Article Per Year (5 Year)
p-Index From 2021 - 2026
3.527
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal SITECH : Sistem Informasi dan Teknologi International Journal of Marine Engineering Innovation and Research Idealis : Indonesia Journal Information System Jurnal Dialektika Informatika (Detika) International Journal of Management Science and Information Technology (IJMSIT) Ilmu Komputer untuk Masyarakat Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Ilmiah Teknik Informatika dan Komunikasi Verfassung: Jurnal Hukum Tata Negara INOVTEK Polbeng - Seri Informatika PAEDAGOGIE Jurnal Pengabdian Masyarakat Inovasi Indonesia Jurnal Panjar : Pengabdian Bidang Pembelajaran Journal of Vocational, Informatics and Computer Education Journal of Soft Computing Exploration Journal of Information System Exploration and Research
Romadhon, Zainur
Universitas Muria Kudus
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 27 Documents
Search

 1   2   3 
A Web-Based Gold Price Prediction Model Using Geopolitical Sentiment from Social Media and Gated Recurrent Unit (GRU) Narendra Saputra; Anteng Widodo; Zainur Romadhon
International Journal of Management Science and Information Technology Vol. 6 No. 2 (2026): July - December 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i2.8297

Abstract

Gold is widely recognized as a safe-haven asset whose price is sensitive to economic and geopolitical uncertainty. This study develops a web-based international gold price prediction system using the Gated Recurrent Unit (GRU) algorithm and evaluates the contribution of geopolitical sentiment from the X social media platform. The study uses historical XAUUSD data and English-language posts related to the Iran–United States–Israel conflict. Text data were processed through cleaning, case folding, tokenization, stopword removal, lemmatization, TF-IDF transformation, and sentiment analysis using VADER. The resulting daily sentiment scores were integrated with historical gold price features and used as an additional input to the GRU model. Two experimental scenarios were evaluated: GRU without sentiment and GRU with sentiment. The results show that the GRU model without sentiment achieved an MAE of 55.07, RMSE of 69.81, MAPE of 1.17%, and R² of 0.9629, while the model with sentiment achieved an MAE of 80.87, RMSE of 92.93, MAPE of 1.72%, and R² of 0.9342. These findings indicate that incorporating daily aggregated social media sentiment did not improve prediction performance for the dataset used. The developed Flask-based web application provides prediction, sentiment analysis, visualization, and model evaluation features, demonstrating the practical implementation of the proposed approach.
Multi-Platform Healthcare Sentiment Analysis Using Kappa-Validated Lexicon Labelling and SMOTE-Enhanced Naive Bayes Fikri Hamdhan Dwi Saputra; Fajar Nugraha; Zainur Romadhon
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 3 (2026): August
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/bn99ce45

Abstract

This study develops an automated sentiment analysis system for classifying reviews of the RSI Sunan Kudus mobile application, collected from Google Play Store, Google Maps, and YouTube (N = 1,428). Sentiment labels were automatically assigned using a domain-adapted Indonesian lexicon (87 positive / 100 negative terms), with label reliability validated against a human-annotated gold-standard subset (n = 100, two annotators; κ = 0.900, almost perfect agreement), yielding 85.71% classifier accuracy against adjudicated labels. After excluding neutral reviews, a binary multinomial Naive Bayes classifier was evaluated via 5-fold stratified cross-validation, achieving 91.01 ± 1.13% accuracy and 89.20 ± 1.48% macro F1-score. Class imbalance (ratio = 2.39:1) was addressed using SMOTE within each training fold in feature-vector space. An ablation study across six model-vectoriser combinations identified LinearSVC with Bag-of-Words as best-performing (Macro F1 = 96.03 ± 1.34%); a preprocessing ablation showed the six-stage normalisation pipeline did not meaningfully improve Macro F1 over simpler variants. Cross-platform transfer experiments revealed substantial generalisation gaps (e.g., Macro F1 dropping from 81.1% to 26.8% for a Google-Maps-trained model applied to YouTube), underscoring the value of multi-platform data collection. Findings indicate three primary service pain points: OTP/login failures, long waiting times, and application connectivity issues.
Analisis Sentimen dan Pemodelan Topik Komentar YouTube tentang Aplikasi AI Menggunakan Naïve Bayes dan Support Vector Machine: Sentiment and Topic Modeling of YouTube Comments on AI Applications Using Naïve Bayes and Support Vector Machine Rosyada, Mila; Setiawan, R. Rhoedy; Romadhon, Zainur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 4 (2026): MALCOM October 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i4.3044

Abstract

Komentar pengguna YouTube mengenai aplikasi kecerdasan buatan (Artificial Intelligence/AI), seperti ChatGPT, Google Gemini, dan Perplexity AI, terus meningkat sehingga menghasilkan opini yang sulit dianalisis secara manual. Penelitian ini menggabungkan analisis sentimen dan pemodelan topik untuk memperoleh gambaran tentang persepsi pengguna terhadap beberapa aplikasi AI. Penelitian ini bertujuan untuk menganalisis sentimen dan mengidentifikasi topik utama dari 5.588 komentar YouTube terkait aplikasi AI. Data diproses menggunakan tahapan preprocessing sederhana yang meliputi cleaning, case folding, normalisasi, tokenisasi, stopword removal, dan stemming. Pelabelan sentimen dilakukan menggunakan pendekatan berbasis leksikon, sedangkan fitur diekstraksi menggunakan Term Frequency–Inverse Document Frequency (TF-IDF). Klasifikasi sentimen dilakukan menggunakan Naïve Bayes dan Support Vector Machine (SVM), sementara pemodelan topik dilakukan menggunakan Latent Dirichlet Allocation (LDA). Hasil pelabelan menunjukkan 4.801 komentar (85,92%) positif dan 787 komentar (14,08%) negatif. Evaluasi menunjukkan Naïve Bayes memperoleh akurasi 94,72%, sedangkan SVM mencapai 98,39%, sehingga SVM memberikan performa terbaik. Selain itu, LDA berhasil mengidentifikasi tiga topik dominan, yaitu pengalaman pengguna, kualitas layanan, dan diskusi umum tentang AI. Hasil penelitian menunjukkan bahwa kombinasi analisis sentimen dan pemodelan topik mampu memberikan gambaran yang lebih komprehensif mengenai persepsi pengguna terhadap aplikasi AI berdasarkan komentar di YouTube.
Engagement–sentiment gap analysis of tiktok skincare content using IndoBERT: a comparative study of random forest and XGBoost Dwita Amalia Rizki; Andy Prasetyo Utomo; Zainur Romadhon
Journal of Soft Computing Exploration Vol. 7 No. 3 (2026): September 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i3.212

Abstract

Social media platforms, particularly TikTok, have become important sources of consumer-generated information that influence product awareness and purchasing decisions in the skincare industry. However, audience engagement metrics alone cannot fully explain audience perception because highly engaging content does not always receive positive responses from users. This study aims to propose and evaluate an Engagement–Sentiment Gap framework that integrates audience interaction and audience perception to provide a more comprehensive evaluation of TikTok skincare content than conventional sentiment analysis or engagement-based approaches. A dataset comprising 20,688 comments and metadata from 603 TikTok videos across ten skincare-related topics was collected through web scraping. After text preprocessing, sentiment classification was performed using a fine-tuned IndoBERT model, followed by video-level sentiment aggregation, engagement score calculation, Engagement–Sentiment Gap categorization, and engagement-level prediction using Random Forest and XGBoost. The results showed that neutral sentiment dominated the discussions (72.5%), while audience sentiment exhibited a statistically significant but weak positive correlation with engagement (r = 0.2283, p < 0.001). Random Forest achieved the best predictive performance, with 65.27% Balanced Accuracy and 68.74% ROC-AUC, while topic was identified as the most influential predictive feature. These findings demonstrate that integrating sentiment and engagement within the proposed framework provides richer insights into audience behavior and content performance than evaluating engagement or sentiment independently, offering a practical analytical approach for social media content evaluation.
Strengthening Literacy and Broadcasting Skills of Individuals with Visual Impairments through the Broadcast Braille Book Much Arsyad Fardani; Siti Masfuah; Fina Fakhriyah; Zainur Romadhon
Jurnal Panjar: Pengabdian Bidang Pembelajaran Vol. 6 No. 1 (2026)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/panjar.v6i1.63084

Abstract

Inclusive education requires accessible learning media for persons with visual impairments. This community engagement program aimed to strengthen literacy, speaking, and public-speaking skills through Broadcast Braille Book. The program was conducted at PPS Pendowo Kudus using a participant active learning–project oriented approach involving teachers and beneficiaries. The activities included needs analysis, training, mentoring, media development, implementation, and evaluation. Broadcast Braille Book integrates Braille text, audio, and QR codes linked to instructional videos. The evaluation involved 15 teachers and 29 beneficiaries. The results showed that 24 beneficiaries (82.7%) experienced improved literacy skills, while 22 beneficiaries (75.8%) improved their speaking and public-speaking skills. In addition, 13 teachers (86.6%) improved their technological pedagogical competence, and 25 beneficiaries (86.2%) were able to use the media effectively. These results indicate that Broadcast Braille Book can support accessible learning, literacy development, communication skills, and learning independence among persons with visual impairments. 
Explainable Ensemble Learning for Identifying Digital Citizenship Drivers Among Generative AI Users: A SHAP-Based Behavioral Analysis Muchammad Thoha; Andy Prasetyo Utomo; Zainur Romadhon
Journal of Information System Exploration and Research Vol. 4 No. 3: July 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i3.79

Abstract

The increasing integration of Generative Artificial Intelligence (Generative AI) into higher education has transformed how university students learn and interact within digital environments. However, the psychological and behavioral factors influencing responsible Digital Citizenship behavior among Generative AI users remain insufficiently understood. Existing studies have largely relied on descriptive, regression-based, or structural modeling approaches that may overlook complex nonlinear behavioral relationships. This study proposes an explainable ensemble learning framework to identify the dominant drivers of Digital Citizenship among university students using Generative AI technologies. A national-scale dataset consisting of 2,008 students from 41 universities across 22 provinces in Indonesia was analyzed using Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). Model performance was evaluated using R², MAE, RMSE, and cross-validation procedures. To improve transparency and interpretability, SHAP (Shapley Additive Explanations) was employed to examine feature importance and nonlinear behavioral effects. The results showed that XGBoost achieved the highest predictive performance. SHAP analysis consistently identified technology anxiety, self-efficacy, and attitude toward behavior as the most influential predictors of Digital Citizenship. The findings further revealed nonlinear effects of technology anxiety on responsible AI participation. This study contributes to explainable educational analytics by providing interpretable insights into responsible AI behavior and supporting evidence-based AI governance and digital education policy in higher education.
Implementasi Algoritma Random Forest Untuk Deteksi Dini Kesehatan Mental Remaja Di Kabupaten Kudus Menggunakan Instrumen Self-Reporting Questionnaire 20 Lestari, Dwi Kusnadia; Supriyono; Romadhon, Zainur
Jurnal Teknologi Informasi dan Ilmu Komputer Vol. 13 No. 5: Oktober 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026135

Abstract

Kesehatan mental remaja ialah tantangan global serius, namun kesadaran untuk melakukan skrining dini masih rendah akibat adanya stigma sosial. Studi ini mempunyai tujuan guna menerapkan algoritma Random Forest dalam mendeteksi dini kondisi kesehatan mental kelompok usia remaja akhir sampai dewasa awal (rentang usia 17-28 tahun) di Kabupaten Kudus. Kuantitatif eksperimental diimplmentasikan selaku metode studi ini dengan pendekatan pengembangan sistem. Data primer didapat melewati teknik purposive sampling dengan jumlah sampel sejumlah 500 responden yang dihitung menggunakan rumus Slovin. Skrining dilakukan dengan mengintegrasikan instrumen klinis baku Self-Reporting Questionnaire 20 (SRQ-20) dari WHO, tersusun atas 20 pertanyaan dikotomis. Jawaban responden kemudian ditransformasikan menjadi data biner untuk diklasifikasikan oleh model machine learning ke dalam kategori "Sehat" atau "Terindikasi gangguan mental". Sistem ini diimplementasikan dalam bentuk aplikasi berbasis web memakai bahasa pemrograman Python serta basis data MySQL. Evaluasi kinerja model dilaksanakan memakai Confusion Matrix guna memastikan tingkat presisi, akurasi, dan recall yang optimal. Platform skrining mandiri ini diharapkan dapat mempermudah remaja di Kabupaten Kudus dalam mendapatkan bantuan awal secara cepat, objektif, dan terstandardisasi tanpa rasa ragu.   Abstract Adolescent mental health is a serious global challenge, yet public awareness of early screening remains low due to social stigma. This investigation aims to implement the Random Forest algorithm for the early detection of mental health conditions among late adolescents and young adults (aged 17-28 years) in Kudus Regency. The method employed is experimental quantitative research combined with system development approaches. Primary data were gathered through a purposive sampling technique, resulting in 500 respondents determined via the Slovin formula. The screening framework integrates the standardized Self-Reporting Questionnaire 20 (SRQ-20) instrument from WHO, which consists of 20 dichotomous questions. Respondents' answers are transformed into binary format to be processed by the machine learning model into two classes: "Healthy" or "Indication of Mental Disorder". The system is deployed as a web-based application built using the Python programming language and MySQL database. Model performance is evaluated using a Confusion Matrix to guarantee optimal accuracy, precision, and recall metrics. This independent screening platform is expected to provide adolescents in Kudus Regency with rapid, objective, and standardized initial assessments, facilitating early intervention without social hesitation.
Co-Authors Afilda Maharani Alvin Rainaldy Hakim Andy Prasetyo Utomo Andy Prasetyo Utomo Anteng Widodo Catur Edi Widodo Diana Laily Fithri Dwita Amalia Rizki Eko Sediyono Fajar Nugraha Fikri Hamdhan Dwi Saputra Fina Fakhriyah Firnanda, Amalia Gudnanto, Gudnanto Henry Suryo Bintoro Hidayat, Hilmi Bayu Intan Firdausatul Salsabilla Lestari, Dwi Kusnadia Mimbar Maulana, Bintang Sultan Mochammad Imron Awalludin Much Arsyad Fardani Muchammad Thoha Muhammad Wifqi Aufal Maulana Myllikha Putri Izza Nanda Bagus Yulianto Narendra Saputra Noor Latifah Prapti Madyo Ratri Pratomo Setiaji R Rhoedy Setiawan Rosyada, Mila Ruby, Arcivid Chorynia Setyawan, Moh Dodi Siti Masfuah Soni Adiyono SUPRIYONO Supriyono Supriyono Supriyono Susanti, Nanik Yudie Irawan