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All Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Internet of Things and Artificial Intelligence Journal International Journal of Research in Mathematics Education
Rozaq, Hasri Akbar Awal
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Public Health

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Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease MAHMUD, Mahmud; BUDİMAN, Irwan; INDRİANİ, Fatma; KARTİNİ, Dwi; FAİSAL, Mohammad Reza; ROZAQ, Hasri Akbar Awal; YILDIZ, Oktay; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.384

Abstract

Hepatitis C, a significant global health challenge, affects 71 million people worldwide, with severe complications such as cirrhosis and hepatocellular carcinoma. Despite its prevalence and availability in rapid diagnostic tests (RDTs), the need for accurate early detection methods remains critical. This research aims to enhance hepatitis C virus classification accuracy by integrating the C5.0 algorithm with Chi-Square feature selection, addressing the limitations of current diagnostic approaches and potentially reducing diagnostic errors. This research explores the development of a machine learning model for hepatitis C prediction, utilizing a publicly available dataset from Kaggle. It encompasses preprocessing techniques such as label encoding, handling missing values, normalization, feature selection, model development, and evaluation to ensure the model's efficacy and accuracy in diagnosing hepatitis C. The findings of this study reveal that implementing Chi-Square feature selection significantly enhances the effectiveness of machine learning algorithms. Specifically, the combination of the C5.0 algorithm and Chi-Square feature selection yielded a remarkable accuracy of 96.75%, surpassing previous research benchmarks. This highlights the potent synergy between advanced feature selection techniques and machine learning algorithms in improving diagnostic precision. The study conclusively demonstrates that machine learning is an effective tool for detecting hepatitis C, showcasing the potential to enhance diagnostic accuracy significantly. As a future recommendation, adopting AutoML is suggested to periodically automate the selection of the optimal algorithm, promising further improvements in detection capabilities.
Implementation of Discovery Learning Assisted by Pythagorean Puzzle to Improve Mathematical Problem-Solving Ability Mutmainah, Fatikhatun; Rozaq, Hasri Akbar Awal
International Journal of Research in Mathematics Education Vol. 1 No. 2 (2023)
Publisher : Faculty of Tarbiya and Teacher Trainning, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/ijrme.v1i2.8676

Abstract

A Mathematical problem-solving ability is the ability of students to solve a mathematical problem. External factors that affect mathematical problem-solving ability are the methods and learning media used. Researchers apply the discovery method assisted by learning media which is one of the alternatives to improve mathematical problem-solving ability, namely the discovery learning model assisted by the Pythagorean Puzzle. This study aims to determine the effectiveness of discovery learning assisted by the Pythagorean Puzzle in improving the mathematical problem-solving ability of class VIII students of MTs Muhammadiyah 01 Purbalingga. This research is quasi-experiment research with a quantitative approach. The population in this study were students of class VIII MTs Muhammadiyah 01 Purbalingga which amounted to 163 students. The samples of this study were class VIII C which amounted to 22 students as the Experiment class and class VIII D which amounted to 22 students as the Control class. Data collection in this study used instruments in the form of observations and tests of mathematical problem-solving skills. Based on the analysis obtained, discovery learning assisted by Pythagorean Puzzle was implemented very well. The results of data analysis using the T-test, and post-test comparison test obtained sig (2-tailled) of 0.000 < 0.05, which means that there is a difference in the average experimental class and control class. So, it can be concluded that the implementation of discovery learning assisted by the Pythagorean Puzzle is effective in improving the mathematical problem-solving skills of class VIII students of MTs Muhammadiyah 01 Purbalingga.
Applying XGBoost-ADASYN in the Classification Process of Bank Customers Who Will Take Time Deposits Abdilah, Muhammad Fariz Fata; Mazdadi, Muhammad Itqan; Farmadi, Andi; Muliadi, Muliadi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.551

Abstract

Investment in the form of time deposits at banks offers stable returns. Identifying and attracting potential customers, however, poses challenges. This research enhances the predictive capabilities of deposit classification models by addressing data imbalance with a combination of XGBoost, ADASYN, and Random Search optimization techniques. The integration of ADASYN improves minority class representation, while Random Search efficiently optimizes model parameters. Our findings show a significant accuracy of 94.93%, benchmarked against baseline models, highlighting our method's effectiveness compared to traditional approaches. This hybrid model advances customer data analysis and achieves our research objectives. We discuss the integration challenges, including computational demands and technique selection. The research underscores the application of machine learning to address financial industry issues, emphasizing the impact of data preprocessing and feature engineering on performance. Future studies might explore AutoML to reduce complexity further and enhance model scalability, promising more innovation in customer data analysis.
The Internet of Things (IoT) Product Design and Modeling Amikom Purwokerto Hand Sanitizer (AMPUH) Syafaat, Alif Yahya; Rozaq, Hasri Akbar Awal; Maulida, Trisna; Zumaroh, Agnis Nur Afa; Ananda, Rona Sepri; Tahyudin, Imam
Internet of Things and Artificial Intelligence Journal Vol. 2 No. 3 (2022): Vol. 2 No. 3 (2022): Volume 2 Issue 3, 2022 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (919.93 KB) | DOI: 10.31763/iota.v2i3.544

Abstract

In previous research, an automatic hand sanitizer prototype tool was made to prevent COVID-19. The product design in this research is quite complicated and too large. Then, in other studies, a prototype tool has also been made to measure body temperature. The study also had an inefficient prototype design. So, it can be concluded that no product design model is efficient, simple, and has its charm. In this research, the concept design method consists of several stages: planning, measurement, design, and implementation. To identify the production needs that will be used in making AMPUH product design, researchers carry out measurement stages based on a schematic series (Schematic) of the product to be made. AMPUH product designs are made with shapes and models that have been adapted to their components without reducing the aesthetic value, compatibility, complexity, and design applicability. The advantage of this product design is that it always looks at the compatibility with the components that will be installed in it so that it is certain that both in terms of form and function will work well. The drawback of this product design may still leave a little free space in it to minimize the remaining free space and repair.
Geographically Weighted Random Forests for Human Development Index of Central Java Prediction Zuhdi, Shaifudin; Fatatik, Isna Nurul; Prihasno, Izlah Nur Fadlila Herawati; Rozaq, Hasri Akbar Awal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5204

Abstract

The geographically weighted regression (GWR) model has been widely used in various types of predictions, including human development index predictions. Similarly, the random forests (RF) model has also been widely used in various value predictions. The GWR model always assumes a local linear relationship between dependent and independent variables. The RF model only produces one global model that cannot represent conditions at each location. The GWR model is susceptible to multicollinearity in each independent variable, which can lead to overfitting if multicollinearity in the model is high. To address the vulnerability of the GWR model to multicollinearity, the RF model and the GWR model can be combined. Since the RF model is not vulnerable to multicollinearity in the independent variables, the modification becomes the geographically weighted random forests (GWRF) model to improve the shortcomings of the GWR and RF models. The GWR and GWRF models were constructed using data from districts and cities in Central Java Province, which was selected as the study area due to evident disparities in human development index achievements. These disparities highlight the presence of spatial heterogeneity that conventional models fail to adequately capture. To rigorously evaluate model performance, data from 2023 were employed as training data, while data from 2024 served as testing data. This research introduces a novel integration of spatial econometric and machine learning approaches, providing a more robust framework for addressing complex spatial variations in human development outcomes. The GWRF model is capable of producing a model that does not overfit when there is multicollinearity among independent variables. The GWRF model offers a novel integration of machine learning and spatial modelling, outperforming both GWR and RF by not only delivering high predictive accuracy under complex variable relationships but also capturing nuanced local spatial heterogeneity that conventional approaches fail to address.
Pelatihan Video Editing dan Pembuatan Konten Digital PCINU Jepang sebagai Media Dakwah Tahyudin, Imam; Arifudin, Dani; Rozaq, Hasri Akbar Awal; Putra, Feishal Azriel Arya
Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Vol 6, No 1 (2023): Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstormin
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/japhb.v6i1.4231

Abstract

Jumlah warga Indonesia yang tinggal di Jepang dan aktif di PCINU Jepang lebih dari 300 orang. PCINU Jepang memiliki banyak program kerja dalam rangka mensyiarkan Islam berhaluan ahli sunah wal jamaah ala Nahdlatul Ulama (NU). Sebagai Langkah kolaborasi untuk mengoptimalkan program kerja tersebut, PCINU jepang bekerjasama dengan Universitas Amikom Purwokerto. Bentuk implementasi Kerjasama tersebut dalam wujud pembekalan keterampilan kemampuan editing video dan pembuatan konten digital sebagai media dakwah melalui media sosial untuk pengurus dan anggota PCINU Jepang. Kemampuan tentang editing video dan pembuatan konten digital dirasa sangat diperlukan saat ini di era digital. Pelatihan ini diadakan secara daring dan pendampingan melalui group media sosial seperti WhatsApp (WA). Pelatihan terlaksana sukses berkat kerjasama banyak pihak khususnya panitia, pemateri dan peserta. Jumlah peserta yang mengikuti acara tersebut sebanyak 30 orang dari banyak daerah di Jepang dengan beraneka ragam latar belakang pendidikan dan pekerjaan. Hasil evaluasi menunjukan adanya peningkatan pemahaman dan keahlian editing video dan pembuatan konten digital antara sebelum dan sesudah mengikuti pelatihan. Pelatihan ini telah dipublikasikan pada media massa cetak Radar Banyumas, serta video kegiatan yang dapat diakses secara publik melalui saluran Youtube. Untuk tahun selanjutnya akan diadakan proses pendampingan untuk optimalisasi website PCINU Jepang.
Implementation of Extra Trees Classifier and Chi-Square Feature Selection for Early Detection of Liver Disease Al Ghifari, Muhammad Akmal; Budiman, Irwan; Saragih, Triando Hamonangan; Mazdadi, Muhammad Itqan; Herteno, Rudy; Rozaq, Hasri Akbar Awal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4261

Abstract

The imbalanced distribution of medical data poses challenges in accurately detecting liver disease, which is crucial as symptoms often remain unnoticed until advanced stages. This study examines the application of the Extra Trees Classifier algorithm and chi-square feature selection for early detection of liver disease. Compared to traditional methods like Random Forest and SVM, the Extra Trees Classifier offers enhanced computational efficiency and better handling of imbalanced datasets, while chi-square feature selection helps identify the most relevant medical indicators. The data consists of five medical variables likely to be laboratory test results from patient samples, with labels indicating classes A and B. The data is randomly divided with a ratio of 80% for each class. To address data imbalance, SMOTE technique was applied before the data was randomly split into a ratio of 80% for training and 20% for testing to ensure effective learning and testing of the model's performance. The results showed that with the help of chi-square feature selection, the Extra Trees Classifier algorithm could provide fairly accurate predictions in liver disease classification, with an accuracy of 82.6%, sensitivity of 85.5%, precision of 78.3%, and F1-Score of 81.7%. These results demonstrate significant improvement over existing methods, and the proposed approach can aid healthcare practitioners in making timely diagnostic decisions, potentially reducing mortality rates through early intervention in liver disease cases.
Prediction of Life Expectancy of Lung Cancer Patients After Thoracic Surgery Using Decision Tree Algorithm and Adaptive Synthetic Sampling Erdi, Muhammad; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Farmadi, Andi; Saragih, Triando Hamonangan; Rozaq, Hasri Akbar Awal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4724

Abstract

This research focuses on predicting the life expectancy of lung cancer patients after undergoing thoracic surgery, using a decision tree classification algorithm (C4.5) combined with adaptive synthetic sampling to handle data imbalance. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. By applying ADASYN, the data distribution becomes more even, thus improving the performance of the C4.5 model. The results showed that combining these methods increased the prediction accuracy from 67% to 87%. In addition, the precision, recall, and f1-score for minority classes have significantly improved, which were previously difficult to identify by the model. Thus, combining the C4.5 algorithm and the ADASYN technique proved effective in dealing with the challenge of data imbalance and resulted in better prediction in the case of lung cancer. This study is expected to contribute to the field of medical classification and serve as a reference for further research on similar cases.
Performance Comparison of AdaBoost, LightGBM, and CatBoost for Parkinson's Disease Classification Using ADASYN Balancing Anshari, Muhammad Ridha; Saragih, Triando Hamonangan; Muliadi, Muliadi; Kartini, Dwi; Indriani, Fatma; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4726

Abstract

Parkinson's disease is a neurodegenerative condition identified by the decline of neurons that produce dopamine, causing motor symptoms such as tremors and muscle stiffness. Early diagnosis is challenging as there is no definitive laboratory test. This study aims to improve the accuracy of Parkinson's diagnosis using voice recordings with machine learning algorithms, such as AdaBoost, LightGBM, and CatBoost. The dataset used is Parkinson's Disease Detection from Kaggle, consisting of 195 records with 22 attributes. The data was normalized with Min-Max normalization, and class imbalance was resolved with ADASYN. Results show that ADASYN-LightGBM and ADASYN-CatBoost have the best performance with 96.92% accuracy, 97.10% precision, 96.92% recall, and 96.92% F1 score. This improvement suggests that combining boosting methods and data balancing techniques can improve the accuracy of Parkinson's diagnosis. These results demonstrate the effectiveness of ADASYN in addressing data imbalance and improving the performance of boosting algorithms for medical classification problems. The findings contribute to the development of intelligent diagnostic systems in the field of medical informatics and computer science. These findings are essential for developing more accurate and efficient diagnostic tools, supporting early diagnosis and better management of Parkinson's disease.
Implementation of Ant Colony Optimization in Obesity Level Classification Using Random Forest Wardana, Muhammad Difha; Budiman, Irwan; Indriani, Fatma; Nugrahadi, Dodon Turianto; Saputro, Setyo Wahyu; Rozaq, Hasri Akbar Awal; Yıldız, Oktay
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4696

Abstract

Obesity is a pressing global health issue characterized by excessive body fat accumulation and associated risks of chronic diseases. This study investigates the integration of Ant Colony Optimization (ACO) for feature selection in obesity-level classification using Random Forests. Results demonstrate that feature selection significantly improves classification accuracy, rising from 94.49% to 96.17% when using ten features selected by ACO. Despite limitations, such as challenges in tuning parameters like alpha (α), beta (β), and evaporation rate in ACO techniques, the study provides valuable insights into developing a more efficient obesity classification system. The proposed approach outperforms other algorithms, including KNN (78.98%), CNN (82.00%), Decision Tree (94.00%), and MLP (95.06%), emphasizing the importance of feature selection methods like ACO in enhancing model performance. This research addresses a critical gap in intelligent healthcare systems by providing the first comprehensive study of ACO-based feature selection specifically for obesity classification, contributing significantly to medical informatics and computer science. The findings have immediate practical implications for developing automated diagnostic tools that can assist healthcare professionals in early obesity detection and intervention, potentially reducing healthcare costs through improved diagnostic efficiency and supporting digital health transformation in clinical settings. Furthermore, the study highlights the broader applicability of ACO in various classification tasks, suggesting that similar techniques could be used to address other complex health issues, ultimately improving diagnostic accuracy and patient outcomes.
Co-Authors Abdilah, Muhammad Fariz Fata Al Ghifari, Muhammad Akmal Ananda, Rona Sepri Andi Farmadi Anshari, Muhammad Ridha Dani Arifudin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Erdi, Muhammad Fatatik, Isna Nurul Fatma Indriani Herteno, Rudy Imam Tahyudin Irwan Budiman Mahmud Mahmud Maulida, Trisna Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Mutmainah, Fatikhatun Prihasno, Izlah Nur Fadlila Herawati Putra, Feishal Azriel Arya Radityo Adi Nugroho Saputro, Setyo Wahyu Saragih, Triando Hamonangan Syafaat, Alif Yahya Syahputra, Muhammad Reza Wahyu Caesarendra Wardana, Muhammad Difha YILDIZ, Oktay Zuhdi, Shaifudin Zumaroh, Agnis Nur Afa