Article Per Year (5 Year)
p-Index From 2020 - 2025
5.294
P-Index
This Author published in this journals
All Journal Jurnal Edukasi Universitas Jember Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Bisma: Jurnal Bisnis dan Manajemen MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Soft Computing Exploration Jurnal Abdimas Ekonomi dan Bisnis Transekonomika : Akuntansi, Bisnis dan Keuangan Reviu Akuntansi, Manajemen, dan Bisnis PENA ABDIMAS : Jurnal Pengabdian Masyarakat Indonesian Journal of Informatic Research and Software Engineering Jurnal Pemberdayaan Ekonomi eProceedings of Management Journal of Student Research Exploration Journal of Information System Exploration and Research Recursive Journal of Informatics IJEB JPM Jurnal Ekonomi, Manajemen, Akuntansi
Budi Prasetiyo, Budi
Unknown Affiliation
Humanities 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 Mathematics Nursing Public Health Social Sciences Other

Published : 48 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 48 Documents
Search

 1   2   3   4   5 
Pelatihan Perencanaan Keuangan pada Bisnis Baru di Rumah Hasanah Margahayu, Bandung Hasanah, Yulia Nur; Rizaldi, Fikri Mohamad; Prasetiyo, Budi
Jurnal Abdimas Ekonomi dan Bisnis Vol. 4 No. 2 (2024): Jurnal Abdimas Ekonomi dan Bisnis
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abdiekbis.v4i2.4366

Abstract

Financial Planning Training for New Businesses plays a crucial role in addressing the challenges faced by new business owners, particularly in managing finances. The main problem often encountered is the lack of knowledge and experience in business financial management. This training aims to provide a deep understanding of the process of building a new business while designing efficient financial planning. The implementation method includes the dissemination of basic financial planning concepts, cash flow preparation practices, and simple financial statement simulations. As a result, participants can manage their resources more intelligently, increase operational efficiency, and maximize business investment returns. This training emphasizes resource limitations, such as limited budgets and a lack of experience in financial planning. Moreover, the training focuses on the importance of financial management in business operations through simple transaction recording. In conclusion, this training helps new business owners run their businesses with effective financial management, establishing a solid foundation for achieving success in a competitive business world.
Sentiment Analysist of the TPKS Law on Twitter Using InSet Lexicon with Multinomial Naïve Bayes and Support Vector Machine Based on Soft Voting Aisy, Salsabila Rahadatul; Prasetiyo, Budi
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/rji.v1i2.68324

Abstract

Abstract. The Indonesian Sexual Violence Law (TPKS Law) is a law that regulates forms of sexual violence. The TPKS Law reaped pros and cons in the drafting process and was officially ratified on April 12th, 2022. However, after being ratified, pros and cons can still be found and supervision is needed over the implementation of the law. Purpose: This study was conducted to identify the application and accuracy of soft voting on multinomial naïve Bayes and support vector machine algorithm, also to find out public opinion on the TPKS Law as a support tool in evaluating the law. Methods/Study design/approach: The method used is InSet lexicon for labeling with the soft voting classification method on the multinomial naive Bayes and support vector machine algorithm. Result/Findings: The accuracy obtained by applying 10 k-fold cross validation in soft voting is 84.31%, which uses a weight of 1:3 for multinomial naive Bayes and support vector machines. Soft voting obtains better accuracy than its standalone predictor, and also works well for sentiment analysis of the TPKS Law. Novelty/Originality/Value: This study using two combined lexicons (Colloquial Indonesian lexicon and the InaNLP formalization dictionary) in normalization process and using InSet lexicon as automatic labeling for sentiment analysis on TPKS Law.
Optimized Support Vector Machine with Particle Swarm Optimization to Improve the Accuracy Amazon Sentiment Analysis Classification Ningsih, Maylinna Rahayu; Unjung, Jumanto; Pertiwi, Dwika Ananda Agustina; Prasetiyo, Budi; Muslim, Much Aziz
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 1, February 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i1.1888

Abstract

Text mining is a valuable technique that empowers users to gain a deeper understanding of existing textual data, ultimately allowing them to make more informed decisions. One important application of text mining is in the field of sentiment analysis, which has gained significant traction among companies aiming to understand how customers perceive their products and services. In response to this growing need, various research efforts have been made to improve the accuracy of sentiment analysis classification models. The purpose of this article is to discuss a specific approach using the Support Vector Machine (SVM) algorithm, which is often used in machine learning for text classification tasks and then combined with the application of Particle Swarm Optimization (PSO), which optimizes the SVM model parameters to achieve the best classification results. This dynamic combination not only improves accuracy but also enhances the model's ability to efficiently handle large amounts of text data to achieve better results. The research findings highlight the effectiveness of this approach. The application of the SVM algorithm with PSO resulted in an outstanding accuracy performance of 94.92%. The substantial increase in accuracy compared to previous studies shows the promising potential of this methodology. This proves that the SVM algorithm model approach with Particle Swarm Optimization provides good performance.
Restricted boltzmann machine and softmax regression for acute respiratory infections disease identification Pranata, Afrizal Rizqi; Alamsyah, Alamsyah; Prasetiyo, Budi; Vember, Hilda
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.90

Abstract

Restricted boltzmann machines (RBM) have attracted much attention lately after being proposed as building blocks of deep learning blocks. RBM is an algorithm that belongs to the artificial neural network (ANN) algorithm. Deep learning models can be used in the health field to identify diseases using medical data records. Acute Respiratory Infection (ARI) is a disease that infects the respiratory tract. A patient infected by ARI diseases is high. To identify ARI can use the symptoms that the patient had experienced. Based on this background, this study aims to help identify ARI disease using its symptoms. The method used for identification is the deep learning model, which was built using the RBM and softmax regression. Three steps were used in this research, which are training, testing, and implementation. The trained deep learning model will be implemented to identify ARI disease. This research will use ARI data from Puskemas Warungasem, Indonesia. From the research result, the deep learning model can get an accuracy of 96%. The deep learning configuration used in this research has 4 RBM layers, 1 Softmax layer as the output layer, and a learning rate value of 0.01 and 1000 iterations. This research can be used as a reference so that the next researcher can add other algorithms to Deep learning to improve accuracy.
News text classification using Long-Term Short Memory (LSTM) algorithm Triyadi, Indra; Prasetiyo, Budi; Nikmah, Tiara Lailatul
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.136

Abstract

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.
Optimizing the implementation of the BFS and DFS algorithms using the web crawler method on the kumparan site Mustaqim, Amirul; Dinova, Dony Benaya; Fadhilah, Muhammad Syafiq; Seivany, Ravenia; Prasetiyo, Budi; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 2 (2024): June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i2.309

Abstract

Efficient access to timely information is critical in today's digital era. Web crawlers, automated programs that navigate the Internet, play an important role in collecting data from websites such as Kumparan, a leading news site in Indonesia. This research shows the effectiveness of the Breadth-First Search (BFS) and Depth-First Search (DFS) algorithms in indexing Kumparan content. The results of the research show that BFS consistently indexes more files comprehensively but with longer execution times compared to DFS, which provides faster initial results but with fewer files. For example, at depth 4 BFS indexed 949 files in 886.94 seconds, while DFS indexed 470 files in 233.02 seconds. These findings highlight the balance between precision and speed when selecting a crawling algorithm tailored to the needs of a particular website. This research provides insights into optimizing web crawler technology for complex websites such as Coil and suggests avenues for further research to improve permission efficiency and adaptability across a variety of crawling scenarios.
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.
Classification of Student Grading Using Naïve Bayes Method with Under-sampling Approach to Handle Imbalance Aziz, Alif Abdul; Prasetiyo, Budi
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.537

Abstract

This study explores the application of the Naive Bayes classification method to predict student grades based on important attributes such as timeliness of assignment submission, attendance rate, and quality of work. This research uses a dataset that includes three attributes, namely timeliness of submission, attendance level in learning, and evaluation of the quality of assignments collected by students. The pre-processing is performed to clean the data, followed by an under-sampling stage to balance the class distribution. Then, the classification model is evaluated and tested using specific data samples to measure prediction accuracy. The results showed a significant improvement in model accuracy after applying under-sampling, highlighting the importance of handling data imbalance in predictive analysis. The implications of these findings are not only relevant in the context of higher education, but also offer opportunities for further development in data-driven decision-making in various fields.
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.
Analisis Sentimen pada Ulasan Aplikasi iPusnas di Google Play Store Menggunakan Naive Bayes Classifier : Sentiment Analysis On Ipusnas Application Reviews In Google Play Store Using Naive Bayes Classifier Naufal Zuhdi, Hamzah; Prasetiyo, Budi
Indonesian Journal of Informatic Research and Software Engineering (IJIRSE) Vol. 5 No. 1 (2025): Indonesian Journal of Informatic Research and Software Engineering (IJIRSE)
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijirse.v5i1.1846

Abstract

Perkembangan teknologi informasi telah membawa perubahan signifikan dalam akses dan literasi informasi, terutama melalui perpustakaan digital. Di Indonesia, iPusnas, aplikasi perpustakaan digital yang dikelola oleh Perpustakaan Nasional Republik Indonesia, menjadi salah satu platform populer yang menawarkan akses gratis ke berbagai buku digital. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna iPusnas di Google Play Store menggunakan algoritma Naive Bayes. Penelitian ini menggunakan 700 ulasan pengguna yang dikumpulkan, diproses, dan diberi label sentimen berdasarkan rating ulasan. Setelah melalui tahap pre-processing, termasuk case folding, tokenization, dan stemming, data dibagi menjadi set data latih dan uji dengan rasio 80:20. Hasil analisis menunjukkan bahwa 75,1% ulasan bersentimen positif, mengindikasikan tingkat kepuasan pengguna yang tinggi. Algoritma Naive Bayes menunjukkan akurasi 58%, dengan nilai precision 69%, recall 81%, dan f1-score 75%. Temuan ini konsisten dengan penelitian sebelumnya yang juga menunjukkan kualitas layanan dan kepuasan pengguna yang tinggi terhadap iPusnas. Hasil penelitian ini dapat digunakan oleh pengembang iPusnas untuk meningkatkan kualitas layanan berdasarkan umpan balik pengguna. Penggunaan analisis sentimen yang lebih canggih dan integrasi teknologi lainnya dapat lebih meningkatkan evaluasi sentimen dan kualitas layanan di masa depan
Co-Authors Afrizal Rizqi Pranata, Afrizal Rizqi Aisy, Salsabila Rahadatul Alamsyah - Amidi Amidi, Amidi Aziz, Alif Abdul Azura, Amberia Narfi Bayuaji, Hibatullah Zamzam Tegar Beta Noranita Biyantoro, Arell Saverro D.W, Made Bagus Paramartha Didimus Tanah Boleng Dinova, Dony Benaya Endang Sugiharti, Endang Fachrezi, Farhan Rifa Fadhilah, Muhammad Syafiq Fadlil, Affan Fata, Muhamad Nasrul Fata, Muhamad Nasrul Ferninda, Varin Fikri Mohamad Rizaldi Fitriana, Jevita Dwi Hakim, Ade Anggian Hakim, M. Faris Al Hakim, Roshan Aland Jumanto Unjung Korina, Nanda Putri Leo nardo Lestari , Apri Dwi Lestari, Fitri Duwi Lintang, Irendra M. Faris Al Hakim Makrina Tindangen Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Munahefi, Detalia Noriza Mustaqim, Amirul Naufal Zuhdi, Hamzah Ndruru, Toni Krisman Nikmah, Tiara Lailatul Ningsih, Maylinna Rahayu Nisa, Intan Khairun Partini, Emilia Pertiwi, Dwika Ananda Agustina Pradana, Fadli Dony Rachmawati, Eka Yuni Rachmawati, Eka Yuni Rahmat Gernowo Ristiawati, Monika Riza Arifudin Rofik Rofik, Rofik S.Pd. M Kes I Ketut Sudiana . Sadid, Moh Naufal Salsabila, Malika Putri Saparina, Iska Ayu Saputra, Angga Riski Dwi Satriawan, Grace Yudha Satrio Ardiansyah, Adi Seivany, Ravenia Septian, M Rivaldi Ali Sulastri, Ai Syaharani, Reisya Triyadi, Indra Vember, Hilda Yulia Nur Hasanah