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All Journal JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN Jurnal Ilmu Komputer JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science Indonesian Journal of Applied Statistics Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) KOMPUTIKA - Jurnal Sistem Komputer JURNAL METEOROLOGI DAN GEOFISIKA Jurnal Aplikasi Statistika & Komputasi Statistik EIGEN MATHEMATICS JOURNAL International Journal of Advances in Data and Information Systems Journal of System and Computer Engineering JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Seminar Nasional Official Statistics Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Meteorologi dan Geofisika Jurnal Statistika Industri dan Komputasi
Arie Wahyu Wijayanto
Politeknik Statistika STIS
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Clustering Regencies/Cities Vulnerable to Air Pollution in the Java Island: Fuzzy Geographically Weighted Clustering Kusuma, Arya Candra; Wijayanto, Arie Wahyu; Arista; Bahar, Vicka Kharisma; Siregar, Tifani Husna
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 17 No 2 (2025): Jurnal Aplikasi Statistika & Komputasi Statistik
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v17i2.618

Abstract

Introduction/Main Objectives: Air pollution has become a critical global concern with substantial effects on human health and the environment. Background Problems: Java Island in Indonesia, recognized for its high population density and industrial activities, necessitates focused effort in resolving this issue. Novelty: While air pollution research has been enormous, there has been no effort to cluster regencies or cities on Java Island utilizing spatially-based data. This research seeks to cluster regencies and cities on Java Island according to air pollution levels and to compare geodemographic and non-geodemographic clustering methodologies. Research Methods: This study employs secondary data regarding air pollution, obtained from the Openweather API. This study employs a geodemographic clustering technique, namely fuzzy geographically weighted clustering (FGWC), optimized by the artificial bee colony (ABC) algorithm. Finding/Results: The study findings indicate that the geodemographic clustering method ABCFGWC surpasses Fuzzy C-Means (FCM) according to the TSS (Tang-Sun-Sun) index. The data reveal that the Greater Jakarta or Jabodetabek area and its adjacent territories are more susceptible to air pollution. The findings of this study are expected to enhance the spatial planning and mapping of air pollution management strategies on Java Island.
Peramalan Volume Timbulan Sampah dengan Memanfaatkan Indeks Google Trends Menggunakan Metode SARIMAX Hidayat, Anang Kurnia; 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.v2025i1.2514

Abstract

In recent years, the Special Region of Yogyakarta has faced a growing challenge of waste generation exceeding its management capacity. This situation underscores the urgency of developing a long-term, data-driven waste management strategy. This study aims to build an accurate forecasting model for waste volume using real-time data from the Google Trends Index (GTI) alongside official statistical data as exogenous variables. The forecasting methods employed are SARIMA and SARIMAX, tested with various parameter and variable combinations. The best-performing model is SARIMAX(1,1,1)(1,0,0)12 with the Production Index (IBS) and the GTI for the keyword “sampah” (waste) as exogenous variables, achieving a MAPE of 5.7873 (classified as very good) and an RMSE of 46.7509. The forecast shows an upward trend in mid-2024, a decline at the end of 2024, and a sharp increase in early 2025. These results can inform adaptive waste management policies, particularly in strengthening upstream strategies such as waste reduction, sorting, and recycling.
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.
Forecasting Indonesian Monthly Rice Prices at Milling Level Using Google Trends and Official Statistics Data Swardanasuta, I Bagus Putu; Sofa, Wahyuni Andriana; Muchlisoh, Siti; Wijayanto, Arie Wahyu
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.521

Abstract

Hunger is a very complex social issue to address. Alleviating hunger is closely related to achieving food security, which is a goal in realizing the second Sustainable Development Goals (SDGs), zero hunger. The most frequently consumed food commodity by the Indonesian population is rice, which has fluctuating prices in the market. Therefore, price forecasting is necessary so that the government can take preventive measures against rice price increases at certain times. Research on rice price forecasting using big data from Google Trends is still very rare in Indonesia, even though Google Trends has great potential to reflect the public's search popularity for certain keywords. Therefore, this study aims to forecast the monthly medium rice price in Indonesia at the milling level using exogenous variables of dried milled grain prices and the popularity index of related keywords on Google Trends. The forecasting is conducted using Seasonal Autoregressive Integrated Moving Average (SARIMA), SARIMA with Exogenous Variables (SARIMAX), and Extreme Gradient Boosting (XGBoost) models. The SARIMAX model has the best performance in forecasting rice prices, with a Root Mean Squared Error (RMSE) of 941.6933, Mean Absolute Error (MAE) of 817.9021, and Mean Absolute Percentage Error (MAPE) of 0.0620.
The Digital Footprint of Public Attention: Forecasting Indonesian Gold Prices using Google Trends Index and Optimized Support Vector Regression Restu Ilahi, Muhammad; Wahyu Wijayanto, Arie
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.730

Abstract

To provide actionable forecasting insights for gold prices in Indonesia’s public sentiment-driven market, this study developed a machine learning framework using the Google Trends Index (GTI) as a sentiment proxy. We employed an Optuna-optimized Support Vector Regression (SVR) model to comparatively evaluate three feature sets (GTI, historical Lag, and a Mix) across seven forecasting horizons (t+1 to t+30). A key advantage of our approach was the identification of horizon-dependent predictor dynamics: results revealed that while historical data excelled for short-term forecasts (MAPE 0.50% at t+5), the contribution of GTI became vital for long-term accuracy, where the hybrid model achieved its peak performance (MAPE 1.92% at t+30). Notably, the GTI-only model showed solid standalone potential (MAPE < 20%). We conclude that a hybrid approach is most effective, validating GTI as a relevant predictor for Indonesia. Furthermore, the proposed SVR-Optuna framework offers a generalizable methodology for forecasting other sentiment-driven assets, providing a clear, actionable guide for model selection based on forecasting horizons.
Analisis Perbandingan Metode Hierarchical dan Non-Hierarchical dalam Pembentukan Cluster Provinsi di Indonesia Berdasarkan Indikator Women Empowerment Pikata Aselnino; Arie Wahyu Wijayanto
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.68876

Abstract

The focus on improving the quality of women’s live to lessen discrimination and gender inequality is set in the fifth’s goals of SDGs. In Indonesia, the RPJMN 2020-2024 contains measure to improve the contribution of women to equitable development. The Central Bureau of Statistics has developed several indicators related to gender, including Gender Development Index (GDI) an Gender Empowerment Index (GEI), which contain women’s improvement on education and health as well as their participation in economic and political fields. The Ministry of Women’s Empowerment and Child Protection did a quadrant analysis to split Indonesia’s 34 provinces into four categories based solely on GDI and GEI using the national average as a constraint. This study compares the Hierarchical, K-Means, and Fuzzy C-Means method to form number of clusters in Indonesia based on the gender development and empowerment in 2021 in order to complement the quadrant analysis. To choose the number of optimum cluster, Elbow method and Calinski-Harabasz Index were used and the best k value is five. From the validation with Silhoutte Index, K-Means was chosen as the best clustering model.Keywords: clustering; fuzzy; k-means; hierarchical; women empowerment
Co-Authors A.A. Ngurah Gede, Wasudewa Achmad Muchlis Abdi Putra Akhmad Fatikhurrizqi Alfina Nurpiana Alvia Rossa Damayanti Alya Azzahra Andriansyah Muqiit Wardoyo Saputra Annisa Firnanda Arbi Setiyawan Arif Handoyo Marsuhandi Arina Mana Sikana Arini, Rechtiana Putri Arista Ariyani, Marwah Erni Atmaja, Anugerah Surya Atut Pindarwati Ayu Aina Nurkhaliza Az-Zahra, Afifah Bagus Almahenzar Bahar, Vicka Kharisma Bony Parulian Josaphat Chisan, Innas Khoirun Daulay, Nur Ainun Desi Kristiyani Dewi, Ni Kadek Ayu Purnami Sari Dwi Karunia Syaputri Dwi Wahyu Triscowati Emir Luthfi Fauzan Faldy Anggita Fauzan, Fardhi Dzakwan Febrian, M. Yandre Feriyanto, Muhamad Ghina Rofifa Suraya He Youshi Hidayat, Anang Kurnia Hutahaean, Yohana Madame Ika Yuni Wulansari Ikhsanudin, Muhammad Rafi Iman, Qonita Intan Kemala Iskanda, Doddy Aditya Iskanda, Watekhi Izzuddin, Kautsar Hilmi Karmawan, I Putu Agus Kurniawan, Bayu Dwi Kusuma, Arya Candra Luthfi, Emir Maghfiroh, Meilinda F N Maghfiroh, Meilinda F. N. Margareth Dwiyanti Simatupang Maria Angelika H Siallagan Maria Shawna Cinnamon Claire Marsisno, Waris Marsisno, Waris Maulana, Farhan Maulidya, Luthfi Muchlisoh, Siti Muhammad Rezza Ferdiansyah Munifah Zuhra Almasah Nabila Bianca Putri Nasiya Alifah Utami Natasya Afira Natasya Afira Ningrum, Icha Wahyu Kusuma Ningsih, I Kadek Mira Merta Nissa Shahadah Qur&#039;ani Nora Dzulvawan Nurafiza Thamrin Nursiyono, Joko Ade Parwanto, Novia Budi Pasaribu, Ernawati Perani Rosyani Permatasari, Noverlina Putri Pikata Aselnino Pindarwati, Atut Pramana, Setia Prasetyo, Rindang Bangun Pratama, Ahmad R. Prayoga, Suhendra Widi Putri, Salwa Rizqina Putri, Salwa Rizqina Rahmawati, Delvina Nur Raisa Rizky Amelia Rahman Raisa Rizky Amelia Rahman Regita Iswari Puri, Ida Ayu Wayan Renata De La Rosa Manik Ressa Isnaini Arumnisaa Restu Ilahi, Muhammad Ridho, Farid Rifqi Ramadhan Rifqi Ramadhan Robert Kurniawan, Robert Rudianto, Regita Dewanti Sakka, Asriadi Salwa Rizqina Putri Siregar, Tifani Husna Sofa, Wahyuni Andriana Suadaa, Lya Hulliyyatus Sugiarto, Sugiarto Swardanasuta, I Bagus Putu Wahidya Nurkarim Wahyuni, Krismanti Tri Watekhi watin, Rahma Wilantika, Nori Windy Rahmatul Azizah Wulansari, Ika Yuni Yeza, Ardhan Yulia Aryani Yuniarto, Budi Zalukhu, Bill Van Ricardo Zanial Fahmi Firdaus