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Prosiding Seminar Nasional Official Statistics
Published by Politeknik Statistika STIS
ISSN : 27721970     EISSN : 27721970     DOI : https://doi.org/10.34123/semnasoffstat.v2022i1
Core Subject : Economy, Humanities, Science, Social,
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences
prosiding seminar ini bertujuan untuk menghasilkan berbagai pemikiran solutif, inovatif, dan adaptif terkait isu, strategi, dan metode yang memanfaatkan official statistics
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 812 Documents
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Klusterisasi Kabupaten/Kota Berbasis Produk Domestik Regional Bruto: Pendekatan Machine Learning Self-Organizing Map Yohannes Kevin; Bagus Sumargo; Dania Siregar
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2671

Abstract

East Java is province with 38 regencies/cities and second-largest contribution to Gross Regional Domestic Product in 2023. With it’s economy dispersed across regions, this study purposes are cluster the economic characteristics of East Java Province to seek economics of scale between regencies/cities. The method used cluster analysis with Self-Organizing Map based on machine learning. The analysis conducted on 17 economic sectors of GRDP production approaches. Before analysis, number of clusters used through internal validation determined. The results: first cluster consists of 34 regencies/cities (excluding Surabaya City and Sidoarjo Regency), characterized predominantly by agriculture, forestry, and fisheries sector as well as mining and quarrying sector. Second cluster consists of Surabaya City and Sidoarjo Regency, characterized by sectors other than those dominant in the first cluster. When the cluster were visualized using the SOM U- matrix and Codes Plot, significant differences were observed between Surabaya City and Sidoarjo Regency compared with other regencies/cities.
Volatility Spillover antara Harga Minyak Mentah Brent dan Minyak Kedelai Andi Muthiah Rahim; Ilyas Ramadhan; Muhamad Fadhlan Hendri; Efriyendi Efriyendi; Aura Mustika Febrina; Fitri Kartiasih
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2672

Abstract

This study examines the dynamic interconnectedness and risk spillovers between the Brent crude oil and soybean oil markets over the 2007–2024 period using 4,573 daily observations. The Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model is employed to estimate time-varying correlations between the two markets. The Augmented Dickey-Fuller (ADF) test confirms that the return series of both commodities are stationary at the level. The presence of volatility clustering and heteroskedasticity justifies the application of the GARCH framework, while the analysis identifies a significant leverage effect in the Brent crude oil market, where negative shocks generate greater volatility than positive shocks. Dynamic correlations rise sharply during the 2008 Global Financial Crisis and the 2022 Russia–Ukraine conflict, indicating strong risk contagion and reduced portfolio diversification benefits during periods of market stress. These findings highlight the importance of dynamic hedging strategies and coordinated energy– food policies to enhance market stability and global food security.
Respons Dinamis Harga Bitcoin, Harga Emas, dan Indeks Harga Saham Gabungan terhadap Guncangan Makroekonomi Global dan Domestik Lydia Aushaf Ozora Siregar; Sarah Nurfajar; Rifki Siboro; Rizky Arby Maulana; Fitri Kartiasih
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2673

Abstract

This study examines the responses of Bitcoin, gold, and the Indonesia Composite Stock Price Index (IDX Composite/IHSG) to U.S. monetary policy, China's economic conditions, global market volatility, and domestic macroeconomic factors using monthly data from 2014–2025 and the Vector Error Correction Model (VECM). The variables include the Federal Funds Effective Rate, the China Manufacturing Purchasing Managers' Index (China PMI), the CBOE Volatility Index (VIX), the IDR/USD exchange rate, and the BI Rate. The results reveal the existence of long-run equilibrium relationships across all models. In the short run, only gold prices exhibit a significant adjustment mechanism toward long-run equilibrium, whereas Bitcoin and the IDX Composite do not. The impulse response function (IRF) and forecast error variance decomposition (FEVD) analyses indicate that Bitcoin is primarily driven by internal shocks, gold is more sensitive to global factors, showing patterns consistent with safe-haven characteristics, while the IDX Composite is influenced relatively evenly by both global and domestic factors. These findings highlight the differing sensitivities of the three assets to global shocks.
Dampak Kebijakan Efisiensi Anggaran dan Realokasi Belanja Sosial terhadap Perekonomian Indonesia Adinda Safira Santoso Ayuningrum; Ahmad Febrian Muharom; Muhammad Afif Wirdiyan Zaldi; Fitri Kartiasih
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2675

Abstract

Limited fiscal space has prompted the Indonesian government to implement budget efficiency measures, including reductions in the Ministry of Public Works' infrastructure budget, to finance the Free Nutritious Meals (FNM) Program. This study simulates the impacts of this policy on output, employment, and household income using the 2024 Indonesian Input–Output Table. Two policy scenarios are examined: budget efficiency in the construction sector and budget efficiency combined with expenditure reallocation through the FNM Program to the accommodation and food service sector. The results indicate that cuts in construction spending reduce economy-wide economic activity, particularly in the transportation, trade, and service sectors. In contrast, expenditure reallocation through the FNM Program increases employment and household income, especially in labor-intensive sectors such as agriculture and manufacturing. These findings suggest that social spending targeted at sectors with high multiplier effects can offset the adverse impacts of reduced capital expenditure while supporting inclusive and sustainable economic growth.
Estimasi Indeks Pembangunan Manusia Tingkat Kecamatan Menggunakan Data Geospasial Remote Sensing, Point of Interest, dan Statistik Resmi dengan Metode Machine Learning Samuel Marvelius Sianturi; Bony Parulian Josaphat
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2677

Abstract

The limited data coverage for measuring the Human Development Index (IPM), like National Socio-Economic Survey (Susenas) causes the Statistics Indonesia (BPS) only publishes the IPM at the district/city level. Meanwhile, assessing the quality of human development would be more effective if the IPM were available at a smaller area, such as the sub-district. This study examines the use of geospatial data and official statistics to estimate the IPM at the sub-district level. The results indicate that the best machine learning model is Random Forest Regression with Root Mean Square Error (RMSE) evaluation value of 3.7817, Mean Absolute Error (MAE) of 2.6509, and Mean Absolute Percentage Error (MAPE) of 4.01%. The estimation results show that sub-districts with high estimated HDI are consentrated in areas with high population densities and better accessibility to health, education, and economic facilities, such as sub-districts in Kupang City.
Segmentasi Wilayah Jakarta Barat Berbasis UNet++: Studi Kasus Bangunan, Jalanan, dan Objek Lainnya Menggunakan Citra Satelit Pleiades Senoaji Pamungkas; Achmad Fauzi Bagus Firmansyah; Vina Ardelia Effendy
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2678

Abstract

Urbanization in West Jakarta was recorded as the highest and most stable among Jakarta’s administrative regions in 2026, which demands the availability of accurate and up-to-date ground-truth spatial mapping data. In response to this demand, the utilization of Big Data in the form of very high-resolution satellite imagery enables the provision of information at a granular level, which can be translated into spatial planning maps through an appropriate modeling approach. In the case of spatial mapping in the West Jakarta region, this study examines the application of a deep learning-based semantic segmentation method using the UNet++ architecture with an InceptionResNetV2 encoder to extract building, road, and other object classes from 0.5-meter spatial resolution Pleiades satellite imagery. In developing this deep learning model, the model was developed and evaluated through an iterative methodological experiment, encompassing CLAHE, label cleaning, Focal Loss, an SCSE module, gamma correction, and an overlapping-tiling patch scheme. The results show an improvement in mIoU from 0.5789 (baseline model) to 0.7145 during the tuning stage, reaching 0.7352 with an F1-score of 0.8467 during the evaluation stage using the Patch 256 + Stride 128 configuration. The resulting map demonstrates potential in supporting spatial planning optimization in rapidly developing urban areas such as West Jakarta.
Otomatisasi Narasi Statistik pada Dashboard Publikasi BPS Menggunakan Table-to-Text Generation Juan Lubvrin Fernandez Sidabukke; Bony Parulian Josaphat
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2679

Abstract

The preparation of descriptive narratives for BPS statistical publications is still performed manually, requiring time and potentially causing errors in numerical interpretation. This study aims to develop PRANATA (Statistical Narrative Automation Portal) to automate statistical narrative generation and integrate it with data visualization in a single dashboard using a Large Language Model (LLM) with Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT). The study adopts the CRISP-ML(Q) framework to evaluate Gemini 3 Flash, GPT-OSS (120B), and Llama 3.3 (70B) using 11 statistical indicators from BPS Pematangsiantar Municipality. Gemini 3 Flash achieved the best factuality performance, with Macro Precision of 94.96%, Macro Recall of 96.44%, Macro F1-Score of 95.47%, and Macro Hallucination Rate of 4.88%. It also achieved Fluency and Completeness scores of 5.69 and 5.60 out of 7. PSSUQ with five BPS employees yielded an overall score of 6.00 out of 7. These results indicate that PRANATA supports integrated statistical information dissemination.
Pemetaan Heterogenitas Kesulitan Usaha Industri Mikro dan Kecil Menurut Kelompok KBLI di Indonesia dengan Analisis Korespondensi Dina Prariesa
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2680

Abstract

Micro and small industries (MSIs) exhibit diverse business characteristics, resulting in different patterns of business difficulties across industry groups. This study aims to identify the heterogeneity of business difficulties among MSIs across Indonesian Standard Industrial Classification (KBLI) groups using Correspondence Analysis (CA). The study uses weighted data from the 2024 Micro and Small Industry Survey (VIMK), conducted by Statistics Indonesia (BPS), to represent national conditions. The analysis covered 23 KBLI groups and six types of business difficulties. The Chi-square test indicated a significant association between KBLI groups and types of business difficulties (p < 0.001), while the first two dimensions of the CA explained 77.91% of the total inertia. The correspondence analysis showed that raw materials, infrastructure, and marketing were the main contributors to the association structure, indicating heterogeneous patterns of business difficulties across industry groups. These findings provide insights into industry-specific difficulties faced by Indonesian MSIs.
Faktor-Faktor yang Memengaruhi Pemanfaatan Fasilitas Pelayanan Kesehatan untuk Pengobatan Depresi pada Usia Muda di Provinsi Jawa Barat Tahun 2023 Rifqi Fakhrizal Adam; Yaya Setiadi
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2685

Abstract

Depression in young people is an important mental health problem that can impair social and academic functioning and increase mental health risks into adulthood. However, low health service utilization hinders appropriate treatment, especially in West Java, which has Indonesia's highest youth depression prevalence. This study identifies factors influencing health care facility (fasyankes) utilization for depression treatment among young residents in West Java in 2023. Using 2023 Indonesian Health Survey (SKI) data with a cross-sectional design, analysis was conducted using Firth Logistic Regression on 277 residents aged 15–24 years with depression. Results showed only 5.5 percent of respondents utilized fasyankes. Age, maternal education, and suicidal ideation had significant effects. Residents aged 20–24 years, those with highly educated mothers, and those experiencing suicidal ideation were more likely to utilize fasyankes. These findings highlight the importance of early detection, improving mental health literacy, and family support to encourage help-seeking before conditions worsen.
Peramalan Harga Minyak Mentah Menggunakan Kombinasi Model ARIMA dan Machine Learning pada Konflik Iran–Amerika Serikat Tahun 2026 Titi Pramudita Wijayanti; Nabilla Andini Putri; Sekar Afifa Cettastami; Utih Amartiwi
Seminar Nasional Official Statistics Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2026i1.2686

Abstract

As an energy source, crude oil price forecasting is crucial for a country's policy decisions. However, crude oil price volatility is influenced by many factors, one of which is geopolitical conditions. During the Iran-United States conflict, which led Iran to close the Strait of Hormuz, an international oil distribution route, oil prices reached a spike that could not be considered by univariate time series models. This study proposes a framework that combines the Autoregressive Integrated Moving Average (ARIMA) model with a machine learning algorithm. ARIMA is used to predict the dollar index and divided into two models: normal conditions and wartime conditions. Furthermore, the Decision Tree, Random Forests, and Extreme Gradient Boosting (XGBoost) machine learning models are used to predict oil prices based on historical information on oil prices and the dollar index, as well as the closure and opening status of distribution routes. The results show that this integration is capable of predicting oil prices with MAE less than 10 in this geopolitics situation.

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