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All Journal JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN Jurnal Ilmu Komputer JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI REKAYASA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Computatio : Journal of Computer Science and Information Systems 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 Indonesian Journal of Statistics and Its Applications The International Journal of Remote Sensing and Earth Sciences (IJReSES)
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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Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting: Perbandingan Metode Hierarchical Clustering, K-Means, K-Medoids, dan Fuzzy C-Means dalam Pengelompokan Provinsi di Indonesia Menurut Indeks Khusus Penanganan Stunting Suraya, Ghina Rofifa; Wijayanto, Arie Wahyu
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p180-201

Abstract

Stunting has been widely known as the highest case of malnutrition suffered by toddlers in the world and has a bad impact on children's future. In 2018, Indonesia was ranked the 31st highest stunting in the world and ranked 4th in Southeast Asia. About 30.8% (roughly 3 out of 10) of children under 5 years suffer from stunting in Indonesia. To support the government policy making in handling stunting, it is undoubtedly necessary to classify the levels of stunting handling in regions in Indonesia. In this work, the hierarchical agglomerative and non-hierarchical clustering is compared and evaluated to perform clustering on stunting data. The agglomerative hierarchical cluster uses Single Linkage, Average Linkage, Complete Linkage, and Ward Method, while the non-hierarchical cluster uses K-Means, K-Medoids (PAM) Clustering, and Fuzzy C-Means. This study uses data from 12 IKPS indicators in 34 provinces in Indonesia in 2018. Based on the results of the evaluation using the Connectivity Coefficient, Dunn Index, Silhouette Coefficient, Davies Bouldin Index, Xie & Beni Index, and Calinski-Harabasz Index, the results show that the Average Linkage is the best cluster method with the optimal number of clusters is four clusters. The first cluster is a cluster with a good level of stunting management which consists of 28 provinces. The second cluster consists of only one province, DI Yogyakarta with a very good level of stunting handling. The third cluster consists of four provinces with poor stunting handling rates. Finally, the last cluster consisting of one province, Papua, has a very poor level of stunting handling.
Pengembangan Web-Based Dashboard untuk Deteksi Umur dan Status Tanam Pohon pada Perkebunan Kelapa Sawit Salsabila, Michellia Cempaka; Wijayanto, Arie Wahyu
Computatio : Journal of Computer Science and Information Systems Vol. 8 No. 1 (2024): Computatio: Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v8i1.29616

Abstract

Adanya kebutuhan terhadap tools untuk pendeteksian kelapa sawit agar dapat digunakan untuk melakukan monitoring, menjadi dasar permasalahan dilakukannya penelitian ini. Tujuan utama dari penelitian ini adalah pembangunan dashboard sederhana sebagai alat implementasi dari model estimasi umur dan status tanam kelapa sawit. Dashboard dibangundengan menggunakan framework Streamlit yang berbasis bahasa pemrograman Python. Dashboard ini memiliki dua halaman, yaitu halaman Home dan halaman pendeteksian. Pada halaman Home, pengguna dapat membaca tutorial penggunaan dashboard dan mengunduh sampel gambar untuk melakukan pendeteksian. Halaman pendeteksian menampilkan informasi evaluasi testing model pendeteksian sekaligus sebagai tempat bagi user untuk mengunggah gambar yang ingin dideteksi. Dashboard ini berpotensi untuk dikembangkan guna memantau pertumbuhan kelapa sawit.
OIL PALM PLANTATION DETECTION IN INDONESIA USING SENTINEL-2 AND LANDSAT-8 OPTICAL SATELLITE IMAGERY (CASE STUDY: ROKAN HULU REGENCY, RIAU PROVINCE) Yunita Nurmasari; Arie Wahyu Wijayanto
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 1 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3537

Abstract

The objective of this work is to assess the capability of multispectral optical Landsat and Sentinel images to detect oil palm plantations in Rokan Hulu, Riau, one of the largest palm oil producers in Indonesia, by combining multispectral bands and composite indices. In addition to comparing two different sets of satellite images, we also ascertain which gives the best performance among the supervised machine learning classifiers CART Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. With the use of multispectral bands and derived composite indices, the best classifier achieved an overall accuracy of up to 92%. The findings and contributions of the study include: (1) insight into a set of feature combinations that provides the highest model accuracy, and (2) an extensive evaluation of machine learning-based classifiers on two different optical satellite imageries. Our study could further be beneficial for the government in providing more scalable plantation statistics.
MACHINE LEARNING APPLIED TO SENTINEL-2 AND LANDSAT-8 MULTISPECTRAL AND MEDIUM-RESOLUTION SATELLITE IMAGERY FOR THE DETECTION OF RICE PRODUCTION AREAS IN NGANJUK, EAST JAVA, INDONESIA Terry Devara Tri Saadi; Arie Wahyu Wijayanto
International Journal of Remote Sensing and Earth Sciences Vol. 18 No. 1 (2021)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2021.v18.a3538

Abstract

Statistics Indonesia (BPS) has been introducing the use of Area Sampling Frame (ASF) surveys from 2018 to estimate rice production areas, although the process continues to suffer from the high costs of human and other resources. To support this type of conventional field survey, a more scalable and inexpensive approach using publicly-available remote sensing data, for example from the Sentinel-2 and Landsat-8 satellites, has been explored. In this research, we compare the performance gain from Sentinel-2 and Landsat-8 images using a multiple composite-index enriched machine learning classifier to detect rice production areas located in Nganjuk, East Java, Indonesia as a case study area. We build a detection model from a set of machine learning classifiers, Decision Tree (CART), Support Vector Machine, Logistic Regression, Ensemble Bagging Methods (Random Forest and Extra Trees), and Ensemble Boosting Methods (AdaBoost and XGBoost). The composite indices consist of the NDVI and EVI for agricultural and forest areas, NDWI for water and cloud, and NDBI, NDTI, and BSI for built-up areas, fallows, and asphalt-based roads. Validated by k-fold cross-validation, Sentinel-2 and Landsat-8 achieved F1-scores of 0.930 and 0.919 respectively at the scale of 30 meters per pixel. Using a 10 meter resolution per pixel for the Sentinel-2 imagery showed an increased F1-score of up to 0.971. Our evaluation shows that the higher spatial resolution imagery of Sentinel-2 achieves a better prediction, not only performance-wise, but also as a better representation of actual conditions.
CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Dwi Wahyu Triscowati; Bagus Sartono; Anang Kurnia; Dede Dirgahayu; Arie Wahyu Wijayanto
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Abstract

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.
Automated Oil Palm Health Assessment Using Object-Based Deep Learning and High-Resolution UAV Imagery in Indonesia Pindarwati, Atut; Wijayanto, Arie Wahyu; Karmawan, I Putu Agus; Yeza, Ardhan; Sakka, Asriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1391

Abstract

Indonesia, as the world’s largest crude palm oil (CPO) producer, faces challenges in plantation monitoring due to reliance on manual data collection methods that are time-consuming, costly, and prone to human error. This study proposes an automated approach for assessing oil palm tree health using high-resolution UAV imagery (5–10 cm) and object-based deep learning models. We evaluate five state-of-the-art detectors—YOLOv5s, Faster R-CNN, Mask R-CNN, SSD, and RetinaNet—to classify individual trees into four health categories: Healthy, Moderately Healthy, Needs Improvement, and Urgent Condition. Using a dataset of 14,749 labeled trees from Kendawangan, Indonesia, YOLOv5s achieved the highest performance with a precision of 0.784, recall of 0.752, and mAP of 0.764. Our findings demonstrate the potential of AI-driven monitoring to enhance plantation management through rapid, accurate, and cost-effective health assessments—contributing a scalable solution to support precision agriculture and sustainable CPO production.
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.
Co-Authors A.A. Ngurah Gede, Wasudewa Achmad Muchlis Abdi Putra Akhmad Fatikhurrizqi Alfina Nurpiana Alvia Rossa Damayanti Alya Azzahra Anang Kurnia Andriansyah Muqiit Wardoyo Saputra Annisa Firnanda Arbi Setiyawan Arif Handoyo Marsuhandi Arina Mana Sikana Arini, Rechtiana Putri Ariyani, Marwah Erni Atmaja, Anugerah Surya Atut Pindarwati Ayu Aina Nurkhaliza Az-Zahra, Afifah Bagus Almahenzar Bagus Sartono Bony Parulian Josaphat Chisan, Innas Khoirun Daulay, Nur Ainun Dede Dirgahayu Desi Kristiyani Dewi, Ni Kadek Ayu Purnami Sari Diarty, Milie 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 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'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 Restu Ilahi, Muhammad Ridho, Farid Rifqi Ramadhan Rifqi Ramadhan Robert Kurniawan, Robert Rudianto, Regita Dewanti Sakka, Asriadi Salsabila, Michellia Cempaka Salwa Rizqina Putri Sofa, Wahyuni Andriana Suadaa, Lya Hulliyyatus Sugiarto, Sugiarto Suraya, Ghina Rofifa Swardanasuta, I Bagus Putu Terry Devara Tri Saadi Wahidya Nurkarim Wahyuni, Krismanti Tri Watekhi watin, Rahma Wilantika, Nori Windy Rahmatul Azizah Wulansari, Ika Yuni Yeza, Ardhan Yulia Aryani Yuniarto, Budi Yunita Nurmasari Zalukhu, Bill Van Ricardo Zanial Fahmi Firdaus