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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 Zero : Jurnal Sains, Matematika, dan Terapan Andragogi: Jurnal Diklat Teknis Pendidikan dan Keagamaan 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 Bulletin of Computer Science Research 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 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 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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School Accreditation Prediction Based on Literacy and Numeracy: Ordinal Logistic Regression vs KNN Syukri, Nabila; Hiola, Yani Prihantini; Putri, Mega Ramatika; Susetyo, Budi
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.861

Abstract

School accreditation in Indonesia has traditionally relied on administrative inputs and institutional documentation, which often fail to capture the actual quality of student learning. In contrast, the National Assessment provides direct evidence of student literacy and numeracy outcomes, offering a more objective and outcome-based measure of educational quality. Leveraging these results as predictors for accreditation rankings is therefore crucial, as they reflect the competencies most relevant to effective learning delivery. This study aims to develop and evaluate classification models for school accreditation rankings using literacy and numeracy results as predictor variables. The dataset consists of secondary data from the 2023 and 2024 National School Assessments, covering 789 schools across four provinces: DKI Jakarta, Yogyakarta, Bali, and Banten. Two methods were applied, Ordinal Logistic Regression and K-Nearest Neighbors (K-NN) under two scenarios: with and without class imbalance handling. To address imbalance, two techniques were employed: Synthetic Minority Oversampling Technique (SMOTE) and Class Weight. The results indicate that K-NN consistently outperformed Ordinal Logistic Regression in both scenarios. On data without imbalance handling, K-NN achieved Accuracy, Precision, Recall, and F1-Score of 0.803, 0.705, 0.587, and 0.619, respectively. with imbalance treatment using SMOTE, the values were 0.753, 0.619, 0.686, and 0.644. While class balancing did not significantly improve overall accuracy, it enhanced the model’s ability to recognize minority classes. These findings highlight the strong relationship between literacy and numeracy outcomes and school accreditation status, demonstrating that outcome-based measures can complement traditional accreditation instruments, and that conventional statistical approaches are still relevant for modeling school accreditation.
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%.
Comparative Study of Hybrid ARIMA-LSTM and CNN-LSTM for Palm Oil Price Forecasting Putri, Rizki Alifah; Notodiputro, Khairil Anwar; Susetyo, Budi
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.
Perbandingan Kinerja Hybrid Classification SVM-RF dan SVM-NN Terhadap Faktor Risiko Anemia Ibu Hamil di Indonesia dengan Pendekatan Clustering K-Means Asyifah Qalbi; Erfiani; Budi Susetyo
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 | DOI: 10.12962/limits.v22i3.8862

Abstract

Klasifikasi merupakan salah satu topik yang paling banyak diteliti oleh para peneliti dari bidang machine learning dan data mining. Metode machine learning yang sering digunakan antara lain Support Vector Machine (SVM), Random Forest (RF) dan Neural Network (NN). Namun, SVM tidak selalu memberikan nilai akurasi yang baik. Sebagai contoh, ketika diterapkan pada data yang sangat tidak seimbang, SVM akan mengalami tantangan. Selain itu, tidak terdapat satu metode terbaik yang bisa diterapkan untuk semua masalah klasifikasi. Saat ini, pendekatan metode hybrid untuk penggunaan data mining menjadi semakin populer seperti metode hybrid SVM-RF, SVM-NN dan KMeans-SVM. Pada penelitian ini, metode hybrid SVM-RF dan SVM-NN digunakan untuk mengklasifikasikan faktor risiko anemia pada ibu hamil di Indonesia dengan pendekatan K-Means untuk mengelompokkan data yang salah klasifikasi oleh SVM. Hasil penelitian menunjukkan bahwa metode hybrid dapat meningkatkan kinerja model SVM. Hybrid SVM-RF memberikan nilai metrik evaluasi yang lebih tinggi dibandingkan dengan SVM-NN. Empat metrik evaluasi yang digunakan, yaitu accuracy, balanced accuracy, sensitivity dan specificity pada SVM-RF masing-masing bernilai sebesar 0,989; 0,989; 0,988; dan 0,989. Peubah yang berkontribusi secara umum berdasarkan SHAP Global terhadap klasifikasi faktor risiko anemia pada ibu hamil secara berurutan adalah Usia, Tablet Fe, Status Bekerja, Pendidikan, Status Gizi dan ANC
Co-Authors Aam Alamudi Aceng Komarudin Mutaqin Aditya Ramadhan adwendi, satria june Agus Mohamad Soleh Ahmad Ansori Mattjik Aji Hamim Wigena Akbar Rizki Amir, Sulfikar Anak Agung Istri Sri Wiadnyani Anang Kurnia Andina Fahriya Anis Sulistiyowati Anisa, Rahma ASEP SAEFUDDIN Asyifah Qalbi 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 Erfiani Fadjrian Imran Fahriya, Andina Faisal Arkan Farit Mochamad Afendi Fitrianto, Anwar H Karwono Hafidz Muksin Hamid, Assyifa Lala Pratiwi Hari Wijayanto Herlina Herlina Hermawati, Neni Hiola, Yani Prihantini 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 Mohammad Masjkur 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 Putri, Mega Ramatika Putri, Rizki Alifah Qalbi, Asyifah Qomariasih, Nurul Rachman, Nurul Aulia Rahma Anisa Rahmawat, NFN Rahmawati, nFN Rais 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 Syukri, Nabila 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