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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Soft Computing Exploration NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Journal of Data Insights
Faiq Madani
Universitas Muhammadiyah Semarang
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Analysis of public opinion sentiment against COVID-19 in Indonesia on twitter using the k-nearest neighbor algorithm and decision tree Pambudi, Ryo; Madani, Faiq
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.88

Abstract

COVID-19 has become an ongoing disease pandemic across the globe. The need for information makes social media such as twitter a place to exchange information. This tweet can be used to see public sentiment towards COVID-19 in Indonesia. Sentiment analysis classifies opinions from tweets that have been processed and classified into different sentiments, namely negative, neutral, or positive. The aim of this paper is to find the algorithm that has the best accuracy. The researcher proposes to compare the K-Nearest Neighbors (KNN) and decision tree algorithms to be used in the classification of sentiment data from tweets related to COVID-19 that took place in Indonesia. The results of the evaluation of performance metrics concluded that the decision tree algorithm has a higher level of accuracy than KNN. Decision tree produces accuracy = 0.765, error = 0.235, recall = 0.76, and precision = 0.767 which is better when compared to KNN which produces accuracy = 0.69, error = 0.31, recall = 0.66, and precision = 0.702.
Peningkatan Kompetensi Guru di SMK Muhammadiyah 2 Malang melalui Pelatihan Pengembangan Media Pembelajaran Berbasis Kecerdasan Buatan Faiq Madani; Ahmad Ilham; Muhammad Sam’an; Rima Dias Ramadhani; Akhmad Fathurrohman; Safuan Safuan; Muhammad Munsarif; Lukman Assaffat; Wendy Sarasjati; Dhendra Marutho
Nusantara: Jurnal Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Februari: NUSANTARA Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/nusantara.v6i1.7796

Abstract

This study aims to evaluate the effectiveness of the Artificial Intelligence-Based Learning Media Development Program (P3MP-AI) in enhancing teachers’ technological and pedagogical competencies at SMK Muhammadiyah 2 Malang. The program employed a descriptive approach using both quantitative and qualitative methods, including pre-test and post-test assessments, as well as direct observation of the training process. A total of 30 teachers from various disciplines actively participated in the program conducted on August 12, 2025. The evaluation results revealed an increase in the participants’ average scores from 100 to 130 out of a maximum of 150, indicating a significant improvement in their understanding of AI concepts and applications in education. Beyond competency enhancement, the training also fostered teachers’ confidence, creativity, and ability to integrate AI-based tools into interactive learning media. However, several challenges were identified, such as limited technological resources and time constraints in classroom implementation. Overall, this program has made a tangible contribution to strengthening teachers’ digital literacy and can serve as a replicable professional development model for other vocational schools seeking to advance AI-based educational transformation.
Heart Disease Prediction Using Optimized Weighted K-Nearest Neighbor (WKNN) Faiq Madani; Kusworo Kusworo; Farikhin Farikhin
Jurnal Penelitian Pendidikan IPA Vol 10 No 11 (2024): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i11.9257

Abstract

Heart disease remains a significant challenge in the medical field, particularly in predictive diagnostics. This research aims to present a comprehensive investigation into the development and evaluation of a novel approach for heart disease detection using a Weighted k-Nearest Neighbors (WKNN) method. The method employs Euclidean distance metrics and Gaussian kernel weighting for optimal classification results. The research dataset consists of 200 data points, each with 10 key indicators such as age, sex, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, and ST depression relative to rest. Through rigorous experimentation, it is identified that the optimal value of K for classification is 11, with a sigma value of 1.5 for the Gaussian kernel weighting. During the training and evaluation phase, the proposed WKNN method achieved impressive performance metrics, with an accuracy of 91.8%, precision of 93%, and recall of 91%. These findings underscore the potential of the WKNN model as a reliable tool for heart disease detection, showing great promise for practical application in clinical settings. The results emphasize that the proposed method can contribute significantly to improving diagnostic accuracy for heart disease patients
Hate Speech Detection on X Using K-Nearest Neighbor with TF–IDF and Cosine Similarity: Deteksi Ujaran Kebencian pada X Menggunakan K-Nearest Neighbor dengan TF–IDF dan Kesamaan Kosinus Faiq Madani; Arvanida Feizal Permana; Abdul Karim; Riyagung Nuryusufa Tranggono Adi Prasetya; Wendy Sarasjati
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1149

Abstract

The rapid growth of social media has increased online interactions but has also accelerated the spread of hate speech content that may negatively impact individuals and communities. X (formerly Twitter), as one of the largest social networking platforms, enables users to share opinions publicly, making automatic hate speech detection increasingly important. This research proposes a hate speech classification approach using the K-Nearest Neighbor (KNN) algorithm combined with Term Frequency–Inverse Document Frequency (TF–IDF) weighting and Cosine Similarity. The dataset consists of 900 social media posts collected through the platform API and manually labeled into hate speech and non-hate speech categories, consisting of 675 training data and 225 testing data. Prior to classification, text preprocessing techniques including tokenization, stopword removal, and stemming were applied to improve text quality. Model evaluation was conducted using 10-fold cross validation to assess classification performance. Experimental results showed that the KNN algorithm with Cosine Similarity distance measurement and K=3 parameter achieved an accuracy of 78.22% in hate speech detection tasks. The findings indicate that KNN combined with TF–IDF and Cosine Similarity provides a reliable approach for social media text classification and can support automated hate speech detection systems.
Co-Authors Abdul Karim Ahmad Ilham Akhmad Fathurrohman Arvanida Feizal Permana Dhendra Marutho Juwanda, Farikhin Kusworo Kusworo Lukman Assaffat Muhammad Munsarif Muhammad Sam’an Ramadhani, Rima Dias Riyagung Nuryusufa Tranggono Adi Prasetya Ryo Pambudi, Ryo Safuan Safuan Wendy Sarasjati Wendy Sarasjati