forest, Central Java-Indonesia, but it is unclear. This study is an extended version of the previous study that explains more about the human-primate interaction at Kalisalak Forest. The method used in-depth interviews with 83 householders that believed in Kejawen's esoteric faith. Ethnobiological studies discussed are in the form of ethnobotany and ethnozoology (ethnoprimatology). This study result shows that there is no visible relationship between Kejawen esotericism and the presence of wild macaques in the Kalisalak forest, as well as indigenous Kejawen esoteric really cares about the wild macaque’s population in other ways—the interaction related to supporting the biodiversity and SDGs 2030's goals.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73072116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kepok banana corm is one of the natural ingredients that has been obtained and can provide many benefits because of its excessive flavonoid content. This study aims to determine the types of flavonoids contained in the kepok banana weevil extract. Methods of detection and discovery of flavonoid measurement methods used include reaction, thin layer chromatography, and LC-MS/MS, and spectrofotometri for total flavonoid content compared to rutin as a flavonoid standard. The results showed that kepok banana corm extract contains a class of flavonoid compounds including rutin, kaempferol 3-O-rhamnoside-7-O-glucoside, quercetin 3 glycocide, and chrysoeriol-7-O-glycuronyl, with a total flavonoid content of 6.19. ±0.65%.
可可香蕉是一种已获得的天然成分，由于其过量的类黄酮含量，可以提供许多好处。本研究的目的是确定香竹香蕉象鼻虫提取物中黄酮类化合物的种类。黄酮类化合物的检测和发现方法采用反应法、薄层色谱法、LC-MS/MS法和分光光度法对总黄酮含量进行比较，以芦丁为黄酮类化合物的标准品。结果表明，香豆提取物中含有芦丁、山奈酚3- o -鼠李糖苷-7- o -葡萄糖苷、槲皮素- 3糖苷和黄菊醇-7- o -糖醛基等类黄酮化合物，总黄酮含量为6.19。±0.65%。
{"title":"Analysis and identification of flavonoid compounds in kepok banana corm extract (musa paradisiaca L)","authors":"Andini Andini, M. Sari, S. J. Raharjo, A. Anneke","doi":"10.54076/jumpa.v3i2.300","DOIUrl":"https://doi.org/10.54076/jumpa.v3i2.300","url":null,"abstract":"Kepok banana corm is one of the natural ingredients that has been obtained and can provide many benefits because of its excessive flavonoid content. This study aims to determine the types of flavonoids contained in the kepok banana weevil extract. Methods of detection and discovery of flavonoid measurement methods used include reaction, thin layer chromatography, and LC-MS/MS, and spectrofotometri for total flavonoid content compared to rutin as a flavonoid standard. The results showed that kepok banana corm extract contains a class of flavonoid compounds including rutin, kaempferol 3-O-rhamnoside-7-O-glucoside, quercetin 3 glycocide, and chrysoeriol-7-O-glycuronyl, with a total flavonoid content of 6.19. ±0.65%.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85869039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A primary direct use of water's small reservoir in West Nusa Tenggara is irrigation. Analysing irrigated agriculture's performances is a valuable way to measure the impact of small reservoirs on the food security and livelihoods of local communities. Embung Ponggong is a small dam in West Nusa Tenggara with a limited capacity for irrigation purposes. The limited capacity of the reservoir and a large amount of water demand causes an imbalance between water availability and demand. This study aims to obtain information about reservoir behaviour by applying many alternative cropping pattern scenarios to meet the most profitable water allocation decisions. This study conducted a reservoir simulation to obtain the best alternative cropping pattern and the highest cropping intensity. The results showed that the potential water availability in the Ponggong Reservoir is 5,057,076.18 m3/year and could meet irrigation water needs of Paddy-Soybean-Soybean cropping pattern that produced a high cropping intensity about 300% and reservoir reliability of 84.72%. The second best cropping pattern is Paddy-Maize-Maize resulted in a cropping intensity of 300% and reservoir reliability of 82.18%. Another suitable cropping pattern is Paddy-Paddy–Maize resulted in a cropping intensity of 245% and reservoir reliability of 80.09%.
{"title":"Small reservoir reliability under cropping pattern scenarios: a case study of embung ponggong in Lombok","authors":"H. Saidah, M. B. Budianto, Riko Salim Nugroho","doi":"10.54076/jumpa.v3i2.316","DOIUrl":"https://doi.org/10.54076/jumpa.v3i2.316","url":null,"abstract":"A primary direct use of water's small reservoir in West Nusa Tenggara is irrigation. Analysing irrigated agriculture's performances is a valuable way to measure the impact of small reservoirs on the food security and livelihoods of local communities. Embung Ponggong is a small dam in West Nusa Tenggara with a limited capacity for irrigation purposes. The limited capacity of the reservoir and a large amount of water demand causes an imbalance between water availability and demand. This study aims to obtain information about reservoir behaviour by applying many alternative cropping pattern scenarios to meet the most profitable water allocation decisions. This study conducted a reservoir simulation to obtain the best alternative cropping pattern and the highest cropping intensity. The results showed that the potential water availability in the Ponggong Reservoir is 5,057,076.18 m3/year and could meet irrigation water needs of Paddy-Soybean-Soybean cropping pattern that produced a high cropping intensity about 300% and reservoir reliability of 84.72%. The second best cropping pattern is Paddy-Maize-Maize resulted in a cropping intensity of 300% and reservoir reliability of 82.18%. Another suitable cropping pattern is Paddy-Paddy–Maize resulted in a cropping intensity of 245% and reservoir reliability of 80.09%.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83023858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
People was afraid to engage in activities outside the home because of Covid-19, which has affected several countries throughout the world, but these activities must be carried out so that people's needs can still be met. The community must abide by established health protocol standards, such as wearing masks, washing hands, and keeping a safe distance, in order to carry out this activity. Interactions between communities that invected by Covid-19 to other communities can transmit the disease. This interactions not only happen betwen sub division and sub region but also happen in larger area for example between provinces. Thus make the Covid-19 spread rapidly and easily. The existence of a relationship between location areas that affect the response variable can be referred to as Spatial Autoregressive Model (SAR). Based on the model, the dominant factors that causing the significant case of Covid-19 infected patients on the island of Sumatra are: income, local temperature and compliance of keeping save distance. The coefficient ? of 0.28309, interprets that the number of Covid-19 patient is affected by the province that surrounds it by 0.28309 times.
{"title":"Poisson spatial autoregressive (SAR) for estimating factors influencing covid-19","authors":"Gusmi Kholijah, Niken Rarasati, Corry Sormin","doi":"10.54076/jumpa.v3i2.287","DOIUrl":"https://doi.org/10.54076/jumpa.v3i2.287","url":null,"abstract":"People was afraid to engage in activities outside the home because of Covid-19, which has affected several countries throughout the world, but these activities must be carried out so that people's needs can still be met. The community must abide by established health protocol standards, such as wearing masks, washing hands, and keeping a safe distance, in order to carry out this activity. Interactions between communities that invected by Covid-19 to other communities can transmit the disease. This interactions not only happen betwen sub division and sub region but also happen in larger area for example between provinces. Thus make the Covid-19 spread rapidly and easily. The existence of a relationship between location areas that affect the response variable can be referred to as Spatial Autoregressive Model (SAR). Based on the model, the dominant factors that causing the significant case of Covid-19 infected patients on the island of Sumatra are: income, local temperature and compliance of keeping save distance. The coefficient ? of 0.28309, interprets that the number of Covid-19 patient is affected by the province that surrounds it by 0.28309 times.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77498321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Corruption is defined as illegal activities such as bribery, fraud and forgery carried out through the abuse of power by public or private officials for personal, financial or other merits. In Indonesia, the Attorney General's Office is one of the institutions that has the authority to handle corruption cases. As the result of the overall business process, public perception is very important. One method to assess public perception is using data collected from social media. Among the many social media, Twitter is known for its high public interaction which then can be used to describe direct people's perceptions. This research aims to create a machine learning model using the Naïve Bayes Classification Algorithm based on Twitter data to determine public sentiment on the Attorney General's Office performance in handling corruption cases. As for the results, we managed to create a model with accuracy, recall, precision, and f-measure values of 74.34%, 71.80%, 73.09%, and 72.44% respectively. From the sentiment analysis result, it can be concluded that the public gives more positive sentiment to the Attorney General's Office in handling corruption cases carried out in the period January 2022 to December 2022 with a percentage of positive sentiment is 61.32% and 38.68% for the negative sentiment.
{"title":"Sentiment analysis of the attorney general's office performance in handling corruption cases on twitter using naïve bayes classification algorithm","authors":"Yuda Hermawan, Harry TY Achsan","doi":"10.54076/jumpa.v3i2.329","DOIUrl":"https://doi.org/10.54076/jumpa.v3i2.329","url":null,"abstract":"Corruption is defined as illegal activities such as bribery, fraud and forgery carried out through the abuse of power by public or private officials for personal, financial or other merits. In Indonesia, the Attorney General's Office is one of the institutions that has the authority to handle corruption cases. As the result of the overall business process, public perception is very important. One method to assess public perception is using data collected from social media. Among the many social media, Twitter is known for its high public interaction which then can be used to describe direct people's perceptions. This research aims to create a machine learning model using the Naïve Bayes Classification Algorithm based on Twitter data to determine public sentiment on the Attorney General's Office performance in handling corruption cases. As for the results, we managed to create a model with accuracy, recall, precision, and f-measure values of 74.34%, 71.80%, 73.09%, and 72.44% respectively. From the sentiment analysis result, it can be concluded that the public gives more positive sentiment to the Attorney General's Office in handling corruption cases carried out in the period January 2022 to December 2022 with a percentage of positive sentiment is 61.32% and 38.68% for the negative sentiment.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91468413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information about the weather is crucial in assisting human activities and labor because the weather is a factor that cannot be separated and is closely related to all human activities. The purpose this study to compare performance the Autoregressive Integrated Moving Average (AIMA) and Long-Short Term Memory (LSTM) algorithm models with case studies of weather forecasting. This study uses comparison of two methods, forecasting using AIMA and LSTM methods. LSTM method provides the best forecasting performance for attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.45 and the Mean Absolute Error value below 1.14. For attributes of average humidity and solar radiation with a Root mean squared error value of 2.62 to 3.82 and a Mean Absolute Error value of 2.21 to 3.2. Precipitation forecasting has the highest error value with a root mean squared error value of 9.99 and a mean absolute error of 6.5. The AIMA method provides the best forecasting performance on the attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.47 and the Mean Absolute Error value below 1.16. For the sun exposure attribute with a Root mean squared error value of 2.91 to 3.05. Whereas the average humidity attribute has the highest error with the Root mean squared error value reaching 4.97 and the Mean Absolute Error reaching 3.99. LSTM method is better in terms of forecasting results and in terms of computation time. From every forecast made, the LSTM method produces a smaller error value.
{"title":"Comparison performance analysis of autoregressive integrated moving average and deep learning long-short term memory forecasting weather data","authors":"","doi":"10.54076/jumpa.v3i1.302","DOIUrl":"https://doi.org/10.54076/jumpa.v3i1.302","url":null,"abstract":"Information about the weather is crucial in assisting human activities and labor because the weather is a factor that cannot be separated and is closely related to all human activities. The purpose this study to compare performance the Autoregressive Integrated Moving Average (AIMA) and Long-Short Term Memory (LSTM) algorithm models with case studies of weather forecasting. This study uses comparison of two methods, forecasting using AIMA and LSTM methods. LSTM method provides the best forecasting performance for attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.45 and the Mean Absolute Error value below 1.14. For attributes of average humidity and solar radiation with a Root mean squared error value of 2.62 to 3.82 and a Mean Absolute Error value of 2.21 to 3.2. Precipitation forecasting has the highest error value with a root mean squared error value of 9.99 and a mean absolute error of 6.5. The AIMA method provides the best forecasting performance on the attribute minimum temperature, maximum temperature, and average temperature with the Root mean squared error value below 1.47 and the Mean Absolute Error value below 1.16. For the sun exposure attribute with a Root mean squared error value of 2.91 to 3.05. Whereas the average humidity attribute has the highest error with the Root mean squared error value reaching 4.97 and the Mean Absolute Error reaching 3.99. LSTM method is better in terms of forecasting results and in terms of computation time. From every forecast made, the LSTM method produces a smaller error value.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88285247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unemployment is one of critical issue in society. It may creates snowball effect towards economic development in a country and leads to the economic recessions. Hence, it is important to solve this issue by implementing the clustering to provide groups of people that have chance for job provision. K-Means Clustering is employed in this study by using 378 of data samples. Ages, marital status, amount of land owned, and income are selected as the attributes. The clustering result pointed out that there are 3 clusters that represent the people chances to get job, namely “High”, “Medium”, and “Low”. To evaluate the proposed cluster, Davis-Boulden index is utilized and presents a proper score. The practical implications are presented and discussed, then suggestions for future research are provided.
{"title":"Implementation of k-means clustering for the job provision in urban village","authors":"","doi":"10.54076/jumpa.v3i1.312","DOIUrl":"https://doi.org/10.54076/jumpa.v3i1.312","url":null,"abstract":"Unemployment is one of critical issue in society. It may creates snowball effect towards economic development in a country and leads to the economic recessions. Hence, it is important to solve this issue by implementing the clustering to provide groups of people that have chance for job provision. K-Means Clustering is employed in this study by using 378 of data samples. Ages, marital status, amount of land owned, and income are selected as the attributes. The clustering result pointed out that there are 3 clusters that represent the people chances to get job, namely “High”, “Medium”, and “Low”. To evaluate the proposed cluster, Davis-Boulden index is utilized and presents a proper score. The practical implications are presented and discussed, then suggestions for future research are provided.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82457464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Infection is a disease that causes high mortality in Indonesia, therefore it is necessary to search for antibacterial S. aureus compounds, one of which is the in silico method. The method in this study was docking using the PLANT software, the materials used were 10 compounds derived from karamunting and protein with the 3mzd code from RCSB, the results of the docking were then seen for interacting residues and types of bonds (hydrogen, electrostatic and hydrophobic) using discovery studio, besides that to predict activity using PASSonline and the data obtained is the value of Pa. The results of this study are docking scores -92.3386 to -66.8339 and Pa values of 0.242 to 0.598. The conclusion of this antibacterial research is that three compounds have the potential to be developed as antibacterials S. aureus, namely 5'-Desgalloylstachyurin, Rhodomyrtosone A and Rhodomyrtosone C.
{"title":"Antibacterial S. Aureus docking test from compounds contained from karamunting (rhodomyrtus tomentosa (aiton) hassk.)","authors":"","doi":"10.54076/jumpa.v3i1.273","DOIUrl":"https://doi.org/10.54076/jumpa.v3i1.273","url":null,"abstract":"Infection is a disease that causes high mortality in Indonesia, therefore it is necessary to search for antibacterial S. aureus compounds, one of which is the in silico method. The method in this study was docking using the PLANT software, the materials used were 10 compounds derived from karamunting and protein with the 3mzd code from RCSB, the results of the docking were then seen for interacting residues and types of bonds (hydrogen, electrostatic and hydrophobic) using discovery studio, besides that to predict activity using PASSonline and the data obtained is the value of Pa. The results of this study are docking scores -92.3386 to -66.8339 and Pa values of 0.242 to 0.598. The conclusion of this antibacterial research is that three compounds have the potential to be developed as antibacterials S. aureus, namely 5'-Desgalloylstachyurin, Rhodomyrtosone A and Rhodomyrtosone C.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77241986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Based on the Central Statistics Agency in 2022, the percentage and number of poor people according to islands in March 2022 explained that the largest number of poor people are on Maluku Island and Papua, 19.89 percent. This research used data mining techniques with the X-Means Clustering method. The data in this study was taken from the website of the Central Statistics Agency (BPS) of Maluku, North Maluku, Papua, and West Papua consisting of 63 regencies/cities with 8 variables. Then all the data was processed with Rapidminner and produced 3 clusters, namely cluster 0 consisting of Buru, Western Seram, Central Halmahera, Sula Islands, South Halmahera, North Halmahera, East Halmahera, Morotai Island, Jayapura, Nabire, Biak Numfor, Paniai, Pucak Jaya, Mimika, Tolikra, Nduga, Puncak and Manokwari. Cluster 1 consists of Tanimbar Islands, Southeast Maluku, Aru Islands, Eastern Seram, Southwest Maluku, South Buru, Tual, West Halmahera, Taliabu Island, Ternate, Tidore Islands, Yapen Islands, Boven Digoel, Mappi, Asmat, Star Mountains, Sarmi, Keerom, Waropen, Supiori, Mamberamo Jaya, Central Mamberamo, Yalimo, Dogiyai, Intan Jaya, Deiyai, Fakfak, Kaimana, Wondama Bay, Bentuni Bay, South Sorong, Sorong, Raja Ampat, Tambrauw, Maybrat, South Manokwari and Arfak Mountains and cluster 2 consisting of Central Maluku, Ambon, Merauke, Jayawijaya, Yahukimo, Lanny Jaya, Jayapura City, and Sorong City.
{"title":"Application of x-means alghorithm for district/city clustering based on povetry rate in Maluku Islands and Papua","authors":"M. Y. Matdoan, L. Igo","doi":"10.54076/jumpa.v3i1.270","DOIUrl":"https://doi.org/10.54076/jumpa.v3i1.270","url":null,"abstract":"Based on the Central Statistics Agency in 2022, the percentage and number of poor people according to islands in March 2022 explained that the largest number of poor people are on Maluku Island and Papua, 19.89 percent. This research used data mining techniques with the X-Means Clustering method. The data in this study was taken from the website of the Central Statistics Agency (BPS) of Maluku, North Maluku, Papua, and West Papua consisting of 63 regencies/cities with 8 variables. Then all the data was processed with Rapidminner and produced 3 clusters, namely cluster 0 consisting of Buru, Western Seram, Central Halmahera, Sula Islands, South Halmahera, North Halmahera, East Halmahera, Morotai Island, Jayapura, Nabire, Biak Numfor, Paniai, Pucak Jaya, Mimika, Tolikra, Nduga, Puncak and Manokwari. Cluster 1 consists of Tanimbar Islands, Southeast Maluku, Aru Islands, Eastern Seram, Southwest Maluku, South Buru, Tual, West Halmahera, Taliabu Island, Ternate, Tidore Islands, Yapen Islands, Boven Digoel, Mappi, Asmat, Star Mountains, Sarmi, Keerom, Waropen, Supiori, Mamberamo Jaya, Central Mamberamo, Yalimo, Dogiyai, Intan Jaya, Deiyai, Fakfak, Kaimana, Wondama Bay, Bentuni Bay, South Sorong, Sorong, Raja Ampat, Tambrauw, Maybrat, South Manokwari and Arfak Mountains and cluster 2 consisting of Central Maluku, Ambon, Merauke, Jayawijaya, Yahukimo, Lanny Jaya, Jayapura City, and Sorong City.","PeriodicalId":17729,"journal":{"name":"Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85196818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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