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ANALISIS PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN JUMLAH DESA/KELURAHAN MENURUT JENIS PENCEMARAN LINGKUNGAN HIDUP TAHUN 2024 MENGGUNAKAN PCA DAN K-MEANS CLUSTERING Rahmadani, Afifah Nisa; Hidayat, revalinaputria; Riza, Fikri Ahmad; Simanungkalit, Ariel Muda; Sari, Surya Puspita; Effendi, Magdalena
BESTARI BPS Kalimantan Timur Vol. 5 No. 2 (2025): Vol. 5 No. 02 (2025): Bestari 10th Edition
Publisher : BPS Kalimantan Timur

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Environmental pollution is a critical issue in Indonesia due to its impact on public health and ecosystem sustainability. Variations in pollution conditions across provinces indicate the need for analyses that can comprehensively describe spatial patterns. This study aims to classify 38 provinces in Indonesia based on the number of villages and urban villages according to types of environmental pollution, including water, soil, air pollution, and areas without pollution, in 2024. The data were obtained from official publications of Statistics Indonesia (BPS). The analysis employed Principal Component Analysis (PCA) as a dimensionality reduction technique, followed by K-Means Clustering to group provinces with similar pollution characteristics. The initial analysis was supported by descriptive statistical exploration and data standardization. The PCA results show that two principal components explain 89.41% of the total data variance. The optimal number of clusters was determined using the Elbow method and Silhouette coefficient, indicating that a two-cluster solution provides the most appropriate clustering structure (Silhouette score = 0.40). The clustering results reveal differences in environmental pollution characteristics between provinces in western and eastern Indonesia. These findings provide an initial, area-based descriptive overview of environmental pollution distribution in Indonesia and can support regional environmental management and more targeted policy formulation.
PERAMALAN KUALITAS UDARA DI KOTA BALIKPAPAN BERDASARKAN INDIKATOR NILAI PM2.5 MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Butar, Judah; Anastasya; Riswanty Margareth Malau; Galavinozky Roigabe Gumilang Rajaguguk; Surya Puspita Sari; Magdalena Effendi
BESTARI BPS Kalimantan Timur Vol. 5 No. 2 (2025): Vol. 5 No. 02 (2025): Bestari 10th Edition
Publisher : BPS Kalimantan Timur

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Abstract

Kualitas udara merupakan indikator penting dalam menilai kondisi lingkungan karena berpengaruh langsung terhadap kesehatan manusia. Peningkatan aktivitas industri, transportasi, serta pembangunan infrastruktur di Kota Balikpapan seiring dengan pengembangan Ibu Kota Nusantara (IKN) berpotensi meningkatkan konsentrasi polutan udara, khususnya partikulat halus PM2.5. Penelitian ini bertujuan untuk menganalisis pola dan meramalkan konsentrasi PM2.5 di Kota Balikpapan. Penelitian ini menggunakan metode Autoregressive Integrated Moving Average (ARIMA) untuk memodelkan data deret waktu PM2.5 harian. Data yang digunakan mencakup periode Januari hingga September 2025 dengan total 273 observasi, yang dibagi menjadi 80% data training yaitu sebanyak 218 observasi dan 20% data testing sebanyak 55 observasi. Metode ARIMA dipilih karena kemampuannya dalam menangkap pola fluktuatif pada data deret waktu. Hasil penelitian menunjukkan bahwa model ARIMA(2,0,1) merupakan model terbaik untuk peramalan konsentrasi PM2.5 di Kota Balikpapan berdasarkan kriteria pemilihan model dan evaluasi kinerja peramalan. Model ini mampu merepresentasikan pola data historis dengan baik dan memberikan hasil peramalan yang cukup akurat pada data pengujian. Kesimpulan dari penelitian ini menunjukkan bahwa model ARIMA(2,0,1) dapat digunakan sebagai alat peramalan kualitas udara, khususnya konsentrasi PM2.5 di Kota Balikpapan, serta berpotensi mendukung pengambilan kebijakan dalam pengendalian pencemaran udara di wilayah tersebut.
Pemodelan PEMODELAN DAN PERAMALAN CURAH HUJAN DI BALIKPAPAN MENGGUNAKAN METODE ARIMAX: Studi Kasus Curah Hujan Kota Balikpapan Bulan Januari-Desember 2024 Septiansyah, Rifky; Hashifah Najma Zahra; Ananda Reza Putra Rahmadan; Sarah Katerina Simbolon; Surya Puspita Sari; Magdalena Effendi
BESTARI BPS Kalimantan Timur Vol. 5 No. 2 (2025): Vol. 5 No. 02 (2025): Bestari 10th Edition
Publisher : BPS Kalimantan Timur

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The city of Balikpapan experiences high and fluctuating rainfall intensity, rendering it vulnerable to hydrometeorological disasters such as floods and landslides. Accurate rainfall forecasting is crucial for risk mitigation. This study aims to model and forecast daily rainfall in Balikpapan using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method, considering environmental factors. This study utilizes daily data from the January-December 2024 period sourced from NASA POWER, encompassing the variables of rainfall, average temperature, air humidity, and wind speed. The data was analyzed using the Box-Jenkins approach, which includes stationarity tests, parameter estimation, and diagnostic checks. The results indicated that the data was not stationary in variance, necessitating a logarithmic transformation. The best-fit model, identified by the lowest AIC value (582.05), was ARIMAX (1, 0, 1). Analysis of exogenous variables identified that Air Humidity and Wind Speed significantly influence rainfall, whereas Average Temperature does not. The Ljung-Box diagnostic test confirmed that the model's residuals behave as white noise (p-value 0.2662). The model's forecasting evaluation yielded an RMSE of 9.9617. The model proved reasonably effective in capturing the general rainfall patterns, despite limitations in predicting extreme spikes. These findings can contribute a scientific basis to support early warning systems and disaster mitigation policies in Balikpapan.
Pengaruh Faktor Ekonomi dan Lingkungan terhadap Deforestasi di Indonesia Elfrida Eka Ayuningtyas; Muhammad Akmal Fadhillah; Nathania, Vanisa Azra; Surya Puspita Sari; Magdalena Effendi
BESTARI BPS Kalimantan Timur Vol. 5 No. 2 (2025): Vol. 5 No. 02 (2025): Bestari 10th Edition
Publisher : BPS Kalimantan Timur

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Indonesia is one of the countries with the highest rate of deforestation in the world, influenced by various economic and environmental factors. This study aims to analyze the effect of Gross Regional Domestic Product (GRDP), forest area, population density, forest fires, and CO? emissions on deforestation in Indonesia during the 2018–2022 period using panel data regression analysis. Based on the Chow, Hausman, and Lagrange Multiplier tests, the Random Effects Model was identified as the most appropriate model to explain the relationships among variables. The results indicate that forest fires, forest area, and CO? emissions have a significant effect on deforestation, while GRDP and population density show no significant effect. Forest fires and CO? emissions positively influence deforestation, and forest area also shows a positive effect, indicating that provinces with larger forest coverage tend to record higher levels of deforestation. These findings suggest that environmental factors play a more dominant role than economic factors in determining the rate of deforestation in Indonesia.
ANALISIS KLASTER WILAYAH PADA PENGELOLAAN SAMPAH MELALUI BANK SAMPAH DI KALIMANTAN MENGGUNAKAN FUZZY K-MEANS UNTUK PENGUATAN KEBIJAKAN LINGKUNGAN Rosady, Erzha Nafilah; Rizqi, Zafyra Nur; Nur, Muhammad Dafiq; Sinambela, Johannes Martin; Sari, Surya Puspita; Effendi, Magdalena
BESTARI BPS Kalimantan Timur Vol. 5 No. 2 (2025): Vol. 5 No. 02 (2025): Bestari 10th Edition
Publisher : BPS Kalimantan Timur

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The increase in waste generation in Kalimantan, along with population growth and economic activity, has become a complex environmental issue. The disparity in management capacity among regions has led to differences in the effectiveness of waste management systems, particularly in the implementation of waste banks as a form of community-based management. This study aims to cluster regencies/cities in Kalimantan based on waste management characteristics using the Fuzzy K-Means method to obtain a spatial mapping that supports the formulation of fairer and more efficient waste management policies. Secondary data were obtained from the National Waste Management Information System (SIPSN) and the Central Statistics Agency (BPS), covering variables such as waste generation, managed waste volume, land area, and population. The analysis results show that the optimal number of clusters is two (2): one cluster representing regions with high waste management performance dominated by major cities such as Balikpapan and Banjarmasin, and another cluster representing regions with low management performance due to limited infrastructure. These findings highlight spatial disparities in the effectiveness of waste bank programs across Kalimantan. The clustering results are expected to serve as a foundation for local governments in developing strategies to strengthen waste management policies, particularly through the implementation of the national program “1 RW 1 Waste Bank” and the adoption of sustainable circular economy principles.