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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Ulul Albab: Jurnal Studi Islam MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Prosiding SNATIKA Vol 01 (2011) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Knowledge Engineering and Data Science Applied Information System and Management Letters in Information Technology Education (LITE) Journal of Applied Data Sciences Science in Information Technology Letters International Journal of Engineering, Science and Information Technology Bulletin of Social Informatics Theory and Application Jurnal ilmiah teknologi informasi Asia
Fachrul Kurniawan
Universitas Islam Negeri Malang
Religion Arts Humanities Astronomy 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 Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analysis of the Impact of Meteorological Factors on Predicting Air Quality in South Tangerang City using Random Forest Method Kadir, Nurchaerani; Faisal, M.; Kurniawan, Fachrul
Applied Information System and Management (AISM) Vol 7, No 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.38466

Abstract

Air pollution has become one of the most significant environmental problems in many cities throughout the world, which can endanger public health and the environment. Understanding the impact of meteorological conditions on air quality is very important to understanding air pollution patterns. This study investigates the influence of meteorological variables on air quality predictions in South Tangerang City, Indonesia, using the Random Forest method. Modeling is carried out by building two scenarios, namely predictions using meteorological variables and predictions without meteorological variables. Prediction performance analysis is measured using MAE, MSE, RMSE, R-square, and accuracy. The accuracy results of the research show that predictions without meteorological variables provide good prediction results with a value of 86.42%, but predictions with meteorological variables have better performance with a value reaching 98.99%. The largest error values from each model were 2.58 MAE, 71.82 MSE, and 8.4747 RMSE obtained in prediction modeling without meteorological variables, while the smallest error values were obtained in prediction modeling using meteorological variables, namely 0.00, 0.01, and 0.0219, respectively, for MAE, MSE, and RMSE. This research contributes to a better understanding of the relationship between meteorology and air pollution and air quality in urban areas and helps develop targeted mitigation strategies to improve air quality and public health, especially in South Tangerang City and the surrounding area.
Retaining humorous content from marked stand-up comedy text Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul; Voliansky, Roman; Cengiz, Korhan
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1812

Abstract

Identifying humor in stand-up comedy texts has distinct issues due to humor's subjective and context-dependent characteristics.  This study introduces an innovative method for humor retention in stand-up comedy content by employing a pre-trained BERT model that has been fine-tuned for humor classification.  The process commences with the collection and annotation of a varied assortment of stand-up comedy writings, categorized as hilarious or non-humorous, with essential comic elements like punchlines and setups highlighted to augment the model's comprehension of humor.  The texts undergo preprocessing and tokenization to be ready for input into the BERT model. Upon refining the model using the annotated dataset, predictions regarding humor retention are generated for each text, yielding classifications and confidence scores that reflect the model's certainty in its predictions.  The criterion for prediction confidence is set to categorize texts as "retaining humor."  The results indicate that prediction confidence is a dependable metric for humor retention, with elevated confidence scores associated with enhanced accuracy in comedy classification.  Nonetheless, the analysis reveals that text length does not affect the model's confidence much, contradicting the presumption that lengthier texts are more prone to comedy.  The findings underscore the significance of environmental and linguistic elements in comedy detection, indicating opportunities for model enhancement.  Future efforts will concentrate on augmenting the dataset to encompass a broader range of comic styles and integrating more contextual variables to improve prediction accuracy, especially in intricate or ambiguous comedic situations
A contest of sentiment analysis: k-nearest neighbor versus neural network Kurniawan, Fachrul; Supriyatno, Triyo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1625-1633

Abstract

Discourse about public matters often encompasses sentences that address topics emerging within societal contexts, including issues related to Islamophobia. Debates surrounding this subject frequently evoke support and opposition within digital platforms and interpersonal interactions. Categorizing such dialogic expressions within online media facilitates an evaluation of their negative and positive implications. This study employs two distinct methodologies, specifically deep learning and machine learning techniques, to visualize the findings by implementing dual algorithms. According to the comparative analysis, deep learning achieves a higher accuracy rate of 78%, whereas machine learning achieves a rate of 71%. Thus, deep learning is a better method for textual data classification.
Enhancing Teks Summarization of Humorous Texts with Attention-Augmented LSTM and Discourse-Aware Decoding Supriyono, Supriyono; Wibawa, Aji Prasetya; Suyono, Suyono; Kurniawan, Fachrul
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.v5i3.932

Abstract

Abstractive summarization of humorous narratives presents unique computational challenges due to humor's multimodal, context-dependent nature. Conventional models often fail to preserve the rhetorical structure essential to comedic discourse, particularly the relationship between setup and punchline. This study proposes a novel Attention-Augmented Long Short-Term Memory (LSTM) model with discourse-aware decoding to enhance the summarization of stand-up comedy performances. The model is trained to capture temporal alignment between narrative elements and audience reactions by leveraging a richly annotated dataset of over 10,000 timestamped transcripts, each marked with audience laughter cues. The architecture integrates bidirectional encoding, attention mechanisms, and a cohesion-first decoding strategy to retain humor's structural and affective dynamics. Experimental evaluations demonstrate the proposed model outperforms baseline LSTM and transformer configurations in ROUGE scores and qualitative punchline preservation. Attention heatmaps and confusion matrices reveal the model's capability to prioritize humor-relevant content and align it with audience responses. Furthermore, analyses of laughter distribution, narrative length, and humor density indicate that performance improves when the model adapts to individual performers' pacing and delivery styles. The study also introduces punchline-aware evaluation as a critical metric for assessing summarization quality in humor-centric domains. The findings contribute to advancing discourse-sensitive summarization methods and offer practical implications for designing humor-aware AI systems. This research underscores the importance of combining structural linguistics, behavioral annotation, and deep learning to capture the complexity of comedic communication in narrative texts.
Clustering-Based Adaptive UX in E-Learning Systems: Aligning Microservices with the 4C Framework Belluano, Poetri Lestari Lokapitasari; Patmanthara, Syaad; Ashar, Muhammad; Kurniawan, Fachrul; Kurubacak, Gulsun
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.884

Abstract

This study introduces a clustering-driven adaptive User Experience (UX) architecture for e-learning systems, aligning machine learning segmentation with the 21st-century 4C educational framework (critical thinking, communication, collaboration, creativity). The objective is to dynamically personalize digital learning interactions through a microservices architecture responsive to users' UX profiles. A quantitative survey was conducted involving 50 active users of Shopee and Tokopedia, whose interaction feedback was mapped using the User Experience Questionnaire (UEQ). Three unsupervised clustering techniques—KMeans, Agglomerative, and DBSCAN—were compared. KMeans outperformed the others with a silhouette score of 0.157, compared to 0.146 for Agglomerative and −0.017 for DBSCAN, identifying three meaningful clusters representing high, medium, and low UX proficiency. A one-way ANOVA test confirmed statistically significant differences (p 0.01) among the clusters in dimensions such as error clarity, support responsiveness, and user confidence. These UX profiles were then mapped to individualized microservices: Cluster 0 received autonomous content with minimal support, Cluster 1 was offered guided prompts, and Cluster 2 was provided with simplified interfaces and proactive assistance. Each cluster was aligned with specific 4C competencies to ensure pedagogical relevance. The proposed architecture, built with gRPC-based microservices, enabled asynchronous, low-latency personalization based on user cluster membership. The novelty of this research lies in its dual alignment—technological (microservices + machine learning) and educational (4C competency mapping)—to construct a scalable and responsive e-learning environment. The system design, although validated through simulation, demonstrates a practical foundation for future deployment in platforms like Moodle or OpenEdX. By linking behavioral UX clustering to pedagogical intervention strategies, this study offers a model for adaptive, data-informed instructional systems that are both scalable and learner-centered.
Prediksi Rating Film IMDb Menggunakan Decision Tree Ilmi, Rifqy Rosdiyah; Kurniawan, Fachrul; Harini, Sri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106615

Abstract

Industri Film bukan hanya industri atau pusat hiburan semata melainkan menjadi pusat bisnis global. Popularitas atau kesuksesan film box office  selalu menjadi perhatian di seluruh dunia. Data kesuksesan atau popularitas film saat ini tersedia secara online. IMDb merupakan satu dari sekian situs daring penyedia informasi yang berkaitan dengan film, acara televisi, yang meliputi sinopsis, daftar pemain, ulasan penilaian, dan tentunya pemberian rating film. Keberhasilan film dapat ditandai dengan perolehan rating yang tinggi. Prediksi rating film menjadi topik menarik untuk menilai keberhasilan film baik yang telah diproduksi maupun yang belum diproduksi. Pada penelitian ini, dilakukan prediksi nilai rating film menggunakan metode decision tree. Hasil dari penelitian ini diperoleh kesimpulan bahwa atribut popularitas film dan nilai vote user pada laman IMDb berpengaruh terhadap nilai rating film. Nilai akurasi penggunaan model decision tree pada data training, validasi dan testing bertuturt – turut adalah 0,7529, 0,7237 dan 0,7079. AbstractThe film industry is not just an industry or entertainment but also a global business center. The popularity or success of box office movies has always been a concern around the world. Data on the success or popularity of a movie is currently available online. IMDb is one of the many online sites that provide information related to movies, television shows, which include synopsis, cast lists, rating reviews, and of course movie rating assignments. Prediction of movie ratings is an interesting topic to assess the success of films that have been produced and those that have not been produced. Prediction of movie ratings values can be modeled through machine learning using the decision tree model. From this research, it can be concluded that the popularity of the film and the value of user votes on the IMDb page have an effect on the film rating value. The accuracy values of using the descision tree model in training data, validation and testing are respectively 0.7529, 0.7237 and 0.7079.
Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions Kurniawan, Fachrul; Astawa, I Nyoman Gede Arya; Atmaja, I Made Ari Dwi Suta; Wibawa, Aji Prasetya
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 2 (2023): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i2.3249

Abstract

The Covid-19 pandemic has made it imperative to prioritize health standards in companies and public areas with a large number of people. Typically, officers oversee the usage of masks in public spaces; however, computer vision can be employed to facilitate this process. This study focuses on the detection of facemask usage utilizing the YOLO-v5 algorithm across various datasets and resolutions. Three datasets were employed: the face with mask dataset (M dataset), the synthetic dataset (S dataset), and the combined dataset (G dataset), with image resolutions of 320 pixels and 640 pixels, respectively. The objective of this study is to assess the accuracy of the YOLO-v5 algorithm in detecting whether an individual is wearing a mask or not. In addition, the algorithm was tested on a dataset comprising individuals wearing masks and a synthetic dataset. The training results indicate that higher resolutions lead to longer training times, but yield excellent prediction outcomes. The system test results demonstrate that face image detection using the YOLO-v5 method performs exceptionally well at a resolution of 640 pixels, achieving a detection rate of 99.2 percent for the G dataset, 98.5 percent for the S dataset, and 98.9 percent for the M dataset. These test results provide evidence that the YOLO-v5 algorithm is highly recommended for accurate detection of facemask usage.
Acceptance and Success Model for AI Use in Higher Education: Development, Instrument Decomposition, and Its Triangulation Testing Subiyakto, Aang; Huda, Muhammad Q; Hakiem, Nashrul; Suseno, Hendra B; Arifin, Viva; Azmi, Agus N; Sani, Asrul; Yuniarto, Dwi; Hartawan, Muhammad S; Suryatno, Agung; Muji, Muji; Kurniawan, Fachrul; Kusumawati, Ririen; Balogun, Naeem A; Ahlan, Abd. Rahman
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.619

Abstract

Prior social computing studies described that the performance of technology products is about how the product use benefits the users, including Artificial Intelligence (AI). To have an impact, ensuring how AI is used is a prerequisite after the development. Furthermore, its use is also influenced by how users accept AI. This study aimed to develop an acceptance and success model of AI use in the higher education world from the user perspective, to decompose the model into its instrument level, and to test the validity and reliability of the research instrument. The researchers developed the model by adopting and combining the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM) and adapting the proposed model in the context of AI use in higher education learning. The measurement items were derived from definitions of the variables and indicators of the model. The instrument was tested sequentially using triangulation methods. The quantitative testing was online survey with about 51 respondents and the qualitative one was interview involving five experts. This study may contribute methodologically as one of the guidance for novice scholars in similar works. It may relate to the clarity of the research procedure and the implementation of the mixed testing methods. Of course, the assumptions, samples, and data used in the study cannot be generalized for the other studies. Referring to the model development, the proposed model may not cover the other factors related to the ethical, cultural, and organizational barriers for adopting AI. These barriers may also affect its acceptance and success. Thus, the adoption of the factors related the barriers may also be interesting to study further.
Systematic Literature Review: Application of Interactive Educational Games ‘Climate Change and Mitigation Effort’ Hadi, Muhammad Aulia Syamsul; Kurniawan, Fachrul
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14092

Abstract

The purpose of this Systematic Literature Review (SLR) is to explore the role of interactive educational games in increasing public awareness about climate change and the mitigation strategies that can be adopted. The review examines how educational games can enhance understanding of climate change by integrating narratives, simulations, and gamification. A systematic approach was used to collect and analyze 28 relevant academic papers, focusing on interactive games used to teach climate change. The methodology involved identifying studies from various databases, applying specific inclusion and exclusion criteria, and synthesizing findings from studies that explore the effectiveness of games in environmental education.The review found that interactive educational games, especially those utilizing augmented reality (AR), simulation, and narrative-based approaches, are effective tools for raising awareness about climate change. These games engage players by simulating real-world environmental challenges and offering mitigation solutions. However, the effectiveness varies depending on the audience's age, background, and technical skills. Challenges such as limited access to technology and differing levels of engagement across age groups were identified, but these can be addressed by using more accessible mobile platforms and gamified learning experiences. This SLR contributes to the understanding of how interactive games can be a valuable tool in climate change education. It highlights the potential of combining emerging technologies like AR and machine learning with traditional educational methods to create engaging and effective learning experiences. The paper provides insights into the current state of research on game-based climate change education. 
A hybrid GoogLeNet–GLCM feature extraction framework for textural representation of post-disaster building damage imagery Amani, Holidiyatul; Almais, Agung Teguh Wibowo; Abidin, Zainal; Nugroho, Fresy; Kurniawan, Fachrul; Sugiharto , Tomy Ivan
Jurnal Ilmiah Teknologi Informasi Asia Vol 20 No 1 (2026): Volume 20 Issue 1 2026 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v20i1.1214

Abstract

Accurate representation of visual characteristics in post-disaster building imagery is crucial for downstream analytical tasks such as damage interpretation, retrieval, and automated assessment. This study presents a focused investigation of feature extraction using a hybrid approach that integrates deep semantic representations from the GoogLeNet architecture with statistical texture descriptors inspired by the Gray-Level Co-Occurrence Matrix (GLCM). The objective of this work is limited strictly to the generation and analysis of semantic–textural feature vectors rather than the development or evaluation of any classification or prediction model. High-level feature maps are obtained from a selected convolutional layer of GoogLeNet, after which statistical texture properties—contrast, energy, and homogeneity—are computed per channel. A representative set of feature channels is analyzed to demonstrate the capabilities of the proposed hybrid extraction pipeline. The results demonstrate the potential of semantic–textural descriptors to provide interpretable feature characteristics in building-damage imagery. This study provides a methodological foundation and analytical insight for future works that may incorporate these feature representations into classification, clustering, or decision-support frameworks.
Co-Authors ., Rahmadwati Aang Subiyakto Abd. Rahman Ahlan Ady Wicaksono Agung Teguh Wibowo Almais Aji Prasetya Wibawa Ajib Hanani Albas, Juan Alfachruddin, M. Nabil Fahd Amani, Holidiyatul Andri Pranolo Anggraini, Desi Anis Anusua Ghosh, Anusua Artanti, Vera Ashar, Muhammad Asrul Sani Atmaja, I Made Ari Dwi Suta Az, Suharyono Azmi, Agus N Balogun, Naeem A Binti Umayah, Binti Camargo, Jair Cengiz, Korhan Dwi Yuniarto Endang Setyati Esther Irawati Setiawan Fachry Khusaini Fresy Nugroho Gianto Widodo Hadi, Muhammad Aulia Syamsul Hani Nurhayati Hartawan, Muhammad S Hoz, César De La Huda, Muhammad Q Hudiono, Subroto Prasetya I Nyoman Gede Arya Astawa Ikhlayel, Mohammed Ilmi, Rifqy Rosdiyah Iqbal Bachtiar, Mohammad Jihad Hammad, Jihad Joan Santoso Kadir, Nurchaerani Karisma, Alfiana Intan Koeshardianto, Meidya Kurubacak, Gulsun Laili, Ufil Hidayatul Leonel Hernandez, Leonel Lokapitasari Belluano, Poetri Lestari M. Faisal Mochammad Hariadi Mubdik, M. Ahsanul Muhammad Faisal Muji, Muji Nashrul Hakiem Nia, Nur Jazilah Purnomo Budi Santoso Purwanto, Christian Nathaniel Qurrohman, Taufiq Ridho, Faiz Mohammad Ririen Kusumawati Siti Helmyati Sri Harini Sugiharto , Tomy Ivan Sunu Jatmika, Sunu Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Supriyono Suryatno, Agung Suseno, Hendra B Suyono Suyono Suyono Syaad Patmanthara Triyo Supriyatno Viva Arifin Voliansky, Roman W, Delliana Eka Wahyu Setiawan Yunifa Miftachul Arif Zainal Abidin