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Iqbal Kharisudin

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Author-ID : 298167

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Pengintegrasian Nilai Karakter dan Nilai Konservasi Pembelajaran Matematika Kurikulum Merdeka di Era Teknologi Society 5.0 Sarah, Caecillia Rafika; Zaenuri, Zaenuri; Mulyono, Mulyono; Walid, Walid; Kharisudin, Iqbal
Suska Journal of mathematics Education Vol 9, No 2 (2023)
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sjme.v9i2.22075

Education is a determinant of the quality of human resources in a nation, which can guide the nation's development in a better direction in various aspects. The implementation of the Independent Curriculum in education is in line with new concepts in the social and technological world, namely the Industrial Revolution Society 5.0, which enables humans (to utilize modern-based knowledge, one of which is in the process of implementing character and conservation values in schools. This research aims to examine the integration of character values and conservation values through independent curriculum mathematics learning in the technological era of society 5.0. The research method used is a literature study through the study of scientific articles, books, journal proceedings, and other scientific literature. The data is analyzed descriptively to determine the relationship between one aspect and another. Based on the results of the literature study, it can be concluded that character and conservation values in mathematics learning can be integrated through a fun learning process in accordance with the concept of an independent curriculum in the era of society 5.0 in 21st century learning, namely a new learning paradigm that has learning objectives, a learning process and an assessment process carried out to ensure that students' character and conservation values are achieved and realized through the pancasila student profile.

EXPLORING MATHEMATICAL MODELING ABILITIES IN SOLVING WORD PROBLEMS Alfath, Maliki; Kharisudin, Iqbal
AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol 14, No 3 (2025)
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ajpm.v14i3.12722

Students' mathematical problem solving ability in solving word problems still faces various challenges, especially in terms of transforming contextual problems into appropriate mathematical models. Many students have difficulty in identifying variables, compiling equations, and validating the solutions obtained, resulting in low success rates in solving complex mathematical problems. Therefore, it is necessary to conduct an in-depth analysis of students' problem solving abilities to identify the specific obstacles they face and the strategies that can be developed to overcome them. This study aims to analyze students' problem solving abilities through mathematical modeling strategies in solving word problems. A qualitative approach with a phenomenological design is used to explore students' thinking processes in depth. The subjects of the study were 31 ninth grade students of SMP Negeri 4 Klaten who were selected by purposive sampling based on variations in academic ability. Data collection was carried out through observation, written tests, and in-depth interviews, then analyzed inductively. The results showed that students who answered correctly were able to understand the context of the problem, compile mathematical models appropriately, complete calculations and interpret the results systematically, especially on questions with low to medium difficulty levels. Conversely, students who answered incorrectly had difficulty in transforming the problem into mathematical form and evaluating the final results, especially on complex questions. The conclusion of this study is that the mathematical modeling strategy is effective in improving problem solving skills, but further emphasis is needed on the transformation and reflection stages to optimize students' understanding and validation of solutions.

COMPARATIVE STUDY OF LSTM-BASED MODELS WITH HYPERPARAMETER OPTIMIZATION FOR SHORT-TERM ELECTRICITY LOAD FORECASTING Kharisudin, Iqbal; Arissinta, Insyiraah Oxaichiko; Aulia, Sabrina Aziz; Dani, Muhamad Abdul Qodir; Wijaya, Galih Kusuma
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0105-0122

This research is focused on the development and comparison of time series models for short-term electrical load forecasting, utilizing several variants of Long Short-Term Memory (LSTM) networks. The specific LSTM variants employed in this study include Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, and Convolutional Neural Network LSTM (CNN-LSTM). We used five years (2016-2020) of daily electricity load data from the Central Java-DIY system, provided by PT PLN (Persero). The primary objective is to ascertain the accuracy and evaluate the performance of these LSTM variants in the context of short-term load forecasting. This is achieved quantitatively through the computation of various error metrics, namely Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared. The results of the study reveal that the CNN-LSTM method outperforms the other variants in terms of the calculated metrics. Specifically, the CNN-LSTM method achieved the lowest values for all metrics: an MSE of 0.007 for training and 0.0010 for testing, an MAE of 0.0050 for training and 0.0062 for testing, and an RMSE of 0.083 for training and 0.099 for testing. Among the evaluated models, CNN-LSTM demonstrates the best trade-off between predictive accuracy and training efficiency, making it the most recommended for short-term electricity load forecasting. While BiLSTM achieves higher accuracy, particularly in terms of MAE, it requires a longer training time. In contrast, Stacked LSTM converges faster with slightly lower accuracy, making it a strong alternative when computational efficiency is prioritized..

Co-Authors achilla, Silky Achmad, Fariz Adi Nur Cahyono Akbar, Mohammad Jefrie Ilham Alfath, Maliki Alifatul Muyasaroh Amin Suyitno Anis Shihafiyatal Abida Anisa Rosdiana, Anisa Arief Agoestanto Arissinta, Insyiraah Oxaichiko Ashim, Muhammad Asih, Tri Sri Noor Astutiningtyas, Luthfiyah Aulia, Sabrina Aziz Ayu Andira Risnawati Aziiza Andanawarih Utoyo Bambang Eko Susilo Budi Waluya Dani, Muhamad Abdul Qodir Ditasari, Dwi Dian Dwijanto Dwijanto Ellya Masturina Hamid Emi Pujiastuti Fatkhurokhman Fauzi Faujiyah, Siti Gunawan Gunawan Habibie, Zulqoidi R. Hardi Suyitno Iis Widya Harmoko Ikrimah, Annisa Isnarto Isnarto Iwan Junaedi Iwan Junaedi Iwan junaedi Jannatun Khustia Lubis Kartono Kartono - Kartono Kartono Kartono, Kartono Kartono, Wardono Khoirunnisa, Farah Dina Korkor, Sarah Lavicza, Zsolt Lina Lutfiyana Luluk Ulfa Chasania Masri'an, Hera Masrukan Masrukan Miftahudin Moh Khubaib Tamami Mohammad Asikin Muhammad Ainuddin Daahiljabir Muhammad Ghozian Kafi Ahsan Muhammad Iqbal Muhammad Iqbal Mulyono Mulyono Mulyono Muna, Trimurtini, Nur Aizatun Mutik, Rossa Muttaqin, Muhammad Nurul Nike Yustina Oktaviani Noviana Dini Rahmawati Nur Fitrianingsih, Riska Nur Hasanah Nurfaidah Nurfaidah Nurhasanah, Rizki Ahid Nurkaromah Dwidayati, Nurkaromah Nurochmah, Yeni Nuryadi Nuryadi Pandi, Eunike Cantika Kusuma Petronela Ivoni Susantya Putri, Sanianajiba Nugroho Radika Widiatmaka Rahanto, Faris Febri Rahman, Alif Aulia Rahmawati, Rofiqo Rizki Ahid Nurhasanah Rochdi Wasono Rochmad Rochmad Ruamba, Marthinus Yohanes S B Waluya Safrudiannur Safrudiannur Sarah, Caecillia Rafika SB Waluya SB Waluya Scolastika Mariani Sebastianus Fedi St. Budi Waluya Sufah Iliya Manazila Sugiman Sugiman Sukestiyarno Sukestiyarno Sukestiyarno, Yulius Leonardus Supriyono Supriyono Sutrisno, Hendrik Tiani Wahyu Utami Tsania Rahma Azzahra Utami, Mira Dwi Utoyo, Aziiza Andanawarih Vera Dewi Susanti Vera Dewi Susanti Wahyu Arif Setyo Pambudi Wahyu Nur Annisa Wahyu Nur Annisa Walid Walid Walid, Walid Wardono Wardono Wardono Wijaya, Galih Kusuma Y. L. Sukestiyarno YL Sukerstriyarno YL Sukestiyarno YL Sukestriyarno Zaenuri Zaenuri M Zaenuri Mastur Zaenuri Zaenuri Zikir, Al Zulkardi

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