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Bagus Sartono
Departmen Statistika Institut Pertanian Bogor Gedung IPB Dramaga, Jl. Meranti Wing 22 Lantai 2 Kampus IPB Darmaga Bogor (16680) Indonesia Phone: +62-811-118-508
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CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH Anisa, Rahma; Sartono, Bagus; Silvianti, Pika; Alamudi, Aam; IJSA, Indonesian Journal of Statistics and Its Applications
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
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.v3i1.198

Abstract

Indonesia is very prone to earthquake disaster because it is located in the Pacific ring of fire. Therefore, a reference level of earthquake disaster exposure likelihood events in Indonesia is needed in order to increase people's awareness about the risks. This study aims to determine the index that describes the risk of possible future earthquake disaster. As initial research, this study is focus on earthquake disasters in Java region, as it has the largest population in Indonesia. Several indicators that are related to the severity of earthquake disaster impact, were used in this study. The weights of each indicators were determined by considering its shapley-value, thus all indicators gave equal contribution to the proposed index. The results showed that shapley-value approach can be utilized to construct index with equal contribution of each indicators. In general, the resulted index had similar pattern with the number of damaged houses in each districts.
PENERAPAN CYLINDRICAL DAN FLEXIBLE SPACE TIME SCAN STATISTIC DALAM MENGIDENTIFIKASI KANTONG KEMISKINAN DI PULAU JAWA TAHUN 2011-2015 Nurrusydah, Zaima; Erfiani, Erfiani; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 3 No 2 (2019)
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.v3i2.274

Abstract

The Indonesian government formed the National Team for the Acceleration of Poverty Reduction (TNP2K) to eradicate poverty. TNP2K requires identification of priority areas or poverty hotspots so that the program can be targeted. Scan statistic is one of the most widely used methods to identify poverty hotspots. Cylindrical STSS uses cylindrical scanning windows while most geographical areas are not circular. Flexible STSS is able to detect poverty hotspots in a flexible form. This study aims to identify poverty hotspots using Cylindrical and Flexible STSS then compare the results of both and then determine the best STSS method. Cylindrical STSS tends to have wider hotspots than Flexible STSS. There are a number of districts that are not eligible to be included as poverty Flexible STSS is able to produce better poverty hotspots by not including these districts Poverty hotspots produced by Flexible STSS have higher LLR values. The more suitable STSS method has optimal K values and high suitability with TNP2K priority areas. Cylindrical STSS has an optimal K value when K = 8 and 9. Flexible STSS has a constant LLR value. Flexible STSS has a higher LLR value than Cylindrical STSS at each K value. Flexible STSS with K = 9 has optimal K and high suitability with TNP2K priority areas so that it is the more suitable STSS method to identify poverty hotspots in Java.
KAJIAN VALIDITAS INSTRUMEN PENGUKURAN SKALA PENGALAMAN KERAWANAN PANGAN DI INDONESIA Herlina, Herlina; Sartono, Bagus; Susetyo, Budi
Indonesian Journal of Statistics and Applications Vol 4 No 1 (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.v4i1.543

Abstract

The results of the FAO study since 2013 through the Voices of Hungry Project (VoH-FAO) have produced measures of the Food Insecurity Experience Scale (FIES). FIES is a global reference scale that becomes a reference for comparing the prevalence of food insecurity between countries and regions. The challenge of using the FIES instrument, each country must carry out linguistic adaptations that are appropriate to the culture and national language. This study aims to analyze the validity of FIES measurements in Indonesia, including internal and external analysis. The Rasch model (RM) used for internal validity analysis. Measurement of the validity and reliability of Indonesian FIES items was calibrated with a global reference scale. Differences in the scale of calibration items with a global reference scale of less than 0.35 indicate that they are standard items. FIES measurements require at least five common items. External analysis of FIES measurements uses the Pearson correlation between district-level aggregation on each FIES item that is answered "yes" and determinant characteristics of household food insecurity. The expected correlation coefficient indicated the direction of a positive correlation and observed the correlation coefficient of item 1501 to 1508, which is getting smaller. Internal analysis of FIES measurements in Indonesia shows the achievement of unidimensional and local independence assumptions. However, item 1501 has identified as an outlier. Then identify unique issues are 1501 and 1504, while unique items in rural subsamples are 1503 and 1508. Unique item differences founded in food expenditure 60 percent or more, i.e., 1502. This shows a discordance with items assumption of parameter invariance. The reliability of the FIES item is 0.78, and this reflects the suitability of the model quite well. External analysis of the FIES measurement identifies item 1501 and 1504 as invalid items (unique items).
Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java: Penanganan Overdispersi pada Model Regresi Poisson dengan Binomial Negatif untuk Jumlah Kasus Baru Kusta di Jawa Ulfa, Yopi Ariesia; Soleh, Agus M; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
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.v5i1p1-13

Abstract

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.
PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE Rakhmalia, Riza Indriani; Soleh, Agus M; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 4 No 3 (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.v4i3.667

Abstract

Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.
Improving Classification Model Performances using an Active Learning Method to Detect Hate Speech in Twitter: Peningkatan Kinerja Model Klasifikasi dengan Pembelajaran Aktif dalam Mendeteksi Ujaran Kebencian di Twitter Abidin, Muhammad Ilham; Notodiputro, Khairil Anwar; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
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.v5i1p26-38

Abstract

Efforts from the police to address hate speech on social media such as Twitter will not be sufficient to rely solely on manual checks. Therefore, it is necessary to use statistical modelling like the classification model to detect hate speech automatically. Classification is a type of predictive modelling to produce accurate predictions based on labelled data. Generally, the available data are usually unlabelled implying that the labelling process needs to be done beforehand. Data labelling is time consuming, high cost, and often fails to produce correct labels. This research aims to improve the performances of classification models by adding a small amount of data through the so called active learning method. The results showed that there was no significant difference in the performances of logistic regression and naïve bayes classification models in detecting hate speech. However, the results also showed that adding data through the active learning method substantially improved the logistics regression performance in detecting hate speech when compared to data addition based on a simple random sampling method. Therefore, the performances of classification models in detecting hate speech on Twitter could be improved by using an active learning method.
Enhancing Employee Innovation Through Digital Talent Development: The Mediation of Digital Competency, Leadership and Culture in the Telecom Industry Fauziah, Nadira Aribah; Sukmawati, Anggraini; Sartono, Bagus
Dinasti International Journal of Economics, Finance & Accounting Vol. 7 No. 1 (2026): Dinasti International Journal of Economics, Finance & Accounting (March-April 2
Publisher : Dinasti Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/dijefa.v7i1.6448

Abstract

In the digital economy, innovation is a strategic imperative for sustaining organizational competitiveness, with employees’ innovative work behavior serving as a crucial micro-level foundation. Accelerated technological change and skill disruption have intensified the need for systematic digital talent development. Despite increasing scholarly attention to digital talent management, empirical research remains limited in explaining how digital talent development interacts with digital leadership, digital competence, and digital culture to shape innovative work behavior. This study addresses this gap by examining the structural relationships among these digital enablers within the context of digital transformation in the telecommunications industry. This study adopts a quantitative approach using survey data collected from 189 certified digital talents across PT Telkom Indonesia. Structural Equation Modeling–Partial Least Squares was employed to test the hypothesized relationships and mediating mechanisms. The results demonstrate that digital talent development influences innovative work behavior through an integrated mediating mechanism involving digital leadership, digital competence, and digital culture. This study advances the digital transformation literature by clarifying the systemic role of organizational enablers in translating digital talent investments into employee-level innovation. It emphasizes the necessity of aligning talent development with leadership capability, competency, and cultural reinforcement to support sustainable innovation in digitally transforming organizations.
Identifying the Types of Future Skills Needed in The Manufacturing Industry: A Systematic Literature Review Kharismatul Zaenab Akhilla; Anggraini Sukmawati; Bagus Sartono
Jurnal Aplikasi Bisnis dan Manajemen Vol. 11 No. 3 (2025): JABM Vol. 11 No. 3, September 2025
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.11.3.1099

Abstract

Background: The advent of Industrial Revolution 4.0 has led to the need to redesign the jobs and skills required across various industries and markets. As the manufacturing industry has the most significant contribution to GDP, it has not escaped the impact of this revolution. The dynamics of industrial growth, which tend to fluctuate from year to year, are also significant indicators of the need for strategic efforts to enhance the sector's resilience and competitiveness. One step that can be taken is to identify the types of future skills required and relevant to the manufacturing industry to adapt to a changing environment. Purpose: This study aims to identify the types of future skills needed by the manufacturing industry through a systematic literature review approach in order to obtain a comprehensive overview of relevant and strategic skill trends. Design/methodology/approach: This research employed the Systematic Literature Review (SLR) method, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. SLR serves as a systematic approach to identifying, evaluating, and synthesizing relevant previous research results, thereby obtaining a comprehensive understanding of the future skills required in the manufacturing industry. In addition, thematic analysis was used to identify and interpret patterns or themes within a dataset, as well as to develop a conceptual model of the study's results. Findings/Results: 63 types of future skills were categorized into eight categories. The classification of future skills in this study consists of: 1) Interpersonal Skills, 2) Leadership and Management, 3) adaptive and resilient mindsets, 4) Higher-Order Thinking Skills, 5) Entrepreneurial and Business Skills, 6) Learning and Development; Social and Ethical Responsibility; and 8) Personal Effectiveness. Conclusion: This research highlights the importance of the manufacturing industry in adapting to the changing demands brought about by Industrial Revolution 4.0 through the development of future skills. This study identified 63 types of future skills. It classifies them into eight main categories, considering the similarity of concepts, interrelationships in their application in the workplace, and the role of each skill in supporting individual readiness to face future challenges. Originality/value: This study has substantial originality value because it fills a significant research gap related to future skills in the manufacturing industry context. The lack of previous studies that specifically address future skill needs in this sector indicates the need for further exploration to strengthen the scientific foundation while making practical contributions. Therefore, this study is the first step in identifying the types of future skills that are relevant and potentially applicable to the manufacturing industry in Indonesia. Keywords: future skills, manufacturing industry, soft skills, systematic literature review, thematic analysis
Co-Authors -, Salsabila Aam Alamudi Abdul Aziz Nurussadad Abidin, Muhammad Ilham Abyan, Muhammad Fatih Achmad Fauzan Achsani, Noer Azham Adi Hadianto Adinna Astrianti Afendi, Farit M Agus M Soleh Agus M Soleh Agus M. Sholeh Agus Mohamad Soleh Agusta, Madania Tetiani Agwil, Winalia Aji Hamim Wigena Akbar Rizki Akbar Rizki Alfa Nugraha Pradana Alfian Futuhul Hadi Alifviansyah, Kevin Alona Dwinata Alwinie, Ade Agusti Amanda, Nabila Tri Amatullah, Fida Fariha Amin, Toufiq Al Amir Abduljabbar Dalimunthe Ananda, Frisca Rizki Anang Kurnia Andi Susanto Andrie Agustino Anggraini Sukmawati Anggraini Sukmawati Ani Safitri Anik Djuraidah Anisa Nurizki Anisa, Rahma Annisa Permata Sari, Annisa Permata Annissa Nur Fitria Fathina Anton Ferdiansyah Anwar Fajar Rizki Ardhani, Rizky Ardiansyah, Muhlis Arie Wahyu Wijayanto Arief Daryanto Arief Daryanto Arief Gusnanto Aris Yaman Aris Yaman Aristawidya, Rafika Aruddy Aruddy Aryasa, Komang Budi Asep Rusyana ASEP SAEFUDDIN Asfar Asrirawan, Asrirawan Audina, Delia Fitri Aulia Rizki Firdawanti Aunuddin Aunuddin Auzi Asfarian Ayu Sofia Azlam Nas Bagus Randhyartha Gumilar Bariq, Muhammad Shidqi Abdul Barokaturrizkia Ameliani Bayu Indrayana Bayu Pranata, Bayu Bayu Suseno Beny Mulyana Sukandar Billy Bimandra Adiputra Djaafara Bonar Marulitua Sinaga Budi Susetyo Budi Susetyo Bukhari, Ari Shobri Cahya, Septa Dwi Carlya Agmis Aimandiga Cici Suhaeni Cici Suhaeni Cici Suhaeni Cintari, Nanda Putri Citra, Reza Felix Dani Al Mahkya Darwis Darwis Dede Dirgahayu Dede Dirgahayu Defri Ramadhan Ismana Deiby T Salaki Deni Achmad Soeboer Deri Siswara Desi Prabandari Kusuma Ningtyas Dessy Rotua Natalina Siahaan Dewi Margareth Lumbantoruan Dhanu Dhanu Saptowulan Dian Ayuningtyas Dian Handayani Dian Kusumaningrum Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Dwi Fitrianti Dwi Wahyu Triscowati Dyah Setyo Rini Eko Ruddy Cahyadi Embay Rohaeti Endriani, Desy Erfiani Erfiani Erira, Salsa Rifda Erliza Noor Erwan Setiawan, Erwan Etis Sunandi EVI RAMADHANI Evita Purnaningrum Fachry Abda El Rahman Fadhila Hijryani FAHREZAL ZUBEDI Farit M. Afendi Farit Mochamad Afendi Fauzi, Fatkhurokhman Fauziah, Nadira Aribah Ferdiansyah, Anton Ferdiansyah, Anton Fitri Mudia Sari Fitrianto, Anwar Frisca Rizki Ananda Galih Hedy Saputra Gerry Alfa Dito Ghiffary, Ghardapaty Ghaly Ginting, Victor Gumilar, Bagus Randhyartha Gustiara, Dela Hanum Rachmawati Nur Hari Wijayanto Hari Yanni, Meri Harianto Harianto Hartoyo Hartoyo Hartoyo Hazan Azhari Zainuddin Hendri Wijaya Hendria, Muhammad Herlin Fransiska Herlina Herlina Herlina Herlina Hidayat, Agus Sofian Eka Hidayat, Muhammad Hilman Dwi Anggana I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Idqan Fahmi IJSA, Indonesian Journal of Statistics and Its Applications Ilma, Hafizah Ilma, Meisyatul Ilmani, Erdanisa Aghnia Iman, Mutiara Nurul INA YATUL ULYA Indahwati Indonesian Journal of Statistics and Its Applications IJSA Intan Arassah, Fradha Irene Muflikh Nadhiroh Irfan Syauqi Beik Ismah, Ismah Isnaini, Mardatunnisa Ita Wulandari Itasia Dina Sulvianti Iwan Kurniawan Jaelani, Raditya Kamila, Sabrina Adnin Khairil Anwar Notodiputro Khairunnajah Khairunnajah Khairunnisa, Adlina Kharismatul Zaenab Akhilla Khikmah, Khusnia Nurul Kudang Boro Seminar Kusman Sadik Kusnaeni Kusnaeni, Kusnaeni Kusuma Ningtyas, Desi Prabandari La Surimi, La Laode Ahmad Sabil Leni Anggraini Susanti Lilik Noor Yuliati Linda Karlina Sari Lisa Amelia Luky Adrianto Lukytawati Anggraeni M. Yunus Magfirrah, Indah Matualage, Dariani Mega Ramatika Putri Megawati - Megawati Simanjuntak Meylisah, Eni Mohamad Agus Setiawan Muhadi, Rizqi Annafi Muhammad Hendria Muhammad Ilham Abidin Muhammad Irfan Hanifiandi Kurnia Muhammad Nur Aidi Muhammad Subianto Muhammad Syafiq Muhammad Yusran Mukhamad Najib Murpraptomo, Saka Haditya MY, Hadyanti Utami Nimmi Zulbainarni Nisa Nur Aisyah Nofrida Elly Zendrato Novian Tamara Nugraha, Adhiyatma Nur Aulia NUR HASANAH NURADILLA, SITI Nurfadilah, Khalilah nurrusydah, zaima Oktaviani, Rina Pardomuan Robinson Sihombing Parwati Sofan, Parwati Pika Silvianti Popong Nurhayati Pratiwi, Windy Ayu Purwanto, Arie Puspita, Novi Qalbi, Asyifah Rachma Fitriati Rahardi, Naufal Rahardiantoro, Septian Rahma Anisa Rahma Dany Asyifa Rahman, Gusti Arviana Rahmatulloh, Febriandi Rais Rakhmalia, Riza Indriani Rere Kautsar Resiloy, Unique Desyrre A. Rhendy K P Widiyanto Riantika, Ines Rina Oktaviani Riska Yulianti, Riska Riza Indriani Rakhmalia Rizal Bakri Rizka Rahmaida Rizqi, Tasya Anisah ROCHYATI ROCHYATI Roy Sembel Rupmana Br Butar Sachnaz Desta Oktarina salsa bila Saptowulan Saputra, Galih Hedy Sarah Putri Sari, Jefita Resti Sentana Putra, I Gusti Ngurah Seta Baehera Setiabudi, Nur Andi Setiadi Djohar Setyowati, Silfiana Lis Shafa, Shalshabilla Sholeh, Agus M. Siregar, Indra Rivaldi Siskarossa Ika Oktora Siti Aisyah Sri Amaliya Suantari, Ni Gusti Ayu Putu Puteri Suhaeni, Cici Suhaeri, ⁠Bulan Cahyani Sukarna Sukarna Sunan, Muh. Suprayogi, Muhammad Azis Susanto, Andi Suseno Bayu Syam, Ummul Auliyah Syarip, Dodi Irawan Syella Zignora Limba Totong Martono Toufiq Al Amin Toufiq Al Amin Triscowati, Dwi Wahyu Tsabitah, Dhiya Ulayya Tsaqif, Denanda Aufadlan Ujang Sumarwan Ulfa, Yopi Ariesia Ulfia, Ratu Risha Utami Dyah Syafitri Valentika, Nina Vera Maya Santi Virgie, Meriza Immanuela Wahida Ainun Mumtaza Wahyudi Setyo Wahyuni, Silvia Tri Waliulu, Megawati Zein Wawan Saputra Yani Prihantini Hiola Yanuari, Eka Dicky Darmawan Yenni Angraini Yoga Primanda Yopi Ariesia Ulfa Yudhianto, Rachmat Bintang Yuliani, Leny Zahra, Latifah Zaima Nurrusydah Zulhijrah Zulmi, Muhammad Indra