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All Journal Jurnal Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Science and Technology Indonesia Jurnal Penelitian Sains BAREKENG: Jurnal Ilmu Matematika dan Terapan Teorema: Teori dan Riset Matematika QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT KOMPUTEK Inferensi Diseminasi : Jurnal Pengabdian kepada Masyarakat Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal Pepadu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Jurnal Matematika Integratif
Endro Setyo Cahyono, Endro Setyo
Jurusan Matematika, Fakultas MIPA, Universitas Sriwijaya
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Journal : Science and Technology Indonesia

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Robust-Set Covering Problem and Sensitivity Analysis to Determine The Location of Temporary Waste Disposal Sites Octarina, Sisca; Bangun, Putra Bahtera Jaya; Cahyono, Endro Setyo; Suprihatin, Bambang; Sarjani, Ita; Puspita, Fitri Maya; Yuliza, Evi
Science and Technology Indonesia Vol. 9 No. 2 (2024): April
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2024.9.2.260-272

Abstract

The increasing population has resulted in a significant increase in the amount of waste. One effort that can be made to overcome the waste problem is to provide a Temporary Waste Disposal Site (TWDS). This research aims to optimize the TWDS in the Bukit Kecil sub-district, Palembang city, by formulating a Robust-Set Covering Problem (Robust-SCP) model and solving the model with the software. Sensitivity analysis is used to analyze the optimal solution. Bukit Kecil sub-district is the sub-district that has the highest number of TWDS in Palembang city. The robust-SCP model obtained 10 optimal TWDS. Therefore, this research recommends the Robust SCP model as the optimal solution for the determination of TWDS in the Bukit Kecil sub-district, namely TWDS Kartini Street, TWDS front of Starbucks KI Street, TWDS Merdeka Street, TWDS Illegal at 26 Ilir Market, TWDS Flat Block 35, TWDS Flat Block 49, TWDS Merdeka Women’s Prison, TWDS Musi Riverbank Park, TWDS Monpera, and TWDS Cinde Market, with the addition of TWDS Mayor’s Office in 22 Ilir village and TWDS Flat Block 01 in 23 Ilir village. The sensitivity analysis results in this study show that the solution remains optimal if the coefficient change is within the coefficient interval value.
MICE and ADASYN for Missing Data Imputation and Imbalanced Data Handling on Heart Disease Classification Desiani, Anita; Dewi, Deshinta Arrova; Amran, Ali; Pratiwi, Ananda; Andriani, Yuli; Cahyono, Endro Setyo
Science and Technology Indonesia Vol. 10 No. 4 (2025): October
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2025.10.4.1020-1030

Abstract

The quality of data is determined by several things, namely the completeness and balance data. The heart disease dataset from the University of California, Irvine (UCI) has missing and imbalanced data, which if it is not handled, can lead to a lack of accuracy in the prediction model and errors in interpreting the data. To overcome missing data, several methods can be used, one of which is data imputation. Attributes with missing data of 5% or less are handled using imputation methods such as Mean, Mode, and MICE. Attributes with numeric types are handled by Mean. Attributes with categorical types are imputed byMode. Attributes with more than 5% missing data are imputed using the MICE method. Imbalanced data can be handled by applying an oversampling method using the Adaptive Synthetic Sampling Approach (ADASYN). The effect of imputing missing data and addressing class imbalance on heart disease classification performance was tested using Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) algorithms. After handling missing values and data imbalance, improvements were observed in the classification results. The accuracy, precision, recall, and F1-score showed excellent performance, above 90% on several classification methods. The results indicate that handling missing and imbalanced data through Mean, Mode, MICE, and ADASYN positively impacts the performance of classifiers on the UCI heart disease dataset.
Co-Authors Agung Alamsyah Alamsyah, Agung Alfensi Faruk Ali Amran Anita Desiani Armalia, Divasanda Asri Kuntari Asri Kuntari, Asri Bambang Suprihatin Des Alwine Zayanti, Des Alwine Dewi, Deshinta Arrova Dian Cahyawati Eka Susanti Eka Susanti Eka susanti Elisa Nurnawati Evi Yuliza, Evi Fitri Maya Puspita Fitri Maya Puspita Indrawati Indrawati Indrawati Muhammad 'Aqil Muhammad Alifian Nuriman Muji Gunarto Nadya Amalia Yulianti Ning Eliyati Novi Rustiana Dewi Oki Dwipurwani Permata, Anggun Pratiwi, Ananda Putra Bahtera Jaya Bangun, Putra Bahtera Jaya Putra BJ Bangun, Putra BJ Rima Kesuma Robinson Sitepu Sarjani, Ita Sisca Octarina Villando, Gio Yanita, Nurul Fadhila Yuli Andriani