Tensor: Pure and Applied Mathematics Journal最新文献_第2页

ICOR Analysis on Inequality in Maluku Development 马鲁古发展不平等的ICOR分析

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-11-14 DOI: 10.30598/tensorvol3iss2pp57-64

Jefri Tipka

引用次数: 0

Combined Model Time Series Regression – ARIMA on Stocks Prices 股票价格的组合模型时间序列回归- ARIMA

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-11-14 DOI: 10.30598/tensorvol3iss2pp65-72

Desi Desi, S. W. Rizki, Yundari Yundari

{"title":"Combined Model Time Series Regression – ARIMA on Stocks Prices","authors":"Desi Desi, S. W. Rizki, Yundari Yundari","doi":"10.30598/tensorvol3iss2pp65-72","DOIUrl":"https://doi.org/10.30598/tensorvol3iss2pp65-72","url":null,"abstract":"Stock price data tend to experience a linear trend and fluctuate over time. So that forecasting is needed to predict stock prices in the next period. The nature of the linear trend can be modeled by linear time series regression and ARIMA. The purpose of this study is to form a combined model time series regression linear – ARIMA and predict stock prices using the combined mode time series regression linear – ARIMA. Combining two models can increase the level of forecasting accuracy compared to using separate models. The data used is the daily closing price of PT Unilever Indonesia Tbk for the period January 4, 2021 to December 30, 2021. The data forms a trend pattern that tends to be linear. The data is divided into in sample and out sample data with a proportion of 80:20. The model time series regression linear is formed by regressing the trend variable and stock closing price variable. From the model time series regression, the residual value is sought that will be used to form the ARIMA model. The model time series regression linear is then combined with the ARIMA model, where the model formed is a combined model time series regression linear – ARIMA (0,1,1) with the MAPE is 1.349906%. The results of PT Unilever Tbk’s stock price forecasting for the period January 3, 2022 to January 21, 2022, continued to decline. The highest forecasting results occurred on January 3, 2022, amounting to 4,091.253. While the lowest forecasting results occurred on January 21, 2022, which amounted to 3,827.192.","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132992610","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}

引用次数: 3

Optimization of Assignment Problems using Hungarian Method at PT. Sicepat Express Ambon Branch (Location: Java City Kec. Ambon Bay) 西派捷运安汶分公司(地点:爪哇市)分配问题的匈牙利法优化安汶湾)

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-06-11 DOI: 10.30598/tensorvol3iss1pp23-32

Ardial Meik, V. Y. I. Ilwaru, Monalisa E. Rijoly, B. P. Tomasouw

{"title":"Optimization of Assignment Problems using Hungarian Method at PT. Sicepat Express Ambon Branch (Location: Java City Kec. Ambon Bay)","authors":"Ardial Meik, V. Y. I. Ilwaru, Monalisa E. Rijoly, B. P. Tomasouw","doi":"10.30598/tensorvol3iss1pp23-32","DOIUrl":"https://doi.org/10.30598/tensorvol3iss1pp23-32","url":null,"abstract":"One of the special cases of problems in linear programming that is often faced by a company in allocating its employees according to their abilities is the assignment problem. The assignment problem can be solved using the Hungarian Method. In applying the Hungarian method, the number of employees assigned must be equal to the number of jobs to be completed. In this study, the Hugarian method was used to optimize the delivery time of goods from PT. SiCepat Express Ambon Branch – Java City. To solve the assignment problem at PT. SiCepat Express Ambon Branch – Java City, the required data includes employee names, destination locations, and delivery times. Before using the Hungarian method, the total delivery time of 7 employees at 10 destinations is 955 minutes. However, after using the Hungarian method, the total delivery time of 7 employees at 10 destination locations was 440 minutes. It can be seen that there are 515 minutes of time effisiency. We also Solved this assignment problem uses the QM For Windows Version 5.2 software and go the same amount of time, which is 440 minutes. \u0000 ","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123097675","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}

引用次数: 0

The total irregularity strength of m copies of the friendship graph m份友谊图的总不规则强度

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-06-11 DOI: 10.30598/tensorvol3iss1pp43-48

M. Tilukay, Harmanus Batkunde

引用次数: 0

Classification of Village Status in Landak Regency Using C5.0 Algorithm 基于C5.0算法的兰达克县村落状态分类

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-06-11 DOI: 10.30598/tensorvol3iss1pp33-42

Mutiara Cindy Nur Fitria, Naomi Nessyana Debataraja, S. W. Rizki

{"title":"Classification of Village Status in Landak Regency Using C5.0 Algorithm","authors":"Mutiara Cindy Nur Fitria, Naomi Nessyana Debataraja, S. W. Rizki","doi":"10.30598/tensorvol3iss1pp33-42","DOIUrl":"https://doi.org/10.30598/tensorvol3iss1pp33-42","url":null,"abstract":": Village development is an effort to improve the welfare and quality of life of rural communities. The development of a village, one of which can be measured by the Village Building Index (VBI). VBI is formed from three indices that are expected to cover all areas of village life. The lower the VBI value of a village, the more backward the village is. Classification of village status is very important for making policies that are in accordance with village conditions. This study used village status data based on the Village Building Index (VBI) of Landak Regency in 2020 obtained from the website of One Data West Kalimantan. The data used consists of Village Status variable ( 𝑌 ) which is the dependent variable and 12 independent variables, namely Health (X 1 ), Education (X 2 ), Social Capital (X 3 ), Settlement (X 4 ), Production Diversity (X 5 ), Trade (X 6 ), Distribution Access (X 7 ), Credit Access (X 8 ), Economic Institutions (X 9 ), Regional Openness (X 10 ), Environmental Quality (X 11 ), and Potential and Disaster Response (X 12 ). The purpose of this study is to classify the status of villages in Landak Regency using C5.0 Algorithm. Classification begins with data collection, then the data is divided into training and testing data in the 90:10 proportion. Next is the formation of a classification model using training data, after that, testing the classification model using data testing. Then the evaluation of the classification model and based on the results of the study obtained an accuracy value of 82.35%, which means the quality of the model is good and can be used, with the variables Health (X 1 ), Education (X 2 ), Settlement (X 4 ), and Credit Access (X 8 ), not too influential and the variable Potential and Disaster Response (X 12 ) the most influential in the classifying village status in Landak Regency.","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"271 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977778","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}

引用次数: 0

Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke 关于早期发现中风的模糊逻辑比较

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-06-11 DOI: 10.30598/tensorvol3iss1pp11-22

D. L. Rahakbauw, Adya Afriananda, H. W. M. Patty

{"title":"Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke","authors":"D. L. Rahakbauw, Adya Afriananda, H. W. M. Patty","doi":"10.30598/tensorvol3iss1pp11-22","DOIUrl":"https://doi.org/10.30598/tensorvol3iss1pp11-22","url":null,"abstract":"Stroke is a neurological function disorder caused by disruption of blood flow in the brain that arises suddenly and acutely within a few seconds or more precisely within a few hours that lasts more than 24 hours with symptoms or signs according to the affected area. Early detection of stroke usually takes a long time. With advances in technology, stroke can be prevented by detecting the risk early so that it can be treated quickly and increase the chances of recovery. The discussion of this research is about early detection of stroke risk by comparing using fuzzy logic Sugeno method and Mamdani method and using patient data at Dr. Hospital. H. Isaac Umarella. By using input variables in the form of: blood pressure, age, LDL, and blood sugar levels. Based on the results obtained from the calculation of Error with Mean Absolute Percentage Error (MAPE), the level of truth of the calculation of the Sugeno method is 87%, while the truth level of the Mamdani method is 85% so that it can be said that both methods get good results but Sugeno's fuzzy logic is superior with a value of small MAPE. In conclusion, fuzzy logic with the Sugeno method can be used in early detection of stroke risk.","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"501 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115984122","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}

引用次数: 1

Forecasting The Production of Crude Palm Oil (CPO) in Indonesia 2022 using the Grey(1.1) Model 运用灰色(1.1)模型预测印尼2022年棕榈油产量

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-06-11 DOI: 10.30598/tensorvol3iss1pp1-10

N. M. Huda, Nurfitri Imro’ah, Dea Rizki Darmawanti

{"title":"Forecasting The Production of Crude Palm Oil (CPO) in Indonesia 2022 using the Grey(1.1) Model","authors":"N. M. Huda, Nurfitri Imro’ah, Dea Rizki Darmawanti","doi":"10.30598/tensorvol3iss1pp1-10","DOIUrl":"https://doi.org/10.30598/tensorvol3iss1pp1-10","url":null,"abstract":"Crude Palm Oil (CPO) is a vegetable oil produced from oil palm fruit plants. Palm oil can be used for many things, including for various foods, cosmetics, hygiene products, and can also be used as a source of biofuel or biodiesel. Indonesia is a country with the most CPO production in the world. However, the development of CPO production must be appropriately managed to meet demand from other countries. Therefore, this study aims to predict CPO production in 2022. One of the appropriate statistical methods is the Grey(1.1) model. This model was chosen based on the availability of CPO production data by the Central Statistics Agency, which only presents annual data from 2017 - 2021. So, the number of observations that can be used to predict CPO production in Indonesia is only five observations. The Grey(1.1) model can cover problems in the availability of small amounts of data. There are three main steps in the modelling procedure with Grey(1,1) model, namely forming an Accumulated Generated Operation (AGO) sequence, then forming a Mean Generating Operation (MGO) sequence, and the last step is a prediction with Inverse AGO (1-AGO). This study obtained the 1-AGO sequence on the Grey(1.1) model for CPO production in Indonesia with outstanding accuracy, namely the Mean Absolute Percentage Error (MAPE) value of 0.01%. In addition, a prediction of CPO production in Indonesia for 2022 is made, which is 52590612.99 (an increase of 2339783.668 from 2021).","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124115631","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}

引用次数: 0

Kajian Grup Galois Isomorfis dengan Grup Alternating A5 与交替群 A5 同构的伽罗瓦群研究

Tensor: Pure and Applied Mathematics Journal Pub Date : 2022-06-11 DOI: 10.30598/tensorvol3iss1pp49-56

Henry W. M. Patty, Fandy Sanudin, F. Y. Rumlawang, D. Patty

引用次数: 0

The Modular Irregularity Strength of Triangular Book Graphs 三角形书本图的模不规则强度

Tensor: Pure and Applied Mathematics Journal Pub Date : 2021-11-25 DOI: 10.30598/tensorvol2iss2pp53-58

M. Tilukay

引用次数: 0

Peramalan Jumlah Penduduk Miskin di Provinsi Maluku Tahun 2021 Dengan Menggunakan Metode Arima 2021年，马鲁库省的穷人通过阿里马法得到了平衡

Tensor: Pure and Applied Mathematics Journal Pub Date : 2021-11-25 DOI: 10.30598/tensorvol2iss2pp77-86

Iksan Mule

{"title":"Peramalan Jumlah Penduduk Miskin di Provinsi Maluku Tahun 2021 Dengan Menggunakan Metode Arima","authors":"Iksan Mule","doi":"10.30598/tensorvol2iss2pp77-86","DOIUrl":"https://doi.org/10.30598/tensorvol2iss2pp77-86","url":null,"abstract":"Studi ini bertujuan untuk meramalan jumlah penduduk miskin di Provinsi Maluku tahun 2021 dengan menggunakan data series tahun 2005-2020 yang bersumber dari website Badan Pusat Statistik Provinsi Maluku. Penelitian serupa masih sangat terbatas di Provinsi Maluku. Metode peramalan yang digunakan dalam penelitian ini adalah metode ARIMA (Autoregresive Integrated Moving Average). ARIMA merupakan metode analisis time series yang baik untuk peramalan jangka pendek. Tahapan penelitian dimulai dari pemeriksaan pola data, pengecekan kestasioneran data, identifikasi model, estimasi parameter, hingga verifikasi model. Setelah melalui tahapan-tahapan tersebut maka model pun dapat digunakan untuk meramalkan data. Dalam pengolahannya menggunakan software Minitab 15 dan SPSS 21. Berdasarkan hasil penelitian, diperoleh model ARIMA(1,0,0) sebagai pilihan terbaik dengan nilai ramalan jumlah penduduk miskin Provinsi Maluku untuk tahun 2021 sebesar 321.094 jiwa. Hal ini menunjukkan terjadi kenaikan sebesar 2.914 jiwa dari tahun 2020. Dengan demikian, penelitian ini diharapkan dapat menjadi pertimbangan bagi pemerintah Provinsi Maluku dalam mengoptimalkan kebijakan pengentasan kemiskinan di seluruh wilayah kepulauan Maluku.","PeriodicalId":294430,"journal":{"name":"Tensor: Pure and Applied Mathematics Journal","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116323276","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}

引用次数: 0