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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Bulletin of Electrical Engineering and Informatics Jurnal Penelitian Pendidikan Kreano, Jurnal Matematika Kreatif-Inovatif International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Buletin Ilmiah Sarjana Teknik Elektro Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Manajemen Transportasi & Logistik (JMTRANSLOG) Journal of Soft Computing Exploration Devotion: Journal of Research and Community Service Unnes Journal of Mathematics Education Unnes Journal of Mathematics Journal of Advances in Information Systems and Technology Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Innovative: Journal Of Social Science Research Recursive Journal of Informatics Scientific Journal of Informatics Journal of Economic Resilience and Sustainable Development Jurnal Abdi Negeri Recursive Journal of Informatics
Endang Sugiharti, Endang
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Recursive Journal of Informatics

 1 
Brain Tumor Classification on Magnetic Resonance Imaging Images using Convolutional Neural Network with Cycle Generative Adversarial Network and Extreme Gradient Boosting Aji, Akbar Lintang; Sugiharti, Endang
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.486

Abstract

Abstract. With the current advancement in technology, image classification process can be carried out through computer processing. This can also be applied to various fields, one of which is the health sector. The health sector is known for its high complexity in pattern recognizing of diseases. One of the diseases that is difficult to classify is brain tumors.             Purpose: This study aims to improve the accuracy of classification in brain MRI images, which are known to have a small and unbalanced sample. This limitation poses challenges in developing an effective classification model. The classification model is highly dependent on the quantity of data used for training. Therefore, data augmentation techniques play a crucial role in influencing the model's performance. Methods/Study design/approach: In this study, CNN model using VGG-19 architecture was used to learn feature of brain tumor in brain MRI images. Additionally, CycleGAN is used to augment and balance the data, addressing issues related to data scarcity and imbalance, thus improving diversity of the dataset. And then, XGBoost is applied to classify the feature learned by the CNN model. Result/Findings: CycleGAN has the ability to generate new image by transferring characteristics between images with different classes, making it a suitable to replace traditional data augmentation techniques in CNN. Additionally, XGBoost can be used to improve the classification results by classifying the features learned by CNN model during the training process. The proposed combination method achieves a highest accuracy of 97.37%. Novelty/Originality/Value: CNN combined with CycleGAN and XGBoost successfully improved the accuracy of the model and addresed data scarcity and imbalances in the dataset used. This combined method can improve the accuracy of the classification models. This is proven by an accuracy increase of 0.36% when compared to previous research.
Combination of Genetic Algorithm and Spiking Neural Network Leaky Integrate-And-Fire Model in Analyzing Brain Ct Scan Image for Stroke Disease Detection Boro, Fabian Dominggus Eka; Sugiharti, Endang
Recursive Journal of Informatics Vol. 3 No. 1 (2025): March 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i1.3492

Abstract

Abstract. Stroke is a condition where there is impaired brain function due to lack of oxygen caused by blockage, breakdown, or blood clots inside brain. Diagnosis of stroke is usually based on symptoms, but symptoms are not always the correct measure. In examining a stroke, the most common way to examine a patient is to perform a CT scan of the brain. Purpose: This study was conducted with the aim of predicting brain scan images consisting of normal brain, ischemic stroke brain, and hemorrhagic stroke brain. It is also to understand how an algorithm works to recognize and predict an image. Methods/Study design/approach: The image data is trained using machine learning algorithm of neural network, specifically spiking neural network (SNN) using leaky Integrate-and-Fire (LIF) method, which practices the biological performance of human nerves. SNN offers an alternative way of a computational algorithm that mimics the workings of the human brain, especially the nerves in the brain at a low computational cost. In addition, this research optimizes SNN parameters using genetic algorithm (GA). GA is proven to be a successful optimization algorithm from many sources. GA is performed after going through the process in the SNN LIF algorithm, then the parameters in SNN are entered into the algorithm operations in GA until it reaches the most optimal parameter value. Although it requires a large amount of computational time and cost, combining it with SNN will be precise and less labor-intensive. Result/Findings: Calculation of accuracy results in this study using confusion matrix, conducted on SNN test with LIF method resulted in 90.27%. While with parameter optimization with GA, the final result of the SNN LIF algorithm produces 96.3% accuracy. Novelty/Originality/Value: This study was conducted to predict stroke disease with human brain images as training data, using the SNN LIF model to train the model and identify patterns that help in predicting stroke risk. For comparison, this research also uses optimization of the model using GA which is useful for determining the core parameters in the training process of the SNN LIF model.
Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients Zaaidatunni'mah, Untsa; Sugiharti, Endang
Recursive Journal of Informatics Vol. 2 No. 2 (2024): September 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/vavw2205

Abstract

Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.
Hyperparameter Optimization Using Hyperband in Convolutional Neural Network for Image Classification of Indonesian Snacks Asyrofiyyah, Nuril; Sugiharti, Endang
Recursive Journal of Informatics Vol. 2 No. 1 (2024): March 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/n9xhbf04

Abstract

Abstract. Indonesia is known for its traditional food both domestically and abroad. Several cakes are included in favorite traditional foods. Of the many types of cakes that exist, it is visually easy to recognize by humans, but computer vision requires special techniques in identifying image objects to types of cakes. Therefore, to recognize objects in the form of images of cakes as one of Indonesian specialties, a deep learning algorithm technique, namely the Convolutional Neural Network (CNN) can be used. Purpose: This study aims to find out how the Convolutional Neural Network (CNN) works by optimizing the hyperband hyperparameter in the classification process and knowing the accuracy value when hyperband is applied to the optimal hyperparameter selection process for classifying Indonesian snack images. Methods/Study design/approach: This study optimizes the hyperparameter Convolutional Neural Network (CNN) using Hyperband on the Indonesian cake dataset. The dataset is 1845 images of Indonesian snacks which consists of 1523 training data, 162 validation data and 160 testing data with 8 classes. In training data, the dataset is divided by 82% on training data, 9% validation, and 9% testing. Result/Findings: The best hyperparameter value produced is 480 for the number of dense neurons 2 and 0.0001 for the learning rate. The proposed method succeeded in achieving a training value of 87.53%, for the validation process it was obtained 66.8%, the testing process was obtained 79.37%. Results obtained from model training of 50 epochs. Novelty/Originality/Value: Previous research focused on the application and development of algorithms for the classification of Indonesian snacks. Therefore, optimizing hyperparameters in a Convolutional Neural Network (CNN) using Hyperband can be an alternative in selecting the optimal architecture and hyperparameters.
Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results Pradana, Dany; Sugiharti, Endang
Recursive Journal of Informatics Vol. 1 No. 1 (2023): March 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/hjs1fd70

Abstract

Abstract. The application of information technology in the field of education produces big data. It retains information that can be treated as useful. Having data mining, can be used to model highly useful student performance for educators performing corrective actions against weak students. Purpose: The study was to identify the application and accuracy algorithm Naive Bayes Classifier to predict students' study results. Methods: The prediction system for student learning outcomes was built using the Naive Bayes Classifier and Laplace Smoothing methods using a combination of two Information Gain and Chi Square feature selections. The experiment was carried out 2 times using different dataset comparisons. Result: In the first experiment using a dataset of 80:20, the accuracy Naive Bayes Classifier method with Laplace Smoothing and without Laplace Smoothing showed the same results as 94.937%. On the second experiment to equate dataset 60:40 results of the Naive Bayes Classifier accurate method without Laplace Smoothing only 86.076%, then score a 91.772% accuracy using the Laplace Smoothing. The improvement is caused by a probability of zero that can be worked out with Laplace Smoothing. Novelty: The selection feature process is very important in the classification process. Thus, in this study, information gain and chi square double selections of such features as information gain and so promote accuracy.
Co-Authors Abas Setiawan Adha, Nugraha Saputra Adi, Pungky Tri Kisworo Adi, Pungky Tri Kisworo Afifah, Eka Nur Afifah, Eka Nur Aji, Akbar Lintang Al Hakim, M. Faris Alamsyah - Amin Suyitno Anggara, Dian Christopher Anggyi Trisnawan Putra Arief Broto Susilo Astuti, Winda Try Astuti, Winda Try Asyrofiyyah, Nuril Atikah Ari Pramesti, Atikah Ari Auni, Ahmad Ramadhan Boro, Fabian Dominggus Eka Budi Prasetiyo, Budi Bunardi, Gunawan Choirunnisa, Rizkiyanti Clarissa Amanda Josaputri, Clarissa Amanda Devi, Feroza Rosalina Devi, Feroza Rosalina Dian Tri Wiyanti Dwijanto Dwijanto, Dwijanto Dwika Ananda Agustina Pertiwi Emi Pujiastuti Fauzan, Riantama Sulthana Fitriana, Erma Nurul Florentina Yuni Arini, Florentina Yuni Hakim, M. Faris Al Hani'ah, Ulfatun Hariyanto, Abdul Hernowo, Mutiara Heryadi, Muhammad Heri Imam Sonny, Imam Indah Urwatin Wusqo Isa Akhlis Juliater Simamarta Jumanto Jumanto, Jumanto Jumanto Unjung Khoirunnisa, Oktaria Gina Korzhakin, Dian Alya Krida Singgih Kuncoro Kuncoro, Rizki Danang Kartiko Kurniawati, Putri Aida Nur Lestari, Dewi Indah Listiana, Eka Malisan, Johny Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Much Aziz Muslim Muhammad Kharis Mulyono Mulyono Muzayanah, Rini Nofrisel, Nofrisel Nugroho, Prisma Bayu Perbawawati, Anna Adi Perbawawati, Anna Adi Pipit Riski Setyorini Pradana, Dany Pradhana, Fajar Eska Purnamasari, Ratnaningtyas Widyani Raharjo, Ahmad Solikhin Gayuh Ratri Rahayu Riza Arifudin Rofik Rofik, Rofik Rupiah, Siti S.Pd. M Kes I Ketut Sudiana . Sampurno, Global Ilham Sampurno, Global Ilham Sari, Firar Anitya Sekartaji, Novanka Agnes Sekarwati Ariadi, Tiara Subarkah, Agus Sugiman Sugiman Sugiman Sukestiyarno Sukestiyarno Sukmadewanti, Irahayu Sukmadewanti, Irahayu Sulis Eli Triliani, Sulis Eli Sungkowo, Nanang Supriyono Supriyono Susanti, Eka Lia Sutarti, Sri Sutarti, Sri Umi Latifah Vedayoko, Lucky Gagah Vedayoko, Lucky Gagah Whisnu Ulinnuha Setiabudi, Whisnu Ulinnuha Wijaya, Henry Putra Imam Zaaidatunni'mah, Untsa Zaenal Abidin