cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rani Nooraeni
Contact Email
raninoor@stis.ac.id
Phone
+6221-8191437
Journal Mail Official
semnas@stis.ac.id
Editorial Address
https://prosiding.stis.ac.id/index.php/semnasoffstat/about/contact
Location
Kota adm. jakarta timur,
Dki jakarta
INDONESIA
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
 72   73   74   75   76 
Anatomi Pergerakan Nilai Tukar di Era Ketidakpastian: Pengujian Efisiensi Pasar dan Proses Martingale Kurs KRW/IDR Melalui Regresi Dinamis ARIMA versus Ensembel Machine Learning Sari Desi Ratna; Sahrudin Sahrudin
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.v2026i1.2604

Abstract

The Korean Won (KRW)/Indonesian Rupiah (IDR) exchange rate is important for trade, investment, and remittances, but its non-stationary, near-random-walk behavior complicates forecasting. This study compares seven statistical, regression, and machine learning models using 2,651 daily observations from December 2018 to April 2026: Naïve, ETS, ARIMA(0,1,2)(0,0,2)[7], deterministic trend with ARIMA errors, stochastic trend, XGBoost, and Random Forest. On a 90- day hold-out set, deterministic trend with ARIMA errors achieved the lowest RMSE (0.147) and remained best across six rolling-origin windows. However, Diebold–Mariano tests found no significant differences from Naïve, ARIMA, stochastic trend, and ETS (p=0.138–0.314). In contrast, all statistical/regression models significantly outperformed XGBoost and Random Forest (MASE > 4). The findings support the Efficient Market Hypothesis and the importance of multi-period evaluation and formal significance testing.
Analisis Deret Waktu Penginderaan Jauh Sentinel-2 untuk Pemantauan Fase Tumbuh dan Estimasi Luas Panen Padi M. Naufal Zamani
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.v2026i1.2607

Abstract

Climate uncertainty demands rapid and adaptive agricultural statistics. This study aims to analyze the trend alignment of Sentinel-2 time-series based on Google Earth Engine (GEE) in monitoring growth phases and estimating rice harvested area in Lombok Island in 2025 to strengthen official statistics. A monthly rice phenology analysis method was developed using NDVI and LSWI spectral indices, then validated against NTB Province BPS KSA data. The satellite estimation recorded a harvested area of 112,343 Ha, showing a systematic underestimation of 28.93% compared to the KSA BPS data (158,069 Ha) due to mixed pixels and cloud cover. Despite the absolute area bias, statistical validation indicated highly synchronized monthly trends with a Pearson correlation (r) of 0.9950 (p < 0.0001) and an RMSE of 5,769.96 Ha. It is concluded that this spatial data science application is reliable in detecting temporal harvest trends and holds potential as a complement to official agricultural statistics.
Analisis Komparatif Kecerdasan Buatan sebagai Alat Pengolahan dan Analisis Data Hasil Belajar Siswa (Studi Kasus: ChatGPT dan Gemini) Yoseph Satria Praka; Ananda Maria; Nurul Wahyuni; Candi Rahayu Tri Prameswari
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.v2026i1.2608

Abstract

The rapid development of Artificial Intelligence (AI) has encouraged its use as a supporting tool for research data processing. This study aims to compare the performance of ChatGPT and Gemini in processing and analyzing student learning outcome data using SPSS as the validation reference. A comparative research method was employed using pre-test and post-test data from 25 students. The data were analyzed to calculate Gain and Normalized Gain (N-Gain) values in SPSS, and the results were compared with outputs generated by ChatGPT and Gemini using identical prompts. The findings indicate that both AI platforms produced Gain and N-Gain values consistent with SPSS, with the only difference being the rounding of the average N-Gain value (0.63 compared to 0.6321 in SPSS). Therefore, ChatGPT and Gemini demonstrate equivalent accuracy and have potential as supporting tools for educational data processing, although validation using statistical software remains essential.
A Analisis Ketimpangan Spasial dan Dinamika Pembangunan Menggunakan Data Citra Satelit: Studi Kasus Provinsi Daerah Istimewa Yogyakarta Innas Khoirun Chisan; Astri Nur Innayah
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.v2026i1.2611

Abstract

This study analyzes economic development and spatial inequality in the Special Region of Yogyakarta during 2014–2024 using multi-source geospatial data, including Nighttime Light Intensity (NTL), Normalized Difference Vegetation Index (NDVI), and population statistics. Trend analysis, economic center of gravity, urban sprawl assessment, and the Gini coefficient were applied to evaluate regional development. Results show that economic activity increasingly expands outward from the urban core while vegetation remains stable. This finding indicates the phenomenon of urban sprawl, the gradual expansion of economic activity and the growth of built-up areas into the city's periphery. The NTL/NDVI ratio identifies the highest urbanization pressure in central Yogyakarta. These findings highlight the potential of geospatial indicators to complement official statistics for monitoring inequality and supporting sustainable spatial planning.
Sistem Pendukung Keputusan Rekomendasi Saham Berbasis Simple Additive Weighting (SAW) dan Algoritma XGBoost Anom Pangestu; Putri Tripangesti; Nur Azizah; Lolanda Hamim Annisa
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.v2026i1.2619

Abstract

The rapid growth of stock investors in Indonesia has not been accompanied by adequate investment analysis skills, increasing the risk of irrational investment decisions. This study aims to develop STOXAREA, a hybrid stock recommendation decision support system integrating Simple Additive Weighting (SAW), Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP). The research employed the Research and Development (R&D) method with a quantitative approach. Research data were obtained from the Yahoo Finance API, including historical stock prices, company fundamental data, and investor risk profiles collected through questionnaires. XGBoost was utilized to generate AI Scores, while SAW was applied to rank stocks based on user preferences. SHAP was implemented to improve prediction transparency. The results show that STOXAREA can produce personalized stock recommendations to assist investors in making more rational investment decisions, demonstrating positive performance in backtesting validation, supported by a Virtual Trading feature for simulation.
Analisis Spasio-temporal Fenomena Urban Heat Island di DKI Jakarta Menggunakan Time Series Clustering Solideo Gracio Bangun; 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.v2026i1.2629

Abstract

Rapid urbanization has intensified the Urban Heat Island (UHI) phenomenon in megapolitan areas. DKI Jakarta, one of the world’s most densely populated megacities, faces increasing thermal pressure due to extensive impervious surfaces and limited green space. This study aims to classify areas in DKI Jakarta based on similarities in Land Surface Temperature (LST) patterns and evaluate clustering performance in explaining UHI dynamics. MODIS-derived LST data from 2009 to 2024 were analyzed using K-Means with Euclidean distance, Dynamic Time Warping (DTW), and K-Shape. Cluster quality was evaluated using WCSS, Silhouette Score, Davies-Bouldin Index, and Dunn Index. K-Means with Euclidean distance and four clusters produced the best performance. The resulting clusters represented different urbanization-related thermal characteristics, with central and northern Jakarta exhibiting the highest LST. Surface UHI analysis showed that 91.9% of urban areas experienced moderate to extreme intensity, indicating that urban densification is a major driver of increasing surface temperatures.
Perbandingan Kinerja Algoritma DE dan PSO dalam Optimasi DTVaR pada Return Harga Propana Mont Belvieu Jernita Bintang Yuliani Manalu; 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.v2026i1.2631

Abstract

Dependent Tail Value-at-Risk (DTVaR) is a measure that quantifies expected loss between two quantiles while accounting for dependence among risk variables. Optimizing DTVaR remains challenging due to the complexity of its objective function. This study compares Differential Evolution (DE) and Particle Swarm Optimization (PSO) for DTVaR optimization using return data of Propane Mont Belvieu price as the target risk variable and Brent crude oil price as the risk variable over 2020–2025. Both algorithms are evaluated using objective values, convergence, and validation tests. The results show that both algorithms converge and produce DTVaR estimates. DE achieves the smallest objective function value. For simulated data, the combination (α= 0,95; δ= 0,8) produces the smallest difference for both algorithms, with PSO yielding the smaller value. For real data, DE achieves the smallest difference at (α= 0,95; δ= 0,8), whereas PSO performs best at (α= 0,95; δ= 0,8). These findings indicate that algorithm performance depends on evaluation criteria and data characteristics.
Analisis Perbandingan Model Arima, RNN, dan N-BEATS pada Prediksi Harga Emas Ainul Mardhiyyah; 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.v2026i1.2637

Abstract

Gold is a strategic investment instrument that serves as a hedge against inflation, although its price movements are characterized by high volatility and complex patterns. This study develops and compares the performance of the conventional statistical approach, Autoregressive Integrated Moving Average (ARIMA), and the deep learning approach, Recurrent Neural Network (RNN) and Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS), in forecasting the weekly average gold prices in Jakarta during the 2022–2024 period. The two methods are evaluated using the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (sMAPE) metrics. The results indicate that N-BEATS outperformed ARIMA in forecasting performance. This is attributed to its basis expansion architecture, which effectively captures nonlinear dynamics and adapts to extreme volatility, making it more accurate and robust than RNN and ARIMA.
Mengungkap Keterkaitan Antarsektor dan Kebocoran Ekonomi di Indonesia: Bukti dari Analisis Input–Output Dustin Raka Widiananta Aslam; Chew Myky Sylvyana; Iben Sebastian; 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.v2026i1.2639

Abstract

Export growth is a cornerstone of Indonesia's economy, yet its benefits remain suboptimal due to a high reliance on imported inputs, which triggers economic leakage. This study examines the degree of import dependence and potential economic leakage across Indonesian economic sectors. Employing an Input-Output (I-O) analysis, this research utilizes the 2016 and 2020 I-O Tables from Statistics Indonesia (BPS) to decompose domestic and imported transactions. The analyzed indicators include the Import Content of Exports (ICoE), Total Backward Linkage (TBL), and Economic Leakage. Findings reveal that most sectors in 2020 recorded an ICoE greater than 1, indicating that imported inputs exceeded export values. The Construction sector exhibited the highest ICoE, while Education and Health Services experienced drastic surges. Furthermore, a positive correlation exists between TBL and Economic Leakage. In conclusion, Indonesia's export production relies heavily on imported inputs, causing significant economic leakage. Strategic import substitution policies are imperative to strengthen the national economic structure.
Hilirisasi Nikel dan Kontribusinya terhadap Nilai Tambah dan Struktur Perekonomian Nasional Bunga Musva Cotva; Nur Faqih Ihsan; Sun Asi Gabriella Sinaga; 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.v2026i1.2644

Abstract

Indonesia has abundant nickel reserves and significant potential to promote economic growth through downstreaming strategies. However, studies analyzing the impacts of nickel downstreaming investment injections on intersectoral output, household income, and value added using the Input-Output approach remain relatively limited. This study aims to analyze the linkages of nickel downstreaming with intersectoral output, household income, and value added in Indonesia. The analysis employs an Input-Output model with an investment injection of IDR 153.2 trillion into the metal, iron, and steel processing industry as a representation of nickel downstreaming activities, using 50%, 100%, and 150% injection scenarios. The results show that the largest impacts on output and value added occur in the non-metallic, iron, and steel processing industry, while the largest household income impact occurs in the same sector, followed by wholesale and retail trade and mining and quarrying. Increasing the injection scenario only changes the magnitude of impacts without altering the intersectoral ranking. Expanding the impacts of nickel downstreaming requires strengthening local inputs, high-value-added downstream industries, technology transfer, and human resource development.

Page 74 of 82 | Total Record : 812

 72   73   74   75   76 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 2026 No 1 (2026): Seminar Nasional Official Statistics 2026 Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025 Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023 Vol 2022 No 1 (2022): Seminar Nasional Official Statistics 2022 Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020 Vol 2019 No 1 (2019): Seminar Nasional Official Statistics 2019