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All Journal Pendas : Jurnah Ilmiah Pendidikan Dasar International Journal of Science and Society (IJSOC) Jurnal Riset Statistika Bandung Conference Series: Statistics Al-fiqh : Journal of Islamic Studies Jurnal Industrikrisna Prawara Jurnal Pengabdian Masyarakat
Suwanda
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Religion Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Penerapan Diagram Kendali T2 Hotelling Bootstrap pada Pemantauan Kualitas Udara Kota Jakarta Isman Putra, Rafly; Suwanda
Jurnal Riset Statistika Volume 5, No. 2, Desember 2025, Jurnal Riset Statistika (JRS)
Publisher : UPT Publikasi Ilmiah Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jrs.v5i2.8460

Abstract

Abstract. Air quality in Jakarta has fluctuated significantly due to vehicle emissions and industrial activities. Although it improved during the 2020 pandemic, data from the Jakarta Environment Agency shows an increase in pollutant concentrations in 2021. Therefore, air quality monitoring is important to detect changes that affect public health. This study applies the T² Hotelling Bootstrap Control Chart for individual data (n = 364) to monitor air quality that are not normally distributed. The individual approach is considered more representative for detecting changes in air conditions directly over time. The variables used were PM2.5, PM10, and NO₂, with daily data from 2021 and 2022. The 2021 data was used to determine the control limits for phase I, while 2022 was used for phase II. The Mardia normality test indicated that the data was not normally distributed, so the Bootstrap approach was used. In phase I, out-of-control points were removed until the conditions were in control. In Phase II, 176 out of 364 points exceeded the Upper Control Limit (UCL = 10.38), indicating a deviation from the reference condition. Investigations revealed that the primary causes were pollutant stagnation, transboundary air pollution, and local emissions contributing to the deterioration of Jakarta's air quality. Abstrak. Kualitas udara Kota Jakarta mengalami fluktuasi signifikan akibat emisi kendaraan dan aktivitas industri. Meski sempat membaik selama pandemi 2020, data Dinas Lingkungan Hidup DKI Jakarta menunjukkan peningkatan konsentrasi polutan pada 2021. Oleh karena itu, pemantauan kualitas udara menjadi penting untuk mendeteksi perubahan yang berdampak pada kesehatan masyarakat. Penelitian ini menerapkan Diagram Kendali T² Hotelling Bootstrap untuk data individu (n = 364) guna memantau kualitas udara yang tidak berdistribusi normal. Pendekatan individu dinilai lebih representatif untuk mendeteksi perubahan kondisi udara secara langsung dari waktu ke waktu. Variabel yang digunakan adalah PM2.5, PM10, dan NO₂, dengan data harian tahun 2021 dan 2022.Data 2021 digunakan untuk menentukan batas kendali fase I, sedangkan 2022 sebagai fase II. Uji normalitas Mardia menunjukkan data tidak normal, sehingga digunakan pendekatan Bootstrap. Pada fase I, terdapat titik out of control yang dihapus hingga kondisi in control tercapai. Di fase II, sebanyak 176 dari 364 titik melampaui Batas Kendali Atas (BKA = 10,38), menunjukkan adanya sinyal penyimpangan dari kondisi referensi. Investigasi menunjukkan penyebab utamanya adalah stagnasi polutan, transboundary air pollution, dan emisi lokal yang berkontribusi terhadap memburuknya kualitas udara Kota Jakarta.
Penerapan Diagram Kendali Shewhart pada Model ARMA-GARCH dalam Memantau Proses Keuangan di Pasar Modal Indonesia Maharani, Renita; Suwanda
Bandung Conference Series: Statistics Vol. 5 No. 2 (2025): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v5i2.20556

Abstract

Abstract. The capital market has significant potential to drive the country's economic growth. The Indonesian capital market is inseparable from market uncertainty which influences risk considerations in investing. Financial data modeling using time series methods is effective in modeling stock market returns and volatility. The ARMA model is unable to handle the problem of non-constant residual variance, requiring advanced modeling, namely ARMA-GARCH. However, this model has limitations in monitoring the stability of financial processes. Therefore, a statistical method capable of monitoring financial process stability, namely the Shewhart control chart, is applied. This study uses the ARMA-GARCH model and the Shewhart control chart to model returns and volatility with statistical control tools useful for providing an overview of investment risk in the Indonesian capital market. This study uses data from the Jakarta Composite Index for the period January 2000 to December 2024, totaling 6,089 observations. The results show that the ARMA (1,2) - GARCH (2,1) model is the best model with the smallest AIC and SIC values. The Shewhart control chart for the ARMA (1,2) - GARCH (2,1) model shows 101 uncontrolled signals. This indicates that there were at least 101 market volatility shocks in the Indonesian capital market from January 2000 to December 2024. Abstrak. Pasar modal memiliki potensi besar menjadi motor penggerak pertumbuhan ekonomi negara. Pasar modal Indonesia tidak terlepas dari ketidakpastian pasar yang berpengaruh pada pertimbangan risiko dalam berinvestasi. Pemodelan data keuangan menggunakan metode deret waktu sangat efektif dalam memodelkan return dan volatilitas pasar saham. Model ARMA tidak mampu menangani masalah ragam sisaan yang tidak konstan sehingga diperlukan pemodelan lanjutan yaitu ARMA-GARCH. Namun, model ini memiliki keterbatasan dalam memantau stabilitas proses keuangan. Maka diterapkan metode statistik yang mampu memantau stabilitas proses keuangan yaitu diagram kendali Shewhart. Penelitian ini menggunakan model ARMA-GARCH dan diagram kendali Shewhart untuk memodelkan return dan volatilitas dengan alat kontrol statistik yang berguna untuk memberikan gambaran risiko investasi di pasar modal Indonesia. Penelitian ini menggunakan data Indeks Harga Saham Gabungan (IHSG) periode Januari 2000 sampai Desember 2024 sebanyak 6.089 pengamatan. Hasil penelitian menunjukkan bahwa model ARMA (1,2) – GARCH (2,1) merupakan model terbaik dengan nilai AIC dan SIC terkecil. Diagram kendali Shewhart pada model ARMA (1,2) – GARCH (2,1) menunjukkan sinyal yang tidak terkendali sebanyak 101 kali. Hal ini menandakan bahwa sepanjang periode Januari 2000 sampai Desember 2024 terjadi setidaknya 101 kali guncangan (market volatility) di pasar modal Indonesia.
Co-Authors 10060119027, Izmi Rizkyanisaputri Abdul Kudus Assidiq , Muhammad Nurhasan Aulia Yasmin Azhwa, Nadya Beni Rahmatullah butarbutar, florida Dina Anika Marhayani Dyah Listya Rini Fhilip Trisa Parera Firmansyah, Ervana Nurmelia Ramdhani Gesti Gilang Utama Auliarahman Hasan, Tajudin Isman Putra, Rafly Kariadi, Dodik Krisna, Rian Kurnia , Ismail Maharani, Renita Mufika Reviana Hidayat Nur Hikmah Qathrunnada Salsabila Rahayu Renaldi Silvi Oktaviani Saridewi Tasya Noor Octa Melana Tiara Erlani Putri Tsaltsa Annisaa, Ayang Wahyu Ramadhan Yani