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All Journal Natural Science: Journal of Science and Technology Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Bulletin of Electrical Engineering and Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Aplikasi Statistika & Komputasi Statistik SANITAS: Jurnal Teknologi dan Seni Kesehatan Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of System and Computer Engineering Parameter: Journal of Statistics Asian Journal of Philosophy and Religion Jurnal Sintak STATISTIKA Journal of Waqf and Islamic Economic Philanthropy Journal of Regional Economics and Development Tadulako Science and Technology Journal Tadulako Social Science and Humaniora Journal
Lilies Handayani
Universitas Tadulako
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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Analysis of Skin Disease Infection After the Palu Earthquake Using Binary Logistic Regression Wahyuni, Selvia Anggun; Lilies Handayani; Muhammad Akriyaldi Masdin; Salmia
Parameter: Journal of Statistics Vol. 2 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2021.v2.i1.15682

Abstract

The incidence of skin disease in Indonesia is still relatively high and is a significant problem. This is evidenced by the 2010 Indonesian Health Profile data which shows that skin and subcutaneous tissue diseases are the third rank of the 10 most common diseases among outpatients in hospitals throughout Indonesia. Skin disease is growing, as evidenced by data from the Indonesian Ministry of Health, the prevalence of skin disease throughout Indonesia in 2012 was 8.46%, then increased in 2013 by 9 %. Palu City is an area that has a high skin disease problem. According to the 2016 BPS of Palu City, skin diseases are among the top 10 diseases in Palu City with a total of 11,363 sufferers. The method used in this research is binary logistic regression. Based on the analysis that has been done, it can be concluded that the best model is formed as follows:. Based on the best model, it is found that the factors that influence the transmission of skin diseases after the Palu earthquake are genetic factors.
Clustering of Province in Indonesia Based on Aquaculture Productivity Using Average Linkage Method Putera, Fachruddin Hari Anggara; Mangitung, Septina F.; Madinawati; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 1 (2021)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2021.v2.i1.15683

Abstract

Fisheries are one of the agricultural sub-sectors that play an important role in contributing to income figures for the state and the region because most of Indonesia's territory is water so that the fisheries sector is a sub-sector that is feasible to be developed in this country, one of which is through aquaculture. One of the efforts that can increase and maintain productivity in the aquaculture sector is to classify provinces that produce aquaculture production into groups based on the similarity of characteristics possessed by each province in Indonesia. In this study, clustering was carried out using cluster analysis using the average linkage method and based on the analysis results obtained showed that cluster 1 consists of 25 provinces, cluster 2 consists of 5 provinces, cluster 3 consists of 2 provinces, cluster 4 consists of 1 province, and cluster 5 consists of 1 province with a standard deviation value within a cluster of 11,729 and a standard deviation between clusters of 118,745.
Modeling of Poverty Level in Central Sulawesi Using Nonparametric Kernel Regression Analysis Approach Sakinah, Nur; Nurfitra; Ihlasia, Nurmasyita; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 3 (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.i3.15743

Abstract

Poverty is defined as a person's inability to meet their basic needs. The level of poverty that exists can be used to assess the good or bad of a country's economy. The kernel regression method is used in this study to model the poverty rate in Central Sulawesi in 2020. According to the findings of this study, comparing poverty rate predictions for the Gaussian Kernel function and the Epanechnikov Kernel function with optimal bandwidth can be said to use different kernel functions with optimal bandwidth for each - each of these kernel functions will produce the same curve estimate. So, in kernel regression, the selection of the optimal bandwidth value is more important than the selection of the kernel function. Because of the use of various kernels functions with optimal bandwidth values results in almost the same curve estimation.
Corn Production Exploration of Central Sulawesi Using Multiplicative Winter Model Putera, Fachruddin Hari Anggara; Amelia, Rezi; Handayani, Lilies
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.i2.15943

Abstract

Corn is a very important food ingredient after rice. Central Sulawesi corn production data is in the form of time series data which every year in certain months increases or decreases in production. Therefore, the method that can be used for forecasting is the winter multiplicative method. This study aims to build the best model for forecasting corn production in Central Sulawesi using the winter multiplicative method. The results of this study are used to explore corn production for the next period. Modeling is done by selecting the best combination of parameters and the best combination of model parameters is obtained with a mean absolute percentage error (MAPE) of 18% with a value of α = 0,5; γ = 0,1; and β = 0,1. The data plot of the forecasted corn production shows fluctuations which indicate seasonal factors and trends in it
REGIONS GROUPING IN CENTRAL SULAWESI PROVINCE BY TRANSMITTED DISEASE USING FUZZY GUSTAFSON KESSEL Fajri, Mohammad; Rais, Rais; Handayani, Lilies
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (367.308 KB) | DOI: 10.30598/barekengvol17iss1pp0275-0284

Abstract

Health is one of the main indicators in determining the human development index. This is in contradiction with the situation in several areas in Indonesia where infectious diseases are the cause of death and have become extraordinary events. It was recorded in Central Sulawesi that in 2020 there were 8 extraordinary events due to infectious diseases which made this province become relatively high infectious diseases. One of the efforts that can be made to identify infectious diseases in an area is to form a grouping of locations into a group that has similarities and same characteristics. This is intended to provide information related to health in each region. Cluster analysis is one of method that can be used to grouping the data. Cluster analysis is the process of dividing data into a group based on the degree of similarity. Data with similar characteristics will be gathered in one group. One of the algorithms in cluster analysis is Fuzzy Gustafson Kessel which can produce relatively better groupings compared to the basic algorithms in cluster analysis. This study will use data on infectious diseases in Central Sulawesi Province with several recorded infectious diseases. From 13 regions, 5 clusters were formed. Clusters 1, 2 and 3 each consist of 3 regions, while clusters 4 and 5 each consist of 2 regions.
Comparison of Gaussian and Epancehnikov Kernels Fadillah, Nur; Audina Dariah, Priliany; Anggraeni, Anisa; Cahyani, Nur; Handayani, Lilies
Tadulako Social Science and Humaniora Journal Vol. 3 No. 1 (2022): Tadulako Social Science and Humaniora Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sochum.v3i1.15745

Abstract

Kernel regression is a nonparametric analysis with smoothing method. Smoothing has become synonymous with nonparametric methods used to estimate functions. The purpose of smoothing is to remove variability from data that has no effect so that the characteristics of the data will appear clear. Kernel regression has a flexible form and the mathematical calculations are easy to adjust. In kernel regression, an estimator is known which is usually used to estimate the regression function, namely the Nadaraya-Watson estimator. This study aims to show how to estimate data using nonparametric regression Gaussian and Eponocvh kernels with the Nadaraya-Watson estimator and the bandwidth selection methods are "Rule of Thumb" bandwidth, Unbiased Cross Validation, Biased Cross Validation and Complete Cross Validation. The results of this study indicate that the MSE value generated by the Epanechnikov kernel function and the Gaussian kernel uses the optimal bandwidth. Statistically, the MSE value generated by the Epanechnikov kernel is almost close to the value in the Gaussian kernel, so it can be said that the MSE value produced by the two kernel functions is almost the same. Based on the plot of estimation results for the Eponocvh kernel function and the Gaussian kernel using the optimal bandwidth, it is very close, so it can be said that the use of a different kernel function with the optimal bandwidth for each of the kernel functions will produce the same estimated regression curve. The results of this study support the opinion expressed by Hastie and Tibshirani, which states that in kernel regression the selection of the smoothing parameter (bandwidth) is much more important than choosing the kernel function.
Improving nutrient prediction models with polynomial and ratio features and mRMR selection Indriani, Fatma; Budiman, Irwan; Kartini, Dwi; Handayani, Lilies
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.9189

Abstract

Due to limited space and regulations, food labels often lack information on micronutrients, i.e., vitamins and minerals. Accurately predicting missing these micronutrient data is essential yet challenging. This study explores the feasibility of using machine learning to predict these missing nutrients based on a limited reported nutrient (protein and carbs). Using the Tabel Komposisi Pangan Indonesia (TKPI) dataset, we evaluated the performance of 12 diverse classifiers to predict binary classes ("low" or "high") for 13 target micronutrients. Random forest emerged as the best performing classifier with an average accuracy of 0.7421 across all target nutrients. Additionally, we introduced feature engineering techniques by incorporating polynomial and ratio features to enhance model performance. Minimum redundancy maximum relevance (mRMR) feature selection was then applied to identify the most informative features. This approach boosted the average accuracy of the random forest classifier to 0.7591. These findings highlight the efficacy of feature engineering and selection in enhancing nutrient prediction models, demonstrating the potential to improve consumer knowledge about unknown nutrients in food.
Co-Authors Adawiyah, Laila Amelia, Rezi Andi Faidilah Anisa Anggraeni, Anisa Aryandi Audina Dariah, Priliany Ayudita Rahmi Aristya Baharuddin Baharuddin Baso Akib Betricia , Jesicca Dwi Kartini, Dwi Fatma Indriani Favorisen R. Lumbanraja Fuad Zubaidi Handayani, Vitri Aprilla Harahap, Diana Sari Hartas Hasbi Herteno, Rudy Ihlasia, Nurmasyita Irwan Budiman Islami, Mira Bela Jassinca Chrissma Audina Jonathan, Steven Kadoena, Faldi Christiawan Lunardi, Louis Madinawati Mangitung, Septina F. Moh. Jusman Mohammad Fajri Muhammad Akriyaldi Masdin Muhammad Reza Faisal, Muhammad Reza Nahrul Hayati Nur Azizah Janad Nur Cahyani Nur Fadillah, Nur Nur Sakinah Nur'eni Nur, Apriyansyah Nureni, Nureni Nurfitra Nurhazanah, Andi Cahyatri Nursyahraini Husen Nur’eni Nur’eni Putera, Fachruddin Hari Anggara Rachman, Rohis Rahayu , Sri Rais Rais Rais Rauf, Wahidah Risnawati Risnawati Sagap, Marni Salmia Saragih, Triando Hamonangan Saskia Amalia Putri septyani kawuwung Sihombing, Urianto Siti Aisyah Solechah Suriani Jihad Surni’a Surni’a Wahyuni, Selvia Anggun Yudy Novindri Tadale