Article Per Year (5 Year)
p-Index From 2021 - 2026
3.761
P-Index
This Author published in this journals
All Journal Prosiding KOMMIT Jurnal Simetris Prosiding SNATIF Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Journal of Information System Jurnal Informatika Upgris Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Devotion: Journal of Research and Community Service Jurnal Pengabdian Literasi Digital Indonesia
Fikri Budiman
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

Published : 28 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 28 Documents
Search

 1   2   3 
Implementasi Website Desa Pucang, Bawang, Banjarnegara Untuk Promosi Desa Dan Peningkatan Layanan Publik Susanto, Ajib; Sudaryanto, Sudaryanto; Kusumawati, Yupie; Budiman, Fikri
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

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

Abstract

Desa Pucang, Kec. Bawang, Kab. Banjarnegara adalah desa target pendampingan Abinayamuda Disporapar Provinsi Jawa Tengah. Kegiatan desa, produk UMKM desa, kegiatan karang taruna, badan usaha milik desa (BUMDES), layanan masyarakat dan berbagai potensi desa selama ini masih dikelola belum memanfaatkan teknologi informasi dengan maksimal, sehingga informasi tentang desa dan kegiatannya masih sering belum tersampaikan ke masyarakat yang membutuhkan serta informasi desa Pucang di internet belum tersebar dengan baik, hal ini diperlukan media yang memudahkan untuk dikelola dan tersebar dengan cepat salah satunya dengan mewujudkan web desa dan pemanfaatan media sosial.Tujuan yang ingin dicapai yaitu mengembangkan dan mengoptimalkan website desa Pucang, Kec. Bawang, Kab. Banjarnegara untuk peningkatan layanan publik dan layanan informasi yang dibutuhkan masyarakat saat ini. Hasil sudah dicapai berupa website desa untuk layanan publik, informasi desa, promosi dan penjualan produk UMKM desa.
Optimasi Metode K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Pengenalan Citra Batik Dengan Ragam Hias Geometris Widyatmoko, Karis; Sugiarto, Edi; Muslih, Muslih; Budiman, Fikri
Jurnal Informatika UPGRIS Vol 8, No 1: Juni 2022
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v8i1.11705

Abstract

Batik is an intangible cultural heritage that has been approved by UNESCO as an Indonesian cultural heritage. Batik is the art of drawing on cloth for clothing. This art of drawing is in the form of a pattern and has a philosophical meaning which is historically very closely related to the philosophy of Javanese culture. The various patterns of this batik need to be preserved, and one of the efforts to preserve this diversity is to maintain the special characteristics of the batik motif and continue to introduce it to future generations. Efforts to facilitate the introduction of batik motifs can be done through technology that is able to automatically recognize batik patterns according to the motif being tested, one of which is pattern recognition technology. This study aims to optimize the KNN method with PSO to increase the accuracy of batik pattern recognition, the research was carried out in several stages starting from data collection, preprocessing, feature extraction, and classification. The research was conducted using 310 data in the form of batik images in 7 different motifs and divided into 240 compositions for training data and 70 for testing data. At the feature extraction stage, the discrete wavelet transform method is used up to level 3, then at the classification stage, the PSO and KNN algorithms are used. The PSO algorithm is used to obtain the most optimal number of k which is used as the input parameter k in the KNN algorithm. The results of the research that have been carried out have proven that with the addition of the PSO algorithm, the accuracy of the KNN method can be increased by 6% compared to the standard KNN method.
Performa Logistic Regression dan Naive Bayes dalam Klasifikasi Berita Hoax di Indonesia Cahyani, Okta Nur; Budiman, Fikri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.28987

Abstract

The spread of false information has become a major challenge in Indonesian society, with 2,484 cases recorded in 2022. This highlights the importance of developing a system that can effectively identify and filter out fake news. This research aims to develop a more accurate fake news detection model by applying logistic regression, which is optimized by grid search and oversampling to overcome data imbalance. The main focus of this research is to improve the performance of the model in detecting fake news on unbalanced datasets. The dataset used is the Indonesian Fake News dataset, which consists of 4,231 entries with two categories: valid (3,465 entries) and hoax (766 entries). Preprocessing steps include stemming, stopword removal, and text normalization using TF-IDF. Random oversampling was applied to balance the data between hoax and valid classes, and parameter optimization was performed using grid search to improve model performance. The results show that the optimized logistic regression achieved the highest accuracy of 93%, surpassing naive bayes, which achieved 86% accuracy. These findings suggest that the developed fake news detection model can be used to improve the social media news monitoring system, and increase digital literacy among Indonesians.
Analisis Sentimen Ulasan Mobile JKN pada Playstore dengan Perbandingan Akurasi Algoritma Naïve Bayes dan SVM Pranata, Eka Arya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7334

Abstract

The facilities provided by BPJS Health by releasing the Mobile JKN application, with this application the administrative process that previously had to be done directly can be done online and more flexibly. This research aims to see the sentiment of the community towards the JKN Mobile application review by comparing the SVM and Naïve Bayes algorithms. As well as optimizing the Naïve Bayes algorithm by using grid search. Reviews are taken from Google play with the help of Google Play Scraper API, the dataset taken amounted to 7,000 reviews. The results of using Naïve Bayes with an accuracy value of 86%, after tuning optimization using Grid Search significantly increases the accuracy value of the Naïve Bayes algorithm to 91% and for the SVM algorithm has an accuracy value of 92%. From the trial, it was found that the SVM algorithm is still better than the Naïve Bayes algorithm even though it has been optimized, but by optimizing the accuracy value Naïve Bayes is closer to SVM performance. This research can provide insight into the comparison of the two algorithms in identifying JKN Mobile reviews and the need for optimization to improve the performance of algorithms in sentiment analysis, besides that this research also contributes to the improvement and development of the JKN Mobile application so that it is useful for the community.
Analisis Sentimen Pengguna X terhadap Kasus Korupsi Gula Tom Lembong Menggunakan Naïve Bayes, SVM, dan Random Forest Kuncoro, Aneira Vicentiya; Budiman, Fikri; Kurniawan, Defri
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8577

Abstract

The alleged sugar import corruption case involving Tom Lembong has become one of the most widely discussed public issues on social media, generating diverse reactions. This phenomenon illustrates how public opinion on legal issues is often influenced by perceptions of the public figures involved. This study aims to analyze public sentiment regarding the case on the social media platform X (formerly Twitter). The dataset consists of 1,802 tweets collected through a crawling process using the X API with the keyword “Tom Lembong.” The research stages include data cleaning, case folding, text normalization, tokenizing, stopword removal, stemming, sentiment labeling using a lexicon-based approach, and feature extraction with the Term Frequency–Inverse Document Frequency (TF-IDF) method. The prepared dataset was then tested using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The results show that the SVM algorithm achieved the highest accuracy (84%), followed by Random Forest (80%) and Naïve Bayes (76%). Based on the sentiment labeling results, positive sentiment dominated with 61%, while negative sentiment accounted for 39%. Although the analyzed issue concerns an alleged corruption case, the dominance of positive sentiment indicates that public opinion tends to focus on Tom Lembong’s personal image or public track record, which is viewed positively rather than on the substance of the legal allegations. These findings demonstrate the effectiveness of the SVM algorithm in analyzing high-dimensional text and provide insights into how public perception of legal issues can be influenced by image factors and the socio-political context on social media.
Prediksi Harga Rumah Menggunakan Algoritma Regresi Linier,Random Forest, Dan Gradient Boosting Akhmadi, Akhmadi; Budiman, Fikri
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9369

Abstract

House price prediction is a crucial issue in the property sector because it is influenced by various interrelated factors, such as building characteristics and environmental conditions. Accurate prediction using conventional approaches is often difficult and can lead to errors in decision-making. Therefore, this study aims to develop and compare the performance of house price prediction models using three machine learning algorithms: Linear Regression, Random Forest, and Gradient Boosting. The dataset used is the Home Value Insights Dataset on Kaggle, which consists of 1,000 houses with eight main attributes. The research stages include data pre-processing, dividing training and test data, model training, parameter optimization using GridSearchCV, and performance evaluation based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²) metrics using the 10-Fold Cross Validation method. The test results show that Linear Regression provides the best performance with an R² value of 0.8539 and a lower prediction error rate than Random Forest and Gradient Boosting. Although the ensemble model shows competitive results, increasing model complexity does not result in a significant increase in accuracy, so Linear Regression is considered the simplest, most efficient, and most easily interpreted approach for house price prediction systems on datasets with characteristics that tend to be linear.
Optimizing Feature Extraction for Naïve Bayes Sentiment Analysis Achmad, Achmad; Budiman, Fikri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12041

Abstract

The rapid growth of e-commerce platforms such as Tokopedia has generated a large volume of user reviews containing diverse opinions about products and services. These reviews reflect consumer perceptions and provide valuable insights for business decision-making. This study aims to enhance sentiment analysis performance by optimizing the Naïve Bayes algorithm through a comparison of two feature extraction techniques, namely Bag of Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF). The dataset consists of 5,400 Tokopedia product reviews obtained from the Kaggle platform, which are categorized into positive and negative sentiments. The research process includes text preprocessing consisting of text cleaning, case folding, tokenization, stopword removal, and stemming, feature extraction using Bag of Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF), handling data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), and model training using the Naïve Bayes. The dataset is divided into 80% training data and 20% testing data, and model performance is evaluated using accuracy, precision, recall, and F1-score. The results show that BoW achieved the highest accuracy of 93%, while TF-IDF reached 83%, indicating that BoW provides more effective feature representation and more stable performance for Naïve Bayes-based sentiment analysis on this dataset.
Comparison of Random Forest and LSTM for Tokopedia Sentiment Analysis Saputra, Fahrizal Denta; Budiman, Fikri
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12042

Abstract

Tokopedia is one of the largest e-commerce platforms in Indonesia, where every transaction generates user reviews containing opinions about the products or services received. These reviews provide important information about product quality, but the very large quantity makes manual analysis inefficient. This study aims to automatically classify Tokopedia review sentiment and compare the performance of machine learning and deep learning methods. The dataset used was obtained from Kaggle and has undergone an initial cleaning stage, including removing irrelevant columns and manually labeling into two sentiment classes, positive and negative. The research methodology includes several stages, namely data preprocessing (cleaning, case-folding, stopword removal, tokenization, normalization, and stemming), feature extraction using TF-IDF for Random Forest and word embedding for LSTM, implementation of Random Forest and Long Short-Term Memory (LSTM) models, and model evaluation using confusion matrix. Experimental results show that LSTM provides the best performance with 94% accuracy, while Random Forest achieves 92% accuracy. These findings indicate that LSTM is more effective in understanding language context, resulting in more accurate sentiment classification and is useful for decision making in the e-commerce field.
Co-Authors A.A. Ketut Agung Cahyawan W Abi Senoprabowo Achmad Achmad Agus Winarno, Agus Ajib Susanto Akhmadi Akhmadi Arief Soeleman, M Arifudin, Agus Asih Rohmani, Asih Awaludin, Yoga Mahendra Bao Sumantoro, Yeremia Joen Cahyani, Okta Nur Candra Irawan Edi Sugiarto Fahmi Amiq Fudhlatina, Dina Hastuti, Nanda Tri Kamarudin, Fatkhurridlo Pranoto Karis Widyatmoko Kuncoro, Aneira Vicentiya Kurniawan, Defri Kusumawati, Yupie Mahendra, Syafrie Naufal Moch Arief Soeleman, Moch Arief Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Noor Hasyim Novi Hendriyanto, Novi Pranata, Eka Arya Rahmawan P, Elkaf Rahmayani, Ririt Sheila Tina S Nurhendratno Saputra, Fahrizal Denta Sri Winarno SUDARYANTO SUDARYANTO Supriyono Asfawi Susanto, Al Birr Karim Yuventius Tyas Catur Pramudi Zaenal Arifin