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Analysis of Inflation Rates During and After the COVID-19 Pandemic Using the K-Means Clustering Method and Kruskal-Wallis Test Fadhila, Riska Nuril; Ulinnuha, Nurissaidah; Hafiyusholeh, Moh
Jurnal Fourier Vol. 14 No. 2 (2025)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2025.142.56-67

Abstract

Inflation occurs when excessive demand results in an overall increase in the prices of goods and services. During the COVID-19 pandemic, the inflation rate in Indonesia leveled off due to the weakening economy. However, in 2022, there was a spike in post-COVID-19 inflation due to increased public demand as pandemic conditions improved. Stable inflation is a requirement for sustainable economic growth and improving people's welfare. In handling inflation problems in various regions, variables and unique circumstances in each region are very important. This research aims to determine whether significant differences exist in the clustering of inflation rates in Indonesia during and after the COVID-19 pandemic. The research results using the Kruskal-Wallis test and the K-Means method obtained that the clustering of inflation rates with k=2 provides good results, as indicated by the Silhouette Coefficient value of 0.66. In addition, there is a significant difference between the current (2020-2021) and post (2022-2023) years of COVID-19 as evidenced by the Kruskal-Wallis test with a p-value < 0.05.
OPTIMIZATION OF PARAMETERS IN MEWMV AND MEWMA CONTROL CHARTS FOR CLEAN WATER QUALITY CONTROL AT PP KRAKATAU TIRTA GRESIK Hafiyusholeh, Moh.; Khaulasari, Hani; Firmansyah, Fery; Ulinnuha, Nurissaidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0729-0742

Abstract

Water is a vital resource whose quality directly affects public health. In Gresik Regency, water treatment processes must be closely monitored, particularly during production. PT PP Krakatau Tirta, a key provider of clean water in the region, plays a strategic role in treating raw water from the heavily polluted Bengawan Solo River. Ensuring that the treated water consistently meets health standards is crucial, highlighting the need for an effective process. This study aims to evaluate the clean water production process and assess the process capability in maintaining the quality of water produced by PT PP Krakatau Tirta Gresik. Laboratory data on key parameters, including pH, dissolved iron, and total dissolved solids, were collected daily from November 25, 2022, to May 31, 2023. These mandatory indicators were analyzed using Multivariate Exponentially Weighted Moving Variance (MEWMV) and Moving Average (MEWMA) control charts to assess process performance. A key contribution of this research lies in optimizing smoothing parameters to enhance control chart performance. Sixteen combinations of (ω,λ) were tested for MEWMV, with the optimal configuration found at (λ = 0.4) and (ω = 0.4), indicating that process variability is statistically stable. For MEWMA, nine values of λ were evaluated, and the optimal weight (λ=0.9) was identified as optimal, yielding a stable process mean after removing two out-of-control points. PT PP Krakatau Tirta, which plays a strategic role in treating raw water from the polluted Bengawan Solo River, was selected as a case study to evaluate the effectiveness of advanced monitoring methods. The results indicate that its clean water production process is well-controlled and capable, with water quality consistently meeting health and safety standards.
Implementasi Chi-Square dan Oversampling Pada Klasifikasi Kesehatan Janin dengan Support Vector Machine Wahyudi, Sharenada Norisdita; Ulinnuha, Nurissaidah; Hafiyusholeh, Moh
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 3 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n3.327-337

Abstract

Pemantauan kesehatan janin menjadi aspek penting karena hal tersebut merupakan bentuk antisipasi terkait deteksi potensi patologis yang berkemungkinan membahayakan janin maupun ibu hamil. Sebagaimana dilansir dalam website resmi UNICEF, setidaknya terdapat 2,3 juta bayi meninggal pada bulan pertama kelahiran dengan 90% dari total keseluruhan merupakan kasus kematian bayi didalam kandungan pada masa kehamilan diatas 20 minggu. Selain membahayakan bayi, kesehatan janin juga berdampak pada keselamatan ibu hamil. Oleh karena itu, perlu dilakukan suatu usaha mitigasi resiko guna memperkecil potensi kematian janin dengan mendeteksi kesehatan janin dengan melakukan klasifikasi dengan algoritma SVM. Data yang digunakan pada penelitian ini adalah hasil pemeriksaan kandungan berupa data cardiotocography, berisikan 2126 data yang berisikan 21 fitur yang terkategorikan menjadi 3 kelas yaitu 1665 normal, 295 kelas suspect dan 176 kelas pathologic. Berdasarkan perbedaan yang cukup signifikan pada jumlah data ditiap kelas, dilakukan balancing data dengan metode Synthetic Minority Over-Sampling Technique (SMOTE). Selain itu, dilakukan seleksi fitur dengan menggunakan Chi-Square pada 21 fitur yang kemudian didapati 12 fitur terpilih untuk diklasifikasikan menggunakan algoritma SVM. Skema klasifikasi dilakukan dengan beberapa tahapan, dan didapati bahwa penambahan seleksi fitur Chi-Square dan SMOTE berhasil meningkatkan akurasi klasifikasi menjadi 98%, dengan nilai presicion sebesar 99%, recall 98% dan F-1 Score sebesar 98%. Fetal health monitoring is an important aspect because it forms for detect potential pathologies that may endanger fetus and pregnant mother. As reported on UNICEF, at least 2.3 million babies die in the first month of birth with 90% of the total being cases of intrauterus fetal death. In addition to endangering the baby, fetal health also has an impact on pregnant mother. As an effort to minimize the potential and risk of fetal death, is classify the health status of the fetus using the SVM algorithm. The data used in this study are gynecological results in the field of cardiotocography data, containing 2126 data that have been categorized into 3 classes, namely normal, suspect and pathologic classes. Cardiotocography data in this study was included 2,126 observations distributed across 21 features grouped into three categories: 1,665 normal, 295 suspect, and 176 pathological. Given the significant variation in the number of observations across each category, a data balancing technique, known as the Synthetic Minority Over-Sampling Technique (SMOTE), was employed to address this imbalance. Furthermore, a feature selection process was implemented, employing the Chi-Square method on the 21 features. This method identified 12 features that were subsequently classified using the SVM algorithm. The classification scheme was executed in multiple stages, and it was observed that the incorporation of both Chi-Square and SMOTE feature selection led to a substantial enhancement in classification accuracy, reaching 98%, accompanied by a 99% precision value, 98% recall, and an 98% F-1 score.