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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Tropical and Infectious Disease JOIN (Jurnal Online Informatika) Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization Paediatrica Indonesiana Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JRST (Jurnal Riset Sains dan Teknologi) Dinamisia: Jurnal Pengabdian Kepada Masyarakat JSiI (Jurnal Sistem Informasi) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Gema Ngabdi Jurnal Repositor Indian Journal of Forensic Medicine & Toxicology AL-UMRON: Jurnal Pengabdian kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Basuki, Setio
Universitas Muhammadiyah Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities 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 Medicine & Pharmacology Public Health Social Sciences Other

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Predicting the Sentiment of Review Aspects in the Peer Review Text using Machine Learning Basuki, Setio; Sari, Zamah; Tsuchiya, Masatoshi; Indrabayu, Rizky
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 4, November 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i4.2042

Abstract

This paper develops a Machine Learning (ML) model to classify the sentiment of review aspects in the peer review text. Reviewers use the review aspect as paper quality indicators such as motivation, originality, clarity, soundness, substance, replicability, meaningful comparison, and summary during the review process. The proposed model addresses the critique of the existing peer review process, including a high volume of submitted papers, limited reviewers, and reviewer bias. This paper uses citation functions, representing the author's motivation to cite previous research, as the main predictor. Specifically, the predictor comprises citing sentence features representing the scheme of citation functions, regular sentence features representing the scheme of citation functions for non-citation sentences, and reference-based representing the source of citation. This paper utilizes the paper dataset from the International Conference on Learning Representations (ICLR) 2017-2020, which includes sentiment values (positive or negative) for all review aspects. Our experiment on combining XGBoost, oversampling, and hyper-parameter optimization revealed that not all review aspects can be effectively estimated by the ML model. The highest results were achieved when predicting Replicability sentiment with 97.74% accuracy. It also demonstrated accuracies of 94.03% for Motivation and 93.93% for Meaningful Comparison. However, the model exhibited lower effectiveness on Originality and Substance (85.21% and 79.94%) and performed less effectively on Clarity and Soundness with accuracies of 61.22% and 61.11%, respectively. The combination predictor was the best for the 5 review aspects, while the other 2 aspects were effectively estimated by regular sentence and reference-based predictors.
Named Entity Recognition in Medical Domain: A systematic Literature Review Kusuma, Selvia Ferdiana; Wibowo, Prasetyo; Abdillah, Abid Famasya; Basuki, Setio
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3111

Abstract

Biomedical Named Entity Recognition (BioNER) is essential to bioinformatics because it identifies and classifies biological entities in biomedical texts. With the increasing number of biomedical literature and the rapid progress of the BioNER approach, it is essential to conduct a systematic literature review (SLR) on BioNER. This SLR consolidates existing information and provides directions for future studies in the BioNER field. This review systematically explores scientific journals and conferences published from 2019 to 2024. This research uses PubMed and Scholar as reference search databases because of their affiliation with other well-known publishers such as IEEE, Elsevier, and Springer. The results show a transition from conventional machine learning to deep learning. Neural networks and transformers show better performance in deep learning methods. The datasets often used in BioNER development are BC2GM, BC5CDR, and NCBI-Disease. Precision, Recall, and F1-Score are used in most papers to evaluate model performance. The performance of these models mostly depends on the availability of big annotated datasets and significant computational tools. Therefore, it is vital for future research to address the issues of annotated data and resource availability to build accurate models. Researchers should investigate the creation of ideal designs that lower computing complexity without compromising performance. Overall, this SLR offers a thorough overview of the latest research on BioNER. It provides significant insights for academics and practitioners in bioinformatics and medical research, helping them understand the innovative aspects of BioNER research.
PENDAMPINGAN PEMBANGUNAN WEBSITE DAN KONTEN DIGITAL KREATIF DI ERA 5.0 BERBASIS GENERATIVE ARTIFICIAL INTELLIGENCE (GEN-AI) Basuki, Setio; Faiqurrahman, Mahar; Putri, Valencia Sefiana; Nugraha, Muhammad Daffa; Shafiyah, Rahajeng Febri
Al-Umron : Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): AL-UMRON : Jurnal Pengabdian kepada Masyarakat
Publisher : LEMBAGA PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (LPPM) UNIVERSITAS NAHDLATUL ULAMA SUNAN GIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/alumron.v6i1.4191

Abstract

The use of Generative Artificial Intelligence (Gen-AI) in digital content development and website development is a new approach to digital marketing in the 5.0 era. The community service program spearheaded by Universitas Muhammadiyah Malang (UMM) aims to help schools improve branding, visibility, and promotional effectiveness with AI technology that generates automated content, so that they can create websites and creative content without coding using Gen-AI. The methods used include (i) observation, (ii) digital marketing strategy development, (iii) training module development, (iv) mentoring implementation, and (v) evaluation of results. The program invited 20 teachers from three Secondary Schools. The effectiveness of this program was evaluated through questionnaires before and after mentoring, related to several aspects, namely (i) Understanding of AI, (ii) Utilization of AI to Create Text Teaching Materials (Textbooks), (iii) Utilization of AI to Create Learning Videos, (iv) Utilization of AI to Create Websites. The results showed a significant increase in the aspect of understanding of AI increased from 62% (pre-test) to 83% (post-test), utilization of AI for text teaching materials increased from 64% (pre-test) to 85% (post-test), and utilization of AI to create learning videos and websites increased from 57% (pre-test) to 82% (post-test).
Implementation of Conditional Random Fields Algorithm for Part of Speech Tagging in Madurese Language Rizky Sulaiman; Setio Basuki
Jurnal Sistem Informasi Vol. 12 No. 1 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i1.9989

Abstract

Penelitian ini berfokus pada penerapan Conditional Random Fields (CRF) untuk Part of Speech (POS) Tagging dalam bahasa Madura. Mengingat keterbatasan sumber daya pemrosesan bahasa alami (NLP) untuk bahasa daerah, khususnya bahasa Madura, studi ini bertujuan untuk mengembangkan model POS tagging yang akurat. Dataset yang digunakan berisi 73.051 kata yang dikumpulkan dari berbagai sumber, seperti media sosial, artikel, dan percakapan sehari-hari. Data ini melalui tahapan pra-pemrosesan, termasuk pembersihan, tokenisasi, dan pelabelan manual dengan kategori POS yang mencakup 15 jenis tag. Model CRF dilatih menggunakan fitur morfologis dan kontekstual untuk mengenali pola linguistik dalam bahasa Madura. Model ini mencapai akurasi yang kompetitif sebesar 95%, yang menunjukkan kemampuannya dalam menangkap pola linguistik bahasa Madura secara efektif. Model ini berkinerja baik dalam kategori POS umum seperti kata benda (NN), kata kerja (VB), dan kata sifat (JJ), dengan F1-score sebesar 0,96 untuk kata benda dan 0,89 untuk kata kerja. Namun, tantangan muncul pada kategori yang lebih jarang seperti Foreign Word (FW) dan Adverb (RB), terutama disebabkan oleh variasi dialek dan penggunaan kata serapan. Penelitian ini memberikan kontribusi penting dalam pengembangan sumber daya NLP untuk bahasa daerah dan dapat digunakan dalam berbagai aplikasi seperti penerjemahan otomatis, asisten virtual, serta pelestarian bahasa Madura. Penelitian mendatang disarankan memperluas dataset dan mengeksplorasi model berbasis neural network untuk lebih meningkatkan kinerja POS tagging.
Peran Pelatihan Dan Peningkatan Keterampilan Tenaga Kesehatan Dalam Penanganan Difteri Di Jawa Timur Pada Tahun 2024 Mustikasari, Rahma Ira; Husada, Dominicus; Kartina, Leny; Basuki, Parwati Setiono; Puspitasari, Dwiyanti; Ismoedijanto, Ismoedijanto; Hilwana, Lutifta; Haq, Arini
Jurnal Gema Ngabdi Vol. 7 No. 2 (2025): JURNAL GEMA NGABDI
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jgn.v7i2.585

Abstract

Diphtheria, caused by Corynebacterium diphtheriae, is a significant public health threat, particularly in areas with low immunization coverage. Despite national immunization programs, sporadic outbreaks continue to occur, especially in East Java, which reported the highest number of cases in 2021. The disease is transmitted through respiratory droplets and can lead to severe complications if not diagnosed and treated promptly. Diphtheria mortality can be reduced with appropriate treatment, along with good immunization status. Diagnosis can be made both clinically and through laboratory tests, including culturing the diphtheria bacteria from swabs of affected tissues. This community service program aimed to enhance the capacity of healthcare workers in East Java, specifically in Sampang Regency, to manage diphtheria through training that included both theoretical and practical components. The training methods used included pre- and post-tests to assess knowledge, mini lectures on epidemiology, clinical symptoms, diphtheria vaccination, and management, along with case simulations to improve participants' practical skills. The program was attended by 42 participants from various healthcare professions, including doctors, nurses, health analysts, and surveillance officers The evaluation demonstrated a significant improvement in participants' knowledge after the training. This program contributed meaningfully to enhancing preparedness among local healthcare providers and is expected to support more robust early detection and response systems for diphtheria outbreaks in the future.
Exploiting Vulnerabilities of Machine Learning Models on Medical Text via Generative Adversarial Attacks Akmal Shahib, Maulana; Basuki, Setio; Aulia Arif, Wardhana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 3, August 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i3.2280

Abstract

Significant developments in artificial intelligence (AI) technology have fueled its adoption across a range of fields. The use of AI, particularly machine learning (ML), has expanded significantly in the medical field due to its high diagnostic precision. However, the AI model faces a serious challenge to handle the adversarial attacks. These attacks use perturbed data (modified data), which is unnoticeable to humans but can significantly alter prediction results. This paper uses a medical text dataset containing descriptions of patients with lung diseases classified into eight categories. This paper aims to implement the TextFooler technique to deceive predictive models on medical text against adversarial attacks. The experiment reveals that three ML models developed using popular approaches, i.e., transformer-based model based on Bidirectional Encoder Representations from Transformers (BERT), Stack Classifier that combines three traditional machine learning models, and individual traditional algorithms achieved the same classification accuracy of 99.98%.  The experiment reveals that BERT is the weakest model, with an attack success rate of 76.8%, followed by traditional machine learning methods and the stack classifier, with success rates of 28.73% and 5.21%, respectively. This implies that although BERT classification demonstrates good performance, it is highly vulnerable to adversarial attacks. Therefore, there is an urgency to develop predictive models that are robust and secure against potential attacks.
Transfer Learning Approaches for Non-Organic Waste Classification: Experiments Using MobileNet and VGG-16 Sari, Zamah; Basuki, Setio
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2319

Abstract

This paper develops machine learning (ML) models for classifying non-organic waste automatically. The goal is to support more effective waste management by increasing recycling rates, reducing landfill use, and minimizing environmental impact. The ML models proposed in this paper classify 20 types of non-organic waste collected from the internet, which consists of 2,552 instances. Our experiments reveal several key findings. First, MobileNet, which achieved 86% accuracy, outperforms VGG-16, which reaches only 72% accuracy. Second, both models show good classification performances in classifying glass bottles, toothbrushes, and cigarette butts. Third, both models suffer from misclassification in visually similar categories, especially when it comes to paper-based waste like books, cardboard, foam packaging, and carton packaging. Fourth, MobileNet has difficulty detecting plastic packaging, carton packaging, and books, while VGG-16 exhibits higher misclassification rates for foam packaging, cardboard, and newspapers. These results pose a further critical development of the model to classify non-organic waste with similar textures and shapes. Moreover, it presents the urgency of improving the model to distinguish visually similar waste materials. Considering the number of labels used in this paper compared with existing studies, the findings demonstrate the competitiveness of our models for non-organic waste classification.
Detect Malware in Portable Document Format Files (PDF) Using Support Vector Machine and Random Decision Forest Charim, Abdachul; Basuki, Setio; Akbi, Denar Regata
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.196

Abstract

Portable Document Format is a very powerful type of file to spread malware because it is needed by many people, this makes PDF malware not to be taken lightly. PDF files that have been embedded with malware can be Javascript, URL access, media that has been infected with malware, etc. With a variety of preventive measures can help to spread, for example in this study using the classification method between dangerous files or not. Two classification methods that have the highest accuracy value based on previous research are Support Vector Machine and Random Forest. There are 500 datasets consisting of 2 classes, namely malicious and not malicius and 21 malicius PDF features as material for the classification process. Based on the calculation of Confusion Matrix as a comparison of the results of the classification of the two methods, the results show that the Random Forest method has better results than Support Vector Machine even though its value is still not perfect.
Automatic Categorization of Mental Health Frame in Indonesian X (Twitter) Text using Classification and Topic Detection Techniques Basuki, Setio; Indrabayu, Rizky; Effendy, Nico Ardia
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3328

Abstract

This paper aims to develop a machine learning model to detect mental health frames in Indonesian-language tweets on the X (Twitter) platform. This research is motivated by the gap in automatically detecting mental health frames, despite the importance of mental health issues in Indonesia. This paper addresses the problem by applying classification and topic detection methods across various mental health frames through multiple stages. First, this paper examines various mental health frames, resulting in 7 main labels: Awareness, Classification, Feelings and Problematization, Accessibility and Funding, Stigma, Service, Youth, and an additional label named Others. Second, it focuses on constructing a dataset of Indonesian tweets, totaling 29,068 data, by filtering tweets using the keywords "mental health" and "kesehatan mental". Third, this paper conducts data preprocessing and manual labeling of a random selection of 3,828 tweets, chosen due to the impracticality of labeling all data. Finally, the fourth stage involves conducting classification experiments using classical text features, non-contextual and contextual word embeddings, and performing topic detection experiments with three different algorithms. The experiments show that the BERT-based method achieved the highest accuracy, with 81% in the 'Others' vs. 'non-Others' classification, 80% in the seven main label classifications, and 92% in the seven main labels classification when using GPT-4-powered data augmentation. Topic detection experiments indicate that the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) algorithms are more effective than the Hierarchical Dirichlet Process (HDP) in generating relevant keywords representing the characteristics of each main label.
Assistance in Preparing Engineering Prompts for Muhammadiyah School Teachers to Optimize the Use of ChatGPT in the World of Education: Pendampingan Penyusunan Prompt Engineering Bagi Guru Sekolah Muhammadiyah Untuk Mengoptimalkan Pemanfaatan ChatGPT Di Dunia Pendidikan Basuki, Setio; Faiqurrahman, Mahar; Marthasari, Gita Indah; Indrabayu, Rizky; Zachra, Fatimatus; Effendy, Nico Ardia
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 3 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i3.19522

Abstract

ChatGPT, a widely used Large Language Model (LLM), enhances productivity in various sectors, including education. However, its extensive usage often lacks proficiency in writing effective prompts, resulting in less optimal, biased, and hallucinated outputs. This community service initiative by Universitas Muhammadiyah Malang (UMM) aims to educate teachers on prompt engineering, enabling them to (i) write effective prompts to utilize ChatGPT's potential, (ii) educate about potential biases and hallucinations of ChatGPT, and (iii) integrate ChatGPT into educational practices with integrity. Partnering with three Muhammadiyah Schools, the program trains 7-8 teachers from each institution. The training covers five key areas: (a) prompt engineering introduction, (b) building optimal prompts, (c) leveraging ChatGPT in teaching and learning, (d) prompt engineering for educational material creation, and (e) ethics of LLM usage in professional and academic settings. The effectiveness of this program is evaluated through pre-test and post-test questionnaires. Results indicate a significant improvement in prompt engineering proficiency, rising from 35.3% (pre-test) to 87.5% (post-test), and in the utilization of ChatGPT for learning support, increasing from 23.5% (pre-test) to 81.3% (post-test).
Co-Authors Abdachul Charim Abdillah, Abid Famasya Agus Eko Minarno Akbi, Denar Regata Akmal Shahib, Maulana Alfira Rizky Alimuddin Hasan Al Kabir Aminudin Aminudin Amrul Faruq Aulia Arif, Wardhana Baiduri, Senja Bangkit Putrawan Charim, Abdachul Diany Yogiantoro Dini Tri Purwaningsih Dominicus Husada Dwiyanti Puspitasari, Dwiyanti Edo Ardhiansyah Effendy, Nico Ardia Faiqurrahman, Mahar Faizun Nuril Hikmah Gita Indah Marthasari Haq, Arini Hariyady Hariyady Hendra Saputra Hilman Hilman Hilwana, Lutifta Husada, Dominicius Indrabayu, Rizky Irfan, Muhammad irma fitriani Irwanto Irwanto Ismoedijanto Izzah, Tsabita Nurul Kartina, Leny Khoirir Rosikin Kusuma, Selvia Ferdiana Lina Dwi Yulianti Mahar Faiqurahman Mauridhi Hery Purnomo Mizwar Mizwar Muhammad Fadliansyah Muhammad Nasrul Tsalatsa Putra Muhammad Rizki Muhammad Yusuf Mustikasari, Rahma Ira Novita Daian Marlissa Nugraha, Muhammad Daffa Nur Hayatin Putri, Valencia Sefiana Rima Mediana Mashita Risa Etika, Risa Rizky Sulaiman Rizky, Alfira S, Vinna Rahmayanti Sari, Zamah Shafiyah, Rahajeng Febri Siti Maghfiroh Soegeng Soegiyanto Sumadi, Fauzi Dwi Setiawan Syafaah, Lailis Titin Eka Puspitawati Tsuchiya, Masatoshi Wibowo, Prasetyo Wicaksono, Galih Wasis Wisnujono Soewono Yuda Munarko Yufis Azhar Zachra, Fatimatus Zakiyah Rakhmawati Zamah Sari