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Iqbal Kharisudin

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Author-ID : 298167

Religion 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..

Mathematical modelling problem solving with respect to students’ mathematical resilience in GeoGebra-assisted mea learning Lutfiyana, Lina; Pujiastuti, Emi; Kharisudin, Iqbal
Alifmatika (Jurnal pendidikan dan pembelajaran Matematika) Vol 7 No 2 (2025): Alifmatika - December
Publisher : Fakultas Tarbiyah Universitas Ibrahimy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/alifmatika.2025.v7i2.233-259

Problem-solving is a crucial 21st-century skill that plays a vital role in mathematics education. One practical approach to developing this skill is through mathematical modelling, particularly by employing GeoGebra-assisted MEA learning. This study aims to: (1) evaluate the quality of GeoGebra-assisted MEA learning; (2) examine the influence of mathematical resilience on Mathematical Modelling Problem-Solving Ability (MMPSA); and (3) describe students’ MMPSA based on their levels of mathematical resilience. A mixed-methods approach was employed using a Sequential Explanatory design, with instruments including questionnaires, tests, observations, and interviews. The sample consisted of Class VII A as the experimental group and Class VII B as the control group, each comprising 30 students. Eight students were selected as qualitative subjects based on their mathematical resilience levels. The results indicate that GeoGebra-assisted MEA learning demonstrates high instructional quality and significantly enhances students’ MMPSA. Quantitative findings show that mathematical resilience has a significant effect, accounting for 30% of the variance in students’ problem-solving performance. These results are further supported by qualitative data obtained through observations and interviews. Students with high resilience tended to be confident, persistent, and effective in solving problems. Those with moderate resilience showed adequate capability but lacked precision, while students with low resilience were easily discouraged and exhibited low self-confidence. In conclusion, integrating quantitative and qualitative findings underscores the importance of fostering mathematical resilience to enhance students’ problem-solving abilities, particularly in the context of GeoGebra-assisted MEA in mathematical modelling.

INTEGRASI KURIKULUM MERDEKA DALAM MODUL AJAR MATEMATIKA MELALUI MODEL INQUIRY LEARNING BASED ON INFORMATION LITERACY (ILBIL) Susanti, Vera Dewi; Sukestiyarno, Yohanes Leonardus; Kharisudin, Iqbal; Agoestanto, Arief
AKSIOMA: Jurnal Program Studi Pendidikan Matematika Vol 14, No 4 (2025)
Publisher : UNIVERSITAS MUHAMMADIYAH METRO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ajpm.v14i4.13790

Rendahnya literasi matematika siswa masih menjadi permasalahan dalam pembelajaran matematika di SMA. Hal ini menunjukkan bahwa pembelajaran belum sepenuhnya mengembangkan kemampuan berpikir kritis, pemecahan masalah, dan pemanfaatan informasi sesuai dengan tuntutan Kurikulum Merdeka. Oleh karena itu, diperlukan pengembangan modul ajar inovatif yang mengintegrasikan pembelajaran inquiry dan literasi informasi. Penelitian ini bertujuan mengembangkan modul ajar kelas X SMA Kyai Ageng Basyariah Madiun yang dirancang menggunakan model Inquiry Learning Based on Information Literacy (ILBIL) dan disesuaikan dengan karakteristik pembelajaran Kurikulum Merdeka. Model pengembangan yang digunakan dalam penelitian ini adalah model 4-D. Namun, ruang lingkup penelitian dibatasi hanya pada tahap define, design, dan develop, tanpa melibatkan tahap disseminate. Hasil penelitian menunjukkan bahwa: (1) tingkat validitas modul ajar berbasis ILBIL mencapai 82,4% sehingga dinyatakan sangat layak digunakan; (2) hasil uji coba memperlihatkan respon positif dari guru dengan skor 86,9% dan dari siswa dengan skor 83,08%; serta (3) nilai rata-rata N-Gain sebesar 0,3956 yang termasuk kategori sedang. Berdasarkan hasil ini, penggunaan modul ajar dengan model ILBIL terbukti dapat meningkatkan hasil belajar siswa pada materi Barisan dan Deret. Kontribusi dari penelitian ini terletak pada integrasi model ILBIL ke dalam modul ajar berbasis Kurikulum Merdeka yang belum banyak diterapkan pada konteks pembelajaran matematika SMA, khususnya materi Barisan dan Deret. Penelitian ini juga memberikan kontribusi praktis berupa modul ajar yang tervalidasi, mudah diimplementasikan, serta efektif dalam meningkatkan literasi matematika siswa. Temuan ini memperkuat bahwa penggabungan inquiry learning dengan literasi informasi merupakan pendekatan inovatif yang relevan untuk meningkatkan mutu pembelajaran matematika pada era Kurikulum Merdeka.

Comparative Study of Autoencoder and LSTM-AE for Extreme Temperature Anomaly Detection in Semarang Kusuma Wijaya, Galih; Anggraeni, Aliyya; Chulaili Sahri Nova, Tsalisa; Alifian yusuf, Muhammad; Kharisudin, Iqbal
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.549

Climate change has increased the frequency and intensity of extreme weather events, including heatwaves and cold spells, posing critical risks to public health and urban infrastructure. This study proposes and compares two deep learning frameworks based on Autoencoders, namely the Long Short-Term Memory Autoencoder (LSTM-AE) and the standard Autoencoder (AE), for detecting extreme temperature anomalies using historical daily data from 2005 to 2025 in Semarang City. Unlike conventional anomaly detection methods, the LSTM-AE introduces temporal learning through recurrent memory cells, enabling it to capture sequential temperature dependencies that static AE models cannot. Both models are trained to reconstruct “normal” temperature patterns, with anomalies identified when reconstruction errors exceed the 95th percentile threshold. The results demonstrate that the LSTM-AE more consistently identifies significant heatwave and cold spell events, with seasonal alarm rates that closely align with local climatic transitions. Several detected peaks coincide with historically documented events such as the 2015–2019 El Niño and 2019–2020 transition periods reported by BMKG, confirming climatological relevance. In contrast, the standard AE detects a higher number of anomalies (726 vs 366 from the LSTM AE) but tends to generate false alarms outside transitional periods. Model performance is evaluated using reconstruction error distributions, Jaccard similarity indices, and monthly alarm rates. This study highlights the potential of LSTM-based architectures for improving anomaly detection in climate data and contributes to developing data-driven strategies for urban climate resilience in tropical regions.

Pemodelan Prediksi Suhu Rata Rata Harian dan Kelembapan Relatif di Kota Semarang Menggunakan LSTM, GRU, dan GRU-LSTM Fadhilah, Nida Nur; Kharisudin, Iqbal
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 8, No 1 (2026): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v8i1.26349

Penelitian ini bertujuan membandingkan kinerja tiga model deep learning, yaitu Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), dan model hibrida GRU-LSTM dalam memprediksi suhu rata-rata harian dan kelembapan relatif di Kota Semarang. Data sekunder diperoleh dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) periode 1 September 2021 hingga 2 Oktober 2025. Setelah dilakukan pra-pemrosesan dan normalisasi, data dibagi menjadi 80% untuk pelatihan dan 20% untuk pengujian. Optimasi hiperparameter dilakukan dengan Bayesian Optimization menggunakan pustaka Optuna. Hasil evaluasi berdasarkan metrik MSE, RMSE, MAE, dan MAPE menunjukkan bahwa ketiga model mampu menangkap pola non-linier dan ketergantungan jangka panjang dalam data dengan baik. Model LSTM dan GRU-LSTM memberikan kinerja paling kompetitif pada prediksi suhu, sementara untuk kelembapan, perbedaan kinerja antarmodel relatif kecil. Prediksi 30 hari ke depan yang dihasilkan konsisten dengan pola musiman di Kota Semarang. Hasil penelitian ini dapat menjadi dasar pertimbangan dalam pengembangan sistem peringatan dini dan strategi adaptasi perubahan iklim berbasis data.

Co-Authors achilla, Silky Achmad, Fariz Adi Nur Cahyono Akbar, Mohammad Jefrie Ilham Alfath, Maliki Alifatul Muyasaroh Alifian yusuf, Muhammad Amin Suyitno Anggraeni, Aliyya 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 Chulaili Sahri Nova, Tsalisa Dani, Muhamad Abdul Qodir Ditasari, Dwi Dian Dwijanto Dwijanto Ellya Masturina Hamid Emi Pujiastuti Fadhilah, Nida Nur Faujiyah, Siti Fauzi, Fatkhurokhman 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, Wardono Khoirunnisa, Farah Dina Korkor, Sarah Kusuma Wijaya, Galih Lavicza, Zsolt Lina Lutfiyana Luluk Ulfa Chasania Lutfiyana, Lina 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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