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All Journal Scientific Journal of Informatics Journal of Economic Education Jurnal Penelitian Pendidikan IPA (JPPIPA) J-SAKTI (Jurnal Sains Komputer dan Informatika) Nusantara Science and Technology Proceedings J-SAKTI (Jurnal Sains Komputer dan Informatika) Recursive Journal of Informatics Jurnal Penelitian Pendidikan International Journal of Active Learning Indonesian Journal of Advocacy and Legal Services Journal of Advances in Information Systems and Technology Jurnal Abdi Negeri Recursive Journal of Informatics Inventa: Journal of Science, Technology, and Innovation
Anggyi Trisnawan Putra
Universitas Negeri Semarang
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics

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Course Scheduling Optimization Using Genetic Algorithms with Fuzzy Tsukamoto-Based Fitness Adjustment Alexander, Taripar Matius; Putra, Anggyi Trisnawan
Jurnal Penelitian Pendidikan Vol. 42 No. 2 (2025): October 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpp.v42i2.31594

Abstract

This study develops a course scheduling optimization system that integrates a genetic algorithm with the Tsukamoto fuzzy inference system to dynamically adjust fitness values. The objective of this research is to overcome the limitations of conventional genetic algorithms that rely on static fitness functions, which only evaluate schedule quality based on the number of constraint violations. The Tsukamoto fuzzy inference system is designed with three input variables: constraint violation level, lecturer workload distribution, and classroom utilization efficiency. It employs 27 fuzzy rules based on triangular and trapezoidal membership functions to produce a fitness adjustment factor.The research methodology consists of four stages: requirements analysis and problem modeling, Tsukamoto fuzzy inference system design, hybrid genetic algorithm implementation, and performance testing and evaluation. Experiments were conducted using a synthetic dataset comprising 50 courses, 20 lecturers, 15 classrooms, and 30 weekly time slots. The results show that the proposed hybrid genetic algorithm achieves 42% faster convergence with an average fitness value of 0.89 compared to 0.76 in the conventional algorithm. Constraint satisfaction improved from 82.4% to 94.7%, lecturer workload distribution became more balanced with the coefficient of variation decreasing from 0.34 to 0.19, and classroom utilization efficiency increased from 76.8% to 88.5%. Statistical tests indicate a significant difference (p-value < 0.001) with a substantial effect size (Cohen’s d = 1.23). This research contributes to the development of a hybrid approach that integrates the Tsukamoto fuzzy inference system into genetic algorithms, resulting in more optimal, adaptive, and efficient course schedules compared to conventional methods.
Analysis of STEM Knowledge of Pre-Service Science and Mathematics Teacher Widiyatmoko, Arif; Putra, Anggyi Trisnawan; Astuti, Budi; Rakainsa, Senda Kartika; Sutarto, Hery; Mustikaningtyas, Dewi; Darmawan, Melissa Salma
Jurnal Penelitian Pendidikan IPA Vol 10 No 3 (2024): March
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i3.5411

Abstract

This research is motivated by the importance of STEM knowledge for pre-service science and mathematics teachers in the 21st century. This research aims to analyze the STEM knowledge of pre-service science and mathematics teachers. The research method was carried out by distributing questionnaires with STEM knowledge indicators to the sample. The sample in this research was 86. They were 59 pre-service science teachers and 27 pre-service mathematics teachers, with different years of education, namely the 1st, 2nd, 3rd, and 4th year of Universitas Negeri Semarang. The results of this research show that the average understanding of pre-service science and mathematics teachers regarding to the STEM-based learning in the definition aspect is 4.11, objectives are 3.94, benefits are 3.94, aspects are 3.69, components are 3.71, characteristics is 3.75, and implementation is 3.86. This proves that pre-service science and mathematics teachers' understanding of the definition of STEM-based learning is very good, while pre-service science and mathematics teachers' understanding of the objectives, benefits, aspects, components, characteristics, and implementation of STEM-based learning is good.
Peningkatan Manajemen Ujian Online Bagi Guru di SMK Negeri 1 Karimunjawa Prasetiyo, Budi; Hakim, M. Faris Al; Purwinarko, Aji; Putra, Anggyi Trisnawan; Subhan, Subhan
Jurnal Abdi Negeri Vol 1 No 1 (2023): Januari 2023
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v1i1.3

Abstract

Program Pembelajaran dan Penilaian online di era pandemi COVID-19 saat ini telah berlangsung selama satu tahun di SMK Negeri 1 Karimunjawa. Penerapan e-learning di sekolah ini berdampak pada bertambahnya tuntutan peningkatan kompetensi guru dalam melakukan penilaian secara online. Bagi siswa, tentu akan berdampak terhadap cara mereka mengikuti ujian di sekolah. Siswa harus membiasakan diri untuk menggunakan fitur-fitur yang terdapat pada aplikasi e-learning. Berdasarkan hasil komunikasi dan observasi dengan Kepala SMK Negeri 1 Karimunjawa, dibutuhkan aplikasi yang efektif untuk digunakan dalam penyelenggaraan penilaian atau ujian online untuk siswa. Aplikasi Ujian online juga diharapkan dapat digunakan untuk simulasi Asesmen Kompetensi Minimal (AKM) pada tahun pelajaran 2021/2022. Oleh karena itu, Jurusan Ilmu Komputer, FMIPA, UNNES menawarkan solusi berupa penerapan aplikasi e-ujian yang merupakan produk penelitian yang telah memiliki hak cipta. Metode yang digunakan dalam kegiatan pengabdian ini terdiri dari 3 tahap yaitu Analisis Kebutuhan, Perancangan Aplikasi, Pengembangan Aplikasi, Pelaksanaan, dan Evaluasi. Hasil dari kegiatan pengabdian masyarakat yang telah dilaksanakan adalah pengurus sekolah dan guru memahami potensi dari manajemen ujian berbasis daring untuk pembelajaran di masa pandemi sebagai upaya untuk menjaga standar proses pembelajaran.
Implementasi E-Ujian Sebagai Sistem Penilaian Pembelajaran Daring di SMP Islam Roudlotus Saidiyyah Semarang Hakim, M. Faris Al; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Abidin, Zaenal; Putra, Anggyi Trisnawan
Jurnal Abdi Negeri Vol 2 No 1 (2024): Januari 2024
Publisher : Informa Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63350/jan.v2i1.18

Abstract

The presence of the COVID-19 outbreak has led to the implementation of online learning from the location of each home. SMP Islam Roudlotus Saidiyyah Semarang has transformed learning by utilizing various applications. However, for the purposes of final semester assessment or integrated assessment, an online exam application is needed that is easy to use and able to provide data on student learning outcomes accurately and quickly. The implementation method consists of preparation, training, and evaluation. The results of the training showed that the E-Ujian application as an application for online assessment has the potential to be applied at SMP Islam Roudlotus Saidiyyah Semarang. The utilization of the E-Ujian Application in learning activities in the partner environment is an effort to maintain the quality of learning.
Implementation of Bidirectional Long-Short Term Memory (Bi-LSTM) and Attention to Detect Political Fake News Using IndoBERT and GloVe Embedding Adham Satria Firmansyah; Anggyi Trisnawan Putra
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.v3i2.159

Abstract

Abstract. Indonesian political news is now increasingly spread through various media, especially social and online media. However, a lot of fake news are spread to bring down political opponents or attract public sympathy in order to find their own supporters. Of course, this news need to be watched out for and preventive measures must be taken so as not to cause misunderstanding in the wider community. Purpose: This study was conducted to detect the political news whether it’s classified as hoax or fact by its narration. Also, understanding how to build the news detector using corresponding architecture and word embeddings. Methods/Study design/approach: The model architecture of Bi-LSTM and attention mechanism is used to reach the goals from this study’s purposes. Many studies have been conducted to detect hoaxes but have not yet paid attention to the context of sentences and the contribution of words in a news text so that this architecture is made to overcome this problem. It uses IndoBERT to optimize the model for Indonesian language and also GloVe to obtain the word weights from pre-trained embedding. Then, the tokenization process is performed with IndoBERT and keras to generate token id and attention mask. After receiving the token id and attention mask as input, the data training process is performed for three architectural scenarios with each configuration of 20 epochs, batch size of 32, and the learning rate is 0.00001. Result/Findings: The results of this study are defined by a confusion matrix which contains accuracy, recall, precision, and F1-score as the evaluation. The combination of Bi-LSTM + Attention + IndoBERT + GloVe obtains the best result of 97,71% of accuracy, 96,33% of precision, 97,93% of recall, and 97,72% of F1-score. Novelty/Originality/Value: This study combines two word embeddings in order to make sure the weight of words is completely defined and optimized into the Bi-LSTM and attention mechanism architecture.
Optimizing Heart Disease Classification Using the Support Vector Machine Algorithm with Hybrid Particle Swarm and Grey Wolf Optimization Luthfi Ilham Agus Pratama; Anggyi Trisnawan Putra
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.v3i1.737

Abstract

Abstract. Heart disease, also known as cardiovascular disease, is a condition that affects the heart and blood vessels, leading to complications such as coronary artery disease, heart failure, arrhythmias, and heart valve disorders. According to the World Health Organization (WHO), approximately 17.9 million people die from heart disease each year. Early detection plays a crucial role in reducing the number of cases and improving patient outcomes.Purpose: In the era of rapid technological advancements, machine learning has been widely utilized for early diagnosis of heart disease. This study aims to enhance classification accuracy by applying a hybrid PSOGWO (Particle Swarm and Grey Wolf Optimization) method for feature selection and a standard scaler for data balancing in SVM classification.Methods/Study design/approach: The research begins with obtaining a heart disease dataset, which undergoes preprocessing steps, including feature selection using hybrid PSOGWO and data normalization with a standard scaler. The dataset is then divided into training and testing sets, where the training data is classified using SVM. Performance evaluation is conducted using a confusion matrix to measure accuracy improvements.             Result/Findings: The proposed method successfully selects 10 significant features out of 13 in the dataset. By integrating hybrid PSOGWO with SVM, the classification accuracy improves to 93.66%, representing a 2.44% increase from the original 91.22% obtained using SVM without feature selection.              Novelty/Originality/Value: This research contributes to the development of more effective heart disease prediction models by optimizing feature selection and classification, leading to improved diagnostic accuracy and potential clinical applications.
Optimizing Random Forest for Predicting Thoracic Surgery Success in Lung Cancer Using Recursive Feature Elimination and GridSearchCV Deonisius Germandy Cahaya Putra; Anggyi Trisnawan Putra
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/cax5k765

Abstract

Abstract. Lung cancer is one of the deadliest forms of cancer, claiming numerous lives annually. Thoracic surgery is a strategy to manage lung cancer patients; however, it poses high risks, including potential nerve damage and fatal complications leading to mortality. Predicting the success rate of thoracic surgery for lung cancer patients can be accomplished using data mining techniques based on classification principles. Medical data mining involves employing mathematical, statistical, and computational methods. In this study, the prediction of thoracic surgery success employs the random forest algorithm with recursive feature elimination for feature selection. The feature selection process yields the top 8 features. The 8 best features include 'DGN', 'PRE4', 'PRE5', 'PRE6', 'PRE10', 'PRE14', 'PRE30', and 'AGE'. Hyperparameter using GridSearchCV is then applied to enhance classification accuracy. The results of this method implementation demonstrate a predictive accuracy of 91.41%. Purpose: The study aims to develop and evaluate a Random Forest model with a Recursive Feature Elimination feature selection and applies hyperparameter GridSearchCV for predicting thoracic surgery success rate. Methods: This study uses the thoracic surgery dataset and applies various data preprocessing techniques. The dataset is then used for classification using the Random Forest algorithm and applies the Recursive Feature Elimination feature selection to obtain the best features. GridSearchCV is used in this study for hyperparameter. Result: The accuracy using the Random Forest algorithm and Recursive Feature Elimination feature selection with hyperparameters tuning GridSearchCV resulted in an accuracy of 91,41%. The accuracy was obtained from the following parameters values: bootstrap set to false, criterion set to gini, n_estimator equal to 100, max_depth set to none, min_samples_split equal to 4, min_samples_leaf equal to 1, max_features set to auto, n_jobs set to -1, and verbose set to 2 with 10-fold cross validation. Novelty: This study comparison and analysis of various dataset preprocessing methods and different model configurations are conducted to find the best model for predicting the success rate of thoracic surgery. The study also employs feature selection to choose the best feature to be used in classification process, as well as hyperparameter tuning to achieve optimal accuracy and discover the optimal values for these hyperparameters.
Optimization of the Convolutional Neural Network Method Using Fine-Tuning for Image Classification of Eye Disease Vivi Wulandari; Anggyi Trisnawan Putra
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: 10.15294/0xga4r13

Abstract

Abstract. The eye is the most important organ of the human body which functions as the sense of sight. Most people wish they had healthy eyes so they could see clearly about life around them. However, some people experience eye health problems. There are many types of eye diseases ranging from mild to severe. With advances in technology, artificial intelligence can be used to classify eye diseases accurately, one of which is deep learning. Therefore, this study uses the Convolutional Neural Network (CNN) algorithm to classify eye diseases using the VGG16 architecture as a base model and will be combined using a fine-tuning model as an optimization to improve accuracy. Purpose:To find out the accuracy results obtained in the fine-tuning optimization model on Convolutional Neural Network (CNN) method in classifying images in eye disease. Methods/Study design/approach: Combining the Convolutional Neural Network (CNN) method with fine-tuning optimization models for image classification in eye disease. The two methods will be compared to determine the best result. Result/Findings: The accuracy results obtained from testing the Convolutional Neural Network method with the VGG16 architecture were 82.63% while the accuracy results from testing the fine-tuning model were 94.13%. Novelty/Originality/Value: The test results on the fine-tuning model have better accuracy than the testing of the Convolutional Neural Network method. This can be seen in the fine-tuning model which has an increase in accuracy of 11.5%.
Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning Resta Adityatama; Anggyi Trisnawan Putra
Recursive Journal of Informatics Vol. 1 No. 2 (2023): September 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/xnw7v590

Abstract

Abstract. The development of information technology in facial recognition is influenced by a faster and more accurate authentication system. This allows the computer system to identify a person's face. Purpose: Similar to fingerprints and the retina of the human eye, each person's face has a different shape and contour. Since it is known that the human face provides a lot of information, as well as topics that attract attention make it studied intensively. Methods/Study design/approach: Several studies examining information from human faces are facial recognition. One of the approaches used to recognize facial imagery is through the use of a Convolutional Neural Network (CNN). CNN is a method in the field of Deep Learning that can be used to recognize and classify objects in digital images. In this study, the method used to implement facial image classification is the Xception architecture CNN algorithm with a transfer learning approach. Result/Findings: The dataset used in this study was obtained from Kaggle, namely the Face Shape Dataset which contains 5000 data. After testing, an accuracy rate of 96.2% was obtained in the training process and 81.125% in the validation process. This study also uses new data to test the model that has been made, and the results show an accuracy rate of 85.1% in classifying facial imagery. Novelty/Originality/Value: Therefore, it can be said that the model created in this study has the ability to classify images of facial shapes Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning.
Deep Learning Transparan untuk Analisis Gagal Bayar Kredit: Kerangka Kerja Jaringan Syaraf Tiruan yang Dapat Dijelaskan dengan Menggabungkan SHAP dan LIME Norma Zuhrotul Hayati; Anggyi Trisnawan Putra
Journal of Science, Technology, and Innovation Vol 1 No 1 (2025): August: Inventa: Journal of Science, Technology, and Innovation
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/w6ne3h43

Abstract

This study introduces a transparent deep learning framework for credit default analysis that integrates Artificial Neural Networks (ANN) with dual interpretability mechanisms SHapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). Using the Default of Credit Card Clients dataset from the UCI Machine Learning Repository, the research develops an optimized model that combines predictive precision with explanatory transparency. The ANN model achieved an accuracy of 81.8% and an AUC of 0.77, outperforming conventional classifiers such as XGBoost and LightGBM while maintaining interpretive clarity. The hybrid SHAP–LIME configuration provides both global and local explanations, identifying repayment status (PAY_0), billing amount (BILL_AMT1), and credit limit (LIMIT_BAL) as the most influential predictors. Empirical findings confirm that interpretability enhances trust, auditability, and regulatory alignment without sacrificing statistical performance. The framework offers a methodological contribution to transparent financial modeling, bridging the gap between algorithmic precision and human interpretive accountability. It advances the paradigm of responsible credit risk management by transforming black-box neural architectures into auditable, evidence-based decision tools for financial institutions
Co-Authors Adham Satria Firmansyah Adityatama, Resta Aji Purwinarko, Aji Alamsyah - Alexander, Taripar Matius Amaliyah, Tazkiyatun Aeni Arif Widiyatmoko, Arif Avi Budi Setiawan Bhekti Kumorowati, Bhekti Budi Astuti Budi Prasetiyo, Budi Cahya Wulandari Darmawan, Melissa Salma Deonisius Germandy Cahaya Putra Dewi Mustikaningtyas Emha, Zidney Ilma Fazaada Endang Sugiharti, Endang Hakim, M. Faris Al Hassan, Muhamad Sayuti Hery Sutarto Husodo, Martanto Setyo Ilham Insani, Muhammad Ilham Insani, Muhammad Luthfi Ilham Agus Pratama Martanto Setyo Husodo Much Aziz Muslim Muh. Sholeh Muh. Sholeh Munawar, Akhmad Norma Zuhrotul Hayati Priliani, Erlin Mega Priliani, Erlin Mega Putra, Deonisius Germandy Cahaya Rakainsa, Senda Kartika Ramadhan, Andika Enggal Resta Adityatama Riza Arifudin S.Pd. M Kes I Ketut Sudiana . Septian, Dinar Diaz Subhan Subhan Sugianto Sugianto Sutarti, Sri Sutarti, Sri uminar Vivi Wulandari Vivi Wulandari Wicaksana, Tania Widyastuti, Ariyani Yanuarto, Alfath Zaenal Abidin