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Iqbal Kharisudin

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Author-ID : 298167

Religion Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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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.

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.

Aplikasi Mobile untuk Pemantauan Shuttle Bus di Universitas Negeri Semarang Muhammad Iqbal; Adi Nur Cahyono; Iqbal Kharisudin; Fadilatul Husna; Nabhan Nabilah; Made Arnandea Fatiha Putri
Jurnal Komputasi Vol. 13 No. 1 (2025)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v13i1.287

Shuttle bus di Universitas Negeri Semarang dapat meningkatkan kualitas transportasi umum dengan menggunakan teknologi yang tersedia. Ponsel pintar sudah ada di mana-mana karena mobilitas dan keserbagunaannya, dengan GPS sebagai komponen penting. Penyajian informasi mengenai shuttle bus secara obyektif dapat dilakukan dengan mengembangkan sistem transportasi yang menampilkan lokasi dan perkiraan pergerakan shuttle bus beserta perlengkapannya melalui smartphone. Sistem seperti ini dapat menyederhanakan pekerjaan pengguna shuttle bus dengan menyoroti berbagai aspek layanan shuttle bus. Teknologi ITS merupakan komponen integral dari inisiatif kampus pintar. Tujuan utama dari penelitian ini adalah untuk menyediakan aplikasi smartphone yang ramah pengguna yang menyajikan informasi tentang shuttle bus dengan cara yang mudah digunakan dan dimengerti. Aplikasi berhasil dikembangkan dan diterapkan di Universitas Negeri Semarang. Hasil pengujian menunjukkan bahwa responden memberikan penilaian yang baik terhadap aplikasi tersebut.

Co-Authors achilla, Silky Achmad Fariz Adi Nur Cahyono Ahmad Halimy Nugroho Akbar, Mohammad Jefrie Ilham Al Zikir Alfath, Maliki Alifatul Muyasaroh Alifian yusuf, Muhammad Amanda Fifi Yaya Mahe Amin Suyitno Anggraeni, Aliyya Anis Shihafiyatal Abida Anisa Rosdiana, Anisa Arief Agoestanto Ashim, Muhammad Asih, Tri Sri Noor Astutiningtyas, Luthfiyah Ayu Andira Risnawati Aziiza Andanawarih Utoyo Bambang Eko Susilo Bambang Eko Susilo Budi Waluya Chulaili Sahri Nova, Tsalisa Ditasari, Dwi Dian Dr. Masrukan, Dr. Dwijanto Dwijanto Ellya Masturina Hamid Emi Pujiastuti Eunike Cantika Kusuma Pandi Fadhilah, Nida Nur Fadilatul Husna Faujiyah, Siti Fauzi, Fatkhurokhman Gunawan Gunawan Habibullah Hardi Suyitno Iis Widya Harmoko Ikrimah, Annisa Isnarto Isnarto Iwan Junaedi Jannatun Khustia Lubis Kartono Kartono Kartono , Kartono, Wardono Kartono` Kartono 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 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 Petronela Ivoni Susantya Putri, Sanianajiba Nugroho Radika Widiatmaka Rahanto, Faris Febri Rahman, Alif Aulia Rahmawati, Rofiqo Rizki Ahid Nurhasanah Rizvi Uzza Awwawina Rochdi Wasono Rochmad - Rochmad Rochmad S B Waluya Safrudiannur Safrudiannur Sarah, Caecillia Rafika SB Waluya SB Waluya Scolastika Mariani Sebastianus Fedi Siti Maslihah St. Budi Waluya Sufah Iliya Manazila Sugiman Sugiman Sugiman Sugiman Sukestiyarno Sukestiyarno 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 Vera Dewi Susanti Wahyu Arif Setyo Pambudi Wahyu Nur Annisa Wahyu Nur Annisa Walid Walid Walid Walid Walid, Walid Wardono Wardono Wardono Y. L. Sukestiyarno YL Sukerstriyarno YL Sukestiyarno YL Sukestriyarno Yohanes Leonardus Sukestiyarno Zaenuri Zaenuri M Zaenuri Mastur Zaenuri Zaenuri Zulkardi Zulqoidi R. Habibie

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