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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Media Infotama Journal of the Indonesian Mathematical Society KEUNIS JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Informatika Pertanian Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) SINTECH (Science and Information Technology) Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics International Journal of Remote Sensing and Earth Sciences (IJReSES) Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Building of Informatics, Technology and Science ESTIMASI: Journal of Statistics and Its Application Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Natural Xplore: Journal of Statistics STATISTIKA Scientific Journal of Informatics Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Anang Kurnia
School Of Data Science, Mathematics And Informatics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Transportation Other

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Analisis Tingkat Kesenjangan Pendapatan antar Provinsi di Indonesia Menggunakan Regresi Data Panel Model Pengaruh Tetap Thooriq Ghaith; Hari Wijayanto; Anang Kurnia
Xplore: Journal of Statistics Vol. 7 No. 3 (2018): 31 Desember 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v7i3.125

Abstract

THOORIQ GHAITH. Analysis of Income Disparity Rates among Provinces in Indonesia Using Panel Data Regression. Supervised by HARI WIJAYANTO and ANANG KURNIA. Income disparities in Indonesia generally and in each province particularly is a serious problem from year to year. It is necessary to find out the factors that affect the income disparity rates (Gini ratio) to be taken into consideration in determining the economic policy. By using data of 33 provinces from 2007 until 2016, panel data regression with provincial fixed effect model approach was used to determine factors that affect Gini ratios in Indonesia and to capture the differences of Gini ratio characteristics of each province in form of intercept. Modeling was done for whole Indonesia and for five regions as well to find out what factors that affect the Gini ratio of provinces in Indonesia generally and what factors affect Gini ratios of provinces in each region particularly. The percentage of poor people is a significant factor to Gini ratio in the model throughout Indonesia and in the model of each region, except in Sumatera. Beside the percentage of the poor people, other explanatory variables affecting Gini ratios are GDP growth rates in Kalimantan, open unemployment rates in Sulawesi, and provincial minimum wage in Nusa Tenggara, Maluku and Papua. All of the predicted models are good enough because they produce MAPE values below 10%.
Analisis Kepuasan Pelayanan dan Literasi TIK Pengunjung Dinas-Dinas di Kota Bogor Ryska Putri Madyasari; Anang Kurnia; Rahma Anisa; Yani Nurhadryani
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.152

Abstract

Determining Public Satisfaction Index using analysis of Importance Performance Analysis (IPA) and Customer Satisfaction Index (CSI) can be utilized to improve service quality of Governmental Departments in X City. Analysis of IPA and CSI were used to measure the level of respondents’ satisfaction regarding the provided services. The departments were selected using purposive sampling method. Four selected departments were Population and Civil Registry Department, Transportation Department, Housing and Settlement Department, and Social Department. The result showed that customers were moderately satisfied with the services, with the following CSI index value: 70.09%, 72.95%, and 76.61% respectively for each departments. Moreover, Social Department’s customers were very satisfied with the CSI index 81.56%. In this study, aspect of Information and Communication Technology (ICT) literacy indicator were more exposing the ability to operate personal computer. There were six indicator of ICT literacy, i.e access, manage, integrate, evaluate, create, and communication. The value of evaluate indicator were quite high, it has reached score higher than 50% for each departments were. However, based on overall score, it was shown that 60% respondents still have low ICT literacy. This study also showed that ICT literacy were related to responden’s education and age. It increased along with the higher level of education that has been completed by respondents, and with the age of 17-39 years old.
Penerapan Metode DBSCAN dalam Memperbaiki Kinerja K-Means untuk Penggerombolan Data Tweet Astri Fatimah; Anang Kurnia; Septian Rahardiantoro; Yani Nurhadryani
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.159

Abstract

Text Mining is collecting text data mining results from a computer to get information contained therein. Text data has a form of data that is not structured and difficult to analyze. The unstructured data can be used as structured data through pre-processing stages. Text data is represented as numerical data after going through the pre-processing stages using vector space model method and weighting method of inverse frequency document frequency so that it can be used for analysis. The K-Means cluster analysis is one method that can be used for unstructured data, but the K-Means method is not robust to noise. Outliers can be detected using Density Based Spatial Clustering of Application with Noise (DBSCAN) cluster analysis. Outliers obtained from DBSCAN results can be omitted in the data. Cluster analysis was carried out again after removal of outliers using the K-Means method with the same number of k clusters. Evaluation of the cluster that is used to see the goodness of the cluster results is Silhouette Coefficient (SC). The SC value of the K-Means method after removal of outliers has a significant increase of 0.21 for a small amount of data. Adding the amount of text data to cluster analysis also affects the number of clusters. This is influenced by the number of katas in a document that is given weight. The fewer katas that are given weight, the more number of clusters will be generated
Penggerombolan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan Metode K-Means dan Fuzzy C-Means . Hanniva; Anang Kurnia; Septian Rahardiantoro; Ahmad Ansori Mattjik
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (973.285 KB) | DOI: 10.29244/xplore.v11i1.855

Abstract

The achievement of the human development index in Indonesia differs between regions with striking gaps occurring in the western and eastern parts of Indonesia. This difference in achievement can be seen more clearly by grouping regencies/municipalities in Indonesia based on the four indicators of the human development index. With this aim, this study uses the k-means and fuzzy c-means methods to determine the optimal cluster size with two distance approaches, namely the Euclidean and Manhattan distances on the human development index indicators data in 2020. In addition, this study also seeks to identify the distribution of regencies/municipalities based on the characteristics of the human development index indicators in the clustering result. The result is that the best distance measure is Euclidean distance with optimal cluster size is four for k-means and six for fuzzy c-means. In addition, the clustering results obtained by the k-means method are more optimal than the fuzzy c-means because the evaluation value is better. In general, the four clusters formed were in accordance with the grouping carried out by BPS with the percentage of conformity reaching 66,54%. In summary, most regencies/municipalities on the Island of Sumatera, Java, Borneo and Sulawesi have higher life expectancy and percapita expenditure than many regencies/municipalities in the Nusa Tenggara Islands (besides Bali), Moluccas and Papua. Very high achievement for each HDI indicators is dominated by the capital city of each province with unfavorable conditions occurring in most regencies/municipalities in Papua Province.
Pendekatan Metode CHAID dan Regresi Logistik dalam Menganalisis Faktor Berpengaruh pada Kejadian Stunting di Provinsi Jawa Barat Fitri Dewi Shyntia; Anang Kurnia; Gerry Alfa Dito
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.259 KB) | DOI: 10.29244/xplore.v11i1.857

Abstract

Stunting is a chronic nutritional disorder characterized by short or very short height compared to the average child of his age. Data on the prevalence of stunting under five collected by the World Health Organization (WHO) in 2018 stated that Indonesia was the third-highest contributor to stunting in the South-East Asia Regional (SEAR) after Timor Leste and India. Indonesia's national stunting prevalence is 29,6%. West Java Province has the 12th the highest prevalence in Indonesia is one of the priority areas in stunting management, with the stunting prevalence rate most similar to the Indonesian national stunting prevalence of 29,2%. This study aims to examine the variables that are indicated to affect the incidence of stunting in children aged 0-59 months based on data obtained from the 2018 Basic Health Research (Riskesdas). Eighteen variables are categorized into child characteristics, nutritional fulfillment, socio-demographic, socialeconomic, and environmental characteristics. The analysis was performed using the logistic regression method and the Chi-Square Automatic Interaction Detection (CHAID) method. The analysis results show that the probability of stunting will increase significantly in children under five with several criteria. These Criteria are mothers with low education, sex of male toddlers, toddlers who do not carry out immunizations, toddlers who are not given additional food (PMT), and infants with households that have a safe place to eat and the disposal of wastewater from the kitchen is not suitable.
Analisis Clustering Time Series untuk Pengelompokan Provinsi di Indonesia Berdasarkan Indeks Pembangunan Manusia Jenis Kelamin Perempuan Dwi Agustin Nuriani Sirodj; I Made Sumertajaya; Anang Kurnia
Statistika Vol. 23 No. 1 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i1.2181

Abstract

ABSTRAK Indeks Pembangunan Manusia (IPM) mencerminkan bagaimana kualitas dari pembangunan suatu wilayah tertentu. Selain adanya ketimpangan nilai IPM antar wilayah provinsi di Indonesia, jika dilihat dari sudut pandang gender, maka kesenjangan IPM laki-laki dan perempuan pun tidak bisa dihindari. Peningkatan pertumbuhan pembangunan di setiap wilayah tentu harus mendorong peningkatan kesetaraan gender pula, dalam hal ini kesenjangan pembangunan antara laki-laki dan perempuan harus mampu diminimalisir sehingga penting untuk melihat bagaimana kondisi IPM perempuan perwilayah provinsi di Indonesia agar dapat dilakukan langkah-langkah intervensi untuk meminimalisir isu ketimpangan yang harapannya dapat mendorong indeks pembangunan di wilayah tersebut. Metode analisis yang digunakan untuk mengelompokkan daerah berdasarkan nilai IPM perempuan adalah Clustering time series. Hasil analisis memperlihatkan metode clustering time series dengan menggunakan jarak dynamic time-warping (DTW) menghasilkan dua kelompok yaitu kelompok 1 (daerah dengan IPM perempuan rendah): daerah Papua dan kelompok 2 (daerah dengan IPM perempuan tinggi): daerah lain selain Papua. Pengelompokan yang dibentuk menghasilkan nilai koefisien Silhouette sebesar 0,74. Nilai tersebut menandakan bahwa kelompok yang dibentuk berada dalam kategori kuat dalam artian bahwa dua kelompok tersebut mempunyai karakteristik yang jelas berbeda sehingga metode pengelompokan dengan jarak DTW dapat digunakan dalam pengelompokan provinsi-provinsi di Indonesia berdasarkan nilai IPM Perempuan. ABSTRACT The Human Development Index (HDI) reflects the quality of development in a particular region. In addition to the inequality of HDI values between provinces in Indonesia, when viewed from a gender perspective, the gap between the HDI of men and women is inevitable. Increased development growth in each region must certainly encourage an increase in gender equality as well; in this case, the development gap between men and women must be able to be minimized, so it is important to see how the condition of the women's HDI per region in Indonesia so that intervention steps can be taken to minimize the issue of inequality. The analysis method used in this paper is Time Series Clustering. The analysis results show that the time series clustering method using dynamic time-warping (DTW) distance produces two groups: group 1 (regions with low female HDI): Papua region and group (2 regions with high female HDI): all provinces except Papua. The grouping formed produced a Silhouette coefficient value of 0.74. This value indicates that the groups formed are in a strong category, so the clustering method with DTW distance can be used in grouping provinces in Indonesia based on the value of Women's HDI.
Mixed Models of Non-Proportional Hazard and Application in The Open Distance Education Students Retention Data Dewi Juliah Ratnaningsih; Anang Kurnia; Asep Saefuddin; I Wayan Mangku
Journal of the Indonesian Mathematical Society VOLUME 28 NUMBER 3 (NOVEMBER 2022)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.28.3.1185.323-344

Abstract

The problem that arises in the Cox model is that there are more than two types of covariates and the presence of random effects is a non-proportional hazard (NPH). One example of a case that involves many factors is student retention. Low student retention can lead to dropping out of college or failure in completing studies. The purpose of this study is to overcome the problem of NPH caused by the presenceof time-independent covariates, time-dependent covariates, and random effects. The research method uses simulation. Some of the modified models are the stratified Cox model, the extended Cox model, and the frailty model. The developed model is applied to distance education student retention data. The results of the study show that frailty and study programs provide considerable diversity in explaining thetotal diversity of the model. It can be concluded that frailty needs to be considered by UT to improve the quality of services to students. In addition, other covariates that have a significant effect on UT student learning retention modeling are age, domicile, gender, GPA, marital status, employment status, number of credits taken, and number of registered courses.
Bahasa Inggris Dhea Dewanti; Kristuisno Martsuyanto Kapiluka; Febryna Sembiring; Ajeng Bita Alfira; Anang Kurnia
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.35584

Abstract

Multilevel binary logistic regression analysis is a development of logistic regression for hierarchical data structures. Hierarchical data is data from a population that has levels. This research examines the relationship model of Life Expectancy, Mean Years of Schooling, Expected Years of Schooling, Regency/City Minimum Wage as explanatory variables at level 1 (Regency) and Gross Regional Domestic Income (GRDP) as an explanatory variable at level 2 (Provincial) against Unemployment Rate (UR) as a response variable. The research results show that Life Expectancy and Minimum Wage at level 1 and GRDP at level 2 have a significant influence on district/city TPT on Java Island in 2022
Prediksi Fenomena Ekonomi Indonesia Berdasarkan Berita Online Menggunakan Random Forest Khairani, Fitri; Kurnia, Anang; Aidi, Muhammad Nur; Pramana, Setia
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11401

Abstract

Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category
Densely Connected dan Residual Convolutional Neural Network Untuk Estimasi Jumlah Keluarga Tingkat Desa Dengan Citra Satelit Siregar, Jodi jhouranda; Kurnia, Anang; Sadik, Kusman
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1191

Abstract

Indonesia conducts a population census every ten years to collect population data. Variables such as family count are collected to provide general population information for policy making and sampling frames. Indonesia as an archipelagic country with an area of 8.3 million km2 will require a lot of resources to collect such data. In the age of big data, satellite imagery has become more available and inexpensive. In this study, we used West Java as a case study for applying deep learning to estimate family counts at the village level. Sentinel-2 and SPOT-67 data are used to model family counts. Using xgboost, we regress the family count with the softmax probability, resulting from family density classification using deep learning (densenet121 and resnet50 ) as the input. With an R2 of 0.93 and a MAPE of 19%, the regression model provides a good prediction of the number of families in the census. Regarding the input data, Sentinel-2 is sufficient to accomplish this task as there is no significant difference from the modeling results with higher resolution images (SPOT 6-7). The input level in the form of a segment of the estimation area and using structured auxiliary variables also deliver better predictions
Co-Authors . Hanniva . Marzuki . Sutriyati Abdullah Ilman Fahmi Achmad Fauzan Achmad Fauzan, Achmad Agus Buono Agus M Soleh Agus Mohamad Soleh Ahmad Ansori Mattjik Ajeng Bita Alfira Aji Hamim Wigena Alkahfi, Cahya Amalia Pasaribu, Asysta Amin, Yudi Fathul Anik Djuraidah Ardiansyah, Muhlis Arie Anggreyani Arief Gusnanto ASEP SAEFUDDIN Astri Fatimah Azka Ubaidillah Bagus Sartono Bambang Sumantri Beny Trianjaya Budi Susetyo Budi Waryanto Cici Suhaeni Citra Jaya Dede Dirgahayu Dede Dirgahayu Deiby T Salaki Dewi Juliah Ratnaningsih Dhea Dewanti Dian Handayani Dian Kusumaningrum Dian Kusumaningrum Dian Kusumaningrum, Dwi Agustin Nuriani Sirodj Dwi Wahyu Triscowati Efriwati Efriwati Erfiani Erfiani Erfiani Erwan Setiawan, Erwan Farit Mochamad Afendi Farit Mohamad Afendi Fauzi, Fatkhurokhman Fauziah, Ghina Febryna Sembiring Fitri Dewi Shyntia Fitrianto, Anwar Fitriyani Sahamony, Nur Gerry Alfa Dito Hamid, Assyifa Lala Pratiwi Hamim Wigena, Aji Haq, Irvanal Hari Wijayanto Hari Wijayanto Hari Wijayanto Hestiani Wulandari Hidayat, Agus Sofian Eka Hidayat, Muhammad I Made Sumertajaya I Wayan Mangku Ikhlasul Amalia Rahmi Ina Widayanty Indah Herlawati Indahwati Indonesian Journal of Statistics and Its Applications IJSA Ita Wulandari Iwan Kurniawan Khairani, Fitri Khairil Anwar Notodiputro Kristuisno Martsuyanto Kapiluka Kusman Sadik Loly, Joao Ferreira Rendes Bean Matualage, Dariani Maulana Achiar, Anshari Luthfi Muhammad Nur Aidi Mulianto Raharjo Nashir, Husnun Newton Newton Nurul Hidayati Pardomuan Robinson Sihombing Pasaribu, Asysta Amalia Pingkan Awalia Pramana, Setia Purba, Widyo Pura Purwanto, Arie Putri, Christiana Anggraeni Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rahman, Gusti Arviana Retsi Firda Maulina Ristiyanti Ristiyanti Rysda Rysda Ryska Putri Madyasari Sahamony, Nur Fitriyani Santoso, Andrianto Santoso, Zein Rizky Sari Agustini Hafman Septiani, Adeline Vinda Setyowati, Indah Rini Siregar, Jodi jhouranda Siskarossa Ika Oktora Siti Muchlisoh Suhaeni, Cici Suprayogi, Muhammad Azis Suprayogi, Muhammad Aziz Teguh Prasetyo Thooriq Ghaith Topan . Ruspayandi Triscowati, Dwi Wahyu Tyas, Maulida Fajrining Utami Dyah Syafitri Viarti Eminita Widiyanto, Rhendy K. P. Widoretno, Widoretno Yani Nurhadryani Yenni Angraini Yenni Kurniawati Yudistira Yudistira Yully Sofyah Waode