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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 Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Learning Mathematical Literacy Across Islands in an Archipelagic Country Through Cross-Cultural STEM Trails Cahyono, Adi Nur; Sukestiyarno, Yulius Leonardus; Kharisudin, Iqbal; Iqbal, Muhammad; Zulkardi; Safrudiannur; Lavicza, Zsolt; Miftahudin
Mathematics Education Journal Vol. 19 No. 4 (2025): Mathematics Education Journal
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

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

This study explored a program aimed at designing cross-cultural STEM trails in three regions of an archipelagic country to enhance students' mathematical literacy. The research employed an exploratory mixed-methods design, involving 30 prospective teachers, in-service teachers, and lecturers and 50 students. Data were collected through observations, interviews, documentation, and mathematical literacy tests. They were analyzed using qualitative coding/triangulation and ANOVA. The study took place in three cities located on different islands during the 2024 academic year. The study began with implementation of a teacher training program to equip them with the necessary skills to design STEM Trails using the stemtrails.id platform. This application was developed in previous research. The trails were designed to integrate STEM contexts, cultural themes, and landmark elements in three Indonesian cities. Students used the trails to learn mathematical literacy and gain insight into the cultures of other regions. The location of tasks and trails in remote areas prompted task authors to innovate task design using technologies such as 3D printing. This technology facilitates student exploration, as with general math trails, but also incorporates miniature object models relevant to the tasks. The teacher selected project-based learning as an appropriate model for this strategy. The results demonstrated measurable improvements in mathematical literacy, with average post-test scores rising from M = 68.4 (SD = 9.3) to M = 81.7 (SD = 8.6), t(142) = 9.21, p < .001. Cross-island cultural triangulation fostered collaborative learning across geographically separated regions, thereby demonstrating a strategy for overcoming archipelagic barriers. The model can be expanded to other regions through digital integration and culturally grounded STEM task design. In this way, it can offer a pathway for nationwide application.

Thematic ethno-based learning math skills in ethnomathematics-based learning Zikir, Al; Walid, Walid; Susilo, Bambang Eko; Kharisudin, Iqbal; Zaenuri, Zaenuri; Sugiman, Sugiman
Gema Wiralodra Vol. 15 No. 1 (2024): Gema Wiralodra
Publisher : Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/gw.v15i1.608

This research aims to determine and describe mathematical abilities in ethnomathematics-based learning. The method used in the research is a systematic literature review. The results of article selection from 53 articles published from 2019-2023 based on the ERIC and Scopus databases found 5 articles that met the research objectives. The results of the identification and analysis of articles found that ethnomathematics-based learning can improve mathematical understanding abilities, problem-solving abilities, creative thinking abilities, higher-order thinking abilities, and mathematical connection abilities. Ethnomathematics-based learning can improve students' mathematical abilities. However, existing research has yet to reveal the full range of students' mathematical abilities in learning mathematics. This can be used as a reference for further research.

Optimization of Mathematical Literacy through the Development of Information Literacy-Based Inquiry Learning in Secondary Education Susanti, Vera Dewi; Sukestiyarno, YL; Kharisudin, Iqbal; Agoestanto, Arief
EDUKASIA Jurnal Pendidikan dan Pembelajaran Vol. 5 No. 2 (2024): Edukasia: Jurnal Pendidikan dan Pembelajaran
Publisher : LP. Ma'arif Janggan Magetan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62775/edukasia.v5i2.1396

The low level of students’ mathematical literacy in Indonesia, as indicated by the results of the Minimum Competency Assessment and various previous studies, highlights the need to improve the instructional models used in schools. This study examines the validity, practicality, and effectiveness of developing Information Literacy-Based Inquiry Learning. The development model used in this study is adapted from Plomp which consists of five phases: (1) the preliminary investigation phase, (2) the design phase, (3) the realization/construction phase, (4) the testing, evaluation, and revision phase, and (5) the implementation phase. The subjects in this study were 63 grade X high school students in Madiun district. Data collection techniques used in this study were validation sheets, response questionnaires, and students' mathematical literacy tests. The average score from the teaching module validation was 3.86, categorized as good, and all validators confirmed that the developed mathematical literacy test instrument was valid. In the trial, the average score from the student response questionnaire was 4.02, which was also in the good category. The average student's mathematical literacy score in this trial was 79.5. Therefore, it can be concluded that the development of the model is valid, practical, and effective.

Bayesian Optimization for Stock Price Prediction Using LSTM, GRU, Hybrid LSTM-GRU, and Hybrid GRU-LSTM Utami, Mira Dwi; Kharisudin, Iqbal
Unnes Journal of Mathematics Vol. 13 No. 2 (2024): Unnes Journal of Mathematics Volume 2, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.11253

Stocks have high price fluctuations, which include high risks and high potential returns for investors. This high potential return has attracted significant interest from investors. This study proposes the use of Bayesian optimization methods with Gaussian Process (GP), Random Forest (RF), Extra Trees (ET), and Gradient Boosted Regression Trees (GBRT) surrogate models to enhance the accuracy of stock price predictions using Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and hybrid models (LSTM-GRU and GRU-LSTM). This study tests the effectiveness of various combinations of hyperparameters optimized using the Bayesian optimization method. The model optimized with the Bayesian approach and the GP surrogate model demonstrates superior results compared to the others. Evaluation is conducted using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) metrics. The results indicate that Bayesian optimization with the GP surrogate model for the GRU-LSTM hybrid model outperforms all other methods in terms of MSE, RMSE, MAE, MAPE, and R2. These findings provide significant contributions to parameter selection for stock price prediction and demonstrate the great potential of using Bayesian optimization methods to improve the accuracy of prediction models.

Stacking Ensemble Modeling of Bidirectional LSTM and Bidirectional GRU for Air Temperature Prediction in Ngawi Nike Yustina Oktaviani; Iqbal Kharisudin
Unnes Journal of Mathematics Vol. 13 No. 2 (2024): Unnes Journal of Mathematics Volume 2, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i2.11518

Artificial Neural Networks (ANN) have rapidly developed and are used in forecasting, classification, and regression by mimicking how the human brain processes data. Recurrent Neural Networks (RNN), such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are effective in processing sequential data and handling long-term dependencies. Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) process data in both directions to enhance accuracy. This study evaluates the implementation of the Stacking Ensemble method using BiLSTM and BiGRU as base learners and Random Forest as the meta learner to predict air temperature in Ngawi Regency. Air temperature prediction is crucial as it affects agriculture, health, and energy sectors. The data used comprises 2282 records from January 1, 2028, to March 31, 2024, processed using Google Colab. The results show that the Stacking BiLSTM-BiGRU model with Random Forest provides the best performance with a Mean Squared Error of 0.0005, Root Mean Squared Error of 0.0233, Mean Absolute Error of 0.0179, and R-squared of 0.9832, outperforming other individual models. This study confirms that the Stacking Ensemble method with BiLSTM and BiGRU significantly improves air temperature prediction accuracy.

Implementation of Auto ARIMA, PSO-LSTM, and PSO-GRU for Time Series Modeling of 3 Telecommunication Company Stock Prices LQ45 Index Astutiningtyas, Luthfiyah; Kharisudin, Iqbal
Unnes Journal of Mathematics Vol. 13 No. 1 (2024): Unnes Journal of Mathematics Volume 1, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i1.11939

The Indonesia Stock Exchange (IDX) issues stock indices to make it easier for investors to choose company shares such as the LQ45 Index. This study focuses on forecasting the share prices of 3 telecommunications companies listed in the LQ45 Index, namely PT Telkom Indonesia Tbk with the stock code TLKM, PT Tower Bersama Infrastructure Tbk with the stock code TBIG and PT Sarana Menara Nusantara Tbk with the stock code TOWR in the future. The algorithms used for forecasting are Auto ARIMA, LSTM and GRU algorithms. In addition, the PSO method is used to find the optimal hyperparameters in the LSTM and GRU algorithms. The results of this study show that the GRU model has the best performance and produces the best model evaluation value compared to other models on TLKM and TBIG stock data, while on TOWR stock data the LSTM model is the best model. The GRU model on TLKM data results in an R Square value of 0,961, RMSE 122,291 on training data and MAPE 3,027% and an R Square value of 0,859, RMSE 114,703 and 2,109% on testing data. On TBIG data, the GRU model results in an R Square value of 0,984, RMSE 71,945 and MAPE 4,206% on training data and R Square an value of 0,967, RMSE 73,627 and 2,165% on testing data. The LSTM model on TOWR data results in an R Square value of 0,943, RMSE 43,824 and MAPE 4,274% on training data and an R Square value of 0,796, RMSE 42,597 and 3,117% on testing data.

Time Series Modeling of Stock Price Using CNN-BiLSTM with Attention Mechanism Nur Fitrianingsih, Riska; Kharisudin, Iqbal
Unnes Journal of Mathematics Vol. 13 No. 1 (2024): Unnes Journal of Mathematics Volume 1, 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujm.v13i1.13451

Indonesia's capital market has experienced rapid development in recent years, marked by an increase in transaction value, the number of investors, and market capitalization. One of the sectors that has garnered attention is the telecommunications industry, which is rapidly growing alongside the increasing number of internet users and the public's demand for more advanced telecommunications services. PT Indosat Ooredoo Hutchison, as one of the leading telecommunications companies in Indonesia, has become an attractive investment choice for investors. However, the stock market is known for its fluctuating and irregular nature. Stock data has complex characteristics such as large data volume, ambiguous information, and non-linearity. Therefore, it is important for investors to understand stock price movements before making investments in order to reduce the risk of significant losses. One method that can be used to address that risk is by forecasting stock prices. Time series forecasting is a prediction about future values based on historical data. Statistical methods in forecasting allow for the identification of patterns and trends in historical data, as well as modeling the relationships between variables over time. One of the techniques that is becoming increasingly popular in forecasting is deep learning. In this study, a combination of \textit{Convolutional Neural Network} (CNN) and \textit{Bidirectional Long Short-Term Memory} (BiLSTM) with an attention mechanism is used. CNN excels at extracting data features, while BiLSTM is better at handling data with long time ranges. The addition of the attention mechanism allows the model to assign different weights to data features, enabling it to focus on the most relevant information. The combination of these three elements (CNN-BiLSTM with an attention mechanism) has the potential to yield higher prediction accuracy. To measure the accuracy of the forecasts, this study uses evaluation metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared (R²). The research results indicate that the CNN-BiLSTM model with an attention mechanism has proven to be the most superior model compared to other models in forecasting the stock price of PT Indosat Ooredoo Hutchison.

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 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 Fadilatul Husna Faujiyah, Siti Fauzi, Fatkhurokhman Gunawan Gunawan Habibie, Zulqoidi R. Hardi Suyitno Iis Widya Harmoko Ikrimah, Annisa Isnarto Isnarto 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 Made Arnandea Fatiha Putri Marthinus Yohanes Ruamba 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 Nabhan Nabilah Nike Yustina Oktaviani Noviana Dini Rahmawati Nugroho, Ahmad Halimy 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 Rochmad 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 Suwarto Suwarto Tiani Wahyu Utami Tsania Rahma Azzahra Utami, Mira Dwi 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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