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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika Jurnal Dimensi PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika JPPM (JURNAL PENELITIAN DAN PEMBELAJARAN MATEMATIKA) JIPMat (Jurnal Ilmiah Pendidikan Matematika) Al Ishlah Jurnal Pendidikan Jurnal Mercumatika : Jurnal Penelitian Matematika dan Pendidikan Matematika International Journal on Emerging Mathematics Education Minda Baharu Jurnal Absis : Jurnal Pendidikan Matematika dan Matematika PHI: Jurnal Pendidikan Matematika Enthusiastic : International Journal of Applied Statistics and Data Science JCP (Jurnal Cahaya Pendidikan) Fakultas Keguruan dan Ilmu Pendidikan Jurnal Pendidik Indonesia CONSISTAN : Jurnal Tadris Matematika
Yudhi Hanggara
Universitas Riau Kepulauan
Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences Other

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Eksplorasi Hambatan Mahasiswa Dalam Menyelesaikan Masalah Matematika Tidak Terstruktur Ros Maliah Ahmad; Aida Nur Aini; Fortuna Cantika Syari; Yudhi Hanggara; Asmaul Husna
CONSISTAN (Jurnal Tadris Matematika) Vol 4 No 01 (2026): Consistan : Jurnal Tadris Matematika
Publisher : Program Studi Tadris Matematika Fakultas Tarbiyah Institut Agama Islam Al-Qolam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35897/consistan.v4i01.2531

Abstract

The failure of students to solve unstructured mathematical problems is a common issue in the learning process. The purpose of this study is to explore the causes of students’ failure to solve unstructured mathematical problems. This study employed a descriptive qualitative approach. The research subjects consisted of 47 mathematics education students, with 6 students—comprising first-, third-, and fifth-semester mathematics education students from the University of Riau Islands—serving as the primary subjects for interviews. Data collection techniques included a written test in the form of unstructured problem-solving questions, interviews, and observations. The test instrument was validated through expert validation to ensure the appropriateness of the mathematical problem-solving indicators. The data analysis in this study followed the framework of Miles and Huberman, which includes data reduction, data presentation, and drawing conclusions. The results of the study indicate that first-semester students predominantly struggle at the stage of understanding and representing problems, while upper-semester students tend to have weaknesses at the stage of evaluating and reflecting on problems. Additionally, students’ failures are influenced by their continued reliance on routine problems, their tendency to use a single algorithmic solution strategy, and their failure to evaluate and reflect on the results of problem-solving.
Household Electricity Demand Forecasting in Batam from 2023 to 2047 Using Multilayer Perceptron Neural Network Ginting, Tiffani Giofanta; Hermansah; Hanggara, Yudhi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art6

Abstract

The rapid growth of electricity demand in Batam, driven by increasing household and industrial consumption, necessitates accurate long-term energy forecasting. This study aimed to forecast household electricity demand in Batam from 2023 to 2047 using the multilayer perceptron (MLP) artificial neural network (ANN) model. Secondary data from PT PLN Batam (2013-2022), including customer numbers, electricity sales volume, and revenue, were analyzed. A total of 200 MLP models were trained, varying the number of hidden layers and nodes, with algorithms including BACKPROP, RPROP+, RPROP−, SAG, and SLR. The partial autocorrelation function (PACF) was used to determine the number of input layer nodes. The optimal model, using the smallest learning rate (SLR) algorithm with four hidden layers and ten nodes, achieved the best performance with the lowest mean squared error (MSE) of 35.93 and mean absolute percentage error (MAPE) of 0.47%. The projection results show a consistent increase in electricity demand, with a peak forecast of 2,114 GWh by 2047. These findings provide valuable insights for long-term energy planning and policy-making, ensuring adequate electricity supply and infrastructure development in Batam.  
Co-Authors Aida Nur Aini Alwahab, Putri Annisa Ananda, Nur Fitri Arnizamiati, Wahyu Asmaul Husna Cholis Sa’dijah Daniel Marpaung, Carlos Bettran Dian, Romika Fauzan Jafri Fitrah Amelia Fitrah Amelia Fitrah Amelia Fortuna Cantika Syari Ginting, Tiffani Giofanta Gustiadi, Ardi Hermansah hermansah hermansah Hermansah Hermansah, Hermansah Ismarti, Ismarti Jaya Dwi Putra, Jaya Dwi Jenny Derfia Sibarani M.J, Febria Ramadhani Mawaddah, Dina Nina Agustyaningrum Nisa, Muthia Khairan Nurliza Adriani Pasaribu, Rotua Saulina Pranyata, Yuniar Ika Putri Qohar, Abd. Qoriaturrosyidah, Fitri Ria Priharamy Hisaty Rio Mardani Suhardi, Rio Mardani Riska Nur Aini Ros Maliah Ahmad Serena Haska Aisyah Sibarani, Jenny Derfia Simanjuntak, Friska Yanti Suryo Hartanto Vina Alfionita, Vina Wajubaidah Wajubaidah, Wajubaidah Wati Ritonga, Wati Wilda Fasim Hasibuan Yesi Gusmania Yesi Gusmania, Yesi Zendato, Irwan Juniman