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All Journal STUDIA ISLAMIKA J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Studia Islamika International Journal on Educational Issues (IJEI)
Awaludin, Aziz
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Energy Languange, Linguistic, Communication & Media Mathematics Other

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Documenting the Half-Century Evolution of Islamic Education Research: A Probabilistic Topic Modeling Study of the Literature from 1970 to 2023 Awaludin, Aziz
Studia Islamika Vol 31, No 3 (2024): Studia Islamika
Publisher : Center for Study of Islam and Society (PPIM) Syarif Hidayatullah State Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36712/sdi.v31i3.41513

Abstract

In this systematic literature review, I used Correlated Topic Modeling (CTM), a machine learning technique, to analyze 1,116 Scopus-indexed documents on Islamic education spanning 54 years (1970-2023). I identified 19 topics grouped into four thematic clusters: Foundational Concepts and Methods, Social Issues, Teaching and Learning, and Education Systems and Settings. My main argument is that Islamic education is inherently interdisciplinary, encompassing history, philosophy, leadership, policy, citizenship, gender, and technology. While some topics, like education history and values education, have seen consistent focus, others, such as citizenship, education policy, and student learning, remain underexplored. My analysis reveals the field’s adaptability to societal and technological changes. Particularly, I discuss the implications for Southeast Asia’s Islamic education, which has balanced modernization and national policies with global trends. By pioneering machine learning applications in this field, this review uncovers new research directions and demonstrates the potential of large-scale text analysis for Islamic education scholarship.
Application of the LSTM Algorithm in Predicting Urea Fertilizer Production at IIB Plant PT. Pupuk Sriwidjaja Palembang Awaludin, Aziz; Ferdiansyah, F; Andri, A; Oktarina, Tri
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.704

Abstract

PT. Pupuk Sriwidjaja Palembang is a pioneer of fertilizer manufacturers in Indonesia. One of the plants at PT. Pupuk Sriwidjaja Palembang, namely the IIB urea plant, has been operating normally since 2017, thereby the data of production results has been collected for more than five years (time series data). The collected data can be used to make predictions of future production using the LSTM (Long Short Term Memory) model. LSTM is an artificial neural network architecture that is suitable for processing sequential data. The research objective to be achieved is to produce a production prediction model using LSTM modeling. Data collected over five years was divided into training data and testing data through data composition trials. The LSTM model training was carried out with a training data composition of 70% of the total data, batch size 64, and epoch 200. Then testing was carried out with data testing as much as 30% of the total data using RMSE and MAPE as model quality assessment parameters. Based on test results, the LSTM model is able to predict production with an RMSE of 11.08 and a MAPE of 6.39%.
Application of the LSTM Algorithm in Predicting Urea Fertilizer Production at IIB Plant PT. Pupuk Sriwidjaja Palembang Awaludin, Aziz; Ferdiansyah, F; Andri, A; Oktarina, Tri
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.704

Abstract

PT. Pupuk Sriwidjaja Palembang is a pioneer of fertilizer manufacturers in Indonesia. One of the plants at PT. Pupuk Sriwidjaja Palembang, namely the IIB urea plant, has been operating normally since 2017, thereby the data of production results has been collected for more than five years (time series data). The collected data can be used to make predictions of future production using the LSTM (Long Short Term Memory) model. LSTM is an artificial neural network architecture that is suitable for processing sequential data. The research objective to be achieved is to produce a production prediction model using LSTM modeling. Data collected over five years was divided into training data and testing data through data composition trials. The LSTM model training was carried out with a training data composition of 70% of the total data, batch size 64, and epoch 200. Then testing was carried out with data testing as much as 30% of the total data using RMSE and MAPE as model quality assessment parameters. Based on test results, the LSTM model is able to predict production with an RMSE of 11.08 and a MAPE of 6.39%.
Documenting the Half-Century Evolution of Islamic Education Research: A Probabilistic Topic Modeling Study of the Literature from 1970 to 2023 Awaludin, Aziz
Studia Islamika Vol. 31 No. 3 (2024): Studia Islamika
Publisher : Center for Study of Islam and Society (PPIM) Syarif Hidayatullah State Islamic University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36712/sdi.v31i3.41513

Abstract

In this systematic literature review, I used Correlated Topic Modeling (CTM), a machine learning technique, to analyze 1,116 Scopus-indexed documents on Islamic education spanning 54 years (1970-2023). I identified 19 topics grouped into four thematic clusters: Foundational Concepts and Methods, Social Issues, Teaching and Learning, and Education Systems and Settings. My main argument is that Islamic education is inherently interdisciplinary, encompassing history, philosophy, leadership, policy, citizenship, gender, and technology. While some topics, like education history and values education, have seen consistent focus, others, such as citizenship, education policy, and student learning, remain underexplored. My analysis reveals the field’s adaptability to societal and technological changes. Particularly, I discuss the implications for Southeast Asia’s Islamic education, which has balanced modernization and national policies with global trends. By pioneering machine learning applications in this field, this review uncovers new research directions and demonstrates the potential of large-scale text analysis for Islamic education scholarship.
Character Education Through Local Wisdom Culture at MA Al Iman Adiwerna Awaludin, Aziz; Jannah, Roikhatul; Khoerul Anam, Muhammad; Mahdiyah, Nishrina; Jamal Maulidhana, Abdul; Apriyani, Afni; Ayu Lestari, Diah
International Journal on Education Issues Vol. 1 No. 1 (2025): JANUARY
Publisher : CV Kalimasada Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59966/caj74b90

Abstract

This investigate was pointed to knowing the usage of character instruction through school culture. with a center on devout character, teach character and free character. This ponder utilized a subjective approach with the plan of the ponder of multi case (multycase studies). The information collection strategy of this inquire about were the perception, interviews, and documentation. The comes about of the ponder appeared that the usage of character instruction through school culture centered on devout character, teach, and freedom. Each character is gotten through.
Co-Authors Andri, A Apriyani, Afni Diah Ayu Lestari Ferdiansyah Ferdiansyah Ferdiansyah, F Jamal Maulidhana, Abdul Jannah, Roikhatul Khoerul Anam, Muhammad Mahdiyah, Nishrina Oktarina, Tri