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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 729 Documents
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Pemetaan Cluster Spasial Produksi Perkebunan di Indonesia Kusuma, Arya Candra; Mawarsari, Ucik
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2559

Abstract

Plantation agriculture in Indonesia plays a crucial role in ensuring national food security and driving regional economic development. This study aims to analyze the spatial patterns of plantation production across 38 provinces in Indonesia using the SKATER (Spatial 'K'luster Analysis by Tree Edge Removal) algorithm. It focuses on ten major plantation commodities: oil palm, rubber, cocoa, coffee, tea, coconut, sago, clove, sugarcane, and tobacco, based on 2023 production data. Descriptive analysis reveals substantial variation in production levels across regions and commodities. Spatial clustering, conducted using GeoDa, groups provinces into six distinct clusters with unique production profiles. These findings highlight local commodity specialization and production potential, which can inform more targeted policy interventions. The study contributes to spatially informed development planning and supports Indonesia’s national agenda on food security and regional development in line with the Asta Cita vision and the Golden Indonesia 2045 mission.
Analisis Kualitas Modal Manusia Tingkat Provinsi di Indonesia Menggunakan K-Means Clustering dan Regresi Logistik Biner Aurellia, Nur Aisya; Sari, Riska Meyliana; Muzakki, Naufal Fadli
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2577

Abstract

The Human Capital Index (HCI) is one of the indicators used in human development evaluations, with the aim of improving the welfare and advancement of human resources in various sectors of life. Limitations in provincial-level HCI data, as well as limitations in the data of HCI components, hinder the HCI calculation process. Therefore, an alternative approach was applied to assess human capital quality by examining components such as life expectancy, average years of schooling, and stunting prevalence using K-Means cluster analysis. The results indicate that provinces in Indonesia form two clusters: the low HCI group and the high HCI group. This study aims to examine the influence of several variables on HCI categories using binary logistic regression analysis. The results show that per capita GDP, internet penetration rates, and rice productivity have a significant positive impact on human capital quality in Indonesia.
Pengaruh Investasi Publik dalam Sektor Kesehatan, Pendidikan, dan Infrastruktur terhadap Pertumbuhan Ekonomi Indonesia : Analisis ARDL Tahun 2002-2023 Nurfadia, Atikah; Nurkarim, Wahidya
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2580

Abstract

Indonesia's Gross Domestic Product (GDP) has steadily increased in the past two decades to reach US$1.4 trillion. This growth is driven by various factors including domestic consumption, investment, and labor. This study aims to analyze the impact of government spending in the health, education, and gross fixed capital formation sectors on Indonesia's economic growth. Annual data from 2002 to 2023 were analyzed using the Autoregressive Distributed Lag (ARDL) approach to examine both short-term and long-term relationships among the variables. The results show that, in the long run, health expenditure and gross fixed capital formation have a positive and significant effect on gross domestic product (GDP) per capita. Conversely, education expenditure exhibits a negative and statistically insignificant effect. In the short run, only health expenditure has a significant influence on economic growth. Diagnostic tests indicate that the ARDL model used satisfies statistical assumptions and is structurally stable. These findings highlight the importance of optimizing education spending and maintaining as well as enhancing investments in the health and infrastructure sectors to support sustainable economic growth.
Pembangunan Dataset Sintetis Klasifikasi Baku Lapangan Usaha Indonesia 2020 dengan Generative Artificial Intelligence Silmi Kaffah, M. Ihsan; Rahman, Dimas Haafizh; Amnur, Muh. Alfian; Montolalu, Cloudya Qashwah; Siregar, Amir Mumtaz; Sinulingga, Geraldo Benedictus; Ayu Alistin, Zharifah Dhiya; Raihannur, Cut Indah; Putri Arivia, Anggi Marya; Rahmawati, Arih; Nauli Sihombing, Fiona Audia; Salsabiela, Rahmadika Kemala; Bahy, Sabastian Alfons; Suadaa, Lya Hulliyyatus; Choir, Achmad Syahrul
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2581

Abstract

The limited quality datasets is a fundamental challenge in developing automatic classification of business description into the Indonesia Standard Industrial Classification (KBLI) using machine learning models. This research aims to develop a synthetic KBLI dataset using Generative AI via ChatGPT chatbot with a one-shot prompting technique. This technique is employed to generate business descriptions based on five-digit KBLI codes in order to address the limitations of labeled data and the variability of existing business descriptions. The dataset generated through prompt engineering and manual validation shows that 93,25% of the business descriptions align with the established KBLI standards. The average number of business descriptions per category demonstrates a fairly uniform distribution, ensuring sufficient representation for each five-digit code. This research makes a significant contribution in providing a dataset for training machine learning models in the automatic classification of business descriptions into the five-digit KBLI categories.
Penerapan Firefly Algorithm dalam Menentukan Hyperparameter pada Support Vector Regression untuk Memprediksi Harga Saham dengan Google Trends Atmaja, Anugerah Surya; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2582

Abstract

Stock fluctuations as well as the tendency to high volatility raise doubts for investors to invest in a company. Efforts that can be made to minimize investment risk are to conduct predictive analysis. The development of machine learning technology and big data can be a support in prediction, one of which is the use of the Support Vector Regression (SVR) method and google trends index data.This research forms a prediction model for PT. BRI (Persero) Tbk. which involves google trend index data using the SVR method. Referring to the constraints in determining the appropriate hyperparameters for the SVR method, the firefly algorithm is used to obtain hyperparameters that optimize the model. Based on modeling, the SVR-FA model involving the google trend index gave the best results, shown by the RMSE and MAPE were 348,47 and 4,12% respectively. This shows that by adding google trend index variables and utilizing machine learning methods in modeling,it will provide better results.
Penggunaan Indeks Google Trends dalam Nowcasting Jumlah Penumpang Pesawat Terbang pada Keberangkatan Domestik dan Internasional di Bandara Soekarno-Hatta Arini, Rechtiana Putri; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2583

Abstract

Soekarno-Hatta International Airport, as Indonesia’s busiest air transit hub, requires swift, data-driven decision-making to implement responsive policies that enhance passenger services. The number of airline passengers serves as a critical indicator for managing passenger traffic flow, demanding timely data insights. However, official statistics often suffer from a 1–2 month reporting lag. To address this, the study applies nowcasting techniques to estimate passenger volumes using Google Trends indices from January 2016 to January 2024. By integrating GT indicators into SARIMAX and Time Series Regression models, airport authorities can access early signals of passenger traffic volumes. Among the models tested, SARIMA(0,1,1)(1,0,0) 12 demonstrated the best performance, achieving a MAPE of 15.15%. This approach offers valuable, near-real-time insights to support operational planning and policy response in a fast-paced transport environment.
Nowcasting Pergerakan Indeks Saham Lingkungan Berdasarkan Minat Publik terhadap Isu Lingkungan Zareka, Andi Muh. Zulfadhil; Ayuningrum, Adinda Safira Santoso; Adnyana, I Kadek Surya Wisesa; Kurniawan, Robert
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2585

Abstract

The growing awareness among investors regarding environmental, social, and governance (ESG) aspects has increased attention toward the performance of environmentally-based stock indices. This condition has created a need for a nowcasting approach that is responsive to real-time public interest dynamics and market sentiment. This study aims to analyze public interest in environmental issues measured using Google Trends web search volume as a proxy for collective sentiment in predicting the movement of environmental stock indices. ARIMAX, SARIMAX, Random Forest, SVR, and XGBoost models are implemented and evaluated for their performance in predicting index movements. The results show that SVR, with an RMSE of 20.3646, is the best-performing model. These findings indicate that public interest in environmental issues has significant potential as an effective indicator for real-time prediction of environmental stock index movements, offering valuable insights for investors and market analysts in developing investment strategies that are more responsive to market dynamics influenced by sustainability factors.
Klasifikasi Status NEET dengan XGBoost di Pulau Jawa Tahun 2023 Nurcahayani, Helida; P. Wirahadi, Rivana Marinda
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2589

Abstract

The proportion of individuals categorized as Not in Education, Employment, or Training (NEET) is one of the key indicators of the success of creative digital economic development among the youth. This study investigates NEET status on the island on Java island in 2023 using a machine learning approach. Despite Java being the economic and infrastructural center of Indonesia, there exist significant disparities in NEET rates across its provinces. These disparities reflect unequal access to education and employment opportunities, thereby hindering the achievement of Sustainable Development Goal (SDG) 8. By employing the XGBoost algorithm, this study successfully developed a classification model with exceptional performance. The XGBoost model, optimized through SMOTENN resampling and hyperparameter tuning, achieved a validation accuracy of 98.69%, a training loss of 0.0320, a validation loss of 0.0491, and a ROC-AUC score of 0.9978. These results represent a substantial improvement over the baseline model, which attained an accuracy of approximately 80%. The findings reveal that the primary factors influencing NEET status include age, marital status, education level, work experience, household size, gender, disability status, and training experience. Furthermore, participation in training programs and residence in urban areas are associated with a lower risk of becoming NEET, as they enhance individual skill sets and facilitate greater access to educational and employment opportunities.
Pengaruh Karakteristik Rumah Tangga dan Kewilayahan terhadap Kepemilikan Akta Kelahiran pada Rumah Tangga dengan Anak Umur 0-4 Tahun di Kawasan Timur Indonesia Tahun 2024 -, Muhammad; Arcana, I Made
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2592

Abstract

The coverage of birth certificate ownership among children aged 0–4 years in Eastern Indonesia (KTI) in 2024 is 78.29 percent, which has not yet reached the 2024 RPJMN target of 100 percent and is projected to remain below the Sustainable Development Goals (SDGs) target by 2030. This study aims to provide an overview of birth certificate ownership among households with children aged 0–4 years in KTI in 2024 and to analyze household and regional characteristics that influence birth certificate ownership. The data used is from the March 2024 Susenas and is analyzed both descriptively and inferentially using multilevel binary logistic regression. The results show that 80.83 percent of households with children aged 0–4 years in KTI possess a birth certificate. Households living in urban areas, with heads of household who have at least a primary education, are non-poor, have internet access, and are located in regions with higher average years of schooling and a higher percentage of births occurring in health facilities are more likely to own a birth certificate.

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