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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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Komparasi U-MIDAS, Elastic Net, dan Random Forest untuk Nowcasting Tingkat Pengangguran Terbuka Muhammad Erza Lesmana
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.v2026i1.2785

Abstract

The release lag in official unemployment rate (TPT) data limits timely regional policy responses. This study compares nowcasting methods—U-MIDAS, Elastic Net, and Random Forest—to estimate TPT in Lampung Province by integrating monthly macroeconomic indicators (Inflation, Exports) and Google Trends Big Data (37 keywords reduced via PCA into GTREND1 and GTREND2). Pseudo-real-time evaluation with a naive baseline shows Random Forest achieves the lowest MAPE (4.89%), but U-MIDAS is recommended as the primary method due to more transparent economic interpretability (MAPE 8.00%). U-MIDAS projects TPT for August 2026 at 3.93% with a 95% prediction interval of [3.36% – 4.50%], relatively stable compared to February 2026 (3.95%). Integrating mixed-frequency indicators within U-MIDAS yields reliable estimates for regional employment policymaking, despite sample size limitations..
P Pengembangan Early Warning System Inflasi Indonesia Berbasis Random Forest Menggunakan Indikator Makroekonomi Dorinda Aiko Blythe Van Wijaya; Nur Arifah Apriliani
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.v2026i1.2786

Abstract

Inflation is one of the key economic indicators used to monitor economic conditions and stability in Indonesia. The utilization of inflation data can be developed not only to describe economic conditions but also to support prediction and early detection of potential changes in inflation. This study aims to develop an inflation prediction model for Indonesia using the Random Forest Regressor algorithm based on macroeconomic indicators, namely the BI Rate, the Rupiah exchange rate against the United States Dollar (USD/IDR), and world oil prices. The study used monthly secondary data from April 2016 to June 2026 obtained from the Central Bureau of Statistics (BPS), Bank Indonesia, and the World Bank. The research stages consisted of data preprocessing, Exploratory Data Analysis (EDA), lag feature construction, model development, and evaluation using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). Two modeling scenarios were implemented: Model A without lag features and Model B with one-period lag features. The results showed that Model B achieved better performance, with an MAE of 0.5109, RMSE of 0.7084, and R² of 0.4764, compared with Model A, which obtained an MAE of 1.5712, RMSE of 1.8152, and R² of −2.4382. Feature importance analysis indicated that Lag_Inflation was the most influential variable, contributing 88.99%. These results indicate that incorporating historical inflation information improves the predictive performance of the Random Forest model and provides a potential basis for developing an Early Warning System (EWS) to support inflation monitoring and policymaking in Indonesia.
Analisis Pengaruh Guncangan Nilai Tukar USD/IDR Terhadap Indeks Harga Saham Gabungan (IHSG) Melalui Sektor Perbankan Menggunakan Pendekatan SEM-PLS Sandy Fredella Elvaretta; Galih Fajar Nugroho; Atlantis Cartenzian Arkadya; Happy Ending Forever; Muhammad Diego Romadhona
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.v2026i1.2790

Abstract

This study aims to analyze the impact of USD/IDR exchange rate shocks (Rupiah depreciation) on the Composite Stock Price Index (IHSG) and the mediating role of the banking sector. The analysis was conducted using 122 monthly secondary data points from January 2016 to March 2026, sourced from Bank Indonesia (USD/IDR exchange rate) and the Indonesia Stock Exchange (Banking Index, Banking Trading Value, and IHSG), employing the Structural Equation Modeling–Partial Least Squares (SEM-PLS) approach. In this study, USD/IDR exchange rate shocks refer to an increase in the USD/IDR rate, i.e., Rupiah depreciation against the US Dollar. The results indicate that the model explains 57.6% of IHSG variance and 20.9% of Banking Index variance. USD/IDR exchange rate shocks (depreciation) have a significant negative effect on the banking sector and IHSG. The indirect effect of USD/IDR on IHSG through the Banking Index (-0.296) is stronger than its direct effect (-0.185), resulting in a total effect of -0.481 (the slight difference is due to coefficient rounding). This highlights that the banking sector acts as a mediator and amplifier of exchange rate shocks on the Indonesian stock market. Conversely, Banking Trading Value does not serve as a significant mediating channel. These findings underscore the importance of maintaining exchange rate stability and banking sector health.
Analisis Middle Income Trap pada Negara-negara di Kawasan ASEAN I Kadek Surya Wisesa Adnyana; I Made Arcana
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.v2026i1.2791

Abstract

Middle Income Trap (MIT) remains a major challenge for ASEAN developing countries in achieving the ASEAN Community Vision 2045. This study aims to analyze the effect of the 3i framework (investment, infusion, innovation) on GDP per capita and to project whether ASEAN developing countries can escape middle-income status by 2045. Using panel data from eight ASEAN countries for 2000–2023 with GCF, HDI, FDI, TO, and internet penetration as variables, the study applies FGLS- SUR panel regression and Support Vector Regression (SVR) projection. Results show that HDI and internet penetration have significant positive effects, GCF and TO have significant negative effects, while FDI is insignificant and removed through backward elimination. Based on the SVR projection scenario, none of the developing ASEAN countries is projected to reach the high-income threshold by 2045, with Malaysia closest to the threshold. These findings suggest that achieving the ASEAN Community Vision 2045 requires stronger physical investment quality and more productive economic openness.
Dinamika Interaksi Variabel Makroekonomi Domestik dan Global terhadap IHSG dan Nilai Tukar Rupiah: Pendekatan VECM Akhmad Hudatullah; Tiara Nur Oktaviani Putri
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.v2026i1.2797

Abstract

Economic uncertainty driven by geopolitical crises triggers sharp asymmetry in domestic financial markets. Previous literature failed to map these dynamics due to reliance on static models. This study aims to examine the dynamic interactions of macro-financial variables using a data science approach based on the Vector Error Correction Model (VECM) across 198 monthly official statistics observations. Cointegration tests confirm the existence of a single long-run equilibrium. The rupiah exchange rate and global oil prices consistently drive JCI strengthening. Inflation and the BI Rate do not directly affect the stock index. The error correction mechanism actively balances the JCI and rupiah equations simultaneously. Variance decomposition establishes global oil prices as the strongest external shock to the JCI. Rupiah volatility is dictated by inflation and interest rates. Monetary authorities must recalibrate their policy mix. Capital market stability is empirically more vulnerable to global energy shocks than internal monetary maneuvers.
Pengembangan Model Deteksi Teks yang Dihasilkan oleh Artificial Intelligence Menggunakan Pendekatan Stylometry Immanuella Risna Sahalana Tumanggor; Muhammad Ridho Purwanto; M.Alfarizi Nul Hakim; Mercy Febriella Liborang; Lya Hulliyyatus Suadaa
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.v2026i1.2803

Abstract

The advancement of generative AI increases challenges in distinguishing human-written from AI-generated text, threatening academic integrity. This study aims to develop an AI-generated text detection model using a stylometric approach and machine learning algorithms. The stylometric approach was employed to extract 31 linguistic features representing lexical, syntactic, and readability characteristics of textual documents as the basis for classification using machine learning. Five machine learning algorithms, namely Naïve Bayes, Logistic Regression, Support Vector Machine (Linear and RBF), and Random Forest, were evaluated to identify the best-performing model. The experimental results indicate that the Random Forest model achieved the highest classification performance, with an accuracy of 97.94%. Feature importance analysis revealed that average word length was the most influential feature for distinguishing human-written text from AI-generated text. The best-performing model was subsequently implemented in a web-based application to support document originality verification. Overall, the findings demonstrate that combining stylometric analysis with machine learning offers a promising approach for detecting AI-generated text and supporting the preservation of academic integrity in the era of generative artificial intelligence.
Mengidentifikasi Faktor Prediktif Struktur Ekonomi Daerah Menggunakan Explainable Machine Learning Berbasis Official Statistics Ulfa Siti Nuraini; Dimas Fatkhul Rahman; Sintiya Risla Miftaqul Nikmah; Krisjen Fraulein Hutagalung
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.v2026i1.2805

Abstract

Regional economic structures reflect the characteristics of economic activity across regions. This study aims to identify predictive factors that contribute to regional economic structures in Indonesia by modeling the contribution of 17 economic sectors to Regional Gross Domestic Product (RGDP). The data cover 34 provinces from 2015 to 2021, comprising 238 observations. Multi-Output Extreme Gradient Boosting (XGBoost) was used to predict the contribution of each sector, while SHapley Additive exPlanations (SHAP) was used to identify the variables with the greatest predictive contribution. The results show high predictive performance, with R² values for the test data ranging from 0.86 to 0.99, while cross-validation yielded average R² values ranging from 0.80 to 0.94. The SHAP analysis indicates that demographic characteristics, investment, and human development have high predictive contributions, with varying patterns across sectors. These findings suggest that XGBoost and SHAP can be used to interpretatively map the predictive patterns of regional economic structures, without claiming causal relationships or statistical significance.
Kerangka Pemodelan Fase Tumbuh Padi Berbasis Machine Learning pada Wilayah dengan Pola Tanam Tidak Beraturan Maria Shawna Cinnamon Claire; Achmad Fauzi Bagus Firmansyah
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.v2026i1.2807

Abstract

Rice phenology information is essential for agricultural statistics and food security policies. Although remote sensing-based machine learning has been widely applied for rice phenology estimation, studies integrating climate information in regions with irregular planting patterns while addressing operational scalability remain limited. This study develops an XGBoost- based framework that integrates Sentinel-1 data, rainfall information, and Area Sampling Frame (KSA) survey data as official-statistics-based ground truth in East Nusa Tenggara Province, Indonesia, where rice cultivation is predominantly rainfed. The results demonstrate that incorporating rainfall information improves model performance. Furthermore, a simplified modeling strategy that accounts for regional heterogeneity achieves high predictive performance without requiring separate models for each regional group, thereby reducing model development and maintenance complexity. These findings suggest that integrating multisource data, official statistics as ground truth, and a simplified modeling strategy provides a more accurate, efficient, and scalable framework for large-scale rice phenology estimation.
Variabel-Variabel yang Mencirikan Ketidaklayakan Upah Pekerja Formal Perempuan Sektor Tersier di Provinsi Lampung Tahun 2024 Nadia Lutfi Meilawati; Siti Muchlisoh
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.v2026i1.2819

Abstract

Wages are still the main challenge in national development. Even though the Indonesian government has set a minimum wage, as many as 52.20 percent of female formal workers in Lampung Province still receive wages below the district/city minimum standard. This condition shows that more than half of female formal workers in Lampung do not receive wages according to minimum standards and makes Lampung the province with the lowest average wages for female formal workers on the island of Sumatra. This problem also occurs in the tertiary sector, which is the sector with the largest contribution and the main absorber of female workers in Lampung. This research analyzes the variables that characterize the inappropriate wages of tertiary sector female formal workers using the Chi-square Automatic Interaction Detection (CHAID) approach. The CHAID results show that there are six variables that are significant in characterizing the inappropriate wages of female tertiary sector formal workers in Lampung, namely education level, trade union participation, tertiary sector business field, working hours, type of work and marital status. The segment that most characterizes wage inequity is characterized by female tertiary sector formal workers who have less than a junior high school education, do not join a trade union, and work in the blue collar sector as well as a group of tertiary sector female formal workers who have a high school or equivalent education, work in the non-dynamic service sector, and have working hours of less than 35 hours.
Analisis Inequality Trap Dalam Menjelaskan Kemiskinan Di Jawa Barat Tahun 2020-2025 Decapryo Rivian Belo; Azka Annisa Ramadhani; Angel Oktavia Br Tampubolon
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.v2026i1.2824

Abstract

Poverty is a multidimensional issue influenced by inequality and human development. This study analyzes the effects of the Open Unemployment Rate (TPT), Human Development Index (HDI), and Gini Ratio on poverty across 27 regencies/cities in West Java from 2020 to 2025. The study employs panel data regression using the Random Effects Model (REM) and the robust Seemingly Unrelated Regression (SUR) method to account for spatial cross-sectional dependence. The results indicate that HDI and the Gini Ratio have significant effects on poverty. The substantial increase in the Gini Ratio coefficient in the SUR model provides evidence of an inequality trap mechanism, in which inequality systematically hinders poverty alleviation. Therefore, poverty alleviation in West Java requires progressive redistributive policies, labor formalization, and the equalization of human capabilities across regions.

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