{"title":"Kajian Matematis Mengenai Strategi Pengembangbiakan Sapi Potong Lokal Guna Meningkatkan Kualitas Daging Sapi","authors":"Zaki Maulana Hidayat, Andzar Tsaqif Laksana, Anita Triska","doi":"10.33369/diophantine.v2i01.27771","DOIUrl":"https://doi.org/10.33369/diophantine.v2i01.27771","url":null,"abstract":"Beef is one of the most demanded food products in Indonesia compared to other meat commodities. The high demand of this commodity causes a shortage of its supply so that the government has to import it from other countries. As the consequences, people in Indonesia prefer to consume imported beef rather than local beef, especially those in the food industry, since imported beef has better quality. Thus, government may establish a program that is able to increase the local beef quality thereby increasing its competitiveness, for instant by cross-breeding of local and imported cattle. This paper is to discuss a cross-breeding simulation to predict the distribution of offspring produced in the next few generations through mathematical approach. Simulations are conducted by following the X-Linked Inheritance concept and algebra. Simulations are carried out by three different scenarios which consider gender of the imported cattle. The simulations show that all scenarios are able to produce local cattle offspring with imported quality. However, the offspring from the cross-breeding still preserve cattle with original local genetic with a different ratio between the first and the second scenario.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"104 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120872461","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}
Siti Aizal Yasni Ellena, Lidia H. Y. A. Rudamaga, Anita Triska
{"title":"Analisis Manajemen Pengelolaan Pohon Gmelina arborea Roxb. pada Hutan Rakyat di Tasikmalaya dan Banjar, Jawa Barat","authors":"Siti Aizal Yasni Ellena, Lidia H. Y. A. Rudamaga, Anita Triska","doi":"10.33369/diophantine.v2i01.27772","DOIUrl":"https://doi.org/10.33369/diophantine.v2i01.27772","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000Forests are natural resources which if it is managed properly can provide the economic benefits to the surrounding. Woods from trees in the forest are one of the economic benefits that can be gained from the forest. However, trees must be logged under a precise calculation and controlled continuously so that they are not extinct. Logging time in a forest is generally determined by the needs of farmers, which may not necessarily provide maximum benefits. Therefore, harvesting management is needed to obtain optimal benefits while still maintaining the forest sustainability. This paper discusses a basic model of the tree harvesting using Linear Algebra which is applied to one of economically valuable trees, i.e., Gmelina arborea Roxb. on the community forest in Tasikmalaya and Banjar, West Java. Initially, the tree population is divided into 16 class intervals based on their diameter. Analysis of the harvesting model implies that the optimal results will be obtained by logging all of trees in one class with the highest selling value. By applying this scenario, all of the Gmelina arborea Roxb. on the community forest in Tasikmalaya and Banjar, West Java must be logged at the 9th class which will provide a maximum profit of IDR 12,491,843.889 for every 1,000 trees harvested. \u0000 \u0000 \u0000 \u0000","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125356119","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}
{"title":"IMPLEMENTASI ALGORITMA GREEDY PADA PEWARNAAN WILAYAH PETA KECAMATAN GELUMBANG MUARA ENIM","authors":"Khuzaimah Al Jufri, Riza Agustiani","doi":"10.33369/diophantine.v2i01.28347","DOIUrl":"https://doi.org/10.33369/diophantine.v2i01.28347","url":null,"abstract":"Sebuah peta akan lebih menarik dan mudah dibaca ketika diberi pewarnaan. Namun pemberian warna yang berlebihan akan membuat peta tersebut tidak efektif. Kecamatan Gelumbang dipilih karena belum ada pewarnaan pada peta Kecamatan Gelumbang tersebut. Teori graf dapat dimanfaatkan dalam persoalan pewarnaan wilayah peta. Kecamatan Gelumbang direpresentasikan ke dalam graf dual yang terdiri dari 23 simpul dan 53 sisi. Algoritma Greedy dipilih sebagai pemecahan masalah optimasi pewarnaan pada wilayah Kecamatan Gelumbang sehingga diperoleh hasil pewarnaan minimum yang menghasilkan empat warna untuk mewarnai seluruh wilayah di Kecamatan Gelumbang yang terdiri dari 23 desa.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816812","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}
{"title":"Klasifikasi Kualitas Air Minum menggunakan Penerapan Algoritma Machine Learning dengan Pendekatan Supervised Learning","authors":"Lidya Savitri, Rahmat Nursalim","doi":"10.33369/diophantine.v2i01.28260","DOIUrl":"https://doi.org/10.33369/diophantine.v2i01.28260","url":null,"abstract":"The need for the provision and service of clean water from time to time is increasing which is sometimes not matched by the ability and knowledge of clean water. The majority of people still do not know whether water is suitable for consumption or not. The quality of drinking water can be distinguished based on the mineral parameters contained in the water. This article will explain the classification of water sample data by applying a Machine Learning Algorithm, which includes modeling with Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier, K- Nearest Neighbor(KNN), XGBoost Classifier. Classification models produce varying degrees of accuracy. The highest accuracy is obtained in the Random Forest Classifier model with an accuracy rate of 78%. Analysis of drinking water quality with machine learning algorithms is very easy to understand, because the results of this study produce very simple results so that they are easy to understand","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128207892","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}
{"title":"PERFORMA TEKNIK REGULARISASI DALAM PENANGANAN MASALAH MULTIKOLINIERITAS","authors":"Alin Febianti Fikri, Winalia Agwil, Dian Agustina","doi":"10.33369/diophantine.v2i01.28480","DOIUrl":"https://doi.org/10.33369/diophantine.v2i01.28480","url":null,"abstract":"Multikolinieritas adalah kondisi terdapat hubungan linier antar variabel independen, dimana diantara variabel independen tersebut saling berkorelasi. Akibatnya akan sulit untuk melihat pengaruh variabel independen terhadap variabel dependen. Penanganan multikolinieritas salah satunya dapat dilakukan menggunakan teknik regularisasi yaitu bentuk regresi yang mengatur atau menyusutkan perkiraan koefisien menuju nol. Teknik regularisasi yang akan dibahas pada penelitian adalah regresi ridge, LASSO dan elastic net. Regresi ridge hanya dapat menyusutkan koefisien regresi menuju angka 0, tetapi tidak pernah tepat ke angka 0. Regresi elastic net dapat menyusutkan koefisien regresi tepat nol, melakukan seleksi variabel secara simultan dan dapat memilih kelompok peubah yang berkorelasi. Sedangkan, regresi LASSO hanya dapat menyusutkan koefisien dan menetapkan koefisien ke angka 0. Oleh karena itu, LASSO dapat menghasilkan model dengan variabel terbaik. Namun, LASSO memiliki beberapa kelemahan. Ketika jumlah variabel independent lebih kecil dibanding jumlah amatan, kinerja LASSO lebih didominasi oleh ridge. Ketika jumlah variabel independent lebih besar dibanding jumlah amatan, maka LASSO hanya memilih n variabel yang diikutkan dalam model. Sehingga, untuk mengatasi high dimensional data yang mengandung multikolinieritas dilakukan penelitian menggunakan teknik regularisasi regresi ridge, LASSO dan elastic net untuk dibandingkan kebaikan modelnya berdasarkan nilai MSE terkecil. Data yang digunakan merupakan data simulasi dan studi kasus dari website resmi BPS serta UCI machine learning repository. Disimpulkan bahwa dari 30 pengacakan, model ridge baik memodelkan dataset dengan p = 20, 40, dan 80 atau kondisi dataset dimana jumlah variabel independent lebih kecil dibanding jumlah amatan dan elastic net baik memodelkan dataset dengan p = 100, 160, dan320.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126992858","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}
{"title":"Analisis Persediaan Bahan Baku Multi Item Usaha Kerupuk Kulit Alhamdulillah Menggunakan Metode Economic Order Quantity","authors":"Vebby Afifah Cahyani Cahyani, Yusmet Rizal","doi":"10.33369/diophantine.v2i01.28208","DOIUrl":"https://doi.org/10.33369/diophantine.v2i01.28208","url":null,"abstract":"Controlling raw material stocks is a crucial aspect of effective inventory management for businesses. The company's objective is to maximise profits. To maximise earnings, the corporation must prudently maintain appropriate inventory levels in order to limit existing inventory expenses. Kerupuk Kulit Alhamdulillah is a small to medium-sized business in the food industry that manufactures skin crackers in various packaging sizes. This is an example of applied research. This study employs the Lilliefors normality test to assess if the data are normally distributed. According to the findings of calculations using the method Economic Order Quantity (EOQ), the entire cost of multi-item raw material inventory according to the Kerupuk Kulit Alhamdulillah is Rp 4,703,520.00, however according to the EOQ method, the total cost is Rp 2,093.00. Kerupuk Kulit Alhamdulillah Business can save Rp 2,694,427.00 by utilising the EOQ approach.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116608078","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}
{"title":"Peramalan Jumlah Penumpang LRT Sumsel dengan Metode Exponential Smoothing","authors":"Riski Rahmatul Lailiyah, Riza Agustiani","doi":"10.33369/diophantine.v1i1.23994","DOIUrl":"https://doi.org/10.33369/diophantine.v1i1.23994","url":null,"abstract":"Forecasting is critical in the pandemic sector as part of the effort to adapt the post-pandemic system, particularly in transportation. This research carried out on the South Sumatra Integrated Railroad (LRT) in the post-pandemic Covid-19 period. Forecasting is done in this study using the exponential smoothing method using alpha that is α=0.1, α=0.5, and α= 0.9. Comparison with the smallest error using the exponential smoothing method dropped the choice at alpha 0.1 with the smallest error calculation value. Forecasting using the exponential smoothing method with 0.1 alpha sample data on the number of LRT Sumsel passengers during the Covid-19 period in 2020 produces a forecast of 66,538 passengers with an error rate of Mean Absolute Deviation (MAD)=9,486, Mean Square Error (MSE)=1,150, and Mean Absolute Percentage Error (MAPE)=24.58%. Meanwhile, from the sampel data on the number of South Sumatra LRT passengers on post-pandemic Covid-19 period in 2022, it produced a forecast of 187,566 passengers with Mean Absolute Deviation (MAD) = 25,816, Mean Square Error (MSE) = 9,477, and Mean Absolute Percentage Error (MAPE) = 16.60%.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128016650","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}
{"title":"Komparasi Penggunaan Matriks Kebalikan Leontief & Ghosian Untuk Peramalan Dalam Model Input Output","authors":"Budi Kurniawan, Susiawati Kristiarini","doi":"10.33369/diophantine.v1i1.25703","DOIUrl":"https://doi.org/10.33369/diophantine.v1i1.25703","url":null,"abstract":"This study is an empirical study that compares the use of two types of inverse matrices in the input output model. The Input Output (IO) model is based on a system of mathematical equations that applies general equilibrium phenomena. The matrix operating system in the equation derived from the IO model allows the Output value (X) to be calculated as an effect of the final demand induction (F) with the formulation X=(I-A)-1F where A is the technical coefficient matrix. This equation model uses the Leontief Inverse Matrix to calculate the impact of output with final demand (F) as a stimulant. Calculation of the impact of the stimulus from the supply side such as added value and the value of intermediate inputs originating from imports (V) uses the Ghosian Inverse Matrix in the equation X=(I-AT)-1V where AT is the usage coefficient matrix. The data used in this study comes from the Bengkulu Province Input Output Tables in 2000 and 2016, each of which has been collected in a common set to see comparability between years of observation. Forecasting results with both types of approaches produce different levels of accuracy for each observation period.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124085364","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}
{"title":"Penerapan Support Vector Machine Dalam Peramalan Nilai Tukar Petani Provinsi Bengkulu","authors":"Yosep Oktavianus Sitohang","doi":"10.33369/diophantine.v1i1.25793","DOIUrl":"https://doi.org/10.33369/diophantine.v1i1.25793","url":null,"abstract":"Pertanian masih menjadi sektor dominan di Provinsi Bengkulu, oleh sebab itu sasaran pembangunan disektor ini, khususnya kesejahteraan petani perlu mendapat perhatian lebih. Salah satu indikator kesejahteraan petani adalah Nilai Tukar Petani (NTP). Agar pembangunan tepat sasaran diperlukan perencanaan menggunakan kajian nilai NTP dimasa depan, sehingga metode peramalan dibutuhkan. Metode konvensional yang sering digunakan adalah ARIMA, namun metode ini memiliki banyak keterbatasan. Teknik Machine Learning yang berkembang di era Soceity 5.0 menjadi alternatif solusi dari permasalahan tersebut. Salah satu metode terbarunya untuk peramalan data deret waktu adalah Support Vector Machine (SVM). Dari hasil penghitungan didapat MAPE sebesar 8,55, termasuk dalam highly accurate forecasting sehingga SVM layak digunakan dalam peramalan.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128677724","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}
{"title":"Kondisi Tenaga Kerja Di Provinsi Bengkulu Di Tengah Pandemi Covid-19","authors":"Nani Sumarni","doi":"10.33369/diophantine.v1i1.25854","DOIUrl":"https://doi.org/10.33369/diophantine.v1i1.25854","url":null,"abstract":"One of which its impact the Covid-19 is job availability. This study aims to analyze the unemployment rate in Bengkulu Province, especially during the Covid-19 pandemic. This research is quantitative with a descriptive statistical approach. The data source used is secondary data from the Central Bureau Statistics for 2017-2020. The results of this study state that the Covid-19 pandemic has an influence in a decrease in the number of businesses by 0,86 percent. Two sectors, which are trade and agriculture sectors, became the people's main choice to survive during the pandemic, compared to the industrial sector which has been most significantly affected by the pandemic. In line with that, the population working as family workers/unpaid workers increased to 3,71 percent. The results of the analysis of the data for the last four years show that in terms of education completed, the employment is still dominated by people with low education, that reached 78,27 percent. However, among the three graduates, high school graduates are the most not being absorbed in the world of work. Meanwhile, the number of unemployed people with diploma education has actually decreased.","PeriodicalId":330009,"journal":{"name":"Diophantine Journal of Mathematics and Its Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123626835","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}