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Ruliana
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Ordinary Least Square Method in Multiple Regression Analysis to Estimating Coefficients of Factors Affecting Human Development Index Suhendra, Ogi; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 1 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience632

Abstract

Analisis Regresi merupakan suatu analisis data yang memperhatikan hubungan antara suatu peubah respon (response variable) dengan satu atau lebih peubah penjelas (explanatory variables). Penelitian ini menggunakan metode Ordinari Least Square (OLS). Metode OLS merupkan metode dasar yang digunakan untuk menyelesaikan suatu masalah data dengan penyelesaian berbentuk model regresi linier. Hasil pemodelan menunjukkan pengaruh variabel Angka Harapan Hidup, Harapan Lama Sekolah, Rata-Rata Lama Sekolah, dan Pengeluaran Perkapita terhadap Indeks Pembangunan Manusia Provinsi Sulawesi Selatan dilihat dari nilai R-Square sebesar 99.63%. menunjukkan bahwa besar persentase variasi Indeks Pembangunan Manusia yang bisa dijelaskan oleh keempat variabel bebas yaitu Angka Harapan Hidup, Harapan Lama Sekolah, Rata-rata Lama Sekolah, dan Pengeluaran Perkapita, sebesar 99.63% sedangkan sisanya sebesar 0.37 dijelaskan oleh variabel-variabel lain diluar penelitian. Artinya semua variabel bebas berpengaruh signifikan terhadap variabel terikat dengan taraf signifikan 5%.
Development of R Package for Regression Analysis with User Friendly Interface Amir, Arfan Shalihin; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 1 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience728

Abstract

The use of pirated software in Indonesia is quite high compared to other countries in the world. One of the efforts made to reduce the level of software piracy is to develop publicly licensed software such as R software which is open source software. The preparation of this package uses the R software and other additional packages, especially packages for regression analysis. Making this package can make it easier for users to perform regression analysis easily and legally. This package is named SLR App (Simple Linear Regression App) and MLR App (Multiple Linear Regression) which are regression analysis packages that have a user friendly interface. From the tests carried out that this package has similarities from the results of the analysis between the SLR App and MLR App.
Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province Safitri, Irma Yani; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience740

Abstract

Spatial regression is a development of classical linear regression which is based on the influence of place or location. To determine the location/spatial effect, a spatial dependency test was performed using the Moran Index, and the Lagrange Multiplier (LM) test was used to determine a significant spatial regression model. In this study, spatial regression was applied to the case of food security in each district in South Sulawesi Province. The results of the analysis show that there is a negative spatial autocorrelation, meaning that the spatial effect does not affect the level of food security. The significant spatial regression model is the SEM (Spatial Error Model) model. The equation of the SEM model produces variables that have a significant effect, namely the ratio of normative consumption per capita to net availability, percentage of population living below the poverty line, percentage of households with a proportion of expenditure on food more than 65 percent of total expenditure, percentage of households without access to electricity, percentage of households without access to clean water, life expectancy at birth, ratio of population per health worker to the level of population density, the average length of schooling for women above 15 years, and the percentage of children under five with height below standard (stunting). Thus, the resulting distribution pattern is a uniform data pattern. This means that each adjacent district tends to have different characteristics.
Comparison of k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) Methods for Classification of Poverty Data in Papua Fauziah; Tiro, Muhammad Arif; Ruliana
ARRUS Journal of Mathematics and Applied Science Vol. 2 No. 2 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience741

Abstract

Classification is a job of assessing data objects to include them in a particular class from a number of available classes. The classification method used is the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) methods. The data used in this study is data on poverty in Papua with the category of the number of low/high level poor people. Of the 29 regencies/cities that were sampled, 15 regencies/cities represent the number of low-level poor people and 14 districts/cities are the number of high-level poor people. The results of the analysis obtained are the k-Nearest Neighbor (k-NN) method with a value of k=15 producing an accuracy of 58.62%, while the Support Vector Machine (SVM) method with Parameter cost = 1 using the RBF kernel produces an accuracy value. by 93.1%. The classification criteria to find the best method is to look at the Root Mean Square Error (RMSE) which states that the Support Vector Machine (SVM) method is better than the k-Nearest Neighbor (k-NN) method.
The Impact of Covid-19 on Stunting Cases in Indonesia: A Bayesian Spatial Modeling Approach Ankaz As Sikib; Aswi, Aswi; Ruliana
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.3065

Abstract

The high number of COVID-19 cases has impacted various sectors. One of the notable consequences of the COVID-19 pandemic is its effect on food security and nutrition. Social restrictions implemented to curb the spread of the virus have resulted in worsening economic conditions, limited access to healthcare facilities, difficulties in obtaining nutritious food, and school closures. Changes in the routines and activities of COVID-19 patients may contribute to an increase in the prevalence of stunting in Indonesia. While research has been conducted on the impact of COVID-19 on the rise of stunting cases in Indonesia, previous studies have typically focused on individual provinces and have not utilized the Bayesian Conditional Autoregressive (CAR) model. This study aims to investigate the relationship between COVID-19 and the increase in stunting cases across Indonesia. We analyze data on stunting cases in each Indonesian province and the number of COVID-19 patients between March 23, 2020, and December 31, 2021. To assess the relationship, we employ the Bayesian spatial CAR Leroux model with several Inverse-Gamma hyperpriors. We compare these models using various fit criteria. The results indicate that the Bayesian spatial CAR Leroux model with Inverse-Gamma hyperpriors (0.1;0.1) performs best, as it yields the smallest Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) values. In conclusion, our analysis reveals a positive correlation between the number of COVID-19 cases and the increase in stunting cases in Indonesia. Approximately 50% of the regions in Indonesia face a high relative risk of stunting, with Nusa Tenggara Timur having the highest relative risk, followed by Kalimantan Barat and Sulawesi Barat
The Support Vector Machine (SVM) And Random Forest Methods For Classification Graduation Rate Ruliana; Rais, Zulkifli; Lili Maghfirah Rahma Sudirman
ARRUS Journal of Engineering and Technology Vol. 4 No. 2 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech3436

Abstract

Efforts towards an independent nation with high competitiveness can’t be separated from educational programs. Therefore, education must be able to produce quality graduates who have knowledge, master technology, and have technical skills, and adequate life skills. The timeliness of students in completing their studies is one of the supports for assessing the quality of higher education. Classification analysis can be used to predict whether a student is said to pass on time or not. Support Vector Machine (SVM) and Random Forest methods are part of the classification method. SVM and Random Forest classification analysis is done by using historical data alumni from FMIPA UNM of the graduation year 2019-2020 which come from the Administration, Academic and Student Affair Bureau of UNM. SVM accuracy level of RBF kernel with optimum value C = 1 and gamma = 1 is 68% and Random Forest accuracy with optimum value m = 2 and k = 500 is 72%. Therefore, the best method for determining the accuracy of the study duration of FMIPA UNM students is Random Forest..
Perbandingan Metode ARIMA dan Single Exponential Smoothing dalam Peramalan Nilai Ekspor Kakao Indonesia Fahmuddin S, Muhammad; Ruliana; Mustika M, Sitti Sri
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 03 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm373

Abstract

Indonesia is a country with an open economy, one of the sources of foreign exchange needed by a country with an open economy is exports. Cocoa is one of Indonesia's main export commodities that makes an important contribution to the country's economy, but the value of Indonesian cocoa exports fluctuates, that is there are inconsistent changes from time to time. The purpose of this study is to determine the results of forecasting the value of Indonesian cocoa exports, as well as to determine the best method for forecasting. This research compares the ARIMA and Single Exponential Smoothing methods to determine the best forecasting method. The best method is selected based on the smallest MAPE value. Based on the results of data analysis, the best forecasting model using the ARIMA method is the ARIMA (1, 0, 1) model, which has a MAPE value of 10.38060%. Meanwhile, the best forecasting model using the Single Exponential Smoothing method is with α = 0.16, which has a MAPE value of 10.92874%. So that the best method for forecasting the value of Indonesian cocoa exports is the ARIMA method.
Implementation of Binary Logistic Regression and Chi-Squared Automatic Interaction Detection (CHAID) to Recipients of the Prosper Family Card Program in Makassar City Rais, Zulkifli; Ruliana; Indrayasaro
Quantitative Economics and Management Studies Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3981

Abstract

The binary logistic regression analysis method is a classification method that forms a relationship between a dichotomous dependent variable and an independent variable, while the chi-squared automatic interaction detection (CHAID) analysis method is a decision tree classification method for studying the relationship between independent variables and variables. bound by using the chi-square test statistic as the main tool. This research aims to determine the magnitude of the resulting accuracy value and what factors influence recipients of the Prosperous Family Card program in Makassar City based on National Socio-Economic Survey data in 2022 using the binary logistic regression method and the chi-squared automatic interaction detection method (CHAID). The results of this research using the binary logistic regression method show that the variables of the highest level of education of the head of the household (X4) and defecation facilities (X7) have a significant effect on recipients of the Prosperous Family Card program in Makassar City with an accuracy value of 75.78%, while the chi-squared automatic interaction detection (CHAID) method also shows that the variables of the highest level of education of the head of the household (X4) and defecation facilities (X7) have a significant effect on recipients of the Prosperous Family Card program in Makassar City with the resulting accuracy value of 75%. Based on the accuracy values of the two methods, the binary logistic regression method is the appropriate method for classifying recipients of the Prosperous Family Card program in Makassar City
Comparison of R-Forecasting and V-Forecasting Singular Spectrum Analysis in Forecasting Farmers' Exchange Rates in Indonesia Fahmuddin S., Muhammad; Ruliana; Ahmad Imdad
ARRUS Journal of Mathematics and Applied Science Vol. 4 No. 2 (2024)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience3905

Abstract

Indonesia is an agricultural country where one of the main sources of income comes from the agricultural sector. One of the indicators often used to assess farmer welfare is the Farmer Exchange Rate (FER) index. Research on FER forecasting using the Singular Spectrum Analysis (SSA) method has been widely conducted, however, to date, there has been no research comparing the recurrent forecasting and vector forecasting methods in Indonesia. The purpose of this study is to obtain FER forecasting results using r-forecasting and v-forecasting, then compare the forecasting results based on MAPE values ​​to obtain the best forecasting results. The results of the study show that v-forecasting produces better forecasting results with a MAPE value of 0.57%. The forecast results for the next 12 months show an increase and decrease of FER in Indonesia. The highest FER value occurred in May 2022 at 103.79, while the lowest value was in September 2021 at 101.80.
PEMANFAATAN G SUITE FOR EDUCATION UNTUK MENINGKATKAN EFEKTIVITAS BELAJAR MENGAJAR DAN KAPASITAS GURU SMPN 4 BONTONOMPO KABUPATEN GOWA Rais, Zulkifli; Annas, Suwardi; Ruliana; Mar’ah, Zakiyah; Fahmuddin, Muh.
ABDI KIMIA: Jurnal Pengabdian Masyarakat Vol 1 No 2 (2024): Jurnal Edisi Juni
Publisher : Jurusan Kimia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/abdi.v1i2.2573

Abstract

SMPN 4 Bontonompo merupakan salah satu SMPN yang berusaha memanfaatkan teknologi informasi dalam metodologi pengajarannya. Mereka tertarik untuk mengadopsi layanan G Suite for Education untuk E-learning. SMPN 4 Bontonompo bekerja sama dengan Fakultas Matematika dan Sains (FMIPA) UNM mengadakan workshop pengenalan G Suite for Education, migrasi dan penyiapan email institusi, serta workshop pelatihan Google Classroom. Layanan ini dilakukan dalam 2 tahap. Fase pertama adalah menyiapkan email sekolah berdasarkan G Suite untuk Pendidikan dan fase kedua adalah pelatihan Google Kelas untuk guru. Dengan menerapkan layanan baru, semua guru dan siswa sekolah dapat menggunakan layanan terintegrasi dari Google, termasuk Google Classroom. Setelah layanan ini, para pengajar dapat mulai menggunakan Google Classroom untuk meningkatkan efisiensi pengajaran di sekolah dan memudahkan setiap siswa untuk mengakses konten karena dapat diakses kapan pun dan di mana pun. Para guru juga puas dengan pelatihan tersebut
Co-Authors Ahmad Imdad Alfairus, Muh. Qodri Amir, Arfan Shalihin Ankaz As Sikib Aswi, Aswi Fahmuddin S, Muhammad Fahmuddin S., Muhammad Fahmuddin, Muh. Fauziah Hardianti Hafid Indrayasaro Lili Maghfirah Rahma Sudirman Mar’ah, Zakiyah Muh. Imam Shadiq Muhammad Arif Tiro, Muhammad Arif Mustika M, Sitti Sri Nurul Fahmi Rahmat Hidayat Rais, Zulkifli Safitri, Irma Yani Sasa Arisa, Sasa Arisa Sitti Masyitah Meliyana R. Sudarmin Suhendra, Ogi Suwardi Annas Zulkifli Rais