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Indahwati
Department Of Statistics, School Of Data Science, Mathematics And Informatics, IPB University, Campus IPB University, Bogor 16680, Indonesia
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Faktor – Faktor yang Memengaruhi Permasalahan Stunting di Jawa Barat Menggunakan Regresi Logistik Biner Silmi Annisa Rizki Manaf; Erfiani; Indahwati; Anwar Fitrianto; Reni Amelia
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 15 No 2 (2022): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol15.no2.a5654

Abstract

Salah satu bentuk akibat dari kurangnya asupan gizi kronis pada balita adalah stunting. Stunting merupakan permasalahan kesehatan yang saat ini sedang digencarkan untuk diturunkan angka prevalensinya. Permasalahan kesehatan ini berhubungan erat pada pertumbuhan tinggi badan yang lebih rendah dengan anak seusianya. Berdasarkan data Kemenkes per Agustus 2021, Provinsi Jawa Barat menduduki posisi pertama dengan angka balita stunting paling tinggi di Indonesia. Penelitian ini bertujuan untuk mengetahui faktor apa saja yang memengaruhi terjadinya stunting pada balita dan memodelkan dengan metode regresi logistik biner untuk wilayah Jawa Barat. Metode ini dapat menunjukkan faktor yang memengaruhi berdasarkan peubah yang signifikan. Regresi logistik biner akan memodelkan hubungan antara satu atau beberapa peubah prediktor dengan peubah respon yang kategorik. Peubah respon didefinisikan sebagai persentase angka balita stunting dan dibagi kedalam dua kategori yakni tinggi dan rendah. Pengategorian kelas didasarkan pada nilai median pada persentase angka balita stunting. Unit penelitian menuju pada 27 wilayah Kabupaten/Kota di Jawa Barat. Hasil analisis menunjukkan dari 11 peubah prediktor, setelah dilakukan pemodelan terdapat 3 peubah yang berpengaruh signifikan pada taraf nyata 0,10 yakni imunisasi dasar lengkap, tempat pengelolaan makanan yang memenuhi syarat kesehatan, dan penduduk miskin. Model yang terpilih berdasarkan nilai akurasi seimbang terbesar dibandingkan model lainnya yakni dihasilkan nilai akurasi seimbang sebesar 81,59%.
Pemodelan Regresi Logistik Berbasis Backward Elimination Untuk Mengetahui Faktor yang Mempengaruhi Tingkat Kemiskinan di Indonesia Tahun 2021 Alfi Indah Nurrizqi; Erfiani; Indahwati; Anwar Fitrianto; Reni Amelia
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06202

Abstract

Kemiskinan seringkali berhubungan dengan masalah kesejahteraan dan menjadi salah satu masalah utama di Indonesia. Kondisi ekonomi akibat Covid-19 berdampak pada tingkat kemiskinan di masyarakat. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi tingkat kemiskinan di Indonesia tahun 2021 menggunakan regresi logistik biner. Regresi logistik biner digunakan untuk memodelkan hubungan antara variabel respon yang terdiri dari dua kategori dengan satu atau lebih variabel prediktor. Hasil penelitian ini menunjukkan proporsi tingkat kemiskinan rendah lebih tinggi dibandingkan tingkat kemiskinan tinggi. Terdapat 18 provinsi dengan tingkat kemiskinan rendah serta 16 provinsi dengan tingkat kemiskinan tinggi. Faktor-faktor yang berpengaruh terhadap kategori pada tingkat kemiskinan yaitu Indeks Pembangunan Manusia (X1) dan Gini Ratio (X2). Ketepatan klasifikasi dari model sebesar 83.33%, yang artinya model baik digunakan.
Regresi Ordinal Logit dan Probit pada Faktor Kesejahteraan Rumah Tangga Petani Tanaman Pangan di Provinsi Sulawesi Tenggara Titin Yuniarty Yuniarty; Erfiani; Indahwati; Anwar Fitrianto; Khusnia N. K.
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06216

Abstract

Sektor pertanian memiliki sumbangsih besar dalam perekonomian Indonesia, tetapi menjadi sektor yang seringkali diidentikkan dengan kemiskinan. Hingga saat ini, pembangunan sektor pertanian belum mampu sepenuhnya meningkatkan kesejahteraan rumah tangga petani. Provinsi Sulawesi Tenggara selama 2013-2021 tercatat memiliki Indeks Nilai Tukar Petani (NTP) untuk subsektor tanaman pangan, stabil di bawah 100. Indeks NTP di bawah 100 menunjukkan bahwa kesejahteraan petani belum begitu baik. Penelitian ini bertujuan menentukan faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara. Status kesejahteraan merupakan peubah respon berskala ordinal dengan tiga kategori yaitu miskin, rentan miskin, dan tidak miskin. Metode regresi yang sesuai untuk peubah respon berskala ordinal adalah regresi ordinal, dengan beberapa kemungkinan fungsi hubung. Dalam kajian ini menggunakan fungsi hubung logit dan probit. Hasil analisis regresi menunjukkan bahwa umur kepala rumah tangga (X3), kepemilikan telepon seluler (X6), sumber penghasilan utama rumah tangga (X9), akses terhadap kredit usahatani (X11), dan keberadaan jaminan sosial rumah tangga (X13) berpengaruh positif dan signifikan terhadap status kesejahteraan rumah tangga petani tanaman pangan, sedangkan jumlah anggota rumah tangga (X4) dan usia kawin pertama (X5) berpengaruh negatif dan signifikan. Dengan membandingkan nilai R2 dan balanced accuracy model logit dan probit, disimpulkan bahwa model logit lebih baik dalam mengidentifikasi faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara daripada model probit.
Synthetic Minority Oversampling Technique Pada Model Logit dan Probit Status Pengangguran Terdidik Fatimah Fatimah; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.17050

Abstract

Educated unemployment is caused by a misalignment of educational development planning and employment development, resulting in underemployed graduates from various educational institutions. Unemployment data in DKI Jakarta shows an unequal class. Unbalanced data is a severe problem of modeling because it can cause prediction errors that affect the accuracy of the resulting model. Using SMOTE to handle unbalanced data will likely increase the model’s accuracy. This study aims to find the best model for identifying the factors influencing the status of educated unemployment using logit and probit models and handling unbalanced data using SMOTE. The results showed that the independent variables that affect the status of educated unemployment in the logit and probit models are the same: age group and participation in training. The independent variables that affect the status of educated unemployment in the logit and probit models with SMOTE are also the same: age group, marital status, and participation in training. Unbalanced data handling using SMOTE can increase the balanced accuracy value significantly. Balanced accuracy values for the logit and probit models with SMOTE are higher than the logit and probit models without SMOTE. The logit model with SMOTE is the best because it has the highest balanced accuracy value compared to other models. According to the logit model with SMOTE, the educated unemployed in DKI Jakarta are young and have never married. There is a need for the government to play a role in improving the quality of educational institutions in producing graduates who meet company qualifications and can be hired by employers. Unemployed people who have attended the training, despite having a higher education, may also become unemployed. The training provided has not been able to reduce the unemployment rate. As a result, the government should be able to provide training to improve entrepreneurship skills while also providing capital in the form of business loans to reduce educated unemployment.
Implementasi Metode CHAID (Chi-Squared Automatic Interaction Detection) pada Segmentasi Trend Penjualan Minuman Ringan di Indonesia Via Sulviana; Aji Hamim Wigena; . Indahwati
Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.657 KB) | DOI: 10.29244/xplore.v2i2.91

Abstract

Currently some outlet sells their products by looking at sales trends over a period of time to continue developing their business and devising effective marketing strategies. CHAID (Chi-Squared Automatic Interaction Detection) method is one of the efficient non-parametric statistical methods to classify any aspects that can increase the sales of soft drinks. CHAID selects significant variables based on the Chi-Square test between categories of explanatory variables with response categories. The CHAID method is used if the response variable is nominal or ordinal. This research aims to classify characteristics that characterize diversity and determine the target market that is able to maximize profits on the sales trend of various types of soft drinks by using CHAID method. Results from CHAID are tree diagrams that divide categories of response variables by segments from explanatory variables packaged into more easily understood information. CHAID method produces 11 of 20 segments that affect the trend of soft drink sales spread across big cities of Indonesia. There are 4 independent variable from segment that form, there are city, type of outlet, source of buying and payment method which accuracy that form from segmentation are 71.4%.
Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel Gaussian pada Kekuatan Sinyal Seluler Logananta Puja Kusuma; . Indahwati; Kusman Sadik
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.v8i1.134

Abstract

Cellular signal strength may be affected by its location, so researches concerning signal strength need information about location and analysis method that observe spatial aspect. Spatial Regression analysis evaluates location in modeling relation between explanatory variables and response variable. One of the spatial regression analyses is Geographically Weighted Regression (GWR). This method utilizes location to create weight matrix using certain weighting function. GWR analysis with Gaussian kernel weighting function creates better model than Ordinary Least Square model. The model created using GWR is local model which parameter estimation differs in each observation point. Clustering of observation point is performed to summarize the result of GWR. The number of optimum clusters in clustering based on coefficient is five clusters while the number of optimum clusters in clustering based on p value of t test is four clusters.
Pemodelan Data Multi-Label dengan Pendekatan Multivariate Generalized Linear Mixed Model (MGLMM) Dairul Fuhron; . Indahwati; Farit Mochamad Afendi
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.v8i1.148

Abstract

Multi-label data refers to a type of categorical data where an object may has more than one corresponding label or possible values. Multi-label data are commonly found in many fields, one of them is market research of the sweetened condensed milk (SCM) and sweetened condensed creamer (SCC) products. According to product characteristic, market research for the aforementioned product is appropriately conducted on the outlet level. An outlet may use more than one product’s brand in the same time frame. That condition inflict brand choice information to be represented under multi-label data. This research used problem transformation method by tranforming a multi-label variable into several single-label variables. Multivariate Generalized Linear Mixed Modeling or MGLMM was selected under consideration of binary multiple responses and correlated responses presumption. Five responses of SCM and SCC brand choice modeling resulted correct model without overdispersion and the scaled pearson chi square statistic is 0.99. Tests of fixed effects indicate three factor significantly affect SCM and SCC brand choice at the 5% level. They are purchase total, province, and type of business. The variance of the random effect intercept is 1.53×10−18 or insignificant, hence MGLMM based model was similar compare to separated GLM based model.
Analisis Kepuasan Terhadap Green Transportation Salvina Salvina; Akbar Rizki; Indahwati Indahwati
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.654 KB) | DOI: 10.29244/xplore.v9i1.251

Abstract

ABSTRACT SALVINA. Analysis of Satisfaction against Green Transportation. Supervised by AKBAR RIZKI and INDAHWATI. One of the stages of the Green Campus 2020 program as an effort of IPB towards World Class University (WCU) is to carry out the Green Transportation (GT) movement. Buses, electric cars, bicycles and electric motorcycle taxis are the GT transportation modes in IPB. The purpose of this study was to determine the level of satisfaction of GT users and identify attributes that are important and need to be improved so that the GT service system can be improved. This study uses survey data conducted by researchers on undergraduate students who use GT transportation mode in the past week. The sampling method used is random layered sampling with layers in the form of faculties. The analytical methods used are Importance Performance Analysis (IPA), Customer Satisfaction Index (CSI), biplot analysis, and simple correspondence analysis. The CSI value obtained is 2.96 (1-4 scale) with a CSI percentage of 74% in other words the user is satisfied with the service he receives. The aspects that need to be improved are aspects of empathy and reliability on electric cars and assurance on bicycles, while other aspects have been considered good. Biplot analysis shows the diversity of satisfaction obtained from aspects (reliability, empathy, tangibles, assurance, and responsiveness) is the same. Simple correspondence analysis shows students of the Faculty of Veterinary Medicine (FKH), Faculty of Animal Husbandry (FAPET), Faculty of Forestry (FAHUTAN), and General Competency Education Program (PPKU) often use electric cars. Faculties that often use buses are Faculty of Agriculture (FAPERTA), Faculty of Agricultural Technology (FATETA), Faculty of Fisheries and Marine Sciences (FPIK) and Faculty of Mathematics and Natural Sciences (FMIPA). The mode of bicycle transportation cannot be characterized in any faculty because at least the respondents use it. Keywords: biplot, green transportation, IPA-CSI, simple correspondence
Perbandingan ARIMA dan Artificial Neural Networks dalam Peramalan Jumlah Positif Covid-19 Di DKI Jakarta Tri Wahyuni; Indahwati Indahwati; Kusman Sadik
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.867 KB) | DOI: 10.29244/xplore.v10i3.846

Abstract

DKI Jakarta is the center of the spread of Covid-19. This is indicated by the higher cumulative number of Covid-19 positive in DKI Jakarta compared to other provinces. The high number of cases in DKI Jakarta is a concern for all groups, so it is necessary to do forecasting to predict the number of Covid-19 positive in the next period. Accurate forecasting is needed to get better results. This study compares the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in predicting the number of Covid-19 positive in DKI Jakarta. Forecasting accuracy is calculated using the value of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The results show that the best model for forecasting the number of Covid-19 positive in DKI Jakarta is ARIMA(0,1,1) with drift, with a MAPE value of 15.748, an RMSE of 268.808, and the correlation between the forecast value and the actual value of 0.845. Forecasting using ARIMA(0,1,1) with drift and BP(3,10,1) models produces the best forecast for the long forecasting period of the next six weeks.
Analisis Regresi Logistik dan Cart untuk Credit Scoring dengan Penanganan Kelas Tak Seimbang Siwi Haryu Pramesti; Indahwati Indahwati; Utami Dyah Syafitri
Xplore: Journal of Statistics Vol. 11 No. 3 (2022): Vol. 11 No. 3 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (537.947 KB) | DOI: 10.29244/xplore.v11i3.1015

Abstract

The absence of collateral for a type of credit will increase the bank's credit risk (failed to pay). Banks apply the precautionary principle by managing their credit portfolios so that potential hazards that occur can be measured and controlled in a model. Credit scoring describes how likely a debtor will fail with payments. This study aimed to compare logistic regression analysis and Classification and Regression Trees (CART) in classifying debtors to evaluate credit policies. One of the problems in classification is unbalanced data. Synthetic Minority Oversampling Technique (SMOTE) is a technique to handle the unbalanced problem in classification. The results show that the logistic regression model with SMOTE has higher sensitivity than the CART model, and there was no difference in Area Under Curve (AUC). The variables that have significant effects on the classification of debtors (good, bad) are level of education, homeownership status, and income.
Co-Authors A. A., Muftih Aam Alamudi Aditya Ramadhan Agus Mohamad Soleh Agustini , Ni Ketut Yulia Agustini, Ni Ketut Yulia Aji Hamim Wigena Akbar Rizki Alahmad, Ali Omar Alfi Indah Nurrizqi Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amelia, Reni Amin, Yudi Fathul Anang Kurnia Andi Harismahyanti A. Anik Djuraidah annida marsa salsabila Antonius Benny Setyawan Ardi Surya Ari Handayani Arie Anggreyani Aristawidya, Rafika ASEP SAEFUDDIN Assyifa Lala Pratiwi Hamid Aunuddin . Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Bukhari, Ari Shobri Cahya, Septa Dwi Cahyani Oktarina Chrisinta, Debora Dairul Fuhron Daswati, Oktaviyani Dea Fisyahri Akhilah Putri Dian Kusumaningrum Erfiani Erfiani Erfiani Erfiani Etis Sunandi Eva Wany, Eva Evita Purnaningrum Farit Mochamad Afendi Farit Mohamad Afendi Fatimah Fatimah Fira Nurahmah Al Aminy Fitrianto, Anwar Fulazzaky, Tahira Ghina Fauziah Hanifa Izzati Hanifa Izzati Hari Wijayanto Hasanah, Lailatul I Gusti Putu Purnaba I Made Sumertajaya Iin Maena Ilham Azagi Indah, Yunna Mentari Intan Juliana Panjaitan Irawan Irawan Jasmin Nur Hanifa Jaya, Eddy Santosa Julianti, Elisa D Kamil, Farid Ikram Karunia, Nia Kayla Fakhriyya Jasmine Kefi Amtiran, Chandraone Putra Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kholidiah, Kholidiah Khusnia N. K. Khusnia Nurul Khikmah Kristorio, Kevin Kusman Sadik Laily Nissa Atul Mualifah Latifah, Leli Lestari, Nila Lili Puspita Rahayu Logananta Puja Kusuma Made Sumertajaya Maghfiroh, Firda Aulia Mega Maulina Miranti, Ita Miranti, Ita Mohammad Masjkur Mualifah, Laily Nissa Muftih Alwi Aliu Muhammad Nur Aidi Naima Rakhsyanda Narindria, Yasmin Nadhiva Nurul Fadhilah Panjaitan, Intan Juliana Pika Silvianti Puput Cahya Ambarwati Putra, Stefanus Morgan Setyadi Perdana Putri, Christiana Anggraeni Ramdani, Indri Rasyid, Baharun Ray Sastri Regan, Regan Reni Amelia Reni Amelia Reza, Charolina Therezia Rifki Hamdani Rindy Anggun Pertiwi Rizka Annisa Mingka Salvina Salvina Setyo Wahyudi Silmi Annisa Rizki Manaf Siti Hafsah Siwi Haryu Pramesti Tina Aris Perhati Titin Agustina Titin Suhartini Titin Suhartini, Titin Titin Yuniarty Yuniarty Tri Wahyuni Utami Dyah Syafitri Vera Maya Santi Via Sulviana Vitona, Desi Wahyudi Setyo Wandee Wanishsakpong Widya Putri Nurmawati Yani Prihantini Hiola Yenni Angraini Yuniarty, Titin Yunna Mentari Indah Zulkarnain, Rizky _ Aunuddin