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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) BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Socioeconomics and Development Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan Indonesian Journal on Learning and Advanced Education (IJOLAE) Jurnal Jamu Indonesia Jurnal Penelitian Kebijakan Pendidikan Indonesian Journal of Educational Assesment Jurnal Pendidikan dan Kebudayaan Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Penelitian Kebijakan Pendidikan Aceh International Journal of Science and Technology Jurnal Bayesian : Jurnal Ilmiah Statistika dan Ekonometrika Xplore: Journal of Statistics 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
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 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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Integrating VAR and CNN Models for Accurate Forecasting of Money Supply in Indonesia Warsono; Sulandra, Ardelia Maharani; Kurniasari, Dian; Usman, Mustofa; Susetyo, Budi
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252230

Abstract

Economic forecasting serves as a fundamental element in supporting decision-making processes across multiple sectors. One of the main areas of interest in this field is the estimation of the money supply within an economy. The Vector Autoregressive (VAR) model is a commonly applied method for forecasting; however, it often encounters limitations when processing data with nonlinear patterns. Convolutional Neural Networks (CNNs) offer an alternative approach, particularly effective in identifying nonlinear structures that are not adequately captured by VAR models. A hybrid VAR-CNN model is therefore proposed, combining the respective strengths of both techniques to improve the accuracy of predictions. This research applies to the hybrid VAR-CNN model to forecast economic variables for the period from July 2022 to June 2023. The model consists of two main components: the first utilizes forecasted values generated by the VAR model, while the second processes the residuals from the VAR output using a CNN. With 80% of the data allocated for training and 20% for testing, the hybrid VAR-CNN model demonstrates improved performance over alternative forecasting methods. Evaluation based on Mean Absolute Percentage Error (MAPE), supremum (D) values, and p-values confirms the effectiveness of this hybrid approach.
Perbandingan Metode GWR, MGWR, dan MGWR-SAR pada Data Persentase Penduduk Miskin di Pulau Jawa Fahriya, Andina; Susetyo, Budi; Sumertajaya, I Made
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%.
Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java Khikmah, Khusnia Nurul; Sartono, Bagus; Susetyo, Budi; Dito, Gerry Alfa
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1012

Abstract

This study aims to compare the classification performance of the random forest, gradient boosting, rotation forest, and extremely randomized tree methods in classifying the food insecurity experience scale in West Java. The dataset used in this research is based on the Socio-Economic Survey by Statistics Indonesia in 2020. The novelty of this research is comparing the performance of the four methods used, which all are the tree ensemble approaches. In addition, due to the imbalance class problem, the authors also applied three imbalance handling techniques in this study. The results show that the combination of the random-forest algorithm and the random-under sampling technique is the best classifier. This approach has a balanced accuracy value of 65.795%. The best classification method results show that the food insecurity experience scale in West Java can be identified by considering the factors of floor area (house size), the number of depositors, type of floor, health insurance ownership status, and internet access capabilities.
Evaluation of Accreditation and National Examination using Multilevel Generalized Structured Component Analysis Susetyo, Budi; Fitrianto, Anwar
Jurnal Pendidikan Progresif Vol 12, No 1 (2022): Jurnal Pendidikan Progresif
Publisher : FKIP Universitas Lampung

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Abstract

Evaluation of Accreditation and National Examination using Multilevel Generalized Structured Component Analysis. Hierarchical elements or higher levels often influence school accreditation and the national exam because education units are nested in the characteristics of the province. Objectives: This study aims to evaluate the relationship between accreditation and the national exam at the level of Junior high school/Madrasa in Java which are nested in province. Methods: The analysis employs multilevel GSCA analysis (MGSCA). Findings: UNBK has good convergent validity and it can explain each of the subjects tested in each province up to more than 90%. Concerning the estimates of path coefficients,  the study found eight patterns of relationship between SNP and UNBK that have a significant effect in the six provinces. Conclusion: The relationship between content and competency standard for UNBK shows that there are significant differences in all provinces in Java island. This shows that provincial characteristics affect school quality. The model can explain the total variability of all variables is 72.44%. Keywords: multilevel generalized structured component analysis, national education standards, national examination.DOI: http://dx.doi.org/10.23960/jpp.v12.i1.202223
Estimating Missing Panel Data with Regression and Multivariate Imputation by Chained Equations (MICE) Susetyo, Budi; Fitrianto, Anwar
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 9, No 1 (2024): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v9i1.24824

Abstract

Missing data may occur in various types of research. Regression and multiple imputation by chained equations (MICE) are two methods that can be used to estimate missing data in panel data types. This study aims to compare the accuracy of the missing panel data estimation using the regression and the MICE methods. The data used in this study are 161 random samples of senior high schools and vocational schools in DKI province for the year 2016-2020. Based on the results of the Chow test, Hausman test, and Lagrange Multiplier test on panel data regression, it shows that the appropriate model for the student-teacher ratio (X5) is random, the percentage of teachers who have an educator certificate (X6) is a fixed model with the specific effect of individual school and time, while the percentage of teachers who hold a bachelor degree (X7) is a fixed model with the specific effect of individual. Based on this model, the estimation of missing data is then carried out. The accuracy of the missing data estimation was carried out by comparing the MAPE, MAE, and RMSE values. The results show that the MICE method is quite good for estimating missing data at X5, quite feasible for estimating X6, and very good for estimating missing data at X7. In general, MICE is more accurate than panel data regression
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.
Analysis of Covid-19 Risk Perception Survey Result Using Generalized Structured Component Analysis: Analisis Hasil Survei Persepsi Risiko Covid-19 Menggunakan Generalized Structured Component Analysis Robert, Zahira Rahvenia; Rizki, Akbar; Susetyo, Budi; Amir, Sulfikar
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, 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.v6i2p336-347

Abstract

The capital city of Indonesia, Jakarta, became the province with the highest number of Covid-19. Response this situation, LaporCovid-19 collaborate with the Social Resilience Lab, Nanyang Technological University conducted a survey to measure how Jakarta residents perceive the risk of Covid-19 from May 29 to June 20 2020. Factors of risk perception are variables that cannot be measured directly, so they are analyzed used a Structural Equation Modeling (SEM) approach, namely Generalized Structured Component Analysis (GSCA). The Likert scale used can be considered as interval or ordinal depending on the point of view of the theory built. Therefore, this study will compare the GSCA method with the nonlinear GSCA and evaluate six variables, namely risk perception, knowledge, information, health behavior , social capital, and economy. Evaluation of the overall model showed that the nonlinear GSCA model can explain the diversity of qualitative data better than the GSCA model with FIT > 0.9. Based on GSCA nonlinear model, information has significantly influence of knowledge, economy and social capital have a real reciprocal relationship, along knowledge and risk perception have significantly influence of health behavior.
Perbandingan Kinerja Hybrid Classification SVM-RF dan SVM-NN Terhadap Faktor Risiko Anemia Ibu Hamil di Indonesia dengan Pendekatan Clustering K-Means Qalbi, Asyifah; Erfiani, Erfiani; Susetyo, Budi
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Classification is one of the most researched topics by researchers from the field of machine learning and data mining. Machine learning methods that are often used include Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). However, SVM does not always provide good accuracy. For example, when applied to highly imbalanced data, SVM will experience challenges. In addition, there is no single best method that can be applied to all classification problems. Currently, hybrid method approaches for data mining applications are becoming increasingly popular such as hybrid SVM-RF, SVM-NN and KMeans-SVM methods. In this study, a hybrid method of SVM-RF and SVM-NN was used to classify risk factors for anemia in pregnant women in Indonesia with a K-Means approach to cluster data misclassified by SVM. The results showed that the hybrid method can improve the performance of the SVM model. Hybrid SVM-RF provides a higher evaluation metric value compared to SVM-NN. The four evaluation metrics used, namely accuracy, balanced accuracy, sensitivity and specificity in SVM-RF are worth 0,989; 0,989; 0,988; and 0,989, respectively. The variables that contribute generally based on SHAP Global to the classification of risk factors for anemia in pregnant women in order are Age, Fe Tablet, Working Status, Education, Nutritional Status and ANC.
Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Amir, Sulfikar Anak Agung Istri Sri Wiadnyani ASEP SAEFUDDIN Aulia Dwi Oktavia Aunuddin Aunuddin Bagus Sartono Bambang H. Trisasongko Bambang Juanda Brian G. Lees Cici Suhaeni Cut N. Ummu Athiyah DAMAYANTI BUCHORI Darfiana Nur 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 H Karwono Hafidz Muksin Hari Wijayanto Herlina Herlina Hermawati, Neni 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 Muh Nur Fiqri Adham Muhammad Amirullah Yusuf Albasia Muhammad Nur Aidi Muhammad Sayuti Mustofa Usman 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 Ratnasari, Andika Putri Rifannisa Bahar Rifki Hamdani Rizki, Akbar Robert, Zahira Rahvenia Safitri, Wa Ode Rahmalia Sanusi, Ratna Nur Mustika Satriyo Wibowo Sembiring, Febryna Sri Ningsih Desi Afriany Sulandra, Ardelia Maharani Sulfikar Amir Suliana Kriswan Supriatin, Febriyani Eka Syahrir, Nur Hilal A. Syahrir, Nur Hilal A. 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 Wulan Andriyani Pangestu Yasmin Erika Faridhan Zahira Rahvenia Robert Zainal A Koemadji