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All Journal Epsilon: Jurnal Matematika Murni dan Terapan JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Jurnal technoscientia Jurnal Penelitian Sains ABDIMAS SILIWANGI Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Abdimasku : Jurnal Pengabdian Masyarakat JURNAL TEKNOLOGI TECHNOSCIENTIA ABDIMASY: Jurnal Pengabdian dan Pemberdayaan Masyarakat Parameter: Journal of Statistics Bulletin of Applied Mathematics and Mathematics Education Jnanadharma Jurnal Statistika Industri dan Komputasi
Kris Suryowati, Kris
Jurusan Statistika, Fakultas Sains Terapan, Institut Sains & Teknologi AKPRIND Yogyakarta
Religion Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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PREDIKSI INDEKS PEMBANGUNAN MANUSIA PADA KABUPATEN/KOTA DI PROVINSI NUSA TENGGARA TIMUR MENGGUNAKAN BACKPROPAGATION DENGAN KOMBINASI LEARNING RATE DAN EPOCH Mercynanda Yuliany Alang; Kris Suryowati; Febriani Astuti
Jurnal Statistika Industri dan Komputasi Vol. 9 No. 1 (2024): Jurnal Statistika Industri dan Komputasi
Publisher : Program Studi Statistika, Fakultas Sains dan Teknologi Informasi, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/statistika.v9i1.4821

Abstract

Penelitian ini dilakukan untuk memprediksi indeks pembangunan manusia (IPM) di Provinsi Nusa Tenggara Timur (NTT) tahun 2023. Dalam implementasinya, data dinormalisasi menggunakan min–max normalization sebelum dilakukan peramalan dengan membandingkan kombinasi parameter-parameter metode backpropagation, yaitu parameter learning rate (laju pembelajaran) dan epoch (iterasi). Dari kombinasi kedua parameter tersebut akan diseleksi kombinasi mana yang paling baik menggunakan Mean Absolute Percentage Error (MAPE). Penelitian ini menggunakan proporsi 90% data latih serta 10% data uji, dan menggunakan arsitektur jaringan 4-3-1. Berdasarkan hasil pengujian kombinasi parameter untuk metode backpropagation, parameter terbaik adalah parameter dengan Epoch = 5000 dan learning rate = 0,15 dengan hasil MAPE yang paling minimum yakni 0,22690492% dan akurasi prediksi 99,77%, terhadap 257 data latih dan 29 data uji. Hasil penelitian ini menunjukan bahwa prediksi IPM Provinsi NTT tahun 2023 adalah 65 dengan kategori sedang.
Applying negative binomial regression analysis to overcome the overdispersion of Poisson regression model for malnutrition cases in Indonesia Setyawan, Yudi; Suryowati, Kris; Octaviana, Dita
Bulletin of Applied Mathematics and Mathematics Education Vol. 2 No. 2 (2022)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v2i2.4948

Abstract

Indonesia is one of the developing countries that is struggling to eradicate malnutrition problem. Malnutrition that occurs over a long period of time can have an impact on deaths for the sufferers and decreasing human’s quality of life. This study aims to model the case of malnutrition that occurred in Indonesia Provinces during 2015, and get the main factors that cause malnutrition problem. Variables studied consists of Malnutrition (Y), Vitamin A consumption (X1), Exclusive breastfeeding (X2), Immunization (X3), Water quality (X4), Healthcare center (X5), and Poverty level (X6). Based on the Kolmogorov-Smirnov test, the results of malnutrition data in Indonesia Province in 2015 does not follow Poisson distribution because of overdispersion. The presence of overdispersion cases in the Poisson regression model will have an impact on the inappropriateness of inferences. An alternative model that can accomodate this case is negative binomial regression model.  By using this model, factors that are considered influencing malnutrition cases in Indonesia provinces in 2015 are Immunization (X3), Water quality (X4), and Poverty level (X6). The best model obtained from negative binomial regression analysis is μ ̂_i=exp(2.5111-0.0338X_3+0.0295X_4+0.0576X_6).
Application Biplot Analysis on Mapping of Non-Convertive Diseases in Indonesia Suryowati, Kris; JP, Maria Titah; Nasution, Nurzaidah
Parameter: Journal of Statistics Vol. 1 No. 2 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (391.713 KB) | DOI: 10.22487/27765660.2021.v1.i2.15518

Abstract

Non-communicable diseases is diseases that are not caused by germs but rather because of physiological or metabolic problems in human body tissues. Usually, this disease occurs due to unhealthy lifestyle. One way to find out how large the spread of non-communicable diseases is by mapping the disease using biplot analysis. Biplot analysis is applied to determine the proximity information between objects, the length of the change vector, the correlation between modifiers, and the value of the change in an object. The study was conducted in 33 provinces with twelve non-communicable diseases. Descriptive analysis of twelve non-communicable diseases averaged the highest joint disease of 10.51 followed by hypertensive disease 8.85 and Stroke 6.42. While the lowest average disease is Heart Failure disease by 0.10 it is still open to research with other methods and also need to add supporting variables
Application of Negative Binomial Regression Analysis to Overcome the Overdispersion of Poisson Regression Model for Malnutrition Cases in Indonesia Setyawan, Yudi; Suryowati, Kris; Octaviana, Dita
Parameter: Journal of Statistics Vol. 2 No. 2 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i2.15903

Abstract

Indonesia is one of the developing countries that is struggling to eradicate the malnutrition problem. Malnutrition that occurs over a long period of time can have an impact on the deaths of sufferers and decrease human quality of life. This study aims to model the case of malnutrition that occurred in Indonesia Provinces during 2015 and get the main factors that cause the malnutrition problem. Variables studied consist of Malnutrition (Y), Vitamin A consumption (X1), Exclusive breastfeeding (X2), Immunization (X3), Water quality (X4), Healthcare center (X5), and Poverty level (X6). Based on the Kolmogorov-Smirnov test, the results of malnutrition data in Indonesia Province in 2015 do not follow Poisson distribution because of overdispersion. The presence of overdispersion cases in the Poisson regression model will have an impact on the inappropriateness of inferences. An alternative model that accommodates this case is the negative binomial regression model. By using this model, factors that are considered influencing malnutrition cases in Indonesia provinces in 2015 are Immunization (X3), Water quality (X4), and Poverty level (X6).
Co-Authors Alfriyani R D Klau Eka Christy Erlin Koni Ngago Daga Etika Permata Sari Etika Permatasari Febriani Astuti Hadi Prasetyo Suseno Inggit Fatika Inggit Fatika Jatipaningrum, Maria Titah JP, Maria Titah Kartiko Kartiko Mardiyanti Dendo Maria Titah Jatipaningrum Maria Titah JP Mercynanda Yuliany Alang Muhammad Sholeh Nasution, Nurzaidah Noviana Pratiwi Nurafidah Nurafidah Nurafidah Octaviana, Dita Rokhana Dwi Bekti Rokhana Dwi Bekti Rokhana Dwi Bekti Rokhana Dwi Bekti Setyawan, Yudi Setyawan, Yudi Setyawan, Yudi Suci Eka Azhari Tegar Rezki Pangestu Un, Libertania Maria Melania Esti Yudi Setyawan