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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) Network Engineering Research Operation [NERO] Indonesian Journal of Electrical Engineering and Computer Science Frontier Energy System and Power Engineering JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 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 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

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Perancangan dan Pembuatan Website Majelis Ulama Indonesia Kota Batu Malang Farokhah, Lia; Noercholis, Achmad; Ahda, Fadhli Almu’iini; Sulistyo, Danang Arbian; Rofiq, Muhammad
Prima Abdika: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): Volume 4 Nomor 1 Tahun 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar Universitas Flores Ende

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/abdika.v4i1.3746

Abstract

Community service is one of the activities required for lecturers at Institut Teknologi dan Bisnis ASIA Malang every semester. This activity is carried out in groups to distribute knowledge to the community. This activity is in partnership with the Batu City MUI in creating a digital website for distribution of information to the wider community. Problems arise when a partner's website is hacked or damaged by a hacker. The service team wanted to teach how to recover or mitigate after damage, but the technical team could not provide information regarding the website and suggested creating a new website. In the initial stage, this service will create a new website. The method of this service approach is to carry out discussions in group discussion forums (FGD). The results of the discussion were realized in the form of a website for the Batu City MUI. Evaluations were carried out regarding design and functionality requirements. The partners are satisfied but it must be developed further. In ongoing collaboration this website will continue to be developed. After that, training in mitigating data when exposed to hackers will be carried out in the next service.
Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation Ahda, Fadhli Almu'iini; Wibawa, Aji Prasetya; Dwi Prasetya, Didik; Arbian Sulistyo, Danang
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1818

Abstract

Language is a tool humans use to establish communication. Still, the language used is one language and between regions or nations with their languages. Indonesia is a country that has a diversity of second languages and is the fourth most populous country in the world. It is recorded that Indonesia has nearly 800 regional languages, but research activities in natural language processing are still lacking. Minangkabau is an endangered language spoken by the Minangkabau people in Indonesia's West Sumatra province. According to UNESCO, the Minangkabau language is listed as a language that is "definitely endangered," with only around 5 million speakers worldwide. This study uses neural machine translation (NMT) to create a formula based on this information. Neural machine translation, in contrast to conventional statistical machine translation, intends to build a single neural network that can be built up to achieve the best performance. Because it can simultaneously hold memory for a long time, comprehend complicated relationships in data, and provide information that is very important in determining the outcome of translation, LSTM is one of the most powerful machine-learning techniques for translating languages. The BLUE score is utilized in the NMT evaluation. The test results use 520 Minangkabau sentences, conducting tests based on the number of epochs ranging from 100-1000, resulting in optimization using Adam being better than optimization RMSprop. This is evidenced by the results of the best BLUE-1 score of 0.997816 using 1000 epochs.
Comparative Analysis of Ahmad-Yusoff and Jaro-Winkler Approaches for Javanese Language Stemming Andira, Aysza Belia Auly; Ahda, Fadhli Almu'iini; Sulistyo, Danang Arbian
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.7879

Abstract

This research presents a performance comparison between two approaches for identifying the base form of affixed Javanese words: the Ahmad Yusoff Sembok (AYS) rule-based stemming algorithm and the Jaro-Winkler (JW) string similarity approach. Javanese was selected as the focus because of its complex morphological structure, encompassing prefixes, suffixes, infixes, and confixes, along with significant speech-level and dialectal variation, which together pose challenges for natural language processing. The dataset comprises 720 manually annotated word lemma pairs. Evaluation was carried out using precision, recall, F1-score, accuracy, and Cohen’s Kappa, complemented by error analysis on over-stemming and under-stemming cases. Results indicate that JW achieves higher overall performance (83.19% accuracy, 83% F1-score) compared to AYS (73.19% accuracy, 73% F1-score), with AYS producing more over-stemming errors (88 cases) and JW showing more under-stemming errors (47 cases). These outcomes suggest that similarity-based approaches are more effective in addressing Javanese morphological complexity, while also contributing a benchmark dataset of manually annotated Javanese word lemma pairs, a comparative evaluation framework between rule-based and similarity-based approaches, and practical insights for the development of stemming tools in regional languages that currently lack NLP resources.
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

Media sosial telah menjadi forum vital untuk opini publik terhadap kebijakan pemerintah, seperti program "Makan Bergizi Gratis" (MBG) di Indonesia. Memahami sentimen ini sangat penting bagi pemangku kepentingan. Penelitian ini bertujuan untuk (1) menganalisis distribusi sentimen publik terhadap kebijakan MBG dan (2) menentukan model machine learning terbaik untuk klasifikasi sentimen tersebut. Penelitian ini menggunakan 12.389 tweet yang dikumpulkan dari platform X. Metode hybrid labeling, yang mengkombinasikan leksikon berbasis domain dengan IndoBERT, diterapkan untuk melabeli data secara otomatis. Untuk klasifikasi, tiga model (Random Forest, XGBoost, dan Ensemble) dibandingkan menggunakan fitur hybrid (TF-IDF trigram, embedding IndoBERT, dan fitur leksikon) pada dataset yang telah diseimbangkan dengan SMOTE. Hasil penelitian menunjukkan bahwa sentimen publik didominasi oleh sentimen negatif (68,6%), diikuti oleh positif (19,5%) dan netral (11,9%). Model Random Forest menunjukkan kinerja tertinggi, dengan pencapaian F1-Score rata-rata 0.9383 pada K-Fold cross-validation dan 0.9363 pada test set final. Studi ini membuktikan bahwa pendekatan hybrid yang diusulkan sangat efektif untuk klasifikasi sentimen publik berbahasa Indonesia pada domain kebijakan pemerintah.
Analisis Sentimen Ulasan Pemain Genshin Impact Menggunakan Kombinasi TF-IDF, Lexicon, dan Support Vector Machine Sulistyo, Danang Arbian; Fahrudillah, Mochammad Fiqi
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.
Co-Authors A.N. Afandi Achmad Noercholis Aji Prasetya Wibawa Andika Dwiyanto, Felix Andira, Aysza Belia Auly Andrew Nafalski Aripriharta - Armanda, David Didik Dwi Prasetya Fadhli Almu’iini Ahda Fahrudillah, Mochammad Fiqi 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 Pratama, Ficho Pranandasya Andrian Rabbani, Muhammad Faruqi Setiadi, Erik Siti Maharani, Farah Shafiyah Suastika Yulia Riska Sulthan Shidqi Sulthan Shidqi Tampubolon, Surya Viari Vivi Aida Fitria Wayan Firdaus Mahmudy Yogie Susdyastama Putra