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All Journal Jurnal Gaussian Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL PANGAN Jurnal Keluarga Berencana Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Al-Muqayyad Mitra Ash-Shibyan: Jurnal Pendidikan dan Konseling International Journal of Industrial Engineering and Engineering Management
Anne Mudya Yolanda
Program Studi Statistika, Jurusan Matematika, Universitas Riau
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Classification of the Human Development Index in Indonesia Using the Bootstrap Aggregating Method Goldameir, Noor Ell; Yolanda, Anne Mudya; Adnan, Arisman; Febrianti, Lusi
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 1 (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11173

Abstract

Successful development of the quality of human life in a region is determined by the Human Development Index (HDI). Human development performance based on the HDI can be measured: long and healthy life, knowledge, and a decent standard of living. The HDI is usually grouped into several categories to facilitate the classification of the HDI level of each region. This study aimed to determine the ability of the bootstrap aggregating (bagging) method to classify the HDI by district/city. Bagging is a stochastic machine learning approach that can eliminate the variance of the classifier by producing a bootstrap ensemble to obtain better accuracy results. The dependent variable in this study was the HDI by district/city in 2020. In contrast, life expectancy at birth, expected years of schooling, mean years of schooling, and real expenditure per capita are adjusted as independent variables. Bagging was applied to the high and low categories of HDI data. The bagging method demonstrated good classification performance due to only eight classification errors, namely the HDI data which should be in the high category but classified into the low category by the bagging method. Based on the results of calculations with 25 replications, it can be concluded that the bagging method has a very good performance, with an accuracy value of 92.3%, the sensitivity of 100%, and specificity of 83.33%. The bagging method is considered very good for the classifying the HDI by district/city in Indonesia in 2020 because it has a balanced accuracy of 91.67%.
Game PAUD Berbasis Matematika (GEMPITA) Guna Meminimalisir Ketakutan Matematika Pada Anak Usia Dini Anne Mudya Yolanda; Imtikhanah Anis Mahmudiati
Mitra Ash-Shibyan: Jurnal Pendidikan dan Konseling Vol. 4 No. 01 (2021): Mitra Ash-Shibyan: Jurnal Pendidikan dan Konseling
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/mash.v4i01.202

Abstract

Math seems to be one of the scourges in learning that children are afraid of, even though the use of math is very close and needed in everyday life. This specter or fear is also born from the assumption that the number of calculations and formulas that must be studied is added to the monotonous and rote-only factor of giving material. Therefore, this research was conducted as an effort to improve mathematical logic skills in early childhood thorugh the use of more interesting method in order to be accepted by children. It was the GEMPITA program (a math-based PAUD game). This study uses a literature approach with documentation techniques as a data collection method. In the implementation, the use of various tools and technology is able to increase children the attractiveness to learning mathematical concepts. So that the results are in the form of recommendations for math-based games that are easy and fun to minimize math fear in early childhood.
Analisis Angka Partisipasi PAUD Untuk Mewujudkan Pendidikan Berkualitas di Provinsi Riau Eva Eriani; Anne Mudya Yolanda
Mitra Ash-Shibyan: Jurnal Pendidikan dan Konseling Vol. 5 No. 01 (2022): Mitra Ash-Shibyan: Jurnal Pendidikan dan Konseling
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/mash.v5i01.470

Abstract

Quality education can be traced back to the level of early childhood education as assessed by the participation rate in Early Childhood Education (ECE). The ECE participation rate is determined by three indicators: the School Participation Rate (SPR), the Gross Enrollment Rate (GER), and the Pure Participation Rate (PPR). However, there are still gaps in the national ECE participation rate, children who have not earned ECE services, and some villages that do not have ECE institutions. Although the Ministry of Education and Culture has established guidelines for one village one ECE. The purpose of this research is to provide an overview of ECE participation rates in Riau Province and to explain what factors influence them. This study used descriptive analysis. Secondary data were obtained from the Central Bureau of Statistics, the Ministry of Education and Culture, and the Ministry of National Development Planning/National Development Planning Agency. The results show that the ECE participation rate, in general, has been fluctuating, especially in the last five years, and that the GER of children attending ECE in Riau province is still lower than the national rate, while the PPR in rural areas is higher than the NER in urban areas. According to PER by gender, girls who attend ECE on time contribute more than boys. Those are influenced by the fact that ECE institutions are not recorded in the education database, and there are social trends that lead parents to prefer homeschooling over ECE institutions, particularly with the impact of the COVID-19.
The Effect of Increasing Daily Case COVID-19 as Moderating Variable on Coal Stock Price Ratna Mustika Dewi; Anne Mudya Yolanda
International Journal of Industrial Engineering and Engineering Management Vol. 3 No. 2 (2021)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v3i2.5547

Abstract

Stock investments in the time of the COVID-19 pandemic have a considerable risk. This happens because of the increasingly uncontrolled movement of stock prices. The potential for steep charts can occur at any time. Sentiment analysis of increasing daily cases of COVID-19 was analyzed to see how much effect it has on stock price movements. This research will analyze stock prices from coal commodities in Indonesia. Researchers choose to discuss coal commodities because, in April 2021, there was a significant increase and highest in November 2021. Because of the data, researchers want to see the influence of some coal companies using selling price data with moderating variables for estimating the stock price. There are 26 coal companies that are listed on Indonesia Stock Exchange. The analysis will be a check on five companies that have the largest investors. The analysis is also carried out on coal sales price movements. Furthermore, five different coal mining companies were analyzed based on the rate of price changes to new selling prices with variable moderation in Indonesia. Increasing daily cases of COVID-19 being variable moderation. The method used for finding the relationship is a linear regression with a moderating variable. According to the analysis, the increasing daily case of COVID-19 as a moderating variable is enough to affect the relationship between the selling price of coal and the stock price of HRUM.JK and PTBA.JK. In stock price HRUM.JK, there is an increasing adjusted R square from 0.5254 to 0.5451. The same conditions apply to PTBA.JK has increased by 0.4040 to 0.4444.
Peramalan Data dengan Teknik Pemulusan Simple Moving Average (Studi Kasus Harga Saham Harian PT Bank BRI Syariah Tbk) Anne Mudya Yolanda; M. Ridhwan
AL-Muqayyad Vol. 3 No. 2 (2020): Al-Muqayyad
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/jam.v3i2.195

Abstract

Time series analysis is used to model time series data and forecast data for future periods. This research was conducted to predict data with a simple smoothing technique, namely the Simple Moving Average of PT Bank BRI Syariah Tbk's stock closing price data. The closing price of shares was analyzed using three average criteria, namely 3, 5, 20, and 100 of the most recent data. Comparison of accuracy with SSE, MSE, and MAPE showed that the best in predicting daily stock closing price data was the Simple Moving Average using the latest 3 data with a prediction result for the future period of Rp. 748, -.
PEMODELAN KLASIFIKASI PADA INDEKS KETIMPANGAN GENDER (IKG) TAHUN 2020 DENGAN METODE NAÏVE BAYES Anne Mudya Yolanda; Arisman Adnan; Azra Aulia Dwiputri
Jurnal Keluarga Berencana Vol. 7 No. 1 (2022): Jurnal Keluarga Berencana
Publisher : Badan Kependudukan Dan Keluarga Berencana Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.468 KB) | DOI: 10.37306/kkb.v7i1.118

Abstract

Indeks Ketimpangan Gender (IKG) merupakan indikator pendukung pembangunan dalam Tujuan Pembangunan Berkelanjutan pada isu gender. Oleh karenanya peneliti tertarik melakukan kajian pemodelan klasifikasi dengan tujuan melakukan prediksi tingkat IKG menurut kabupaten/kota di Indonesia tahun 2020 menggunakan algoritma machine learning. Algoritma yang diterapkan pada data IKG dan indikatornya adalah metode naïve bayes. Adapun indikator penyusun yang digunakan yaitu proporsi persalinan tidak di fasilitas kesehatan, proporsi perempuan berusia 15-49 tahun yang pernah kawin dan saat melahirkan hidup pertama, persentase keterwakilan di parlemen, proporsi penduduk laki-laki dan perempuan dengan pendidikan minimal SMA, dan tingkat partisipasi angkatan kerja. Analisis dengan metode naive bayes pada empat kategori: rendah, menengah bawah, menengah atas, dan tinggi memberikan hasil klasifikasi yang baik terutama dalam mengklasifikasi kelas positif. Hasil akurasi keseluruhan data training sebesar 82.86 %, sedangkan pada data testing sebesar 83.72 %. Hasil klasifikasi dapat digunakan untuk peramalam IKG dan landasan pengambilan kebijakan dan penyusunan program untuk mengatasi ketimpangan pembangunan berbasis gender di Indonesia.
SEGMENTASI PROVINSI BERDASARKAN SARANA DAN PERLENGKAPAN FASKES KELUARGA BERENCANA TAHUN 2021 Anne Mudya Yolanda; Kristiana Yunitaningtyas
Jurnal Keluarga Berencana Vol. 6 No. 1 (2021): Jurnal Keluarga Berencana
Publisher : Badan Kependudukan Dan Keluarga Berencana Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.875 KB) | DOI: 10.37306/kkb.v6i1.70

Abstract

Fasilitas kesehatan Keluarga Berencana (faskes KB) masih menjadi perpanjangan tangan pertama dalam melaksanakan program berdasarkan kebijakan dan strategi BKKBN pada tahun 2020-2024 untuk meningkatkan akses dan kualitas penyelenggaraan KB yang komprehensif berbasis kewilayahan. Dalam melihat proses peningkatan akses dan kualitas penyelenggaraan di faskes KB, salah satu yang dapat menjadi perhatian adalah sarana dan perlengkapan yang tersedia untuk mendukung program KB. Kajian ini memetakan segmentasi jumlah sarana dan perlengkapan yang bisa digunakan pada faskes KB di seluruh provinsi di Indonesia pada tahun 2021 menggunakan analisis gerombol (cluster analysis). Analisis gerombol yang digunakan adalah K-Means Cluster Analysis yang membagi data seluruh provinsi berdasarkan jenis sarana dan perlengkapan faskes KB ke dalam empat gerombol. Segmentasi ini diharapkan dapat menjadi bahan dukungan dan evaluasi dalam menyusun strategi berkelanjutan terutama terkait peningkatan akses dan kualitas penyelenggaraan KB.
ANALISIS CLUSTER TERHADAP INDIKATOR DATA SOSIAL DI PROVINSI NUSA TENGGARA TIMUR MENGGUNAKAN METODE SELF ORGANIZING MAP (SOM) Nurul Imani; Achmad Isya Alfassa; Anne Mudya Yolanda
Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.3.458-467

Abstract

The Human Development Index (HDI) is used to assess the quality of life in a given area. In general, the HDI of Nusa Tenggara Timur (NTT) Province increased by 0.88 percent per year from 2011 to 2020 and fell by 0.06 percent in 2019-2020. The characteristics of the current situation of HDI in all districts/cities in NTT were defined using 9 variables in this study. The goal of this study is to combine clustering analysis with a Self-Organizing Map (SOM). Based on the analysis, it was found that NTT has four clusters based on HDI, with clusters 1, 2, 3, and 4 having 16, 3, 2, and 1 member(s) respectively. The cluster findings are meant to be utilized as a guide by the government when developing public policy or making decisions, given the seriousness of the Covid-19 pandemic. These findings could be used to address social issues in NTT, as well as be supported by beneficial policies.
The Comparison of Accuracy on Classification Climate Change Data with Logistic Regression Arisman Adnan; Anne Mudya Yolanda; Gustriza Erda; Noor Ell Goldameir; Zul Indra
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11914

Abstract

Machine learning methods can be used to generate climate change models. The goal of this study is to use logistic regression machine learning algorithms to classify data on greenhouse gas emissions. The data used is climate change data of several countries obtained from The World Bank, with total greenhouse gas emissions as the response variable and 61 other attributes as explanatory variables. This data is preprocessed using min-max normalization to handle unbalanced ranges, and then the data is split into 70% training data and 30% testing data. Based on the logistic regression modeling, it was discovered that the data from the min-max transformation resulted in better modeling than the data modeling without the transformation process. The accuracy, precision, sensitivity, and specificity of the transformation are 87.60%, 87.76%, 87.04%, and 88.14%, respectively
Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen Anne Mudya Yolanda; Arisman Adnan; Rustam Efendi; Haposan Sirait; Irfansyah Irfansyah; Okta Bella Syuhada; Rahmad Ramadhan Laska; Riko Febrian
AL-Muqayyad Vol. 5 No. 2 (2022): Al-Muqayyad
Publisher : STAI Auliaurrasyidin Tembilahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46963/jam.v5i2.766

Abstract

Inflation of a region can be measured from the Consumer Price Index (CPI) by spending group. The aim is to look at the factors that influence monthly inflation based on the CPI for 2021. Principal Component Analysis is used to reduce the expenditure group variables in the CPI, followed by biplot analysis to display the visualization of the first two main components of the PCA in a two-dimensional graph. The results of the main component analysis, (1) the primary expenditure component consists of housing, water, electricity and household fuel variables; equipment, tools and household routine maintenance; transportation; information, communication and financial services; recreation, sports and culture, (2) secondary expenditure components include food, drink and tobacco variables; health; education; general, and (3) complementary expenditure components, namely clothing and footwear variables; personal equipment and other services. These three components simultaneously can represent 88.1% of the diversity of the data. Biplot analysis succeeded in describing the similarity and position of the variables with a total variance of 75%
Co-Authors Achmad Isya Alfassa Agus Kurniawati Andriani, Yulia Arisman Adnan Ayu Agustiani Azra Aulia Dwiputri Bustami Bustami Bustami Ecelly Indriani Putri Eva Eriani Fanny Septya Febrianti, Lusi Goldameir, Noor Ell Gustriza Erda Haposan Sirait Haposan Sirait Imtikhanah Anis Mahmudiati Irfansyah Irfansyah Isti Yuliani Kristiana Yunitaningtyas M. Ridhwan Nisha Thahira Noor Ell Goldameir Nurul Imani Okta Bella Syuhada Putri Sion Cahayana Rahmad Ramadhan Laska Ratna Mustika Dewi Raudatul Yusra Ridho Tri Mulya Riko Febrian Rustam Efendi Tira Mei Darnis Windy Maya Sari Zul Indra