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All Journal JIEET (Journal of Information Engineering and Educational Technology) KOMPUTEK Building of Informatics, Technology and Science JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika Terpadu Journal of Artificial Intelligence and Digital Business SCIENTIA JOURNAL : Jurnal Ilmiah Mahasiswa
Rudiman, Rudiman
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mechanical Engineering

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SENTIMENT ANALYSIS DENGAN NAÏVE BAYES BERBASIS ORANGE TERHADAP RESIKO PEMBANGUNAN IKN Munawaroh, Al; Ridhoi, Reno; Rudiman, Rudiman
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 1 (2024): JATI Vol. 8 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i1.8454

Abstract

Keputusan pemindahan Ibu Kota Negara (IKN) ke Kalimantan Timur memicu perubahan signifikan dalam pembangunan nasional. Studi ini menganalisis sentimen masyarakat terhadap risiko pembangunan IKN, fokus pada ketidakpastian dan dampak lingkungan. Ketidakpastian dan dampak lingkungan pembangunan IKN menjadi perhatian utama masyarakat. Penelitian ini bertujuan memahami opini dan pandangan masyarakat terkait risiko tersebut. Penelitian bertujuan mengidentifikasi sentimen publik, khususnya perasaan resah dan kekhawatiran, terhadap pembangunan Ibu Kota Negara di Kalimantan Timur. Dengan menggunakan metode analisis sentimen Naïve Bayes dan data dari media sosial, termasuk Twitter, Instagram, dan Facebook, penelitian ini melakukan preprocessing, klasifikasi, dan evaluasi sentimen. Hasil analisis sentimen menunjukkan bahwa masyarakat mengekspresikan kekhawatiran, terutama terkait dampak lingkungan dan kurangnya kepastian. Metode Naïve Bayes memberikan akurasi sebesar 87%, menghasilkan enam jenis emosi, dengan sentimen suka cita mendominasi. Studi ini memberikan dasar data untuk pengambilan keputusan dan menyoroti pentingnya memperhatikan pandangan masyarakat dalam kebijakan Pembangunan.
Klasifikasi Teks Quick Count Pemilihan Presiden 2024 pada Twitter menggunakan Metode TF-IDF dan Naive Bayes Pranata, Aditya; Rudiman, Rudiman; Verdikha, Naufal Azmi
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1279

Abstract

The 2024 Indonesian Presidential Election generated various responses on X Twitter platform related to the Quick Count. The large number of diverse opinions makes identifying and categorizing sentiments difficult. This study aims to evaluate the accuracy of the Naive Bayes method with TF-IDF weighting in text classification regarding the Quick Count of the 2024 Presidential Election on X Twitter. Data was obtained through crawling, resulting in 2113 tweets, which experts in data labelling then labelled. The preprocessing stage includes case folding, cleansing, stopword removal, and stemming. Words are weighted using TF-IDF, and then the data is divided into 80% for training and 20% for testing. Text classification using the Naive Bayes algorithm achieved an accuracy of 74.46%, indicating a pretty good accuracy in classifying text related to the 2024 Presidential Election Quick Count on X Twitter.
Penerapan Metode GA-TL Pada Algoritma Naive Bayes Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Widyastuti, Dessy; Siswa, Taghfirul Azhima Yoga; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6737

Abstract

The Indonesia Smart Card (KIP) Scholarship Program aims to support students from underprivileged families in pursuing higher education, yet the distribution of recipient data often experiences class imbalance, leading to inaccuracies in scholarship allocation. This imbalance, characterized by disproportionate data between recipient and non-recipient groups, affects classification model performance, causing models to favor the majority class and overlook the minority class, potentially excluding eligible recipients. To address this issue, this study combines the Genetic Algorithm for feature selection and optimization with Tomek Links-Random Undersampling for data balancing. The research process includes data preprocessing, 10-fold cross-validation, and performance evaluation using a confusion matrix. Results indicate that without Tomek Links-Random Undersampling, Naïve Bayes accuracy increased from 65.2% to 66.0% after feature selection and optimization using the Genetic Algorithm, while applying Tomek Links-Random Undersampling improved accuracy from 56% to 63%. This method also enhanced fairness in recipient classification, promoting a more equitable distribution of benefits. The improved model accuracy significantly aids future scholarship selection processes, demonstrating that integrating efficient machine learning approaches optimizes the KIP Scholarship Program by ensuring beneficiaries are appropriately targeted based on predetermined criteria.
Penerapan Metode GA-CBU Pada Algoritma Logistic Regression Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Poernamawan, Ahmad Nugraha; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6747

Abstract

The issue of class imbalance often poses a challenge in data analysis, where the number of instances in the majority class is significantly higher than that in the minority class. This can lead classification models to be biased towards predicting the majority class, resulting in low accuracy in identifying the minority class. This research aims to implement the Logistic Regression (LR) algorithm combined with the Clustering Based Undersampling (CBU) method as an undersampling technique, feature selection, and optimization using Genetic Algorithm (GA) in classifying KIP-College scholarship data at Muhammadiyah University of East Kalimantan. In addition, this research also evaluates the performance of the model with 10-Fold Cross Validation and Confusion Matrix techniques as accuracy metrics and aims to overcome the problem of class imbalance in the data of scholarship recipients (KIP) at Muhammadiyah University of East Kalimantan. The data used consists of 1075 records with 37 features related to the socio-economic factors of scholarship recipients. The results from the application of the CBU method indicate an increase in the accuracy of the Logistic Regression model from 62.51% to 67.68%. Furthermore, the combination of GA and CBU has providing more stable results in classifying minority classes. It is hoped that this research can make a significant contribution to the development of a more accurate and efficient scholarship recipient selection system, as well as serve as a reference for future studies in the fields of data mining and machine learning.
Penerapan Metode GA-NM Pada Algoritma SVM Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Abror, Irfan Fiqry; Siswa, Taghfirul Yoga Azhima; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6756

Abstract

Class imbalance is a common challenge in data analysis, especially when the number of instances in the majority class significantly exceeds that in the minority class. This imbalance can cause classification models to favor the majority class, resulting in low accuracy in identifying the minority class. In this study, the Support Vector Machine (SVM) method combined with Near Miss and Genetic Algorithm (GA) is used to address the class imbalance problem in the scholarship recipient data of the Kartu Indonesia Pintar (KIP) program at Universitas Muhammadiyah Kalimantan Timur. The dataset consists of 1,075 records with 27 features representing the socio-economic factors of the scholarship recipients. Near Miss was applied to undersample the majority class, producing a more balanced data distribution. Subsequently, the SVM algorithm was utilized as the primary classification model, with feature selection and parameter optimization conducted using GA. The results indicate that the combination of SVM, Near Miss, and GA improved classification performance in identifying the minority class. The initial accuracy obtained without the method was 60.55% and after implementation it increased to 76.88%. This approach not only enhances the overall accuracy of the model but also ensures more stable performance, particularly for the minority class. Therefore, this study is expected to provide a significant contribution to the development of a more accurate and efficient scholarship selection system, as well as serve as a reference for future research in data mining and machine learning.
Penerapan Metode GA-RU Pada Algoritma Random Forest Untuk Mengatasi Class Imbalance Data Beasiswa KIP-Kuliah Rahman, Febrian Nor; Siswa, Taghfirul Azhima Yoga; Rudiman, Rudiman
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6757

Abstract

Class imbalance is a common challenge in data analysis, where the majority class significantly outnumbers the minority class. This condition causes classification models to lean toward predicting the majority class, resulting in low accuracy in identifying the minority class. This study proposes the application of Genetic Algorithm (GA) combined with Random Undersampling (RU) on the Random Forest algorithm to address class imbalance issues in the dataset of Indonesia Smart Card (KIP) scholarship recipients at Universitas Muhammadiyah Kalimantan Timur. The dataset comprises 1,080 records with 37 features related to the socio-economic factors of the scholarship recipients. After data cleaning, 1,075 records were retained. The results indicate that the Random Undersampling method improved the accuracy of the Random Forest model from 84.27% to 85.06%. Although this improvement appears modest, it is significant as it demonstrates increased model stability in classifying the minority class, which previously had low accuracy. The combination of GA and RU proved effective in enhancing model performance, resulting in more stable classification for the minority class. This study is expected to contribute to the development of more accurate and efficient scholarship selection systems and serve as a reference for research in data mining and machine learning.
Analisis Faktor-Faktor yang Mempengaruhi Kepatuhan Pajak Pribadi di Kota Batam Rudiman, Rudiman; Ompusunggu, Hermaya
SCIENTIA JOURNAL Vol 5 No 4 (2023): Scientia Journal
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/scientiajournal.v5i4.7747

Abstract

The purpose of this study was to find out whether tax sanctions, taxpayer awareness and tax knowledge affect personal taxpayer compliance in the city of Batam. In this study, researchers used a questionnaire method. The population in this study is the number of taxpayers who report at KPP Pratama Batam Selatan. Using the Slovin formula, this study's sample represents 10 percent of the total population. This investigation utilises primary data. This study's data collection method was a questionnaire. This investigation employs the Statistical Package for the Social Sciences (SPSS) for data analysis. With a Tcount of 3.068 > Ttable of 1.98489 and a Sig. 0.003 0.05, tax sanctions on personal taxpayer compliance are obtained with a Tcount of 3.068 > Ttable of 1.98489. This indicates that Ha is approved and Ho is rejected. Therefore, it can be partially concluded that tax sanctions have a substantial impact on taxpayer compliance in the city of Batam. The Tcount value is 1.676 Ttable 1.98489, and the Sig. is 0.097 > 0.05, based on the research findings regarding taxpayer awareness of compliance. This indicates that Ho is recognised while Ha is not. Therefore, it can be partially concluded that taxpayer awareness has no significant effect on taxpayer compliance in Batam. Based on the research results of tax knowledge on taxpayer compliance, the Tcount value is 0.817 <Ttable 1.98489 and the Sig. 0.416 > 0.05. This means that Ho is accepted and Ha is rejected. So it can be concluded partially that tax knowledge has no significant effect on taxpayer compliance in the city of Batam. Based on the research results of tax sanctions, taxpayer awareness and knowledge of taxation on taxpayer compliance, the Fcount value is 23.074 > Ftable 2.7 and the value of Sig. of 0.000 <0.05. This means that Ho is rejected and Ha is accepted. So it can be concluded that simultaneously tax sanctions, taxpayer awareness and tax knowledge have a significant effect on taxpayer compliance in the city of Batam.
Analisis Sentimen Terhadap Calon Wakil Presiden Gibran Rakabuming Raka Menggunakan Algoritma Naive Bayes Nursyarif, Muhammad Khumaidi; Tirta, Muhamad Wahyu; Hidayat, Muhammad Rahman; Rudiman, Rudiman
KOMPUTEK Vol. 8 No. 1 (2024): April
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v8i1.2509

Abstract

The turnover of the president and vice president in Indonesia occurs every 5 years. In the year 2024, there will be an election, and one of the vice presidential candidates, listed as candidate number 2, is Gibran Rakabuming Raka, who is the son of Mr. Jokowi, currently serving as the 7th President. Many opinions have been expressed by the public regarding Mas Gibran, especially considering his age of 36 years, which is perceived as relatively young to lead the nation of Indonesia. Therefore, we intend to conduct research with the aim of identifying practical implications related to public perceptions of the potential vice presidential candidate. Data from comments on the YouTube video titled "[FULL] Gibran in Between Ganjar and Prabowo, Which One to Choose? | ROSI" underwent a classification process using the Naive Bayes algorithm for sentiment analysis. The accuracy obtained is 92.5%, with an f1 Score of 92.4%, Precision of 93.5%, and Recall of 92.5%.
Analisis Sentiment Cyberbullying pada media Youtube menggunakan Algoritma Naïve Bayes Elfansyah, Muhammad Rayhan; Perdana, Muhammad Reifin; Ihram Nabawi, Ikhsan Nuttakwa Takbirata; Rudiman, Rudiman
KOMPUTEK Vol. 8 No. 1 (2024): April
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research focuses on analyzing cyberbullying sentiment on YouTube using the Naive Bayes algorithm. This study involved data collection and data pre-processing techniques to analyze comments related to Manchester United. The Orange Data Mining application is used for data modeling and analysis. The research methodology and sentiment analysis using Naive Bayes are explained in detail. Data pre-processing includes steps such as removing URLs, tokenization, filtering, and normalization. Analysis uses Naïve Bayes which produces 81% accuracy, 79% precision and 81% recall. The process includes dividing the data into training data and testing data, and the results can be visualized using a confusion matrix. The references include various studies on sentiment analysis using different methods and platforms.
Perbandingan Metode Naive Bayes Dan Support Vector Machine Terhadap Ulasan Aplikasi Ojol The Game. Anggi, Saputra; Ali, Sultan; Sidiq, Rezki Subhan Insani; Rudiman, Rudiman
JIEET (Journal of Information Engineering and Educational Technology) Vol. 8 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v8n2.p84-89

Abstract

Dengan kemajuan teknologi transportasi, internet sekarang sangat memengaruhi kehidupan masyarakat dalam menjalan aktivitas dimasyarakat, dalam penelitian ini, pengguna Google Play Store meningkat sebagai platform di mana pengguna dapat memberikan ulasan tentang produk yang mereka manfaatkan bersama jumlah pengguna yang meningkat, ulasan pengguna menjadi sumber penting bagi perusahaan untuk memperbaiki dan meningkatkan produk di masa depan. Penelitian ini bertujuan untuk melakukan perbandingan metode Naive Bayes dan Support Vector Machine (SVM) dalam menganalisis ulasan pengguna aplikasi "Ojol The Game". Ulasan pengguna aplikasi Ojol The Game diklasifikasikan ke dalam dua tingkatan, yaitu positif dan negatif. Hasil penilaian dari penelitian membuktikan bahwa akurasi mencapai nilai sebesar 92%, presisi sebesar 33%, recall sebesar 6% dan f_1 score sebesar 11% untuk metode Naïve Bayes, dan untuk metode Support Vector Machine menunjukkan hasil Accuracy sebesar 90%, presisi sebesar 30%, recall sebesar 2% dan f_1 score sebesar 24%. Penelitian ini bertujuan untuk memperbaiki pengalaman dan layanan pengguna aplikasi Ojol The Game dengan memahami sentimen pengguna terhadap aplikasi tersebut. Dengan menggunakan metode Naïve Bayes dan Support Vector Machine dan metode seleksi fitur TF-IDF, perusahaan dapat mengkategorikan ulasan pengguna dengan lebih efisien. Kata Kunci Ulasan, Naive Bayes, Support Vector Machine, Ojol The Game, Accuracy.
Co-Authors Abror, Irfan Fiqry Ali, Sultan Anggi, Saputra Elfansyah, Muhammad Rayhan Fiqri, Mohammad Hiqmal Halim, Andi Nur Hidayat, Muhammad Rahman Ihram Nabawi, Ikhsan Nuttakwa Takbirata Munawaroh, Al Naufal Azmi Verdikha Nursyarif, Muhammad Khumaidi Ompusunggu, Hermaya Perdana, Muhammad Reifin Poernamawan, Ahmad Nugraha Pranata, Aditya Rahman, Febrian Nor Ridhoi, Reno Sidiq, Rezki Subhan Insani Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tirta, Muhamad Wahyu Verdikha, Nauval Azmi Widyastuti, Dessy