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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 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 Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Social Sciences Other

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Forecasting Jumlah Penumpang Pesawat Yogyakarta International Airport dengan Big Data Google Trends dan Variabel Makroekonomi untuk Mendukung Official Statistics Chisan, Innas Khoirun; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2123

Abstract

Aviation is an important element to support human connectivity and mobility so it is important to carry out an analysis of the number of airplane passengers. BPS releases data on the number of airplane passengers with a lag of around thirty days. In addition, the use of search engines is increasingly being used nowadays. This research aims to predict the number of Yogyakarta International Airport (YIA) airplane passengers in 2024 using Google Trends and macroeconomic data. To carry out this forecast, the SARIMA and SARIMAX models will be compared with several combinations of external variables. The research results show that the use of Google Trends Index variables and macroeconomics can increase forecasting accuracy. The best model selected was SARIMAX with external variables Google Trends Index and macroeconomics. The forecast results for the number of airplane passengers in January 2024 are 332 thousand passengers and in February 2024 there are 292 thousand passengers. Accurate predictions can help flight planning so that this research can play a role in improving the quality of official statistics in the field of air transportation.
Peramalan Migrasi Masuk di Indonesia Menggunakan Data Google Trend Fauzan, Fardhi Dzakwan; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2237

Abstract

Migration plays an important role that needs to be considered in the national development strategy. The rapid rate of migration require attention such as demand for urban infrastructure, housing, and public services. Google Trend is one of the Big Data sources that can be used to see the possibility of migration through certain keyword searches. This study focuses on using of Google Trend data as additional data to improve forecasting accuracy. The forecasting is carried out using two time series methods, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Vector Autoregression (VAR) with or without Google Trend variables to see model performance. As a result, using Google Trend data helps improve model performance to predict the possibility of migration in the short and long term, as indicated by a decrease in statistical measures such as RMSE, MSE, and MAE when the model is used to predict short and long-term inbound migration.
Prediksi Jumlah Wisatawan Mancanegara Yang Masuk Melalui Bandara Kualanamu Menggunakan Big Data Google Trends Febrian, M. Yandre; Wijayanto, Arie Wahyu
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2273

Abstract

The number of foreign tourists that continues to increase in North Sumatra makes the government must prepare appropriate strategies for policy-making. The data released by the Central Statistics Agency (BPS), as the responsible institution, still has shortcomings, particularly the time gap between data collection and publication. Using Google Trends as supplementary data to fill this time gap is feasible, as Google Trends data can be accessed in real time. This study aims to examine the relationship between Google Trends data and official statistical data, compare the use of SARIMA and SARIMAX models, and forecast the number of tourists for the next year. The results show a moderate correlation between the Google Trends index and official statistics, with a correlation value of 0.592. The most suitable model for this data is the Seasonal Autoregressive Integrated and Moving Average (SARIMA) (0,1,1) (1,0,1)12, with a Root Mean Square Error (RMSE) of 10.223.
Dampak Investasi pada Pembangunan Ekonomi Inklusif di Indonesia: BPS, Politeknik Statistika STIS Iskanda, Watekhi; A.A. Ngurah Gede, Wasudewa; Wijayanto, Arie Wahyu; Iskanda, Doddy Aditya; Watekhi; Regita Iswari Puri, Ida Ayu Wayan
Jurnal Ekonomi dan Kebijakan Pembangunan Vol. 13 No. 2 (2024): Jurnal Ekonomi dan Kebijakan Pembangunan
Publisher : IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jekp.13.2.2024.116-132

Abstract

Capaian pembangunan ekonomi inklusif di Indonesia berkaitan erat dengan kapasitas fiskal pemerintah. Hingga kini kemampuan anggaran pemerintah dalam mewujudkan pembangunan ekonomi yang inklusif masih terbatas. Sebagai alternatifnya, pemerintah dapat menggunakan dana yang bersumber dari investasi untuk meningkatkan capaian pembangunan ekonomi inklusif di Indonesia. Tujuan dari penelitian ini adalah mengukur besarnya pengaruh dari adanya investasi tersebut terhadap tingkat pembangunan ekonomi inklusif di Indonesia. Pengaruh tersebut kemudian dianalisis secara lebih spesifik dengan memperhatikan klasifikasi wilayah antara Indonesia Bagian Barat dan Timur. Berbeda dengan penelitian lainnya, pada penelitian ini pembangunan ekonomi inklusif dimaknai lebih komprehensif dan analisis dilakukan dengan memperhatikan heterogenitas wilayah. Penelitian ini menggunakan pendekatan kuantitatif dengan analisis regresi data panel. Hasil dari penelitian ini memperlihatkan bahwa terdapat pengaruh positif dari kenaikan nilai investasi terhadap tingkat pembangunan ekonomi inklusif. Lebih spesifik, pengaruh tersebut terlihat nyata untuk Indonesia Bagian Timur. Penelitian ini juga memperlihatkan bahwa investasi memiliki pengaruh yang lebih positif terhadap capaian pembangunan ekonomi inklusif dibandingkan dengan dana perimbangan pemerintah pusat ke daerah (dana alokasi umum dan dana alokasi khusus).
Comparison of Machine Learning Algorithms in Classifying Districts/Cities in Indonesia According to the Human Development Index (HDI) in 2021 Dewi, Ni Kadek Ayu Purnami Sari; Wijayanto, Arie Wahyu; Nursiyono, Joko Ade
Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi Vol. 4 No. 1 (2025)
Publisher : Department of Informatics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/snati.v4.i1.4

Abstract

The human development index (HDI) is one of the measuring tools for achieving the quality of life of a region or even a country, including Indonesia. There are 3 basic components of the HDI, namely the dimensions of health, knowledge, and decent living. Development in Indonesia is uneven as indicated by the Human Development Index (HDI) of districts/cities in 2021 which varies greatly. The purpose of this study is to compare several machine learning algorithms to classify districts/cities in Indonesia according to the Human Development Index (HDI) in 2021. There are six machine learning algorithms used in this study, namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearset Neighbor (K-NN), Random Forest, Decision Tree, and Naive Bayes. The k-Fold Cross Validation method is applied to form the training set and testing set, with 10 folds and 1 repetition. The results of the study showed that the classification results of the SVM algorithm using the Radial Basis Function (RBF) kernel parameters with sigma = 0.4864648 and C = 1 were the best among the other five algorithms with an average accuracy of 76.08% and a maximum accuracy of 88.24%.
Deep Learning and Remote Sensing for Agricultural Land Use Monitoring: A Spatio-Multitemporal Analysis of Rice Field Conversion using Optical Satellite Images Wijayanto, Arie Wahyu; Zalukhu, Bill Van Ricardo; Putri, Salwa Rizqina; Wilantika, Nori; Yuniarto, Budi; Kurniawan, Robert; Pratama, Ahmad R.
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1385

Abstract

Rice is a staple food for over half of the global population, making its production crucial for food security, especially in Indonesia, the world's third-largest rice consumer. Population growth and urban expansion have led to agricultural land conversion, necessitating efficient monitoring methods. Traditional approaches, such as area sample frameworks and tile surveys, are costly and time-consuming, prompting the need for remote sensing and deep learning solutions. This study utilizes medium-resolution Sentinel-1, Sentinel-2, and Landsat-8 optical satellite imagery from 2013 and 2021 to analyze land cover changes in West Bandung and Purwakarta Regencies, key agricultural regions in Indonesia. A deep learning model is developed to classify land cover, validated through ground-truth evaluation, and applied to assess spatio-multitemporal land use conversion, paddy field estimation, and conversion rates. Results show that deep learning models effectively classify land cover with high accuracy, revealing significant agricultural land loss due to urban expansion. This research contributes to artificial intelligence (AI)-driven land monitoring, particularly in tropical regions, and supports policymakers in sustainable food agriculture land management. The findings highlight the potential of integrating remote sensing and deep learning for cost-effective agricultural monitoring, ensuring food security and sustainable land use. Future research should explore higher-resolution imagery and advanced AI techniques to enhance predictive accuracy and decision-making.
Drivers and Impacts of Agricultural Land Conversion: Regression Modelling with Spatial Dependence in West Bandung and Purwakarta Regencies, Indonesia Wijayanto, Arie Wahyu; Prasetyo, Rindang Bangun; Putri, Salwa Rizqina; Sugiarto, Sugiarto; Marsisno, Waris; Wahyuni, Krismanti Tri; Pasaribu, Ernawati; Maghfiroh, Meilinda F N
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.23939

Abstract

The rapid conversion of farmland to non-agricultural uses in West Java threatens food security, farmer livelihoods, and environmental sustainability. This study investigates the causes and consequences of land conversion in West Bandung and Purwakarta Regencies using a mixed-source data, including geotagging, CAPI, and secondary data from satellite images, focusing on landowners who converted farmland between 2013 and 2021. Multiple linear regression and spatial models, including Spatial Lag Model (SLM), were applied to assess key determinants. The results revealed economic pressures as the main driver, with rice fields most affected and various geographic and infrastructure factors influencing outcomes. The findings underscore the need for targeted policies to balance development with sustainable land and food system management.
Multi-Source Data Fusion For Data Extraction and Integration of Scientific Publications in Academic Institution STIS Maulidya, Luthfi; Suadaa, Lya Hulliyyatus; Wijayanto, Arie Wahyu; Ridho, Farid
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.87050

Abstract

Scientific research publication data is one of the most important data required by academic and research institution because it can be used as a reference to measure the performance of lecturers in research activities, to assess study programs and university accreditation, to identify research trends, and to plan research development policies and strategies. However, to fulfill these data needs, research data must be collected and integrated from various data sources due to the diversity of databases. One of the portals that provides scientific research publication data for universities in Indonesia is Sinta (Science and Technology Index). The integrated research databases in Sinta are Scopus, Web of Science (WoS), Garba Rujukan Digital (Garuda), and Google Scholar. However, there are limitations, namely that some scientific research publication metadata in Sinta are still not covered, such as Digital Object Identifier (DOI), abstract, author's full name, publication/journal name, publication type, and number of citations. In addition, each data source has a different data format, which requires data processing so that it can be integrated. Processing and integrating research data from different sources will be very inefficient if it is done manually and not computerized. Therefore, this study proposes a data engineering pipeline framework for the extraction and integration of scientific research publication data from various data sources using the multi-source data fusion method with the Unified Cube methodology approach, which is then implemented by building a web interface. We use Politeknik Statistika STIS, Jakarta as a case study. This framework refers to the data engineering lifecycle and multi-source data fusion method based on abstraction levels for the extraction and integration of scientific research publication data. Then, the transformed data will be classified using rule-based classification. The results show that the accuracy of the framework was more than 90% and the accuracy of the classification results was 87.5%.
Integrating Satellite Imagery and Multicriteria Decision Analysis for High-Resolution Flood Vulnerability Mapping: A Case Study of Jakarta, Indonesia Pindarwati, Atut; Wijayanto, Arie Wahyu; Rosyani, Perani; Maghfiroh, Meilinda F. N.
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1388

Abstract

Jakarta, Indonesia, ranks among the most flood-prone megacities in the world, with hydrometeorological factors placing up to 98% of its area at flood risk. Its population density—approximately 15,900 individuals per square kilometer—compounds the impacts of flooding through intensified exposure and socio-economic vulnerability. This study presents a novel, data-driven methodology for flood vulnerability assessment in the Jakarta Special Capital Region (DKI Jakarta), integrating satellite remote sensing and geospatial analysis with a Multicriteria Decision Analysis (MCDA) framework. Employing the Analytical Hierarchy Process (AHP) to systematically weight environmental and socio-economic criteria, a Flood Vulnerability Index (FVI) was developed and spatially modeled at a 500-meter grid resolution. The resulting FVI map categorizes vulnerability into five levels: very low, low, moderate, high, and very high. Findings indicate an index range between 0.36 and 0.70, highlighting predominantly moderate to high vulnerability zones across the region. This high-resolution assessment provides actionable insights for disaster risk reduction, urban resilience planning, and targeted policy interventions to mitigate flood-related hazards in Jakarta.
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 Ariyani, Marwah Erni Atut Pindarwati Ayu Aina Nurkhaliza Az-Zahra, Afifah Bagus Almahenzar 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 Hutahaean, Yohana Madame Ika Yuni Wulansari Ikhsanudin, Muhammad Rafi Iman, Qonita Intan Kemala Iskanda, Doddy Aditya Iskanda, Watekhi Izzuddin, Kautsar Hilmi Kurniawan, Bayu Dwi 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 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'ani Nora Dzulvawan Nurafiza Thamrin Nursiyono, Joko Ade Parwanto, Novia Budi Pasaribu, Ernawati Perani Rosyani Permatasari, Noverlina Putri 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 Ridho, Farid Rifqi Ramadhan Rifqi Ramadhan Robert Kurniawan, Robert Rudianto, Regita Dewanti Salwa Rizqina Putri Suadaa, Lya Hulliyyatus Sugiarto, Sugiarto Wahidya Nurkarim Wahyuni, Krismanti Tri Watekhi watin, Rahma Wilantika, Nori Windy Rahmatul Azizah Wulansari, Ika Yuni Yulia Aryani Yuniarto, Budi Zalukhu, Bill Van Ricardo Zanial Fahmi Firdaus