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All Journal JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN Jurnal Manajemen dan Organisasi MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Media Statistika Jurnal Agro Ekonomi Analisis Kebijakan Pertanian Indonesian Journal of Business and Entrepreneurship (IJBE) Jurnal Pembangunan dan Alam Lestari Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Manajemen Teori dan Terapan Jurnal Ilmu Keluarga dan Konsumen Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan IJID (International Journal on Informatics for Development) Jambura Journal of Mathematics Jurnal Kesehatan Ibu dan Anak ComTech: Computer, Mathematics and Engineering Applications Jurnal Matematika UNAND Indonesian Journal of Electrical Engineering and Computer Science Jurnal Agro Ekonomi Analisis Kebijakan Pertanian International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics ARRUS Journal of Mathematics and Applied Science Journal Of Sustainability Perspectives Makara Journal of Technology Prosiding Seminar Nasional Official Statistics Journal of Sustainability Perspectives Jurnal Ilmu Kehutanan Xplore: Journal of Statistics Scientific Journal of Informatics Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications

Hari Wijayanto

Department Of Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia

Author-ID : 275693

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture 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 Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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STUDY ON EMD METHOD FOR PREDICTING THE PRICE OF CURLY RED CHILI IN INDONESIA Zilrahmi Zilrahmi; Hari Wijayanto; Farit M Afendi; Rizal Bakri
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
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.v4i2.600

The fluctuations of curly red chili price affect the inflation rate in Indonesia. So that, the basic characteristics of price movement and correctly prediction for curly red chili price become concern in various studies. Empirical Mode Decomposition (EMD) method helps to examine behavioral characteristics of curly red chili prices in Indonesia easily. Ensemble EMD (EEMD) and modified EEMD are the decomposition method of time series which is development of EMD method. The decomposed data with EMD methods can also used for price forecast. The forecasting with ARIMA and trend polynomial performed to assess the effect of decomposition with EMD methods for forecast stability of curly red chili price in Indonesia under various conditions. The results show the most influence factor for price fluctuation of curly red chili in Indonesia is season and growing season. In this case, the ability of a decomposition method to produce the actual components that describe the pattern of data signals affect the accuracy of the predicted value obtained using the model. The predicted value using the decomposed data by modified EEMD always better than EEMD on the overall condition.

A Study on Accuracy of Paddy Harvest Area Estimation on Area Sampling Frame Method: Kajian Ketepatan Pendugaan Luas Panen Padi pada Metode Pengambilan Kerangka Sampel Area Mulianto Raharjo; Anang Kurnia; Hari Wijayanto
Indonesian Journal of Statistics and Applications Vol 6 No 1 (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.v6i1p41-49

There was unsynchronized national rice data until 2017, which indicating that influenced by the differences in calculation methods between government agencies. The Indonesian Central Bureau of Statistics (BPS Statistics), the most responsible agency for national rice data, collected rice plant areas data using the paddy statistical assessment method (SP-Padi). Subjective elements from various parties potentially influenced the result of this assessment method. The development of a new method to overcome this matter has been started by the government since 1993. In 2018 the method, which is named the Area Sample Frame (ASF) method, was officially used by the government under the coordination of BPS. The ASF method divides the area into grids: blocks, segments, and sub-segments. This new method has several issues related to the methodology used in determining the sampling method. This study was conducted to evaluate the accuracy of paddy harvest area estimation on the ASF method through a sampling simulation process of the ASF method with various scenarios. With 20 simulated scenario combinations, it was found that the difference percentage average between the harvested area of the population and the harvested area of the sample to the sub-district area was 0.00062%, and the mean square error (MSE) was 0.0041%. So it can be concluded that the ASF methodology is an unbiased method and is good enough to accommodate various strata diversity in any region.

Sentiment Classification on the 2024 Indonesian Presidential Candidate Dataset Using Deep Learning Approaches Cici Suhaeni; Hari Wijayanto; Anang Kurnia
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
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.v8i2p83-94

This study aims to compare the performance of three deep learning models (LSTM, BiLSTM, and GRU) in the task of sentiment classification for the 2024 Indonesian Presidential Candidate dataset, focusing specifically on the case of Prabowo Subianto. The dataset comprises social media X posts sourced from kaggle, and the analysis investigates the effectiveness of different variants of recurrent neural network architectures in identifying public sentiment. The models were evaluated on accuracy and F1 score. The results demonstrate that BiLSTM outperformed both LSTM and GRU models in all metrics, achieving a testing accuracy of 80.70% and an F1 score of 86.86%, compared to LSTM and GRU which both achieved a testing accuracy of 72.56% and an F1 score of approximately 84%. The higher performance of BiLSTM is attributed to its ability to capture bidirectional context within the text, thereby understanding complex sentiment patterns more effectively. LSTM and GRU models displayed similar performance, therefore BiLSTM is the best model for this dataset. These results indicate that BiLSTM is especially well-suited for analyzing public sentiment towards political figures like Prabowo Subianto, offering significant insights into public discussions surrounding the 2024 Indonesian Presidential Election. This study recommends exploring transformer-based models like BERT or GPT variants to enhance sentiment classification accuracy in this domain.

CLUSTER ANALYSIS OF MULTIVARIATE PANEL DATA ON DATA CONTAINING OUTLIERS Kristuisno Martsuyanto Kapiluka; Hari Wijayanto; Anwar Fitrianto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0439-0452

One clustering method for panel data is K-Means Longitudinal (KML), which considers only a single trajectory per subject over time. To address this limitation, KML was extended into K-Means Longitudinal 3D (KML3D), which enables clustering of joint or multivariate longitudinal data by considering multiple trajectories measured simultaneously for each subject. Both KML and KML3D provide new insights into clustering panel data using a non-hierarchical K-means approach. Hereinafter, this method is referred to as KML3D K-Means. KML3D K-Means implements the K-Means algorithm, specifically designed to cluster trajectories in panel data, and uses the mean as the clustering centroid. In practice, the K-Means algorithm is less effective in clustering data with outliers. This issue can be overcome by KML3D K-Medoids, a method based on KML3D that uses the median as the centroid. This study aims to determine cluster analysis for multivariate panel data on data containing outliers with KML3D K-Means and KML3D K-Medoids. Both methods are applied to panel data of social and welfare statistical data from 34 provinces observed for 8 years (2016 – 2023). The comparison of methods is based on the Calinski–Harabasz index. The results of the study show that KML3D K-Medoids has a Calinski-Harabasz index that is higher than KML3D K-Means in clustering multivariate panel data with outliers. The analysis identified three optimal clusters (k = 3) based on the Calinski–Harabasz (CH) index, which can be categorized as the “more prosperous”, “moderately prosperous”, and “less prosperous” groups. The growth rate analysis reveals disparities in development trajectories across clusters, with cluster 3 showing the most consistent improvements, cluster 1 moderate progress, and cluster 2 lagging in key social and welfare indicators.

Comparison of Tree-Based Survival Models for Predicting Graduate Study Duration and Risk Stratification Ahmad Syauqi; Anwar Fitrianto; Hari Wijayanto
Scientific Journal of Informatics Vol. 13 No. 3: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i3.48890

Purpose: This study aims to develop and evaluate a tree-based survival machine learning framework for predicting graduate study duration and identifying students at risk of delayed graduation. The study addresses the growing need for accurate educational time-to-event prediction to support academic monitoring and data-driven decision-making in higher education institutions, including IPB University. Methods: A quantitative predictive analysis was conducted using data from 3,417 master students from the 2020–2022 cohorts. Four tree-based survival models were evaluated, namely Survival Tree (ST), Extremely Randomized Survival Tree (EST), Random Survival Forest (RSF), and Gradient Boosting Survival (GBS). The analysis used right-censored survival data with a 42-month observation period. Model evaluation was conducted using repeated stratified random split validation (10 repetitions) with Concordance Index (C-index) and Integrated Brier Score (IBS) metrics. Risk stratification was subsequently performed using the best-performing model based on predicted survival probabilities at a 24-month time horizon. Result: GBS achieved the best overall predictive performance with the highest mean C-index (0.659) and the lowest mean IBS (0.194), indicating superior discrimination and prediction accuracy compared to ST, EST, and RSF. The repeated evaluation results also demonstrated stable predictive performance across data partitions. Risk stratification successfully separated students into low-, medium-, and high-risk groups with significantly different survival patterns. High-risk students generally tended to be older, have lower undergraduate GPA, were more often male, and more frequently originate from private undergraduate institutions. Novelty: This study provides a comparative evaluation of multiple tree-based survival machine learning models within an educational time-to-event framework. The integration of repeated survival model evaluation with practical student risk stratification offers both methodological and applied contributions for academic monitoring and early intervention strategies in higher education.

PATIENT EMPOWERMENT INDEX OF DIABETES MELLITUS PATIENTS Agus Heru Darjono; Ujang Sumarwan; Lilik Noor Yuliati; Hari Wijayanto
Jurnal Ilmu Keluarga dan Konsumen Vol. 12 No. 3 (2019): JURNAL ILMU KELUARGA DAN KONSUMEN
Publisher : Faculty of Social Science and Human Ecology, IPB University

The measurement of patient empowerment is important in the health care of chronic diseases, especially diabetes mellitus. The purpose of the study was to develop the patient empowerment index (‘IKP’/Indeks Keberdayaan Pasien) and its dimensions (patient knowledge, patient control, and patient participation) in patients with diabetes mellitus. The research utilized factor analysis in developing patient empowerment index for data analysis. Purposive sampling has been conductedwith 330respondents of diabetes mellitus patients from 26 hospitals in Jabotabek (Jakarta, Bogor, Tangerang, and Bekasi). The variables measured using the Likert scale with a scale of 1 to 5, in which 1 indicates the level of strongly disagree and 5 indicates the level of strongly agree. The collected data were analysed with factor analysis.The results showed that the patient empowerment index consisted of 25.84 percent of knowledge dimensions, 33.44 percent of control dimensions and40.76 percent of participation dimensions. The total score of patient empowerment index value is 68.84 that is in the critical category, which means that consumers have control for the management of their disease conditions in their daily lives. The managerial implication based on the result was the emerging issues of the government to develop an empowerment index for each province in Indonesia that can be used as a benchmark and key performance indicator (KPI) to measure the governance of health programs so the patient empowerment can be increased.

A Sustainability Dashboard: Utilization of ICT towards Effective Management of Sustainable Campus Infrastructure Heriansyah Putra; Ibnul Qayim; Anisa Dwi Utami; Zainab Ramadhanis; Maulina Lamuse; Hari Wijayanto; Alim Setiawan Slamet
Journal of Sustainability Perspectives Vol 6, No 1 (2026)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsp.2026.28599

Information and Communication Technology (ICT) has become a pivotal element in managing the sustainability parameters of higher education institutions. This study aims to presents the development of ICT as an effective management strategy for a sustainable campus at IPB University, focusing on integrating digital tools and systems to improve operational efficiency, resource management, and environmental impact. Through a qualitative approach, this study shows the implementation of ICT-driven initiatives, such as setting infrastructure, transportation, and energy management systems. The study highlights the significant benefits of ICT in promoting sustainability goals, such as monitoring land use, the carbon footprint, energy usage, and fostering a culture of environmental awareness among campus civitas. Moreover, this study identifies challenges and opportunities in advancing ICT adoption within the university's sustainability framework. The findings emphasize the strategic and beneficial use of ICT to enhance operational performance by providing a real-time and accurate monitoring system.

Landscape study of land use in Manggis Village and Manggis Gadungan nature reserve as a basis for sustainable environmental resource management planning Rahmania, Farhani Nurshafa; Leksono, Amin; Wijayanto, Hari; Ghada Abd Elsattar Mohammed, Oraby
Jurnal Pembangunan dan Alam Lestari Article in Press
Publisher : Graduate School of Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jpal.699

Manggis Gadungan Nature Reserve (NR) is a nature conservation area that plays a vital role in protecting biodiversity, as a water catchment area, and a life support system. The natural ecosystem in Manggis Gadungan NR has become increasingly important to preserve and defend since it was designated as a Pusat Ficus Nasional (PFN) in 2022. The balance of the ecosystem in Manggis Gadungan NR, with its various essential functions, must be maintained with the support of its buffer zone. Therefore, optimal management is not only for the Manggis Gadungan NR area but also for its buffer zone. A land-use inventory study can serve as the basis for sustainable management planning. Thus, this study aims to determine the land use inventory in the Manggis village and Manggis Gadungan NR as a basis for sustainable management planning. This study uses the Manggis Gadungan Nature Reserve as the study area, with spatial analysis and literature review to determine the land-use percentages in the Manggis Village and Manggis Gadungan NR, as a basis for sustainable environmental resource management. The study found five land-use types in the Manggis Village and Manggis Gadungan NR: production forests, settlements, rice fields, yards, and cultivated land. The most extensive land use is rice fields, covering 44 percent of the total area of 311.83 Ha, and the smallest is settlements. The land-use variations in the Manggis Village and Manggis Gadungan NR can be used to develop sustainable management plans that incorporate agroforestry, farmer groups, and reforestation models, and to assist villages in supporting environmental, social, and economic sustainability

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsp.2026.28599

Comparison of Extremely Randomized Survival Trees and Random Survival Forests: A Simulation Study Mohamad Solehudin Zaenal; Anwar Fitrianto; Hari Wijayanto
Scientific Journal of Informatics Vol. 11 No. 3: Special Issue
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.8464

Abstract. Purpose: This simulation study investigates the Extremely Randomized Survival Trees (EST) model, a machine learning technique expected to handle survival analysis, particularly in large survival datasets, effectively. The study compares the performance of the EST model with that of the Random Survival Forest (RSF) model, focusing on the C-index value to determine which model performs better. Methods: The analysis begins with the generation of 540 simulated datasets, created by combining three levels of sample sizes, two levels of censoring proportions, three types of hazard functions, and 30 repetitions for each scenario. The simulation data were split into 80% training and 20% testing data. The training data were used to build the EST and RSF models, while the test data were used to evaluate their performance. The model with the highest C-index value was deemed the best performer, as a higher C-index indicates superior model performance. Result: The results indicate that the sample size, type of hazard function, and the method used influence that model performance. The EST model significantly outperformed the RSF model when the sample size was large, though no significant difference was observed when the sample size was small or medium. Additionally, the EST model consistently demonstrated faster computation times across all simulation scenarios. Novelty: This study provides a pioneering exploration into applying decision tree algorithms, specifically EST and RSF, in survival analysis. While these methods have been extensively studied in regression and classification contexts, their application in survival analysis remains relatively unexplored.

Co-Authors . Aunuddin . Barizi . Gunawan Aan Kardiana Afnan, Irsyifa Mayzela Agus Heru Darjono Agus M Soleh Agus M. Sholeh Agus Mohamad Soleh Ahmad Syauqi Aji Hamim Wigena Akhmad Fauzi Aldi Cahyanugroho Alim Setiawan Slamet Amin Leksono Anadra, Rahmi Anang Kurnia Anas Miftah Fauzi Andres Purmalino Anggraini Sukmawati Anindia Nufitasari Arief Hendarto Arif Handoyo Marsuhandi Aris Purwanto Aruddy Aruddy ASEP SAEFUDDIN Astridina, Astridina Aunuddin Aunuddin Ayunda Pratiwi Baba Barus Bagus Sartono Bambang Hendro Trisasongko Barizi . Basita G. Sugihen Bertho Tantular Boedi Tjahjono Budi Susetyo Cici Suhaeni Cici Suhaeni Cut Zaraswati DAHRUL SYAH Dede Dirgahayu Domiri Dedi Budiman Hakim Denanda Aufadlan Tsaqif Devi Andrian Dyah R Panuju Dyah R. Panuju Dyah R. Panuju Edi Abdurrachman Eko S. Pribadi Erfiani Erfiani Erliza Noor Fachry Abda El Rahman Fani Fahira Farit M Afendi Farit Mochamad Afendi Farly Shabahul Khairi fatimah Fatimah Femmy Diwidian Fitria Hasanah Fitrianto, Anwar Ghada Abd Elsattar Mohammed, Oraby H S, Rahmat Hefni Effendi Heny K Daryanto Heriansyah Putra Hikmah, Zetil Hwan-Seung Yong I K Marla Lusda I Made Sumertajaya I Nyoman Jaya Wistara IBNUL QAYIM Ilma, Meisyatul Ina Widayanty Indah Dwi Pratama Indahwati Indahwati Inna Firindra Fatati Intan Nirmala Irzaman, Irzaman Istiqlaliyah Muflikhati Itasia Dina Sulvianti Jajah K. Wagiono Jayawarsa, A.A. Ketut Khairil Anwar Notodiputro Kristuisno Martsuyanto Kapiluka Kurnia Suci Indraningsih Kusman Sadik La Ode Abdul Rahman La Ode Abdul Rahman Laily Nissa Atul Mualifah Leny Maryesa Lilik Noor Yuliati Lina Karlinasari Luvy Mayanda M. Syamsul Maarif Mahmud A. Raimadoya Mahmud A. Raimadoya Masrukin Masrukin Maulina Lamuse Merry Sabed Mohamad Solehudin Zaenal Muhammad Nur Aidi Mulianto Raharjo Musa Hubeis Nunung Nurjanah Nurrahman, Fathu Oktaviyani Daswati Panca Wiputra Pang S. Asngari Pannu, Abdullah Prabowo Tjitrpranoto Rachma Venita Rahmania, Farhani Nurshafa Ramadhanis, Zainab Riana Riskinandini Rizal Bakri Rizky Nurkhaerani Rysda Rysda Sachnaz Desta Oktarina Sri Kustiara Sutoro Sutoro Thooriq Ghaith Ujang Sumarwan Utami Dyah Syafitri Utami, Anisa Dwi Widiananda Putri Hutami Yandhika Hary Wibowo Yarah, Helena Ramadhini Yenni Angraini Yonny Koesmaryono Yuli Sukmawati Yuni Suci Kurniawati Zilrahmi, Zilrahmi Zuhdiyah Izzatun Nisa'

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