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Magdalena Effendi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences

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ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI JUMLAH TIMBULAN SAMPAH DI PROVINSI JAWA TIMUR MENGGUNAKAN METODE RANDOM FOREST REGRESSION Alamsyah, Meuthia Nur Azizah; Sofia Dawani Silaban; Dave Agles Rizky Nor; Lalu Muhamad Rifani Fadli; 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

Permasalahan timbulan sampah di Provinsi Jawa Timur terus meningkat seiring dengan pertumbuhan penduduk dan aktivitas ekonomi. Penelitian ini bertujuan untuk menganalisis faktor-faktor sosial ekonomi yang memengaruhi jumlah timbulan sampah tahun 2024 menggunakan metode Random Forest Regression (RFR). Data yang digunakan merupakan data sekunder dari SIPSN dan BPS yang mencakup 38 kabupaten/kota dengan lima variabel prediktor: luas wilayah, pertumbuhan penduduk, kepadatan penduduk, rata-rata pengeluaran per kapita, dan PDRB. Parameter optimal model diperoleh pada nilai mtry = 2 dan ntree = 500. Hasil penelitian menunjukkan bahwa variabel PDRB dan luas wilayah merupakan faktor yang paling berpengaruh berdasarkan analisis feature importance. Nilai R² sebesar 0,198 menunjukkan bahwa masih terdapat variabel lain di luar model yang berpengaruh terhadap variasi timbulan sampah. Penelitian ini diharapkan dapat memberikan wawasan berbasis data bagi pemerintah daerah dalam merumuskan kebijakan pengelolaan sampah yang lebih efektif dan berkelanjutan.
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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Air quality is an important indicator in assessing environmental conditions because it directly affects human health. The increase in industrial activities, transportation, and infrastructure development in Balikpapan City along with the development of the Nusantara Capital (IKN) has the potential to increase the concentration of air pollutants, particularly fine particulate matter PM2.5. This study aims to analyze patterns and forecast PM2.5 concentrations in Balikpapan City. This study uses the Autoregressive Integrated Moving Average (ARIMA) method to model daily PM2.5 time series data. The data used covers the period from January to September 2025 with a total of 273 observations, divided into 80% training data, which is 218 observations, and 20% testing data, which is 55 observations. The ARIMA method was chosen because of its ability to capture fluctuating patterns in time series data. The research results indicate that the ARIMA(2,0,1) model is the best model for forecasting PM2.5 concentration in Balikpapan City based on model selection criteria and forecast performance evaluation. This model is able to represent historical data patterns well and provides fairly accurate forecasting results on the test data. The conclusion of this study shows that the ARIMA(2,0,1) model can be used as an air quality forecasting tool, particularly for PM2.5 concentration in Balikpapan City, and has the potential to support policy-making in controlling air pollution in the area.
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.
Co-Authors Alamsyah, Meuthia Nur Azizah Ananda Reza Putra Rahmadan Anastasya Butar, Judah Dave Agles Rizky Nor Elfrida Eka Ayuningtyas Galavinozky Roigabe Gumilang Rajaguguk Hashifah Najma Zahra Lalu Muhamad Rifani Fadli Muhammad Akmal Fadhillah Nathania, Vanisa Azra Riswanty Margareth Malau Sarah Katerina Simbolon Septiansyah, Rifky Sofia Dawani Silaban Surya Puspita Sari, Surya Puspita