Article Per Year (5 Year)
p-Index From 2021 - 2026
8.753
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Infomedia JURNAL PengaMAS MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Information System Journal (INFOS) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Pengabdian Mitra Masyarakat (JPMM) Malcom: Indonesian Journal of Machine Learning and Computer Science International Journal of Advanced Science Computing and Engineering SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Scientific Journal of Informatics SWAGATI: Journal of Community Service Edu Komputika Journal Jurnal Pengabdian Masyarakat Inovasi Indonesia
Yoga Pristyanto
Universitas Amikom Yogyakarta
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 Medicine & Pharmacology Public Health Social Sciences Other

Published : 53 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 53 Documents
Search

 2   3   4   5   6 
Generative AI Image Sentiment Analysis on Social Media X using TF-IDF and FastText Saputra, Rahman; Pristyanto, Yoga; Fajri, Ika Nur
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10627

Abstract

This research investigates public opinion on AI-generated images on Social Media X using machine learning-driven text classification. Three classification models were evaluated: Complement Naïve Bayes (CNB) utilizing TF-IDF features, Support Vector Machine (SVM) merging TF-IDF with FastText embeddings, and IndoBERT as a modern transformer-based baseline. A total of 1,958 Indonesian tweets were collected via web scraping with relevant keywords, followed by a pipeline involving text cleaning, manual labeling into positive, negative, and neutral categories, and data balancing using the Synthetic Minority Over-sampling Technique (SMOTE) for the classical models (with class weighting applied for IndoBERT). Results show that the SVM model outperformed the others, achieving 68.7% accuracy with average precision, recall, and F1-score of 0.69, 0.69, and 0.68, respectively; CNB attained 64.1% accuracy with average metrics of 0.64; while IndoBERT recorded 58.2% accuracy with average precision, recall, and F1-score of 0.58, 0.58, and 0.57. Confusion matrix analysis revealed SVM's superior ability to distinguish positive and neutral sentiments in casual language, though IndoBERT demonstrated potential for capturing deeper semantic nuances despite underperforming due to dataset size and informal text. The findings highlight the efficacy of integrating statistical and semantic representations for improved sentiment analysis on unstructured, noisy social media data related to AI-generated imagery, while suggesting that transformer models like IndoBERT may benefit from larger datasets for optimal performance.
Sentiment Classification Analysis of Tokopedia Reviews Using TF-IDF, SMOTE, and Traditional Machine Learning Models Barus, Herianta; Fajri, Ika Nur; Pristyanto, Yoga
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10524

Abstract

This study explores sentiment classification on Tokopedia user reviews using TF-IDF for feature extraction and SMOTE to handle class imbalance. From nearly one million raw reviews sourced from Kaggle ("E-Commerce Ratings and Reviews in Bahasa Indonesia"), a final set of 6,477 relevant entries was obtained after rigorous preprocessing, including case folding, noise removal (emojis, URLs, numbers), normalization to KBBI standards, tokenization, stopword removal, and stemming with Sastrawi. The dataset consisted of 5,213 positive and 1,264 negative reviews (80.4% positive). SMOTE balanced the classes to 10,426 reviews with a 1:1 ratio for training. Five traditional machine learning models were evaluated: Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Decision Tree, and Random Forest. Assessments were based on accuracy, precision, recall, F1-score, ROC-AUC, and computational time, using an 80:20 stratified split and 5-fold cross-validation. Random Forest achieved the best overall performance (accuracy: 0.9163, F1-score: 0.9133, ROC-AUC: 0.9784), while tuned SVM (C=10, RBF kernel) attained the highest accuracy of 0.9473 and F1-score of 0.9321. Cross-validation on Naive Bayes showed consistent results with an average accuracy of 88.09%. Further analysis using Logistic Regression coefficients identified influential features: positive sentiment associated with words like "mantap", "mudah", and "sukses", while negative sentiment correlated with "kecewa", "parah", and "lemot". These insights provide practical value for Tokopedia's teams to enhance user experience, such as improving app speed and addressing complaints. The findings demonstrate the effectiveness and efficiency of traditional machine learning techniques for sentiment analysis in Bahasa Indonesia contexts.
Public Sentiment Analysis on Corruption Issues in Indonesia Using IndoBERT Fine-Tuning, Logistic Regression, and Linear SVM Kono, Maria Fatima; Fajri, Ika Nur; Pristyanto, Yoga
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10537

Abstract

Sentiment analysis is a method in Natural Language Processing (NLP) that aims to understand public perceptions based on textual data from social media. Opinions expressed in digital platforms play an important role as they reflect public trust and attitudes toward strategic issues in Indonesia. This study aims to compare the performance of three IndoBERT-based approaches for sentiment classification, namely IndoBERT with full fine-tuning, IndoBERT as a feature extractor combined with Logistic Regression, and IndoBERT as a feature extractor combined with Linear SVM. The dataset was collected through the Twitter API, consisting of 2,012 tweets, which after preprocessing and balancing resulted in 2,252 labeled data for positive and negative sentiments. The preprocessing stage included cleansing, normalization, tokenization, stopword removal, and stemming. The dataset was then split into 80% training data, 10% validation data, and 10% testing data. Experimental results show that IndoBERT with full fine-tuning achieved the best performance, with an accuracy of 82.67%, an F1-score of 82.35%, and an AUC value of 0.87. Logistic Regression and Linear SVM produced lower accuracies of 80.20% and 78.22%, respectively. These findings indicate that fine-tuned IndoBERT is more effective in capturing the semantic nuances of the Indonesian language, while the non fine-tuning approaches offer better computational efficiency at the cost of reduced accuracy. This study contributes to the development of NLP methods for the Indonesian language, particularly in sentiment analysis, and highlights the potential of transformer-based models for analyzing strategic issues in social media.
Transfer Learning-Based Convolutional Neural Network for Accurate Detection of Rice Leaf Disease in Precision Agriculture Sari, Bety Wulan; Prabowo, Donni; Pristyanto, Yoga; Aminuddin, Afrig
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 3 (2025): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.3.420-432

Abstract

Background: Traditional approaches to rice disease identification depend mainly upon visual examination, which is quite labor-intensive and generally demands a certain skill level from people engaged in this activity. However, these approaches suffer from high time costs and potential errors and are impractical for large-scale daily monitoring. The recent rise of deep learning has offered opportunities for automated detection process improvement, which needs to be fast-accurate as good farmer-centric.   Objective: This study aims to enhance the accuracy of image rice leaf disease classification via feature extraction for rice leaf disease in four instances of pre-trained CNN models and provide an automated solution for early detection ahead of timely care by obtaining insights into crop production through precision agriculture. Methods: This study combined transfer learning with four pre-trained CNN models - InceptionResNetV2, MobileNetV2, DenseNet121, and VGG16. Results: The outcome of this research enables the identification of the optimal model to relate datasets where DenseNet121 achieved the highest accuracy, i.e. 99.10%, followed by MobileNetV2, having a precision of 97.10%. Conclusion: The new framework results in a highly accurate and high-throughput early disease detection element in precision agriculture, better than state-of-the-art approaches based on traditional techniques. Keywords: Deep Learning, DenseNet121, Image Classification, Rice Leaf Diseases, Transfer Learning
Sistem Pendukung Keputusan Penilaian Kinerja Guru Di SMK Muhammadiyah Imogiri Menggunakan Metode Profile Matching Rospita, Andri; Pristyanto, Yoga; Dahlan, Akhmad
Eksplora Informatika Vol 12 No 1 (2022): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i1.613

Abstract

Guru mempunyai andil yang besar terhadap keberhasilan pembelajaran di sekolah, sehingga penting untuk dilakukan penilaian kinerja guru untuk mengetahui sejauh mana kemampuan guru dalam menjalankan tugasnya. Saat ini penilaian kinerja guru di SMK Muhammadiyah Imogiri masih dilakukan secara manual sehingga sering terjadi kesalahan dalam melakukan perhitungan nilai dan memerlukan waktu yang lama untuk mengelola data tersebut. Berdasarkan permasalahan yang ada maka dirancanglah sistem pendukung keputusan dengan menerapkan metode profile matching. Dengan adanya sistem ini penilaian kinerja guru dapat lebih efektif dan efisien. Proses pada metode profile matching adalah dengan membandingkan antar kompetensi individu ke dalam potensi suatu jabatan sehingga dapat diketahui perbedaan kompetensinya (GAP). Metode tersebut memiliki tingkat objektifitas yang lebih baik dibanding metode lain dan mempertimbangkan konsistensi yang logis dalam penilaian. Hasil pengujian didapatkan akurasi sistem 93,33% terhadap 30 data yang diuji, hal ini menunjukkan bahwa penerapan metode profile pada sistem mempunyai tingkat akurasi yang baik dan dapat mengatasi permasalahan yang ada.
Comparative Performance of SVM and Multinomial Naïve Bayes in Sentiment Analysis of the Film 'Dirty Vote' Iedwan, Aisha Shakila; Mauliza, Nia; Pristyanto, Yoga; Hartanto, Anggit Dwi; Rohman, Arif Nur
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.10290

Abstract

Purpose: The purpose of this research is to analyze and compare the performance of two machine learning models, Support Vector Machine (SVM) and Multinomial Naive Bayes, in conducting sentiment analysis on YouTube comments related to the film "Dirty Vote." Methods: The study involved collecting YouTube comments and preprocessing the data through cleaning, labeling, and feature extraction using TF-IDF. The dataset was then divided into training and testing sets in an 80:20 ratio. Both the SVM and Multinomial Naive Bayes models were trained and tested, with their performance evaluated using accuracy, precision, recall, and F1-score metrics. Result: The results revealed that both models performed well in classifying sentiments, with SVM slightly outperforming Multinomial Naive Bayes in terms of accuracy and precision. Particularly, SVM showed superior performance in detecting positive comments, making it a more reliable model for this specific sentiment analysis task. Novelty: This study contributes to the field of sentiment analysis by providing a detailed comparative analysis of SVM and Multinomial Naive Bayes models on YouTube comments in the context of an Indonesian film. The findings highlight the strengths and weaknesses of each model, offering insights into their applicability for sentiment analysis tasks, particularly in analyzing social media content. This research also suggests potential future directions, including the exploration of advanced NLP techniques and different models to enhance sentiment analysis performance.
Otomatisasi Penerusan Laporan Pengaduan Menggunakan Neural Network Khoiruddin, Lukman; Sidauruk, Acihmah; Pristyanto, Yoga; Yudiyanto, Muhammad Resa Arif; Kurniawan, Hendra
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 2 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i2.6662

Abstract

Saat ini terdapat sistem laporan pengaduan masyarakat yang sudah terintegrasi ke berbagai instansi. Sistem ini dikembangkan oleh Pemerintah Republik Indonesia bernama Sistem Layanan Aspirasi dan Pengaduan Online Rakyat (LAPOR!). Berdasarkan sistem LAPOR jumlah aduan yang masuk terus meningkat. Dengan adanya sistem ini berbagai aduan yang disampaikan oleh warga masyarakat dapat terintegrasi ke instansi yang berwenang menangani aduan tersebut. Dengan terintegrasinya sistem maka jumlah pengaduan yang masuk sangat banyak sehingga terdapat kendala pada saat proses verifikasi pengaduan yang nantinya akan diteruskan ke pihak yang berwenang. Tujuan penelitian ini adalah melakukan klasifikasi terhadap setiap laporan pengaduan dan mengetahui pengaruh terhadap Replace Slang Word. Dalam proses klasifikasi di penelitian ini menggunakan algoritma Artificial Neural Network. Jumlah data pengaduan yang digunakan adalah sebanyak 750 data pengaduan. Data tersebut terbagi menjadi 3 kategori yaitu bidang pendidikan, kesehatan dan infrasturktur. Untuk pembagian jumlah data dilakukan sama di setiap kategori. Pada tahapan Preprocessing menggunakan replace slang word sebagai penggati kata slang terhadap kata aslinya. Hasil dari penelitian ini adalah menunjukkan nilai tinggi terhadap nilai Accuracy yaitu sebesar 99,33% untuk F1 score, Precission, dan Recall memiliki nilai yang sama yaitu 99,09%. Dengan hasil yang tinggi maka metode yang diusulkan dapat digunakan untuk melakukan pengklasifikasian terhadap laporan pengaduan.
Pemanfaatan Sistem Informasi Berbasis Website untuk Mendukung Pengelolaan Administrasi Data Karyawan Yayasan Taruna Alquran Sleman Yogyakarta Nurmasani, Atik; Dyah Anggita, Sharazita; Dwi Hartanto, Anggit; Pujastuti, Eli; Asti Astuti, Ika; Pristyanto, Yoga; Nur Fajri, Ika
Jurnal Pengabdian Masyarakat Inovasi Indonesia Vol 3 No 4 (2025): JPMII - Agustus 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jpmii.829

Abstract

Penerapan sistem informasi pada suatu institusi penting untuk mendukung proses bisnis. Yayasan Taruna Al-Quran ingin memaksimalkan teknologi dalam mengelola administrasi data unit kerja. Masalah yang dialami pada pengelolaan administrasi data yaitu keterbatasan dalam pengelolaan arsip dan tidak optimalnya proses pencarian data. Sistem informasi berbasis website dibuat untuk mengatasi masalah pengelolaan administrasi dan kemudahan akses bagi seluruh unit kerja. Metode yang diterapkan pada kegiatan terdiri dari perencanaan, pelaksanaan, dan evaluasi. Hasil kegiatan perencanaan berupa perencanaan yang sesuai kebutuhan sebagai dasar pelaksanaan.  Hasil kegiatan pelaksanaan berupa sistem informasi yang siap diserahkan kepada mitra. Hasil evaluasi berupa masukan pengguna dari mitra terhadap sistem informasi, dimana pengguna mudah menggunakan sistem informasi dengan skor 5.9 atau 86%. Sistem informasi yang diterapkan dapat membantu mitra mengelola administrasi data karyawan dengan mudah. Seluruh pengguna dapat mengakses data secara online sesuai kebutuhan.
Extreme Gradient Boosting Algorithm to Improve Machine Learning Model Performance on Multiclass Imbalanced Dataset Pristyanto, Yoga; Mukarabiman, Zulfikar; Nugraha, Anggit Ferdita
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1102

Abstract

Unbalanced conditions in the dataset often become a real-world problem, especially in machine learning. Class imbalance in the dataset is a condition where the number of minority classes is much smaller than the majority class, or the number is insufficient. Machine learning models tend to recognize patterns in the majority class more than in the minority class. This problem is one of the most critical challenges in machine learning research, so several methods have been developed to overcome it. However, most of these methods only focus on binary datasets, so few methods still focus on multiclass datasets. Handling unbalanced multiclass is more complex than handling unbalanced binary because it involves more classes than binary class datasets. With these problems, we need an algorithm with features that can support adjustments to the difficulties that arise in multiclass unbalanced datasets. One of the algorithms that have features for adjustment is the ensemble algorithm, namely Xtreme Gradient Boosting. Based on the research, our proposed method with Xtreme Gradient Boosting showed better results than the other classification and ensemble algorithms on eight datasets with five evaluation metrics indicators such as balanced accuracy, the geometric-mean, multiclass area under the curve, true positive rate, and true negative rate. In future research, we suggest combining methods at the data level and Xtreme Gradient Boosting. With the performance increase in Xtreme Gradient Boosting, it can be a solution and reference in the case of handling multiclass imbalanced problems. Besides, we also recommended testing with datasets in the form of categorical and continuous data.
Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine Fadhilah Dwi Ananda; Yoga Pristyanto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 20 No. 2 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v20i2.1130

Abstract

Media sosial saat ini merupakan media komunikasi yang sering digunakan oleh kalangan masyarakat Indonesia dalam menyampaikan sebuah opini. Salah satu media yang sering digunakan masyarat adalah twitter. Twitter merupakan media sosial yang memberikan banyak informasi melalui tweet, dari informasi yang ditulis tersebut terdapat data yang dapat diolah. Penelitian ini menggunakan teknik text mining dengan menerapkan algoritma Support Vector Machine dipergunakan untuk klasifikasi sentimen pengguna twitter terhadap layanan internet Biznet. Kernel yang digunakan adalah kernel Linear dan kernel RBF. Pengujian dilakukan dengan 3 skenario, pada skenario 1 menggunakan 800 data, skenario 2 menggunakan 900 data dan skenario 3 menggunakan 1000 data, untuk pembagiannya yaitu 90% data training dan 10% data testing dari masing-masing skenario. Berdasarkan hasil pengujian yang dilakukan menggunakan kernel linear dan kernel RBF dapat diambil kesimpulan sebagai berikut. Algoritma SVM menggunakan dengan kernel linear maupun kernel RBF memiliki hasil kinerja evaluasi baik dari sisi akurasi, presisi dan recall yang relatif sama. Sehingga dapat dikatakan bahwa algoritma SVM baik dengan kernel RBF maupun Linear sama sama dapat digunakan dengan baik dalam menentukan sentimen pengguna internet Biznet. Selain itu dengan 3 skenario pengujian dengan jumlah data yang berbeda algoritma SVM baik dengan kernel RBF maupun Linear sama sama konsisten kinerjanya.
Co-Authors Acihmah Sidauruk Aditya Yoga Pratama Afrig Aminuddin Aisha Shakila Iedwan Akhmad Dahlan Alfian Ramadhan Alvin Rahman Al Musyaffa Andi Sunyoto Angelina Putri Ariani Anggi Thoat Ariyanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggita, Sharazita Dyah Anna Baita Ariefhan Maulana arif nur rohman Arif Nur Rohman Arif Nur Rohman Asti Astuti, Ika Atik Nurmasani ATIK NURMASANI Atik Nurmasani Barus, Herianta Bety Wulan Sari Bety Wulan Sari, Bety Wulan Bligania Bligania Cherfly Kaope Dewi Ayu Murtiningsih Donni Prabowo Donni Prabowo, Donni Dwi Hartanto, Anggit Dyah Anggita, Sharazita Eli Pujastuti, Eli Eza Nanda Fadhilah Dwi Ananda Fajri, Ika Nur Fauzy, Marwan Noor Gagah Gumelar Gita Cahyani Hanif Al Fatta Heri Sismoro Hidayat, Kardilah Rohmat Ibnu Hadi Purwanto Ibnu Hadi Purwanto Ibrahim Aji Fajar Romadhon Iedwan, Aisha Shakila Ike Verawati Ikmah Ikmah Irfan Pratama Irma Rofni Wulandari Istikomah Khoiruddin, Lukman Kono, Maria Fatima Kristianti, Fanny Novatriana LILIS DWI FARIDA Lucky Adhikrisna Wirasakti Mambaul Hisam Marcheilla Trecya Anindita Mauliza, Nia Mukarabiman, Zulfikar Mulia Sulistiyono Natasaskara, Nandana Ayudya Nia Mauliza Nia Mauliza Norhikmah Nugraha, Anggit Ferdita Nuri Cahyono Nurindah A Amari Nurwijayanti Purwati, Sintia Eka Putra, Frahma Aditya Rahman Saputra, Rahman Rifda Faticha Alfa Aziza Rizky Hafizh Jatmiko Rohmad Fajarudin Rohman, Arif Nur Romadhon, Ibrahim Aji Fajar Rospita, Andri Sabella, Cindy Dinda Sifa’ul Husna, Siti Okta Sumarni Adi Utama, Hastari Windarni, Vikky Aprelia Wirantanu, Dipa Wirasakti, Lucky Adhikrisna Wiwi Widayani Yanuar Nur Kholik Yogata Rama Guninta Yudiyanto, Muhammad Resa Arif Yuli Astuti Zein, Aditya Ahmad