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All Journal FORUM STATISTIKA DAN KOMPUTASI Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma Journal of Mathematical and Fundamental Sciences JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Socioeconomics and Development JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan Jurnal Jamu Indonesia Jurnal Penelitian Kebijakan Pendidikan Inferensi Indonesian Journal of Educational Assesment Jurnal Pendidikan dan Kebudayaan Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Penelitian Kebijakan Pendidikan Aceh International Journal of Science and Technology Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Xplore: Journal of Statistics Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Jamu Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Integra: Journal of Integrated Mathematics and Computer Science Limits: Journal of Mathematics and Its Applications Indonesian Journal on Learning and Advanced Education (IJOLAE)
Budi Susetyo
Departemen Statistika, IPB University
Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Efek Sinergis Bahan Aktif Tanaman Obat Berbasiskan Jejaring Dengan Protein Target Syahrir, Nur Hilal A.; Afendi, Farit Mochamad; Susetyo, Budi
Jurnal Jamu Indonesia Vol. 1 No. 1 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v1i1.6

Abstract

Medicinal plants contain inherently active ingredients. Such ingredients are beneficial to prevent and cure diseases, as well as to perform specific biological functions. In contrast to synthetic drugs, which is based on one single chemicals, medicinal plants exert their beneficial effects through the additive or synergistic action of several chemical compounds. Those chemical compound act on single or multiple targets (multicomponent therapeutic) associated with a physiological process. Active ingredients combinations show a synergistic effect. This means that the combinational effect of several active ingredients is greater than that of individual one acting separately. A network target can be used to identify synergistic effects of plants active ingredients. The method of NIMS (Network target-based Identification of Multicomponent Synergy) is a computational approach to identify the potential synergistics effect of active ingredients. It also assessess synergistic strength of any active ingradients at the molecular level by synergy scores. We investigate these synergistic on a Jamu formula for diabetes mellitus type 2. The Jamu formula is composed of four medicinal plants, namely Tinospora crispa , Zingiber officinale, Momordica charantia, and Blumea balsamivera. Our work succesfully demonstrates that the highest synergy scores on medicinal plants synergy can be seen in pairs of several active ingredients in Zingiber officinale. On the other hand, the synergy of pairs of active ingredients in Momordica charantia and Zingiber officinale posseses a relatively high score. The same occurs in Tinospora crispa and Zingiber officinale.
Analisis Gerombol Simultan dan Jejaring Farmakologi antara Senyawa dengan Protein Target pada Penentuan Senyawa Aktif Jamu Anti Diabetes Tipe 2 Qomariasih, Nurul; Susetyo, Budi; Afendi, Farit Mochamad
Jurnal Jamu Indonesia Vol. 1 No. 2 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v1i2.16

Abstract

Selama ini pembuatan obat untuk menyembuhkan suatu penyakit masih menargetkan hanya satu protein khusus yang menjadi penyebab penyakit tersebut, yang tentu hanya menggunakan satu senyawa aktif. Padahal selain menimbulkan efek samping, penanganan suatu penyakit perlu menyasar banyak protein sekaligus. Sehingga, baru-baru ini terjadi perubahan paradigma dari “one drug, one target” menjadi “multi-components, network target”. Paradigma baru ini telah melahirkan beberapa penelitian untuk menghasilkan formulasi jamu, hal ini dikarenakan konsep formulasi jamu memerlukan beberapa senyawa aktif yang terlibat. Formula jamu yang diteliti sebagai upaya menyembuhkan penyakit Diabetes Melitus (DM) tipe 2 terdiri dari 4 tanaman yaitu Pare (Momordica charantia), Sembung (Blumea balsamifera), bratawali (Tinospora crispa), dan jahe (Zingiber officinale) berdasarkan hasil penelitian Nurishmaya tahun 2014 serta berdasarkan ramuan jamu yang sedang dikembangkan di Pusat Studi Biofarmaka, Bogor. Evaluasi senyawa yang berkaitan dengan DM tipe 2 dilakukan dengan terlebih dahulu menambahkan 19 obat sintetis yang ditujukan untuk DM tipe 2 dari basis data Drug Bank. Sehingga terdapat total sebanyak 74 senyawa aktif yang terdiri dari 55 senyawa alami dari tanaman dan 19 senyawa sintetis obat. Sebanyak 100 protein yang berkaitan erat dengan masing-masing senyawa diperoleh melalui hasil skor konkordan DrugCHIPER. Skor konkordan tersebut kemudian digunakan dalam analisis gerombol simultan antara senyawa dan protein target. Plot komponen utama dan submatrix penggerombolan simultan menunjukkan 2 dari 3 senyawa dari bratawali sangat dekat dengan kelompok sintetis. Selain itu, ada 11 dari 44 senyawa dari Jahe terkumpul bersama dengan senyawa sintetis tetapi dalam jarak yang jauh. Sedangkan berdasarkan jejaring kemiripan, lebih spesifik lagi terdapat 17 dari 19 senyawa obat sintetis yang memiliki kemiripan berdasarkan protein target dengan 2 senyawa tanaman Bratawali dan 5 senyawa tanaman Jahe.
The Innovation and the Transformation of Indonesian Schools Accreditation Management System Budi Susetyo; Sylvia P. Soetantyo; Muhammad Sayuti; Darfiana Nur
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 4, No. 2, May 2022
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v4i2.17113

Abstract

All schools at the primary and secondary education levels in Indonesia must be accredited. An independent body called the National Accreditation Board for Schools/Madrasah (BAN-S/M) as an external quality assurance agency, accredits schools throughout Indonesia. Since 2005, the percentage of schools accredited in levels A and B has always increased from year to year based on the accreditation results. However, the improvement of school quality based on accreditation did not strongly correlate with the national exam and PISA results. This article discusses the facts of the experience of implementing accreditation for 15 years which became the basis for accreditation reform in Indonesia. BAN-S/M started the reformation in 2020 with three fundamental changes. First, the change in the accreditation instrument from compliance-based to performance-based. Second, the recruitment of new assessors based on cognitive competence and personality. Third, the changes of the accreditation business process through the dashboard monitoring system that will select schools with automatic accreditation extensions without visitation and schools that assessors must visit. implementation of Innovation and accreditation management reform can reduce accreditation costs by more than 60% and is expected to increase the accuracy of school quality assessment results. The findings strengthen the current transformation to the new, more efficient, rational accreditiation management system for schools/madrasah.
Perbandingan Metode GWR, MGWR, dan MGWR-SAR pada Data Persentase Penduduk Miskin di Pulau Jawa Andina Fahriya; Budi Susetyo; I Made Sumertajaya
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 2 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i2.3057

Abstract

The primary goal of Sustainable Development Goals (SDGs) is to end poverty everywhere in all its forms. Poverty is defined as the inability to meet basic needs, such as food, clothing, shelter, education, and healthcare. In Indonesia, the poor population has reached 26.36 million people, with half of them residing on Java Island. Extensive research has been conducted on poverty, particularly using a spatial approach. Spatial regression is a statistical method that explicitly incorporates geographical aspects into a model framework. In spatial regression, two main challenges arise: spatial dependence and heterogeneity. These two effects are inherently interconnected and must be considered simultaneously. Mixed Geographically Weighted Regression with Spatial Autoregressive (MGWR-SAR) is a combination of Mixed Geographically Weighted Regression (MGWR) and Spatial Autoregressive (SAR). MGWR-SAR effectively addresses both spatial dependence and spatial heterogeneity simultaneously. This study aims to determine the best method for modeling the percentage of poor population on Java. The variables used included PPM, BPJSPBI, PPKM, PLSMP, PPTB, BPNT, NCPR, and IPM. The kernel function was selected based on the smallest cross-validation (CV) value, which was a Fixed Gaussian with a CV of 603.8268. Based on the GWR model, the global variables identified were PPTB, BPNT, and IPM, whereas the remaining variables were local. The MGWR-SAR method was found to be the best model for predicting the percentage of poor population, with an AIC = 448.9645, RMSE = 1.9075, and  = 75.23%.
Evaluation of Tree-Based Models for Predicting Social Assistance Recipient Status Based on National Socio-Economic Survey (SUSENAS) 2024 Yani Prihantini Hiola; Zulhijrah; I Gusti Ngurah Sentana Putra; Syella Zignora Limba; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xyyv0f37

Abstract

Abstract. Poverty is a major socioeconomic challenge in Indonesia that affects the effectiveness of social protection programs. In response to this challenge, the government has created social assistance programs to improve the welfare of the people. However, the distribution of social assistance is often considered to be inaccurate, resulting in households that are deemed eligible for social assistance not being identified as recipients. One solution to improve the accuracy of distribution is the application of machine learning in the context of classification. Several tree-based models, such as LightGBM, Random Forest, and XGBoost, were selected because of their superior capabilities compared to classical models such as logistic regression, especially in handling complex data and fulfilling model assumptions. This study compares the performance of these three models in predicting social assistance recipient status using data from the 2024 West Java Provincial National Socioeconomic Survey (SUSENAS). Model evaluation was conducted on several data pre-processing scenarios involving outlier handling, class balancing, and feature engineering. The results show that LightGBM consistently outperforms the other models on six metrics, namely Accuracy, Balanced Accuracy, F1-Score, ROC-AUC, PR-AUC, and Brier Score, out of a total of eight evaluation metrics used. SHAP analysis identifies Social Assistance History and Asset Score as the most influential features for model prediction. Friedman and Nemenyi nonparametric tests confirmed significant performance differences between LightGBM and other models based on the F1-Score, PR-AUC, and Brier Score metrics. These findings indicate that tree-based models, particularly LightGBM, can support the development of a more targeted and data-driven social assistance targeting system. Keywords: Social Assistance; Tree-Based; SHAP; SUSENAS; Hybrid Bayesian Optimization
Analysis of the Relationship between Literacy, Numeracy and School Accreditation Rankings in Sulawesi Using Ordinal Logistic Regression and K-Nearest Neighbors Andi Illa Erviani Nensi; Dela Gustiara; Shalshabilla Shafa; Budi Susetyo
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/Jmathcos11260

Abstract

This study aims to analyze the relationship between literacy and numeracy achievement and school accreditation rankings in the Sulawesi region and to compare the performance of two classification methods, namely ordinal logistic regression and the nearest neighbor method. The data used came from the results of the 2023 and 2024 national school assessments with response variables in the form of tiered school accreditation rankings and predictor variables in the form of literacy and numeracy scores. The analysis began with data exploration to understand the characteristics of distribution and class imbalance, then continued with modeling using two scenarios, namely without and with extreme value handling. Ordinal logistic regression was constructed using a cumulative probability approach and tested through assumption checking, parameter significance, and performance evaluation. The nearest neighbor method was applied through data normalization and parameter tuning to obtain the optimal configuration, and compared between conditions with and without class balancing. The results showed that literacy, especially in 2024, had a significant effect on increasing the probability of higher school accreditation, with an ordinal logistic regression model accuracy rate of around 58% and a balanced accuracy of around 65%. The KNN method produced higher prediction accuracy, around 66%, but had limitations in distinguishing minority classes. These findings emphasize the importance of literacy as a key indicator of school quality and provide a basis for selecting classification methods according to the analysis objectives.
Comparative Study of Hybrid ARIMA-LSTM and CNN-LSTM for Palm Oil Price Forecasting Rizki Alifah Putri; Khairil Anwar Notodiputro; Budi Susetyo
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.27631

Abstract

The forecasting of highly volatile time series data remains a significant challenge due to complex, non-linear patterns. This study compared the performance of two hybrid frameworks, ARIMA-LSTM and CNN-LSTM, which were designed to integrate the statistical strengths of traditional models with the computational power of deep learning. In these architectures, the ARIMA component was utilized to extract linear trends, while the LSTM and CNN layers were employed to identify and manage non-linear dynamics within the data. Utilizing 384 monthly palm oil price data points (1993-2024) sourced from FRED, the models were evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results demonstrated that the hybrid CNN-LSTM outperformed the ARIMA-LSTM and individual models, achieving a superior MAPE of 6.69%. These findings indicated that the integration of Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks was more effective in capturing the complexities of price fluctuations. Practically, the study concluded that accurate forecasting served as a critical tool for market stabilization, thereby supporting broader goals of financial certainty and ecological sustainability.
Analysis of Indonesia’s Gross Regional Domestic Product using a Spatially Filtered Unconditional Quantile Regression Approach Sri Amaliya; Anik Djuraidah; Budi Susetyo
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.37875

Abstract

Analyzing Indonesia’s Gross Regional Domestic Product (GRDP) is crucial for understanding regional economic disparities characterized by heterogeneity and spatial dependence. However, previous studies using mean regression or standard Unconditional Quantile Regression (UQR) often ignore spatial dependence, potentially biasing distributional estimates. Substantively, this study aims to examine how socioeconomic factors influence regional economic performance across different levels of GRDP in Indonesia. To address the methodological gap, this study applies Spatially Filtered Unconditional Quantile Regression (SF-UQR), which captures heterogeneous effects across the GRDP distribution while accounting for spatial dependence. Using cross-sectional data of Indonesian districts and cities from Statistics Indonesia (BPS) in 2023, GRDP is specified as the response variable, with five explanatory variables: human development index, minimum wage, number of workers, original local government revenue, and poverty rate. The analysis compares UQR and SF-UQR across selected quantiles. The results reveal substantial heterogeneity. Human development index and original local government revenue consistently show positive effects, poverty rate negatively affects lower quantiles, minimum wage exhibits a shifting pattern, and number of workers is significant mainly at middle and upper quantiles. SF-UQR outperforms standard UQR, achieving an adjusted R² of 0,67 compared to 0,52 under UQR. Methodologically, this study highlights the relevance of incorporating spatial filtering into UQR when analyzing regional economic data characterized by spatial dependence, providing an alternative distributional perspective on regional economic dynamics. From a policy perspective, the findings indicate that development strategies should consider both distributional heterogeneity and spatial dependence. Overall, the results highlight the necessity of spatially informed and distribution-sensitive policy design to reduce regional economic disparities in Indonesia.
Performance Analysis of Tree-Based Models for Classifying Complete Basic Childhood Immunization in West Java Windi Pangesti; Mega Maulina; Hazelita Dwi Rahmasari; Bagus Sartono; Budi Susetyo; Aulia Rizki Firdawanti; Gerry Alfa Dito
Inferensi Vol 9 No 1 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i1.9167

Abstract

Complete basic immunization is a key public health indicator, and disparities in coverage remain a major concern in West Java. The 2024 Universal Child Immunization (UCI) rate in West Java reached only 77.47%, declining from 2023 and reflecting persistent disparities in access to immunization services, particularly between urban and rural areas. This study aims to identify the determinants of complete basic immunization among children in West Java using SUSENAS 2024 survey data (n = 4,672). Three tree- based classification algorithms CART, Random Forest, and LightGBM were applied, with class imbalance addressed using SMOTE, Tomek Links, and the SMOTE–Tomek Links hybrid method. Model performance was evaluated using balanced accuracy. The Random Forest model combined with SMOTE achieved the highest performance, with a balanced accuracy of 60.3% and an overall accuracy of 70.3%. This model also demonstrated superior capability in identifying children with incomplete immunization. Global feature importance results indicate that household spending category, KIA book ownership, maternal age at first birth, and maternal education are the strongest predictors of complete basic immunization. SHAP analysis reveals contrasting patterns: knowledge- based factors dominate in urban areas, while structural and socioeconomic constraints are more influential in rural areas. These findings underscore the importance of geographically targeted immunization strategies to support equitable access across urban and rural communities in West Java.
Evaluasi Perbandingan Model XGBoost, Random Forest, LightGBM, dan Artificial Neural Network dalam Klasifikasi Kerawanan Pangan Mardatunnisa Isnaini; Dela Gustiara; Rizqi Annafi Muhadi; Shalshabilla Shafa; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36227

Abstract

Food insecurity remains a serious household-level issue, particularly in densely populated regions such as West Java, highlighting the need for analytical approaches capable of accurately identifying vulnerable groups. Machine learning algorithms offer the potential to improve the accuracy and precision of food insecurity classification based on survey data. This study aims to compare the predictive performance and variable importance identification of four machine learning algorithms—Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN)—in predicting household food insecurity status. The analysis employs SUSENAS 2023 data covering 26,012 households with 14 predictor variables, and food insecurity is classified using the Food Insecurity Experience Scale (FIES). Class imbalance is addressed using the Synthetic Minority Over-sampling Technique (SMOTE) within a 10-fold cross-validation framework. The results show that XGBoost achieves the highest accuracy of 71%, while Random Forest provides the best balanced accuracy under the SMOTE scenario. Moreover, all algorithms consistently identify the Wealth Index as the most influential predictor based on their respective Variable Importance measures, followed by variables related to water access and food assistance. Accordingly, XGBoost is recommended in terms of accuracy, whereas Random Forest demonstrates superior balanced accuracy and prediction stability.
Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Agus Mohamad Soleh Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Anak Agung Istri Sri Wiadnyani Andi Illa Erviani Nensi Andina Fahriya Anik Djuraidah ASEP SAEFUDDIN Aulia Dwi Oktavia Aulia Rizki Firdawanti Aunuddin Aunuddin Bagus Sartono Bambang H. Trisasongko Bambang Juanda Brian G. Lees Cici Suhaeni Cici Suhaeni Cut N. Ummu Athiyah DAMAYANTI BUCHORI Darfiana Nur Dela Gustiara Dewi Jasmina Dewi Jasmina, Dewi Dhea Dewanti Dian Kurniasari Dito, Gerry Alfa Dyah R. Panuju Endah Febrianti Erfiani Erfiani Fadjrian Imran Fahriya, Andina Farit Mochamad Afendi Fitrianto, Anwar Gerry Alfa Dito H Karwono Hafidz Muksin Hari Wijayanto Hazelita Dwi Rahmasari Herlina Herlina Hermawati, Neni I Gusti Ngurah Sentana Putra I Made Sumertajaya Inayatul Izzati Diana Yusuf Indahwati Indahwati Indahwati Indahwati, NFN Intan Juliana Panjaitan Iswan Achlan Setiawan Izzati Rahmi HG Jap Ee Jia Jia, Jap Ee Karwono, H Kesuma Millati Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kristuisno Martsuyanto Kapiluka Kriswan, Suliana Kusman Sadik Kusni Rohani Rumahorbo La Ode Abdul Rahman La Ode Abdul Rahman La Ode Abdul Rahman M Nur Aidi M Nur Aidi, M Nur Mahmud A. Raimadoya Mardatunnisa Isnaini Mega Maulina Muh Nur Fiqri Adham Muhammad Amirullah Yusuf Albasia Muhammad Nur Aidi Muhammad Sayuti Mustofa Usman Nur'aini Nurfadilah, Khalilah Nurfajrin, Tria Ermina Nurul Qomariasih Pannu, Abdullah Pika Silvianti Pika Silvianti Qalbi, Asyifah Qomariasih, Nurul Rachman, Nurul Aulia Rahma Anisa Rahmawat, NFN Rahmawati, nFN Rais Ratnasari, Andika Putri Rifannisa Bahar Rifki Hamdani Rizki Alifah Putri Rizqi Annafi Muhadi Safitri, Wa Ode Rahmalia Sanusi, Ratna Nur Mustika Satriyo Wibowo Sembiring, Febryna Shalshabilla Shafa Sri Amaliya Sri Ningsih Desi Afriany Sulandra, Ardelia Maharani Sulfikar Amir Suliana Kriswan Supriatin, Febriyani Eka Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. Syella Zignora Limba Sylvia P. Soetantyo Tina Aris Perhati Tiya Wulandari Ulfa Afilia Shofa Utami Dyah Syafitri Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Warsono Windi Pangesti Wulan Andriyani Pangestu Yani Prihantini Hiola Yasmin Erika Faridhan Zahira Rahvenia Robert Zainal A Koemadji Zulhijrah