Article Per Year (5 Year)
p-Index From 2021 - 2026
4.495
P-Index
This Author published in this journals
All Journal Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics The Asian Journal of Technology Management (AJTM) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Knowledge Engineering and Data Science Network Engineering Research Operation [NERO] Indonesian Journal of Electrical Engineering and Computer Science Frontier Energy System and Power Engineering Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal ilmiah teknologi informasi Asia Cendekia: Jurnal Pengembangan Kurikulum dan Pendidikan Journal of Information Technology Jurnal Abdimas Mahakam
Sulistyo, Danang Arbian
Institut Teknologi Dan Bisnis ASIA Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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

Found 28 Documents
Search

 1   2   3 
Analisis Sentimen Kebijakan Makan Bergizi Gratis Menggunakan IndoBERT dan Machine Learning Sulistyo, Danang Arbian; Setiadi, Erik
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10546

Abstract

Social media has increasingly become a primary channel for the public to express opinions and reactions toward government policies, including Indonesia’s Makan Bergizi Gratis (MBG) program. The large volume of online discussions and the diversity of publicperspectives highlight the importance of sentiment analysis to assess public acceptance and identify potential challenges in policy implementation. This study aims to analyze the distribution of public sentiment toward the MBG policy and to evaluate the performance of machine learning models for sentiment classification. The dataset consists of 12,389 Indonesian-language tweets collected from platform X. Sentiment labeling was performed automatically using a hybrid labeling approach that combines a domain-specific lexicon-based method with the IndoBERT deep learning model to improve contextual understanding. Sentiment classification was conducted using hybrid features, including TF-IDF trigrams, IndoBERT embeddings, and lexicon-based features. Three classification modelsRandom Forest, XGBoost, and an Ensemble modelwere trained and evaluated on a SMOTE-balanced dataset to address class imbalance. The results reveal that public sentiment toward the MBG policy is predominantly negative (68.6%), followed by positive (19.5%) and neutral (11.9%) sentiments. Among the evaluated models, Random Forest achieved the best performance, obtaining an F1-score of 0.9383 using K-Fold cross-validation and 0.9363 on the final test set. This study concludes that the proposed hybrid approach is effective and reliable for classifying public sentiment toward government policies in the Indonesian language.
Analisis Sentimen Ulasan Pemain Genshin Impact Menggunakan Kombinasi TF-IDF, Lexicon, dan Support Vector Machine Fahrudillah, Mochammad Fiqi; Sulistyo, Danang Arbian
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10553

Abstract

The rapid growth of the digital gaming industry in Indonesia has been accompanied by a significant increase in user-generated reviews on distribution platforms such as Google Play Store. This condition necessitates automated methods capable of efficiently interpreting player perceptions on a scale. This study conducts sentiment analysis on player reviews of Genshin Impact by developing a seven-stage analytical pipeline consisting of data preparation, lexicon-based labeling, TF-IDF feature extraction, Support Vector Machine (SVM) training, multi-metric evaluation, rule-based post-processing, and automated summarization using a Large Language Model. A total of 40,000 reviews from 2023 until 2025 were collected through web scraping and processed through text cleaning, slang normalization, tokenization, stopword removal, and stemming. Initial labels were generated using an updated domain-specific sentiment lexicon and subsequently refined through a rule-patch mechanism that handles negation, contrastive expressions, and domain-specific technical cues such as lag, bug, and crash. The SVM model was trained using a TF-IDF configuration (1–3 grams) and evaluated across 10 runs with different random seeds, producing an average accuracy of 0.945, a macro-F1 of 0.900, and stable performance across iterations. Visualization of sentiment distribution and WordClouds highlights prominent thematic patterns within each class, while automated summarization using IBM Granite provides qualitative insights into player appreciation of visual and character design, alongside complaints related to performance issues and the game’s gacha system. Overall, the integration of statistical, rule-based, and LLM-driven approaches demonstrates an effective and contextually robust framework for sentiment analysis in game analytics
Peningkatan Literasi Pengetahuan Kesehatan dan Teknologi untuk Pencegahan dan Deteksi Penyakit Menggunakan Digital Image processing Lia Farokhah; Achmad Noercholis; Fadhli Almuiini Ahda; Muhammad Rofiq; Danang Arbian Sulistyo
Jurnal Abdimas Mahakam Vol. 5 No. 02 (2021): Juli
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

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

Abstract

Pada era sekarang, penyakit muncul bervariasi. Alat kesehatan di Indonesia sangat bergantung dengan impor karena beberapa produk yang dibutuhkan tidak diproduksi di dalam negeri. Selain itu, harganya menjadi cukup mahal. Adapun tujuan dari pengabdian ini adalah meningkatkan literasi mengikuti model The European Health Literacy Survey: the 12 subdimensions. Adopsi model ini diharapkan akan pada tahap dimensi menilai atau mengevaluasi informasi yang relevan dengan kesehatan. Metode yang digunakan adalah edukasi masyarakat khususnya perguruan tinggi yang memiliki fokus keilmuan teknologi dan kesehatan untuk meningkatkan literasi kesehatan. Adapun hasil yang didapatkan selama pengabdian melalui kolaborasi webinar adalah cukup bagus untuk meningkatkan literasi kesehatan. Hal ini didasarkan atas fakta saat proses tanya jawab dalam penggalian informasi. kolaborasi dua keilmuan yaitu kesehatan dan teknologi bisa membuat alat deteksi maupun pencegahan penyakit yang lebih murah namun akurat menggunakan sistem cerdas.
Deteksi Bahasa Isyarat Menggunakan Arsitektur YOLOv8 Berbasis Website Sulistyo, Danang Arbian; Rabbani, Muhammad Faruqi
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11070

Abstract

Communication difficulties between the general public and people with hearing impairments due to limited access to real-time detection tools are the primary urgency of this research. This research aims to develop a cross-platform and easily accessible website-based sign language detection system, while implementing the YOLOv8 variant to remain accurate on devices with limited computing resources. The method used is Research and Development (R&D) with the AI Project Cycle framework, which includes data collection, preprocessing, modeling using the YOLOv8n variant, and implementation. The data used is sourced from the Roboflow platform, consisting of hand gesture images divided into 70% training data, 20% validation, and 10% testing. The results show that the YOLOv8n model provides high performance with a precision of 0.932, recall of 0.997, and mAP50 value of 0.995. Additionally, the model achieves an efficient inference speed averaging 2.1 ms. In conclusion, the implementation of YOLOv8 on a website-based successfully creates an accurate and responsive sign language detection system, making it suitable for assisting communication in real-world scenarios
Analisis Sentimen Ulasan Game Stardew Valley pada Steam dan Google Play Tampubolon, Surya Viari; Sulistyo, Danang Arbian
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11217

Abstract

The large number of user reviews on Steam and Google Play platforms makes manual analysis difficult and prone to subjective bias. This study aims to analyze and compare user sentiment toward Stardew Valley game reviews on both platforms using a text mining approach. The data used consist of 25,099 Steam reviews and 25,594 Google Play reviews. The text preprocessing stage includes case folding, cleansing (removal of punctuation and non-alphabetic characters), tokenization, stopword removal, and lemmatization to produce more structured data. Sentiment labeling is performed using the VADER method, followed by feature extraction using TF-IDF and classification using the Multinomial "Naïve Bayes" algorithm. Model evaluation is conducted using 5-Fold Cross Validation with accuracy, precision, recall, and F1-score as evaluation metrics. The results show that most reviews on both platforms have positive sentiment. The classification model achieves an average accuracy of 0.8151 on Steam and 0.8382 on Google Play. In addition, the model obtains an average F1-score (macro average) of 0.55 on Steam and 0.40 on Google Play. These results indicate that the model performs adequately in sentiment classification, although it still has limitations in identifying minority sentiment classes such as negative and neutral.
A Comparative Study of Machine Learning Models for Javanese Wuku Classification: Exploring SVM, Naïve Bayes, and CNN for Cultural Texts Sulistyo, Danang Arbian; Prasetya Wibawa, Aji; Prasetya, Didik Dwi; Ahda, Fadhli Almu'iini; Utama, Agung Bella Putra
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and Area Under the Curve (AUC) under repeated stratified 5-fold cross-validation (10 repeats; 50 runs) to ensure robust estimates. CNN achieved the best performance with Accuracy = 0.92 ± [SD], Macro-F1 = 0.90 ± [SD], and AUC = 0.93 ± [SD], outperforming SVM (Accuracy: 0.87; F1-score: 0.84) and Naïve Bayes (Accuracy: 0.82; F1-score: 0.78). The results underscore CNN’s strong effectiveness for nuanced, context-rich text classification, offering a vital contribution to cultural heritage preservation and advancing Natural Language Processing (NLP) for under-resourced languages. From a knowledge-engineering perspective, predicted Wuku labels can serve as structured metadata to support computational indexing and retrieval of Wuku narratives in cultural information systems. Methodologically, our CNN is a lightweight, small-corpus design that uses tuned regularization (dropout/early stopping) and multi-scale convolution to capture culturally salient n-gram cues, rather than relying on a fixed default TextCNN configuration. Future work involves expanding the dataset and exploring advanced deep learning architectures.
Implementation of SerpApi-Based Google Scholar Monitoring System for Faculty Publication Evaluation Danang Arbian Sulistyo; Farhan Nafi Emillul Fata
J-INTECH ( Journal of Information and Technology) Vol 14 No 02 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i02.2298

Abstract

Evaluating faculty performance based on scientific publication metrics is a crucial tool in higher education quality assurance and accreditation reporting. However, conventional web scraping techniques based on HTML DOM parsing are highly vulnerable to layout changes and anti-bot blocking on Google Scholar. This study aims to develop a web-based Faculty Publication Monitoring System using the Django framework and SerpApi as a stable data extraction solution, following the Waterfall Software Development Life Cycle (SDLC) model. The results demonstrate that the API integration successfully bypasses anti-bot mechanisms with a 100% data extraction accuracy rate within the validated sample, validated through a cross-referencing protocol involving manual checks of 10 randomly sampled faculty profiles against the live Google Scholar database. Furthermore, mass data synchronization for 63 active faculty profiles was securely completed in approximately 180 seconds, while negative testing confirmed successful data normalization of null values. In conclusion, the system enhances institutional administrative efficiency in providing valid publication datasets to support institutional accreditation needs, delivering an estimated time-reduction of over 99%. Given the performance and stability delivered by this architecture, it can be adopted by other institutions facing similar challenges in automated bibliometric monitoring.
Outcome-Based Education Curriculum Development: Conceptual Foundations and Implementation Challenges in Indonesian Higher Education Fadhli Almu'iini Ahda; Suastika Yulia Riska; Danang Arbian Sulistyo
Cendekia: Jurnal Pengembangan Kurikulum dan Pendidikan Vol. 3 No. 1 (2026): Transforming Education in the Digital Era
Publisher : CV Faliha Cendekia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64683/cendekia.v3i1.57

Abstract

The adoption of Outcome-Based Education (OBE) has become the mainstream of higher education curriculum reform in Indonesia, driven by the Indonesian National Qualifications Framework (KKNI), the National Standards for Higher Education, the Merdeka Belajar–Kampus Merdeka (MBKM) policy, and national as well as international accreditation requirements. Its implementation, however, often stops at the documentary level and has yet to reach the transformation of learning processes and assessment. This article is an integrative literature review that aims to (1) examine the conceptual foundations of OBE, (2) synthesise models of outcome-based curriculum development, (3) analyse implementation challenges in the Indonesian context, and (4) formulate a coherent development framework. A search was conducted across 64 sources from the Scopus, ERIC, DOAJ, and Garuda databases for the period 1994-2025, then analysed thematically using Spady’s OBE principles and Biggs’ constructive alignment. The review shows that effective OBE curriculum development requires vertical alignment from the Graduate Profile down to lesson-level learning outcomes, constructive alignment among outcomes, learning experiences, and assessment, and a continuous quality improvement mechanism that closes the loop. The main challenges include shifting lecturers’ mindsets, assessment workload, learning-outcome inflation, limited information systems, and the gap between the written and the enacted curriculum. The article proposes a capacity-building framework and an agenda for further research.
Co-Authors A.N. Afandi Achmad Noercholis Aji Prasetya Wibawa Andika Dwiyanto, Felix Andrew Nafalski Aripriharta - Armanda, David Didik Dwi Prasetya Fadhli Almu'iini Ahda Fadhli Almu’iini Ahda Fahrudillah, Mochammad Fiqi Farhan Nafi Emillul Fata Farokhah, Lia Fransiska Sisilia Mukti Gunawan Gunawan Hanip Afandi I Nyoman Gede Arya Astawa Ida Wahyuni Lilis Widayanti Machrus Tohir Machrus Tohir Moh Ashari Muhammad Rofiq Muhammad Rofiq Philip Faster Eka Adipraja Rabbani, Muhammad Faruqi Setiadi, Erik Siti Maharani, Farah Shafiyah Suastika Yulia Riska Sulthan Shidqi Sulthan Shidqi Tampubolon, Surya Viari Utama, Agung Bella Putra Vivi Aida Fitria Wayan Firdaus Mahmudy Yogie Susdyastama Putra