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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) JE (Journal of Empowerment) Journal of Engineering and Scientific Research (JESR) Indonesian Journal of Data and Science Jurnal Scientia Journal of Computer Networks, Architecture and High Performance Computing Jurnal Krisnadana Jurnal Krisnadana KOMET
Radhitya, Made Leo
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences

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Improving Investment Understanding For Millennial Generation Through Digital Teaching Media Innovation Bagus Kusuma Wijaya; Made Leo Radhitya; Ni Putu Widantari Suandana
Jurnal Scientia Vol. 13 No. 03 (2024): Education and Sosial science, June - August 2024
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/scientia.v13i03.2520

Abstract

This research explores the low financial literacy among millennials despite their dominance in the use of information technology. Various surveys show that most millennials cannot answer basic questions about finance and investment correctly, indicating an urgent need to improve financial and investment literacy. This lack of understanding of investment instruments and financial strategies negatively impacts their financial stability. Initial questionnaire results from 30 students showed that 70% felt unfamiliar with investments, while 30% felt familiar. In addition, students were most familiar with stocks (40%), followed by mutual funds (30%), cryptocurrencies (15%), and bonds (10%). The study also found that investment literacy and education activities can increase millennials' understanding and motivation to invest. Technology plays an important role in financial education, with well-designed digital learning media, such as e-books and other digital tools, significantly improving learning outcomes. Recommendations from this research include the provision of interactive and fun teaching materials that are easily accessible through digital media, covering the introduction of basic investment concepts, long-term returns, practical guidance on starting an investment, and risk management. This research emphasizes the importance of greater financial education and literacy efforts to empower millennials to manage their finances more effectively and wisely.
Electroencephalogram-Based Emotion Classification Using Machine Learning and Deep Learning Techniques Mastrika Giri, Gst Ayu Vida; Radhitya, Made Leo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.96665

Abstract

 Electroencephalogram (EEG) records brain activity as electrical currents to discern emotions. As interest in human-computer emotional connections rises, reliable and implementable emotion recognition algorithms are essential. This study classifies EEG waves using machine and deep learning. A four-channel Muse EEG headband recorded neutral, negative, and positive emotions for the publicly available Feeling Emotions EEG dataset. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized for deep learning, while SVM, K-NN, and MLP were used for machine learning. The models were assessed for accuracy, precision, recall, and F1-Score. SVM, K-NN, and MLP have accuracy scores of 0.98, 0.95, and 0.97. Deep learning methods CNN, LSTM, and GRU had 0.98, 0.82, and 0.97 accuracy. SVM and CNN surpassed other approaches in accuracy, precision, recall, and F1-Score. The research shows that machine learning and deep learning can classify EEG signals to identify emotions. High accuracy results, especially from SVM and CNN, suggest these models could be used in emotion-aware human-computer interaction systems. This study adds to EEG-based emotion classification research by revealing model selection and parameter tweaking strategies for better categorization.
Analisis Sentimen Pada Pembelajaran Daring Di Indonesia Melalui Twitter Menggunakan Naïve Bayes Classifier Sarasvananda, Ida Bagus Gede; Selivan, Diana; Radhitya, Made Leo; Putra, I Nyoman Tri Anindia
SINTECH (Science and Information Technology) Journal Vol. 5 No. 2 (2022): SINTECH Journal Edition Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v5i2.1241

Abstract

Education is one of the areas most affected by the covid-19 pandemic. Education during the pandemic must continue. To reduce the spread of covid-19 and learning activities can run as usual, the government, in this case the Ministry of Education and Culture, has implemented a distance education system in Indonesia. In addition, the response from the community is very important for an evaluation of the applied online learning. With the implementation of the policy regarding online learning in Indonesia, it is necessary to conduct a sentiment analysis to find out how the responses, opinions, or comments from the public and online learning actors related to online learning are currently being implemented. So the author conducted a research entitled Sentiment Analysis on Online Learning in Indonesia Through Twitter Using the Naïve Bayes Classifier Method to measure student responses regarding online learning during the covid -19 pandemic in Indonesia. The results of the accuracy of this study is 99.8% and the classification error is 0.12%. Of the total data entered, 83 tweets or 20% were included in the positive class, the negative class was 317 tweets or 80%.
Product Layout Analysis Based on Consumer Purchasing Patterns Using Apriori Algorithm Radhitya, Made Leo; Widiantari, Ni Komang Mira; Asana, Made Dwi Putra; Wijaya, Bagus Kusuma; Sudipa, I Gede Iwan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4400

Abstract

In every self-service store, it is certain to have a sales transaction data, where the data will continue to grow every day. But in self-service stores the data is only a record of sales at the store. Whereas transaction data can be used as information on how consumer purchasing patterns when shopping at the store, but not all supermarkets know this. So this research aims to find information on these purchasing patterns, where to do this research using the apriori algorithm which is part of the association technique which is also part of data mining, where in its application it will calculate the support value, confindence value and will be tested using the lift ratio. And after the calculation is carried out, optimization will be carried out using the high utility itemset mining variable which will calculate the highest profit value on the product, so that based on the calculation, the final result is obtained with a support value of 85%, a confidence value of 86%, a lift ratio test of 1.01 and the high utility gets the highest result of Rp. 567,000.
Musical Instrument Classification using Audio Features and Convolutional Neural Network Giri, Gst. Ayu Vida Mastrika; Radhitya, Made Leo
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8058

Abstract

This research classifies acoustic instruments using Convolutional Neural Network (CNN). We utilize a dataset from Kaggle containing audio recordings of piano, violin, drums, and guitar. The training set consists of 700 guitar, percussion, violin, and 528 piano samples. The test set contains 80 samples of each instrument. Features such as Mel spectrograms, MFCCs, and other spectral and non-spectral characteristics are extracted using the Librosa package. Three feature sets"”spectral-only, non-spectral-only, and a combined set"”are employed to evaluate the efficacy of CNN models. Various CNN configurations are tested by adjusting the number of convolutional filters, learning rates, and epochs. The combined feature set achieves the highest performance, with a validation accuracy of 71.8% and a training accuracy of 76.9%. In comparison, non-spectral features achieve a validation accuracy of 68.4%, and spectral-only features achieve 69.3%. These findings highlight the benefits of using a comprehensive feature set for accurate classification.
Comparison of the Packet Wavelet Transform Method for Medical Image Compression Atmaja, I Made Ari Dwi Suta; Triadi, Wilfridus Bambang; Astawa, I Nyoman Gede Arya; Radhitya, Made Leo
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1732

Abstract

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.
Comparison of SVM & Naïve Bayes Methods in Sentiment Analysis of Electric Vehicle Subsidy Policy Based on X Data Wiguna, I Wayan Darma; Waas, Devi Valentino; Wiguna, I Komang Arya Ganda; Radhitya, Made Leo
Journal of Engineering and Scientific Research Vol. 6 No. 1 (2024)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v6i1.158

Abstract

The policy of subsidizing electric vehicles has become a widely discussed issue on social media platform X. The provision of electric vehicle subsidies by the Indonesian government aims to stimulate higher adoption of electric vehicles, with the overarching goal of mitigating air pollution. However, the presence of electric vehicle subsidies continues to elicit both support and opposition among the public. On social media platform X, there is a wealth of data suitable for text mining, particularly concerning the current hot topic of electric vehicle subsidies. This research aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes methods in conducting sentiment analysis on discussions related to the electric vehicle subsidy policy on social media platform X. The testing technique involves using 20% of the total dataset, comprising 5553 data points, and employing 10-fold cross-validation. The results from the 20% test data indicate that the Support Vector Machine (SVM) method's confusion matrix performance is superior, with the highest values achieved using the RBF kernel: accuracy 83.02%, precision 84.61%, and recall 83.02%. In the performance evaluation testing with 10-fold cross-validation, the Support Vector Machine (SVM) method outperforms, especially with the RBF kernel, yielding an average accuracy of 82.88% over 10 iterations.
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Pelatihan dan Pendampingan Implementasi Aplikasi Iuran Digital dalam Mendukung Penerapan E-Governance pada Tingkat Dusun Sudipa, I Gede Iwan; Radhitya, Made Leo; Wijaya, Bagus Kusuma
JE (Journal of Empowerment) Vol 5, No 2 (2024): DESEMBER
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/je.v5i2.4649

Abstract

AbstrakPengelolaan iuran secara manual di Dusun Tegal Kori Kaja, Denpasar Utara, menyebabkan inefisiensi dan ketidakpercayaan warga. Tujuan pengabdian ini adalah meningkatkan transparansi, akuntabilitas, dan efisiensi pengelolaan dana melalui penerapan aplikasi iuran digital berbasis web dan mobile. Metode yang digunakan meliputi sosialisasi, pelatihan, dan pendampingan pada kelompok pecalang (10 orang) dan warga (40 orang). Evaluasi dilakukan dengan pre-test dan post-test untuk mengukur peningkatan pemahaman. Hasil menunjukkan peningkatan pemahaman pecalang sebesar 80% dan warga sebesar 85%. Aplikasi iuran digital memudahkan proses pencatatan, pelaporan, serta pembayaran secara digital, yang disambut dengan kepuasan pengguna mencapai rata-rata 79,22% untuk warga dan 77,56% untuk pecalang. Implementasi ini diharapkan dapat memperkuat e-governance di tingkat dusun dan direplikasi pada wilayah dusun lain pada Desa Ubung Kaja, Denpasar Utara. Abstract Manual management of dues in Tegal Kori Kaja Hamlet, North Denpasar, causes inefficiency and distrust of residents. The purpose of this service is to increase transparency, accountability, and efficiency of fund management through the application of web-based and mobile digital dues applications. The methods used include socialization, training, and mentoring to pecalang groups (10 people) and residents (40 people). Evaluation was conducted with pre-test and post-test to measure the improvement of understanding. The results showed an increase in understanding of pecalang by 80% and residents by 85%. The digital dues application facilitates the process of recording, reporting, and payment digitally, which is welcomed by user satisfaction reaching an average of 79.22% for residents and 77.56% for pecalang.This implementation is expected to strengthen e-governance at the hamlet level and be replicated in other hamlet areas in Ubung Kaja Village, North Denpasar.
Performance Comparison of MobileNet and EfficientNet Architectures in Automatic Classification of Bacterial Colonies Wahyudi, I Putu Alfin Teguh; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Radhitya, Made Leo; Asana, I Made Dwi Putra
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.218

Abstract

Bacterial colony classification is crucial in microbiology but remains labor-intensive and time-consuming when performed manually. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automated classification, improving accuracy and efficiency. This study compares MobileNetV2 and EfficientNet-B0 for bacterial colony classification, evaluating the impact of data augmentation on model performance. Using the Neurosys AGAR dataset, preprocessing techniques such as histogram equalization, gamma correction, and Gaussian blur were applied, while data augmentation (rotation, noise addition, luminosity adjustments) improved model generalization. The dataset was split (80% training, 20% testing), and models were trained with learning rates (0.0001, 0.001) and epochs (100, 150, 200). Results show EfficientNet-B0 outperforms MobileNetV2, achieving higher validation accuracy and stability, with optimal performance at 150–200 epochs and a lower learning rate (0.0001). Data augmentation significantly improved accuracy and reduced overfitting. While MobileNetV2 remains a lightweight alternative, its performance is heavily reliant on augmentation. These findings highlight EfficientNet-B0 as the superior model, supporting the automation of microbiological diagnostics. Future research should explore hybrid CNN architectures, Vision Transformers (ViTs), and real-time implementation for improved classification efficiency.
Co-Authors Admojo, Fadhila Tangguh Agus Harjoko Asana, I Made Dwi Putra Asana, Made Dwi Putra Atmaja, I Made Ari Dwi Suta Atmaja, Ketut Jaya Bagus Kusuma Wijaya Bagus Tresna Bramstya Felix Andika Dwiyanto Giri, Gst. Ayu Vida Mastrika Gst. Ayu Vida Mastrika Giri Gunawan, I Komang Agus Bryan Hamada Zein I Dewa Gede Agung Pandawana I Gede Iwan Sudipa I Made Subrata Sandhiyasa I Nyoman Alit Arsana I Nyoman Alit Arsana I Nyoman Gede Arya Astawa I Nyoman Tri Anindia Putra I Putu Eka Andika Cakra I Wayan Raka Ardana I Wayan Supriana Ida Bagus Gede Sarasvananda Ketut Laksmi Maswari Libraeni, Luh Gede Bevi Naswin, Ahmad Ni Putu Widantari Suandana Raharja, Made Agung Selivan, Diana Triadi, Wilfridus Bambang Waas, Devi Valentino Wahyudi, I Putu Alfin Teguh Widiantari, Ni Komang Mira Wiguna, I Komang Arya Ganda Wiguna, I Wayan Darma Wijaya, Bagus Kusuma