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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Jurnal Pendidikan Agama Islam Jurnal Tarbiyatuna : Kajian Pendidikan Islam DIMAR: Jurnal Pendidikan Islam Journal of Soft Computing Exploration Jurnal Pendidikan Islam Journal of Student Research Exploration Journal of Information System Exploration and Research Jurnal Ilmiah Riset dan Pengembangan
Rofik Rofik, Rofik
FITK UIN Sunan Kalijaga Yogyakarta
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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The Optimization of Credit Scoring Model Using Stacking Ensemble Learning and Oversampling Techniques Rofik, Rofik; Aulia, Reza; Musaadah, Khalimah; Ardyani, Salma Shafira Fatya; Hakim, Ade Anggian
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.203

Abstract

Credit risk assessment plays an important role in efficient and safe banking decision-making. Many studies have been conducted to analyze credit scoring with a focus on achieving high accuracy. However, predicting credit scoring decisions also requires model construction that handles class imbalance and proper model implementation. This research aims to increase the accuracy of credit assessment by balancing data using Synthetic Minority Oversampling (SMOTE) and applying ensemble stacking learning techniques. The proposed model utilizes a base learner consisting of Random Forest, SVM, Extra-Tree Classifier, and XGboost as a meta-learner. Then to handle unbalanced classes using SMOTE. The research process was carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. The model was tested using the German Credit dataset by applying cross-validation. The evaluation results show that the stacking ensemble learning model developed has optimal performance, with an accuracy of 83.21%, precision of 79.29%, recall of 91.78%, and f1-score of 85.08%. This research shows that optimizing the stacking ensemble learning model with data balancing using SMOTE in credit scoring can improve performance in credit scoring.
Breast Cancer Diagnosis Utilizing Artificial Neural Network (ANN) Algorithm for Integrating Multi-Omics Data and Clinical Features Rofik, Rofik; Artiyani, Fani; Pertiwi, Dwika Ananda Agustina
Journal of Information System Exploration and Research Vol. 2 No. 2 (2024): July 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i2.249

Abstract

Breast cancer is one of the most common diseases affecting women worldwide, with a significant impact on patient's health and quality of life. Despite advances in medical technology and research, breast cancer diagnosis remains a challenge due to its complexity involving various biological and clinical factors. Several previous studies have focused on detecting this disease with optimal accuracy, but the selection of appropriate algorithms and methods is key to achieving this goal. This study aims to improve the accuracy of breast cancer diagnosis by using the ANN algorithm and data balancing method, SMOTE. This research uses Multi-Omic data and Clinical Features obtained in general from Kaggle. The research process is carried out in several stages, namely Data Collection, Preprocessing, Oversampling, Modeling, and Evaluation. This research successfully obtained an increase in accuracy, which was able to achieve an accuracy of 99.30%.  This research shows that early detection of breast cancer with ANN algorithm and data balancing using SMOTE can improve accuracy performance in early detection of breast cancer. Given the use of data in this study is not too large, it is recommended for further research to use a larger dataset to validate the strength of the model that has been built on more varied data.
Factors Shaping the Evolution of the Islamic Cultural History (SKI) Curriculum in Madrasahs (1973-2013) Rofik, Rofik; Hasan, Noorhaidi; Hak, Nurul
Jurnal Pendidikan Islam Vol. 12 No. 1 (2023): JURNAL PENDIDIKAN ISLAM
Publisher : Faculty of Tarbiyah and Education State Islamic University (UIN) Sunan Kalijaga Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jpi.2023.121.73-85

Abstract

Purpose – This research undertakes an exploration of the factors shaping the evolution and continuity of the Islamic Cultural History (SKI) curriculum in madrasahs between 1973 and 2013. It zeroes in on five predominant influences: political, economic, social, cultural, and ideological. Design/methods/approach – A historical research method was the primary tool, with data from various written records—comprising legal documents, regulations, and textbooks. A chronological lens was employed for analysis, with categorizations influenced by pertinent regulations and the prevailing zeitgeist. Findings – Of the factors, political dimensions, especially government stances and policies, took precedence in curriculum development. Furthermore, aspects like economic strides, prevailing social conditions, and national ideologies such as Pancasila (Indonesian state philosophy) bore significance in shaping the curriculum. The insights gathered suggest a pivotal role of socio-political dynamics and scientific progress in dictating madrasah curriculum changes. Research implications/limitations – While this investigation furnishes deep insights into a specific period, its temporal scope poses limitations, suggesting a potential exploration post-2013 and scrutiny of other influencing variables.
SEJARAH PERKEMBANGAN LEMBAGA PENDIDIKAN ISLAM DI INDONESIA Herlambang, M.; Muqowim, Muqowim; Rofik, Rofik
Tarbiyatuna Kajian Pendidikan Islam Vol 8 No 2 (2024): (September 2024)
Publisher : LPPM Institut Agama Islam Ibrahimy Genteng Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69552/tarbiyatuna.v8i2.2512

Abstract

Islamic education in Indonesia has a long and complex history, with developments that have occurred since the beginning of Islam's arrival in the archipelago. This article discusses how Islamic educational institutions in Indonesia have developed over time, and how they have adapted to social and cultural changes in society. In this article, we will discuss the development of Islamic educational institutions in Indonesia, starting from the early days of informal Islamic education to the present day with Islamic education integrated with the national education system.
Improving car price prediction performance using stacking ensemble learning based on ann and random forest Tanga, Yulizchia Malica Pinkan; Simanjuntak, Robert Panca R.; Rofik, Rofik; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.462

Abstract

Determining the right selling price for a car can be a challenge for car sales companies. The selling price of a car is highly influenced by car characteristics such as brand, type, year of production, fuel type, and mileage. Therefore, the research aims to develop a more accurate model of car price prediction model by using a stacking ensemble technique that combines Random Forest and ANN. Random Forest is effective in handling outliers and reducing the risk of overfitting, while ANN has the advantage of capturing complex nonlinear patterns. The results show that the stacking ensemble model combining ANN and Random Forest can predict car sales prices by achieving an R2 value of 0.97. The results of this study can help distributors in selling cars make the right decisions regarding the sales price of cars. To improve the generalization of the model, future research is recommended to try a combination of different ensemble methods and the use of larger and more diverse datasets.
Religious Moderation in Walisongo Material in the Textbook of History and Culture of Islam Class VI Madrasah Ibtidaiyah Ministry of Religious Affairs 2016 Rofik, Rofik; Pratidinal Jadid, Rosyid
Jurnal Pendidikan Agama Islam Vol. 18 No. 1 (2021): Jurnal Pendidikan Agama Islam
Publisher : Yogyakarta: Jurusan Pendidikan Agama Islam Fakultas Ilmu Tarbiyah dan Keguruan UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jpai.2021.181-04

Abstract

This research is focused on the content of religious moderation in Walisongo Material in The Textbook of History and Culture of Islam Class VI Madrasah Ibtidaiyah Ministry of Religious Affairs 2016. This research is motivated by the appeal of the Ministry of Religious Affairs of the Republic of Indonesia that madrasa educational institutions should cover the concept of religious moderation in the textbooks, in this case the History and Culture of Islam. The research method used is qualitative research. The type of this research is library research, more precisely the analysis of textbooks. The results of this study indicate that: (1) Each sub-lesson material of walisongo in the textbook contains indicators of religious moderation (national commitment, tolerance, non-violence, accommodative to local culture), except for Sunan Bonang sub-lessons which do not contain indicators of national commitment and Sunan Giri sub-lessons which do not contain indicators of tolerance. Indicators of religious moderation in Walisongo material can be known with two events. First, directly from the sentence that shows the indicator word itself. Second, the sentence structure of the material is in accordance with the indicator definition. (2) The excess content of religious moderation in walisongo material in the book is that there are indicators of religious moderation contained in the sentence directly and according to the definition of each indicator, and there are illustrations both in the form of pictures and writings, from the real form of religious moderation even though not all of them exist.
Integration of Pancasila Values in Islamic Cultural History Subjects: A Content Analysis Faaza, Muhammad; Rofik, Rofik
Jurnal Pendidikan Agama Islam Vol. 19 No. 2 (2022): Jurnal Pendidikan Agama Islam
Publisher : Yogyakarta: Jurusan Pendidikan Agama Islam Fakultas Ilmu Tarbiyah dan Keguruan UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jpai.2022.192-07

Abstract

Purpose – This study aims to determine Pancasila's values, which are integrated into student books for class X Islamic Cultural History. Design/methods/approaches – To collect data, this library research uses documentation. The data is collected from relevant sources such as student books, journals, news, and related laws and regulations. Data analysis was performed using the content analysis method. Findings – The results of this study indicate that the Islamic Cultural History student book at Madrasah Aliyah Class X contains Pancasila values that can be integrated with the material. It shows that 1) there are 17 Pancasila values contained in the Islamic Cultural History MA student book for class X, namely in the first precepts points 1, 2, and 7; the second precept of the 9th, 10th, 11th, 13th, 14th, and 15th points; the third precept point 18 and 19; the fourth point of the 27th precept; fifth precept on points 37,40, 41, 42, and 45. 2) Integrating the values of Pancasila with the material in the Islamic Cultural History MA Class X student book is expected to be able to increase the spirit of nationalism and be able to prevent the spread of anti-Pancasila ideology, which is currently rife in Indonesia, especially among Muslim students because Pancasila is the foundation for Islamic Religious Education in Indonesia, especially the History of Islamic Culture to maintain national unity and integrity. Research limitations – This research has limitations in scope, it can only cover available sources, so it may not include all relevant literature. Originality /value – Teachers can use the findings from the research in teaching Islamic cultural history subjects that integrate with Pancasila values.
Enhancing costumer churn prediction with stacking ensemble and stratified k-fold Rofik, Rofik; Unjung, Jumanto; Prasetiyo, Budi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8112

Abstract

In the era of rapid technological advancement, the telecommunications industry undergoes significant changes. Factors such as the speed of technological change, high customer expectations, and changing preferences are the main obstacles that affect the dynamics of telecommunications companies. One major issue faced is the high customer churn rate, adversely impacting company revenue and profitability. Previous studies indicate that customer churn prediction remains complex in the telecommunications industry, with opportunities to optimize algorithm selection and prediction model construction methods. This research aims to improve the accuracy of customer churn prediction by employing a complex model that utilizes stacking ensemble learning techniques. The proposed model combines 6 base algorithms: extreme gradient boosting (XGBoost), random forest, light gradient boosting machine (LightGBM), support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN), with XGBoost as the meta-learner model. The research process involves preprocessing, class data balance with synthetic minority oversampling technique (SMOTE), training using stratified k-fold, and model evaluation. The model is tested using the Telecom Churn dataset. The evaluation results show that the constructed stacking model achieves 98% accuracy, 98.74% recall, 98.03% precision, and 98.38% F1 score. This study demonstrates that optimizing the stacking ensemble model with SMOTE and stratified k-fold enhances customer churn prediction accuracy.
Comparison of naïve bayes and support vector machine methods for jkt48 music video comment classification Abdul Aziz, Alif; Rofik, Rofik
Journal of Student Research Exploration Vol. 3 No. 1 (2025): January 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i1.389

Abstract

The research was conducted to discuss the classification of comments on music video JKT48 "Magic Hour" in YouTube using method Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). YouTube monitors viewer emotion by adjective comments Adjectives are the descriptive powers of human communication we use to help personify how different types, i.e. different "personalities" flavors and depths reflect artistic expressions The place where interactivity meets with digital marketing signifying a shared contribution to music lore In this work, we study the comparison of The Support Vector Machine (SVM) and Naive Bayes Classifier in terms of Accuracy, Precision & Recall. This Project includes data pre-processing, collecting the data by YouTube API and build classification models which involves Support Vector Machine and Naive Bayes Classifier. SVM displayed more stable performance than NBC, showing consistent results across different data split ratios. SVM achieved its highest accuracy of 93.42% at an 80:20 ratio, with precision and recall rates reaching 92.57% and 93.42%, respectively.
Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE Unjung, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1627

Abstract

Parkinson's disease is one of the major neurodegenerative diseases that affect the central nervous system, often leading to motor and cognitive impairments in affected individuals. A precise diagnosis is currently unreliable, plus there are no specific tests such as electroencephalography or blood tests to diagnose the disease. Several studies have focused on the voice-based classification of Parkinson's disease. These studies attempt to enhance the accuracy of classification models. However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. This research aims to improve the accuracy of speech-based Parkinson's disease prediction by addressing class imbalance in the data and building an appropriate model. The proposed new model is to perform class balancing using SMOTE and build an ensemble voting model. The research process is systematically structured into multiple phases: data preprocessing, sampling, model development utilizing a voting ensemble approach, and performance evaluation. The model was tested using voice recording data from 31 people, where the data was taken from OpenML. The evaluation results were carried out using stratified cross-validation and showed good model performance. From the measurements taken, this study obtained an accuracy of 97.44%, with a precision of 97.95%, recall of 97.44%, and F1-Score of 97.56%. This study demonstrates that implementing the soft-voting ensemble-SMOTE method can enhance the model's predictive accuracy.
Co-Authors Abdul Aziz, Alif Alamsyah - Ardyani, Salma Shafira Fatya Aribah, Lutfina Artiyani, Fani Aulia, Reza Budi Prasetiyo, Budi Dwi Hermawan, Guntur Dwi Jayanti, Oni Endang Sugiharti, Endang Faaza, Muhammad Hakim, Ade Anggian Herlambang, M. Jumanto Unjung Much Aziz Muslim Muqowim, Muqowim Musaadah, Khalimah Noorhaidi Hasan Nurul Hak Pertiwi, Dwika Ananda Agustina Pratidinal Jadid, Rosyid Riza Arifudin Sabarudin Sabarudin Simanjuntak, Robert Panca R. Tanga, Yulizchia Malica Pinkan