Jaringan syaraf tiruan telah digunakan dalam berbagai bidang. Salah satunya untuk memperoleh suatu model prediksi. Pada penelitian ini model prediksi kadar polutan ozon troposfer di Kota Mataram diperoleh menggunakan jaringan syaraf tiruan backpropagation. Prediksi kadar polutan ozon troposfer diperlukan agar diketahui kualitas udara di hari-hari berikutnya sehingga dapat diambil suatu keputusan untuk mencegah dampak negatif dari polutan yang lebih besar. Variabel-variabel yang dijadikan masukan (prediktor) pada jaringan ini adalah temperatur udara , arah angin , kecepatan angin , kelembaban udara , intensitas sinar matahari , kadar NO2 , kadar SO2 dan kadar O3 satu hari sebelumnya pada periode 6 Juli 2018 sampai dengan 31 Mei 2019. Data-data tersebut diperoleh dari Dinas Lingkungan Hidup dan Kehutanan Provinsi Nusa Tenggara Barat. Berdasarkan hasil penelitian ini, didapatkan model jaringan terbaik untuk memprediksi kadar polutan ozon di Kota Mataram adalah jaringan dengan arsitektur 8-20-1 dengan fungsi aktivasi logsig-purelin dan fungsi pembelajaran trainlm. Performa model pelatihan berdasarkan indikator RMSE, MAPE dan berturut-turut sebesar , , dan . Sedangkan, performa model pengujian berdasarkan indikator RMSE, MAPE dan berturut-turut sebesar , , dan . Dari delapan variabel prediktor kadar polutan ozon pada model, variabel yang memiliki pengaruh paling besar terhadap kadar polutan ozon berdasarkan metode Connection Weight Approach adalah variabel temperatur udara sedangkan berdasarkan metode Garson’s Algorithm adalah variabel kadar polutan ozon satu hari sebelumnya.
{"title":"Jaringan Syaraf Tiruan untuk Memprediksi Kadar Polutan Ozon di Kota Mataram","authors":"Nurul Hikmah, syamsul bahri, Irwansyah Irwansyah","doi":"10.29303/emj.v5i2.129","DOIUrl":"https://doi.org/10.29303/emj.v5i2.129","url":null,"abstract":"Jaringan syaraf tiruan telah digunakan dalam berbagai bidang. Salah satunya untuk memperoleh suatu model prediksi. Pada penelitian ini model prediksi kadar polutan ozon troposfer di Kota Mataram diperoleh menggunakan jaringan syaraf tiruan backpropagation. Prediksi kadar polutan ozon troposfer diperlukan agar diketahui kualitas udara di hari-hari berikutnya sehingga dapat diambil suatu keputusan untuk mencegah dampak negatif dari polutan yang lebih besar. Variabel-variabel yang dijadikan masukan (prediktor) pada jaringan ini adalah temperatur udara , arah angin , kecepatan angin , kelembaban udara , intensitas sinar matahari , kadar NO2 , kadar SO2 dan kadar O3 satu hari sebelumnya pada periode 6 Juli 2018 sampai dengan 31 Mei 2019. Data-data tersebut diperoleh dari Dinas Lingkungan Hidup dan Kehutanan Provinsi Nusa Tenggara Barat. Berdasarkan hasil penelitian ini, didapatkan model jaringan terbaik untuk memprediksi kadar polutan ozon di Kota Mataram adalah jaringan dengan arsitektur 8-20-1 dengan fungsi aktivasi logsig-purelin dan fungsi pembelajaran trainlm. Performa model pelatihan berdasarkan indikator RMSE, MAPE dan berturut-turut sebesar , , dan . Sedangkan, performa model pengujian berdasarkan indikator RMSE, MAPE dan berturut-turut sebesar , , dan . Dari delapan variabel prediktor kadar polutan ozon pada model, variabel yang memiliki pengaruh paling besar terhadap kadar polutan ozon berdasarkan metode Connection Weight Approach adalah variabel temperatur udara sedangkan berdasarkan metode Garson’s Algorithm adalah variabel kadar polutan ozon satu hari sebelumnya.","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125484063","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}
Mega Silfiani, F. Hayati, Surya Puspita Sari, Agung Prabowo
COVID 19 merupakan penyakit yang disebabkan oleh SARS-CoV-2. Virus COVID 19 tidak hanya berdampak pada aspek kesehatan, melainkan aspek kehidupan lainnya Pariwisata Bali telah tumbuh berkembang sedemikan rupa dan memberikan sumbangan yang besar terhadap pembangunan daerah langsung maupun tidak langsung. Produk Domestik Regional Bruto atau disingkat dengan PDRB memiliki peran penting dalam meningkatkan pertumbuhan ekonomi suatu daerah, dimana semakin tinggi PDRB maka dapat dikatakan bahwa pertumbuhan ekonominya juga tinggi. Berdasarkan hal tersebut, maka perlu dilakukannya suatu peramalan untuk mengetahui dampak COVID 19 terhadap PDRB Bali. Penelitian ini bertujuan untuk menganalisis dampak COVID 19 terhadap PDRB Provinsi Bali menggunakan model intervensi. Data yang digunakan dalam penelitian ini merupakan data sekunder dari PDRB triwulanan atas dasar harga berlaku sektor penyediaan akomodasi, makanan dan minum. Data dihimpun dari kuartal I 2010 sampai dengan kuartal IV 2021. Berdasarkan pemodelan yang telah dilakukan dengan model intervensi, model terbaik untuk meramalkan dampak COVID 19 terhadap PDRB di Provinsi Bali adalah ARIMA(0,1,0)(1,0,0)4 r=1 dengan nilai SMAPE 8,327 dan MdAPE sebesar 0,067.
COVID 19是由SARS-CoV-2引起的一种疾病。科维德19病毒不仅影响了健康，而且影响了巴厘岛生活的其他方面，旅游业已经发展成型，为直接或间接地区的发展做出了巨大贡献。国民生产总值或简称PDRB的国内生产总值在促进某一地区的经济增长方面发挥着重要作用，PDRB越高，其经济增长也就越高。在此基础上，有必要进行先知计算，看看科维德19对巴厘岛PDRB的影响。本研究旨在分析COVID 19对巴厘岛PDRB的影响，采用干预模式。本研究中使用的数据是基于现行价格提供食宿部门的PDRB季度数据的次要数据。从2010年1月25日到2021年4月25日，数据收集。根据干预模式所做的模型，预测科维德19在巴厘岛对PDRB的影响的最佳模型是ARIMA(0.1 0)(1.0 0)4 r=1, sm p 8.327和MdAPE为0.067。
{"title":"Analisis Dampak COVID 19 terhadap PDRB Provinsi Bali dengan Model Intervensi","authors":"Mega Silfiani, F. Hayati, Surya Puspita Sari, Agung Prabowo","doi":"10.29303/emj.v5i2.141","DOIUrl":"https://doi.org/10.29303/emj.v5i2.141","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000COVID 19 merupakan penyakit yang disebabkan oleh SARS-CoV-2. Virus COVID 19 tidak hanya berdampak pada aspek kesehatan, melainkan aspek kehidupan lainnya Pariwisata Bali telah tumbuh berkembang sedemikan rupa dan memberikan sumbangan yang besar terhadap pembangunan daerah langsung maupun tidak langsung. Produk Domestik Regional Bruto atau disingkat dengan PDRB memiliki peran penting dalam meningkatkan pertumbuhan ekonomi suatu daerah, dimana semakin tinggi PDRB maka dapat dikatakan bahwa pertumbuhan ekonominya juga tinggi. Berdasarkan hal tersebut, maka perlu dilakukannya suatu peramalan untuk mengetahui dampak COVID 19 terhadap PDRB Bali. Penelitian ini bertujuan untuk menganalisis dampak COVID 19 terhadap PDRB Provinsi Bali menggunakan model intervensi. Data yang digunakan dalam penelitian ini merupakan data sekunder dari PDRB triwulanan atas dasar harga berlaku sektor penyediaan akomodasi, makanan dan minum. Data dihimpun dari kuartal I 2010 sampai dengan kuartal IV 2021. Berdasarkan pemodelan yang telah dilakukan dengan model intervensi, model terbaik untuk meramalkan dampak COVID 19 terhadap PDRB di Provinsi Bali adalah ARIMA(0,1,0)(1,0,0)4 r=1 dengan nilai SMAPE 8,327 dan MdAPE sebesar 0,067. \u0000 \u0000 \u0000 \u0000 \u0000","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124310209","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}
Penelitian ini membahas metode numerik untuk menentukan gelombang stasioner sebagai solusi dalam persamaan Nonlinier Schrödinger (NLS). Secara umum, solusi untuk persamaan diferensial parsial dapat diselesaikan secara analitik. Namun, sebagian besar solusi persamaan gelombang nonlinier sulit ditentukan secara analitik. Oleh karena itu, diperlukan sebuah pendekatan numerik untuk menentukan solusi persamaan bertipe NLS. Salah satu metode numerik yang dapat digunakan untuk menentukan solusi persamaan bertipe NLS adalah metode iterasi Petviashvili. Pada studi kasus, persamaan NLS telah dibangkitkan oleh teori kondensasi Bose-Einstein yang mengandung fungsi potensial . Untuk mengatasi masalah ini, kami melakukan pengembangan metode iterasi Petviashvili agar dapat dimanfaatkan untuk menentukan solusi stasioner yang diharapkan. Hasil yang paling menarik dari penelitian ini adalah dengan modifikasi metode iterasi Petviashvili, kita dapat dengan mudah menentukan solusi gelombang stasioner untuk persamaan Schrodinger nonlinier yang memuat fungsi potensial pada teori kondensasi Bose-Einstein .
{"title":"Pengembangan Metode Iterasi Petviashvili dalam Penentuan Solusi Gelombang Stasioner pada Persamaan Bertipe Schrödinger Nonlinear dengan Fungsi Potensial V(x)","authors":"Nuzla Af’idatur Robbaniyyah, I. -","doi":"10.29303/emj.v5i2.146","DOIUrl":"https://doi.org/10.29303/emj.v5i2.146","url":null,"abstract":"Penelitian ini membahas metode numerik untuk menentukan gelombang stasioner sebagai solusi dalam persamaan Nonlinier Schrödinger (NLS). Secara umum, solusi untuk persamaan diferensial parsial dapat diselesaikan secara analitik. Namun, sebagian besar solusi persamaan gelombang nonlinier sulit ditentukan secara analitik. Oleh karena itu, diperlukan sebuah pendekatan numerik untuk menentukan solusi persamaan bertipe NLS. Salah satu metode numerik yang dapat digunakan untuk menentukan solusi persamaan bertipe NLS adalah metode iterasi Petviashvili. Pada studi kasus, persamaan NLS telah dibangkitkan oleh teori kondensasi Bose-Einstein yang mengandung fungsi potensial . Untuk mengatasi masalah ini, kami melakukan pengembangan metode iterasi Petviashvili agar dapat dimanfaatkan untuk menentukan solusi stasioner yang diharapkan. Hasil yang paling menarik dari penelitian ini adalah dengan modifikasi metode iterasi Petviashvili, kita dapat dengan mudah menentukan solusi gelombang stasioner untuk persamaan Schrodinger nonlinier yang memuat fungsi potensial pada teori kondensasi Bose-Einstein .","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130094219","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}
Fermat's Last Theorem is a well-known classical theorem in mathematics. Andrew Willes has proven this theorem using the modular elliptic curve. However, the proposed proof is difficult for mathematicians and researchers to understand. For this reason, in this study, we provide evidence of several properties of Fermat's Last Theorem with a simple concept. We use Newton's Binomial Theorem, well-known in Fermat's time. In this study, we prove Fermat's Last Theorem for case . We also use the Newton’s Binomial theorem to verify several cases .
{"title":"Proving The Fermat Last Theorem for Case q≤n","authors":"B. D. A. Prayanti, Maxrizal Maxrizal","doi":"10.29303/emj.v5i2.137","DOIUrl":"https://doi.org/10.29303/emj.v5i2.137","url":null,"abstract":"\u0000\u0000\u0000\u0000Fermat's Last Theorem is a well-known classical theorem in mathematics. Andrew Willes has proven this theorem using the modular elliptic curve. However, the proposed proof is difficult for mathematicians and researchers to understand. For this reason, in this study, we provide evidence of several properties of Fermat's Last Theorem with a simple concept. We use Newton's Binomial Theorem, well-known in Fermat's time. In this study, we prove Fermat's Last Theorem for case . We also use the Newton’s Binomial theorem to verify several cases .\u0000 \u0000\u0000\u0000\u0000 \u0000\u0000\u0000\u0000 ","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133316759","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}
Z. W. Baskara, Lisa Harsyiah, D. N. A. Paramartha, Qabul Dinanta Utama
In recent years, there has been a positive trend in coffee consumption in Indonesia. Dringking coffee which were originally identical as oldmans drinks, is starting to be liked by teenagers to children. This is because coffee contains caffeine which can have an addictive effect. Drinking coffee in the right dose can have positive effects for the drinker, such as stimulating the ability of brain function and also as an antioxidant. However, if you drink more coffee than your body can tolerate, it will cause symptoms of insomnia, excessive anxiety and increased blood pressure. Various attempts have been made to reduce the caffeine content in coffee (decaffeination), one of which is by mixing coffee with chayote juice (Sechium edule) as has been done by Paramartha (2021). Furthermore, this article classifies the characteristics of decaffeinated products, caffeine content, moisture content, total acid content of titration, ash content, hue color, and L value. By using factor analysis, it is known that the characteristics can be mapped into three main factors, where the first main factor consists of variables of caffeine content, water content, and hue color value. The second main factor consists of variables of ash content, and total acid content titration, and the third major factor, this factor consists only of the characteristic L. It is also known that 74.2% of the diversity of origin can be explained by the three main factors.
{"title":"Analisis Faktor Untuk Pemetaan Karakteristik pada Percobaan Dekafeinasi Kopi Robusta","authors":"Z. W. Baskara, Lisa Harsyiah, D. N. A. Paramartha, Qabul Dinanta Utama","doi":"10.29303/emj.v5i1.139","DOIUrl":"https://doi.org/10.29303/emj.v5i1.139","url":null,"abstract":"In recent years, there has been a positive trend in coffee consumption in Indonesia. Dringking coffee which were originally identical as oldmans drinks, is starting to be liked by teenagers to children. This is because coffee contains caffeine which can have an addictive effect. Drinking coffee in the right dose can have positive effects for the drinker, such as stimulating the ability of brain function and also as an antioxidant. However, if you drink more coffee than your body can tolerate, it will cause symptoms of insomnia, excessive anxiety and increased blood pressure. Various attempts have been made to reduce the caffeine content in coffee (decaffeination), one of which is by mixing coffee with chayote juice (Sechium edule) as has been done by Paramartha (2021). Furthermore, this article classifies the characteristics of decaffeinated products, caffeine content, moisture content, total acid content of titration, ash content, hue color, and L value. By using factor analysis, it is known that the characteristics can be mapped into three main factors, where the first main factor consists of variables of caffeine content, water content, and hue color value. The second main factor consists of variables of ash content, and total acid content titration, and the third major factor, this factor consists only of the characteristic L. It is also known that 74.2% of the diversity of origin can be explained by the three main factors.","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134062585","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}
Gross Regional Domestic Product (GRDP) expenditure describes the final result of the production process within a region's territorial boundaries. Knowing GRDP expenses can describe the level of welfare economics, develop policy formulation, taxation, and export-import study. In estimating the GRDP of expenses in the following year, it is necessary to have a method of calculating systematically, one of which is forecasting. Some research showed that trend moment method and naive method produce higher accuracy than other methods. This method can be used in long-term forecasting and does not require the amount of data to be odd or even. The method is compared to get one of the best methods and has the highest accuracy value using MAPE calculation. The smaller MAPE, the better the forecasting accuracy. Comparing the two methods shows that the Naive method is the best method based on the MAPE criteria with an accuracy of 0.976 %. The result of data forecasting shows a decrease in GRDP Blitar Regency year 2021 and 2022.
{"title":"Comparison of the Trend Moment and Naive Methods in Forecasting Gross Regional Domestic Product in Blitar Regency","authors":"Umi Habibah, Rizka Rizqi Robby, M. N. Qomaruddin","doi":"10.29303/emj.v5i1.121","DOIUrl":"https://doi.org/10.29303/emj.v5i1.121","url":null,"abstract":"Gross Regional Domestic Product (GRDP) expenditure describes the final result of the production process within a region's territorial boundaries. Knowing GRDP expenses can describe the level of welfare economics, develop policy formulation, taxation, and export-import study. In estimating the GRDP of expenses in the following year, it is necessary to have a method of calculating systematically, one of which is forecasting. Some research showed that trend moment method and naive method produce higher accuracy than other methods. This method can be used in long-term forecasting and does not require the amount of data to be odd or even. The method is compared to get one of the best methods and has the highest accuracy value using MAPE calculation. The smaller MAPE, the better the forecasting accuracy. Comparing the two methods shows that the Naive method is the best method based on the MAPE criteria with an accuracy of 0.976 %. The result of data forecasting shows a decrease in GRDP Blitar Regency year 2021 and 2022.","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116788427","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}
I. G. A. W. Wardhana, Muhammad Rijal Alfian, Fariz Maulana, N. W. Switrayni, Q. Aini, Dwi Noorma Putri
One of the sciences used in digital security systems is cryptography. Cryptography is closely related to the integer system, especially prime numbers. Prime numbers themselves have been abstracted a lot. One form of abstraction of prime numbers is the prime ideal. Previous studies have proven that an Ideal is said to be a prime ideal on if and only if I is constructed by a prime element. Other studies have also shown how the prime ideal develops. One of them is the research result of Dauns, where the prime ideal form is developed in the form of a prime submodule. A prime submodule is one of the objects in the module, which is an abstraction of prime numbers. Based on these things, it is exciting if the properties of the prime submodule are applied to other module forms, one of which is the integer module.
{"title":"Prime submodul of an integer over itself","authors":"I. G. A. W. Wardhana, Muhammad Rijal Alfian, Fariz Maulana, N. W. Switrayni, Q. Aini, Dwi Noorma Putri","doi":"10.29303/emj.v5i1.132","DOIUrl":"https://doi.org/10.29303/emj.v5i1.132","url":null,"abstract":"One of the sciences used in digital security systems is cryptography. Cryptography is closely related to the integer system, especially prime numbers. Prime numbers themselves have been abstracted a lot. One form of abstraction of prime numbers is the prime ideal. Previous studies have proven that an Ideal is said to be a prime ideal on if and only if I is constructed by a prime element. Other studies have also shown how the prime ideal develops. One of them is the research result of Dauns, where the prime ideal form is developed in the form of a prime submodule. A prime submodule is one of the objects in the module, which is an abstraction of prime numbers. Based on these things, it is exciting if the properties of the prime submodule are applied to other module forms, one of which is the integer module.","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116063262","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}
This study discusses the agricultural production of rice plans. The variabel used to predict the level of rice production in Tanete Riaja District, Barru Regency, are as the total production of rice plants. This research is an applied research with double exponential smoothing method. The purpose of this study was to determine the amount of rice production in Tanete Riaja District, Barru Regency in 2021 to 2025. The results of forecasting the amount of rice production ini 2021 were 24134.18 tons, in 2022 25235.25 tons, in 2023 26336.32 tons, in 2024 it will be 27437.38 ton, and in 2025 it will be 28538.45 ton. Based on the results of forecasting the amount of rice production, it can be concluded that from year to year increased.
{"title":"APPLICATION OF EXPONENTIAL SMOOTHING METHOD TO FORECASE THE AMOUNT OF RICE PRODUCTION IN TANATE RIAJA DISTRICT, BARRU REGENCY","authors":"Khalilah Nurfadilah, W. Saputri, Adnan Sauddin","doi":"10.29303/emj.v5i1.127","DOIUrl":"https://doi.org/10.29303/emj.v5i1.127","url":null,"abstract":"This study discusses the agricultural production of rice plans. The variabel used to predict the level of rice production in Tanete Riaja District, Barru Regency, are as the total production of rice plants. This research is an applied research with double exponential smoothing method. The purpose of this study was to determine the amount of rice production in Tanete Riaja District, Barru Regency in 2021 to 2025. The results of forecasting the amount of rice production ini 2021 were 24134.18 tons, in 2022 25235.25 tons, in 2023 26336.32 tons, in 2024 it will be 27437.38 ton, and in 2025 it will be 28538.45 ton. Based on the results of forecasting the amount of rice production, it can be concluded that from year to year increased.","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124926870","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}
Kemiskinan merupakan persoalan mendasar karena menyangkut pemenuhan kebutuhan dasar masyarakat. Di Provinsi NTB, tidak sedikit rumah tangga yang hidup di bawah garis kemiskinan. Salah satu penyebabnya adalah belum optimalnya upaya pemerintah dalam menurunkan tingkat kemiskinan. Oleh karena itu, perlu diklasifikasi faktor-faktor yang mempengaruhi tingkat kemiskinan sehingga dapat digunakan sebagai acuan dalam mengambil kebijakan untuk mengurangi tingkat kemiskinan. Salah satu metode untuk klasifikasi adalah metode Random Forest. Metode Random Forest dengan nilai mtry dan ntree optimal masing-masing yaitu dan menghasilkan tingkat akurasi sebesar 81,3%. Hal ini berarti ketepatan metode klasifikasi Random Forest untuk data ini sudah sangat baik. Adapun faktor yang paling berpengaruh dalam menentukan status kemiskinan berdasarkan analisis Random Forest adalah variabel penghasilan dengan dengan nilai Mean Decrease Accuracy sebesar 23,92%. Variabel ini yang memiliki paling nilai Mean Decrease Accuracy tinggi diantara variabel atribut yang lainnya. Keywords: Kemiskinan, Random Forest, Mean Decrease Accuracy
{"title":"Klasifikasi Status Kemiskinan Menggunakan Algoritma Random Forest","authors":"Syaidatussalihah -, Abdurahim -","doi":"10.29303/emj.v5i1.133","DOIUrl":"https://doi.org/10.29303/emj.v5i1.133","url":null,"abstract":"Kemiskinan merupakan persoalan mendasar karena menyangkut pemenuhan kebutuhan dasar masyarakat. Di Provinsi NTB, tidak sedikit rumah tangga yang hidup di bawah garis kemiskinan. Salah satu penyebabnya adalah belum optimalnya upaya pemerintah dalam menurunkan tingkat kemiskinan. Oleh karena itu, perlu diklasifikasi faktor-faktor yang mempengaruhi tingkat kemiskinan sehingga dapat digunakan sebagai acuan dalam mengambil kebijakan untuk mengurangi tingkat kemiskinan. Salah satu metode untuk klasifikasi adalah metode Random Forest. Metode Random Forest dengan nilai mtry dan ntree optimal masing-masing yaitu dan menghasilkan tingkat akurasi sebesar 81,3%. Hal ini berarti ketepatan metode klasifikasi Random Forest untuk data ini sudah sangat baik. Adapun faktor yang paling berpengaruh dalam menentukan status kemiskinan berdasarkan analisis Random Forest adalah variabel penghasilan dengan dengan nilai Mean Decrease Accuracy sebesar 23,92%. Variabel ini yang memiliki paling nilai Mean Decrease Accuracy tinggi diantara variabel atribut yang lainnya. Keywords: Kemiskinan, Random Forest, Mean Decrease Accuracy","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128262313","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}
The clean water is one of the basic needs with unlimited use even in the economic field. The opportunities provided can be utilized by companies that produce bottled drinking water (AMDK). The existence of defective products is obtained in production so that the need for quality analysis of the product is still within the control limits on the P chart. This is done by knowing the highest value in the influential failure mode. So that suggestions for improvement with kaizen can be given. Based on the control P chart obtained, all points of defective products in the production process are within control limits with a UCL limit of 0.00804 and an LCL limit of 0.00602. This indicates that the defective product is statistically controlled. The FMEA method assigns a priority value to each failure mode. The value is the Risk Priority Number (RPN). The biggest RPN is that the cover does not stick to the surface of the cup, with an RPN value of 240. The proposed improvement using the Kaizen method is to increase inspections and routine repairs on the machine. Keywords: Failure Mode and Effect Analysis (FMEA), Kaizen, Quality Control, Statistical Process Control (SPC)
{"title":"Analisis Pengendalian Kualitas Air Minum dalam Kemasan Menggunakan Metode FMEA dan Penerapan Kaizen (Study Kasus di PT.Lombok Pusaka Adam, Jelantik Lombok Tengah)","authors":"Lailatul Pahmi, E. Sulistiowati, Lisa Harsyiah","doi":"10.29303/emj.v5i1.126","DOIUrl":"https://doi.org/10.29303/emj.v5i1.126","url":null,"abstract":"The clean water is one of the basic needs with unlimited use even in the economic field. The opportunities provided can be utilized by companies that produce bottled drinking water (AMDK). The existence of defective products is obtained in production so that the need for quality analysis of the product is still within the control limits on the P chart. This is done by knowing the highest value in the influential failure mode. So that suggestions for improvement with kaizen can be given. Based on the control P chart obtained, all points of defective products in the production process are within control limits with a UCL limit of 0.00804 and an LCL limit of 0.00602. This indicates that the defective product is statistically controlled. The FMEA method assigns a priority value to each failure mode. The value is the Risk Priority Number (RPN). The biggest RPN is that the cover does not stick to the surface of the cup, with an RPN value of 240. The proposed improvement using the Kaizen method is to increase inspections and routine repairs on the machine. Keywords: Failure Mode and Effect Analysis (FMEA), Kaizen, Quality Control, Statistical Process Control (SPC)","PeriodicalId":281429,"journal":{"name":"EIGEN MATHEMATICS JOURNAL","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128441862","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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