Article Per Year (5 Year)
p-Index From 2021 - 2026
9.543
P-Index
This Author published in this journals
All Journal Jurnal Sarjana Teknik Informatika Jurnal Infinity Scientific Journal of Informatics Jurnal Pengabdian UntukMu NegeRI Jurnal Khatulistiwa Informatika Insect (Informatics and Security) : Jurnal Teknik Informatika Anterior Jurnal Martabe : Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Jurnal Pengabdian Dharma Laksana Jurnal Inti Talafa : Jurnal Teknik Informatika JURNAL TEKNIK JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sains Komputer dan Teknologi Informasi International Journal of Engineering, Science and Information Technology Abdimas: Papua Journal Of Community Service Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Parta: Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Program Pengabdian Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA) JOINCS (Journal of Informatics, Network, and Computer Science) J-ENSITEC (Journal of Engineering and Sustainable Technology) Solo Universal Journal of Islamic Education and Multiculturalism Bulletin of Social Informatics Theory and Application PEDAMAS (Pengabdian Kepada Masyarakat) Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Jurnal Sistem Informasi, Manajemen dan Teknologi Informasi Journal of Computer-based Instructional Media Jurnal Infinity Jurnal Abdimas UM Jambi Jurnal Informatika Medis (J-INFORMED) ABDIMASTEK Profetika: Jurnal Studi Islam Jurnal Wawasan Sarjana
Ika Safitri Windiarti
Universitas Muhammadiyah Palangkaraya
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences 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 Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 66 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 66 Documents
Search

 3   4   5   6   7 
Dari Statis ke Adaptif: Literatur Sistematis Riview tentang Evolusi Sistem Pendukung Keputusan Berbasis Machine Learning Zulmi; Ika Safitri Windiarti
Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Vol. 4 No. 1 (2026): JURNAL PENGEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIAKSI (JUPTIK)
Publisher : Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/juptik.v4i1.4188

Abstract

Penelitian ini bertujuan untuk mengkaji evolusi Decision Support System (DSS) dari sistem statis menuju sistem adaptif berbasis machine learning melalui pendekatan Systematic Literature Review (SLR). Studi ini menganalisis artikel ilmiah dalam rentang waktu 2005-2025 dengan menggunakan kerangka PRISMA untuk memastikan proses seleksi yang sistematis dan transparan. Hasil kajian menunjukkan adanya pergeseran signifikan dalam pengembangan DSS, dari pendekatan berbasis aturan (rule-based) menuju sistem berbasis data dan kecerdasan buatan. Pada periode awal, DSS didominasi oleh model deterministik, kemudian bertransisi pada pendekatan data-driven (2005-2015), hingga berkembang menjadi Adaptive Decision Support Systems (ADSS) pada periode 2015-2025, yang ditunjukkan oleh dominasi sekitar 70% studi berbasis machine learning. Integrasi machine learning terbukti meningkatkan akurasi keputusan (65-75%) dan efisiensi sistem (±60%), serta memungkinkan pembelajaran adaptif dan personalisasi rekomendasi. Namun, penelitian ini juga mengidentifikasi sejumlah tantangan utama, seperti kompleksitas model, rendahnya explainability, keterbatasan pendekatan berbasis pengguna, serta belum adanya kerangka konseptual yang terpadu. Dengan demikian, pengembangan DSS ke depan perlu menekankan keseimbangan antara kecerdasan sistem, transparansi, dan kebutuhan pengguna agar menghasilkan sistem yang adaptif, dapat dipercaya, dan implementatif.
From Scalability to Sustainability: A 20-Year Retrospective on Deep Learning and Parameter-Efficient Fine-Tuning for Text Classification: Dari Skalabilitas ke Keberlanjutan: Tinjauan 20 Tahun tentang Pembelajaran Mendalam dan Penyesuaian Parameter yang Efisien untuk Klasifikasi Teks Andry Rachmadany; Ika Safitri Windiarti
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1711

Abstract

In the area of natural language processing (NLP), especially regarding text classification, earlier methods that relied on traditional machine learning are being increasingly replaced by neural network designs like convolutional neural networks and recurrent neural networks. Additionally, the rise of transformer-based models has led to considerable improvements in performance, though this comes with higher demands for computing power and energy usage. This paper provides a look back at the development of deep learning and Parameter-Efficient Fine-Tuning (PEFT) methods for text classification from 2005 to 2025. The research explores important technological advancements, evaluates the balance between performance, scalability, and efficient computing, and points out the rising concern for sustainability in the development of artificial intelligence. The findings show a transition from strategies aimed at simply increasing scale to those that focus on more efficiency. In this setting, PEFT has become an important advancement in easing the computing load without greatly impacting performance, although it still faces challenges in flexibility and energy consciousness. These insights are anticipated to lay the groundwork for more research into creating environmentally friendly NLP technologies.
Context-Aware Transformer-Based Model for Aspect-Based Sentiment Analysis: A Systematic Literature Review: Model Berbasis Transformer yang Sadar Konteks untuk Analisis Sentimen Berbasis Aspek: Tinjauan Literatur Sistematis Moch. Fauzan; Ika Safitri Windiarti
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1712

Abstract

Aspect-Based Sentiment Analysis (ABSA) is a critical natural language processing task aimed at identifying specific aspects within text and determining the sentiment polarity toward each aspect. Transformer-based models, particularly BERT and its variants, have demonstrated significant advances in ABSA through powerful contextual representations. However, challenges in capturing target-specific context and managing inter-subtask dependencies remain. This Systematic Literature Review (SLR) identifies, evaluates, and synthesizes current research on context-aware transformer models for ABSA, with emphasis on context-aware mechanisms, multi-task learning approaches, and BERT-family models. Following the PRISMA 2020 protocol, a structured search was conducted on the Scopus database using three Boolean queries, yielding 851 initial records. After deduplication (n=70), title/abstract screening (n=554 excluded), retrieval (n=147 not retrieved), and full-text eligibility assessment (n=48 excluded), 32 studies were included for synthesis. Three primary model categories were identified: (1) BERT baselines establishing strong end-to-end ABSA performance; (2) context-aware variants employing context-guided attention (CG-BERT, QACG-BERT, LCF-ATEPC, cascade models); and (3) multi-task transformers (BERT-MTL, RoBERTa-MTL, MTL-AraBERT, SABKG, MLEGCN) handling ABSA subtasks jointly. Reported F1-scores ranged from 50–89% across SemEval-2014/2015/2016 and domain-specific datasets. ntext-aware and multi-task transformer models represent the state of the art in ABSA. Open challenges include implicit aspect handling, cross-domain generalization, model efficiency, and evaluation of large generative language models (LLMs) for fine-grained sentiment tasks.
ANALISIS POLA PENYEBAB KARHUTLA KALIMANTAN TENGAH MENGGUNAKAN INDOBERT DAN CAUSAL PATTERN MINING Fahrizal Maulana; Kusrini; Ika Safitri Windiarti; Sutami
INTI TALAFA Vol. 111 No. 111 (2026)
Publisher : Program Studi Teknik Informatika Universitas Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32534/int.v18i2.8638

Abstract

Forest and land fires in Central Kalimantan are environmental problems influenced by natural factors and human activities, resulting in complex causal relationship patterns. This study aims to analyze wildfire cause patterns in Central Kalimantan using an IndoBERT-based NLP and AI approach integrated with causal pattern mining on online news articles. Data were collected through web scraping from six local and national news portals using nine Google search queries related to wildfires, resulting in 436 relevant articles as the main corpus. The methodological stages included text preprocessing, semantic representation using a 768-dimensional IndoBERT transformer model, topic discovery using BERTopic, and causal pattern analysis through co-occurrence analysis and contextual relation mining. The topic discovery results identified 10 main topics, with the topic “extreme dry season” dominating with 104 data points, followed by “hot weather and drought” (54 data points). Anthropogenic factors such as intentional land burning, land clearing activities, and human negligence were also identified as significant causes. Contextual relation mining results showed that the words “land” (1,473 occurrences), “smoke” (938), “dry season” (334), and “peatland” (288) were the most dominant causal contexts, while co-occurrence analysis generated 37,581 word pairs forming a causal network. Future studies are recommended to integrate spatial data, expand the dataset, and implement knowledge graphs to support real-time wildfire disaster intelligence systems.
ANALISIS POLA PENYEBAB KARHUTLA KALIMANTAN TENGAH MENGGUNAKAN INDOBERT DAN CAUSAL PATTERN MINING Fahrizal Maulana; Kusrini; Ika Safitri Windiarti; Sutami
INTI TALAFA Vol. 333 No. 333 (2026)
Publisher : Program Studi Teknik Informatika Universitas Muhammadiyah Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32534/int.v18i2.8638

Abstract

Forest and land fires in Central Kalimantan are environmental problems influenced by natural factors and human activities, resulting in complex causal relationship patterns. This study aims to analyze wildfire cause patterns in Central Kalimantan using an IndoBERT-based NLP and AI approach integrated with causal pattern mining on online news articles. Data were collected through web scraping from six local and national news portals using nine Google search queries related to wildfires, resulting in 436 relevant articles as the main corpus. The methodological stages included text preprocessing, semantic representation using a 768-dimensional IndoBERT transformer model, topic discovery using BERTopic, and causal pattern analysis through co-occurrence analysis and contextual relation mining. The topic discovery results identified 10 main topics, with the topic “extreme dry season” dominating with 104 data points, followed by “hot weather and drought” (54 data points). Anthropogenic factors such as intentional land burning, land clearing activities, and human negligence were also identified as significant causes. Contextual relation mining results showed that the words “land” (1,473 occurrences), “smoke” (938), “dry season” (334), and “peatland” (288) were the most dominant causal contexts, while co-occurrence analysis generated 37,581 word pairs forming a causal network. Future studies are recommended to integrate spatial data, expand the dataset, and implement knowledge graphs to support real-time wildfire disaster intelligence systems.
Impact of Information Technology: Developments the Related Research in the Last 2 Decades Irman Amri; Ika Safitri Windiarti
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.916

Abstract

This study outlines the potential future direction of research in information technology impact. Based on current findings, we identify key areas where further investigation is needed to advance the understanding and application of information technology. Our discussion highlights gaps in the literature, proposes new methodologies, and suggests an interdisciplinary approach to answering unsolved questions. This research highlights the significant impact of information technology in improving operational efficiency and quality in the education, health, and economic sectors over the past two decades. Using the PRISMA method for systematic literature analysis, this study identifies key trends and developments in information technology and provides recommendations for evidence-based practices and policies. The results of this study are expected to make an essential contribution to theory and practice, offering insights for more informed decision-making and more effective implementation strategies in the use of information technology.
Co-Authors Achmad Rizal Kurniawan Agung Prabowo Agung Prabowo Agung Prabowo - STMIK Palangka Raya Ahyar Junaedi Aidin Najihi Alberto Emmanuel Conti Morales Amar Ma’ruf Andriawan, Deden Andry Rachmadany Auratania Hadisah Sugianto Bella Mailinda Citra Amalia DAFIT AFIANTO Deden Andriawan Desy Selawaty Diki Hidayat Doddy Teguh Yuwono Dwi Haryanto Dwi Haryanto Ellathodi, Mohammed Razi Eltom Ishaq Osman Musa Fahrizal Maulana Fitriani Fitriani Hairil Anwar Haryadi Haryadi Haryadi - Irman Amri Irvan Irwandi Istiawan, Deden Jarinah, Jarinah Jaya Anggatama Kusrini Lutfi Ali Muharrom Meika, Ika Miftahurrisqi Miftahurrisqi Miftahurrisqi, Miftahurrisqi Miftahurrizqi Miftahurrizqi Miftahurrizqi Miftahurrizqi Miftahurrizqi Miftahurrizqi Miftahurrizqi, Miftahurrizqi Mita Sari Moch. Fauzan Mr. Anat Maisu Muh Ashari Muhammad Awaluddin Muhammad Awaluddin Muhammad Haris Qamaruzzaman Muthoifin Najwa Aulia Suwandini Nenden Suciyati Sartika Norhalisa, Siti Norhayati Norjanah Siti Nurul Huda Nurul Huda Pebriansen, Yairus Pratama, Agus Putri, Cici R Rosidin Rachmat Hidayat Rafii, Mohamad Rahmadana, Muhammad Akbar Ramadhan, Haris Hiqqal Rida Priyanti Robetson, Robetson Rommi Kaestria Rufi'i Safrizal Sam'ani Sam'ani Santoso, Arif Gunawan Setio Ardy Nuswantoro Setio Ardy Nuswantoro Sholehah, Fitri Amalia Sidik Praptomo Sujana, Asep Sulistyowati Sulistyowati Sutami Suwandini, Najwa Aulia Syamsul Bahri Ulfi, Muhammad Wawan Joko Pranoto Wawan Joko Pranoto Wiwinti Wiwinti Yaakub, Saleh Yelia, Cica Yeni Dwi Rahayu Zaenal Hakim Zulmi