Garuda - Garba Rujukan Digital

PKM Harmoni Religi, Edukasi, dan Keterampilan bagi Anak Anak Mirah Seruni Makassar Risal, Andi Akram Nur; NFH, Alifya; Akbar, Muhammad; Kaswar, Andi Baso; Surianto, Dewi Fatmawati
TEKNOVOKASI : Jurnal Pengabdian Masyarakat Volume 2: Issue 3 (September 2024)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/teknovokasi.v2i3.5347

Kampung Mirah Seruni adalah komunitas di mana sebagian besar penduduknya tidak bekerja dan sebagai ojek online atau pemulung sampah untuk dijual atau didaur ulang. Kehidupan di Kampung Mirah Seruni sering kali diwarnai oleh tantangan sosial, ekonomi, dan kesehatan. Mereka menghadapi keterbatasan akses terhadap layanan dasar seperti pendidikan yang terabaikan dan kesehatan yang diabaikan. Untuk mengatasi masalah di Kampung Mirah Seruni, pendidikan dan pengembangan keterampilan memegang peran penting. Penyediaan pendidikan formal yang berkualitas dan pusat pendidikan di Kampung Mirah Seruni dapat memberikan akses pendidikan sementara kepada anak-anak dan remaja. Selain itu, program pelatihan keterampilan dapat membantu meningkatkan keterampilan masyarakat setempat. Selain pendidikan formal dan pelatihan keterampilan, kegiatan sosial seperti ice breaking, games, dan penghargaan kepada peringkat pertama dapat digunakan sebagai metode untuk memotivasi dan melibatkan masyarakat Kampung Mirah Seruni dalam proses pembelajaran. Serta Kegiatan keagamaan selama bulan ramadan, juga dapat menjadi sarana untuk memperkuat pendidikan dan nilai-nilai keagamaan di Kampung Mirah Seruni. Kegiatan seperti ceramah agama, lomba adzan, menghafal surah pendek, dan dongeng. Kegiatan ini dapat mempererat kepedulian sosial dapat membantu memperkuat ikatan komunitas dan memberikan nilai-nilai positif kepada masyarakat Kampung Mirah Seruni.

Evaluasi Kinerja Karyawan PT. XYZ dengan Pendekatan Metode Fuzzy Mamdani Muharni, Muharni; Awaliah, Widiarti; Surianto, Dewi Fatmawati
Indonesian Technology and Education Journal Volume 3 No. 1 Februari 2025
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/itej.v3i1.567

Employee performance evaluation is vital for organizational effectiveness, ensuring fair and objective assessments. PT. XYZ, a cement distributor, struggles with subjective evaluation criteria, prompting the need for a more accurate approach. This study applies the Fuzzy Mamdani method to improve performance assessment. Data from 31 employees were collected through literature reviews, interviews, and observations. The system uses salary, age, and years of service as input variables, while the output variable represents employee performance. The Fuzzy Mamdani method processes data through fuzzification, fuzzy inference, and defuzzification to handle uncertainty and enhance evaluation fairness. The results show that 29 employees fall under the "Good" performance category, while 2 employees are classified as "Very Good." This demonstrates that the method provides more precise and consistent assessments compared to traditional techniques. Implementing this approach enables companies to make better-informed human resource decisions and promote employee growth. This study underscores the potential of fuzzy logic in refining performance evaluations, contributing to a more transparent and equitable assessment system

Enhancing K-Means Clustering for Journal Articles using TF-IDF and LDA Feature Extraction Surianto, Dewi Fatmarani; Surianto, Dewi Fatmawati
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5547

Clustering is a fundamental technique in data analysis, particularly in unsupervised learning, to group data with similar characteristics. However, the effectiveness of the K-Means algorithm in text clustering heavily depends on proper feature extraction. This study proposes an enhanced feature extraction approach by integrating Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) to improve clustering performance on journal article datasets. The dataset consists of 427 journal article abstracts collected from Google Scholar. The preprocessing steps include tokenization, stopword removal, and TF-IDF vectorization, followed by topic extraction using LDA, which serves as input features for the K-Means clustering algorithm. The optimal number of clusters is determined using the Silhouette Score, with the best result obtained at k=9, achieving a score of 0.6806. The practical implications of this study include improved accuracy in academic document clustering, with applications in journal recommendation systems, digital library indexing, and research trend analysis. The results demonstrate that the combination of TF-IDF and LDA produces more informative text representations, significantly enhancing clustering quality. This study contributes to text mining and data science by proposing a systematic preprocessing framework for document clustering. Future research could explore its application to full-text articles, hierarchical clustering, or deep learning-based models to further improve clustering performance.

Enhancing K-Means Clustering for Journal Articles using TF-IDF and LDA Feature Extraction Surianto, Dewi Fatmarani; Surianto, Dewi Fatmawati
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5547

Clustering is a fundamental technique in data analysis, particularly in unsupervised learning, to group data with similar characteristics. However, the effectiveness of the K-Means algorithm in text clustering heavily depends on proper feature extraction. This study proposes an enhanced feature extraction approach by integrating Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA) to improve clustering performance on journal article datasets. The dataset consists of 427 journal article abstracts collected from Google Scholar. The preprocessing steps include tokenization, stopword removal, and TF-IDF vectorization, followed by topic extraction using LDA, which serves as input features for the K-Means clustering algorithm. The optimal number of clusters is determined using the Silhouette Score, with the best result obtained at k=9, achieving a score of 0.6806. The practical implications of this study include improved accuracy in academic document clustering, with applications in journal recommendation systems, digital library indexing, and research trend analysis. The results demonstrate that the combination of TF-IDF and LDA produces more informative text representations, significantly enhancing clustering quality. This study contributes to text mining and data science by proposing a systematic preprocessing framework for document clustering. Future research could explore its application to full-text articles, hierarchical clustering, or deep learning-based models to further improve clustering performance.

Sistem Pengambilan Keputusan Untuk Menentukan Tingkat Kecanduan Game Online Menggunakan Metode Weighted Product Sam, Muh. Hadal Ali; Farid, Muhammad Miftah; Surianto, Dewi Fatmawati
Progressive Information, Security, Computer, and Embedded System Vol. 3, No. 1 Maret (2025)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v3i1.700

Playing online games is one of the activities that many people do to entertain themselves in the midst of their busy daily lives. The existence of several genres in online games certainly makes the game more exciting and entertaining. However, excessive and unlimited use as a means of entertainment can have a negative impact, such as online game addiction. In addition, not everyone realizes that they have developed this type of addictive behavior towards the game. As a result, a person who experiences online game addiction tends to be less interested in other activities, feels restless when not playing online games, decreased academic achievement, social relationships and health. For this reason, the utilization of a decision-making system with the weighted product method is very necessary to determine the level of online game addiction. Therefore, the purpose of this research is to develop a decision-making system to determine the level of online game addiction using the weighted product (WP) method. This research consists of several assessment criteria, namely playing time, frequency of play, level of satisfaction playing games, financial expenses, social interactions and physical problems. in this study, manual calculations were carried out with excel and also used a system to determine the level of online game addiction from several alternatives.

Transformasi Literasi Digital melalui Pelatihan Pemanfaatan Media Sosial secara Efektif Surianto, Dewi Fatmawati; Baso, Fadhlirrahman; Surianto, Dewi Fatmarani; Mappangara, Surianto; Rifqie, Dary Mochamad
Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Volume 3 Issue No. 1: July 2025
Publisher : Lontara Digitech Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/kreativa.v3i1.20265

Integrasi media sosial dalam kehidupan sehari-hari telah membawa perubahan besar terhadap pola komunikasi masyarakat Indonesia. Media sosial bukan hanya menjadi sarana interaksi dan hiburan, tetapi juga berperan dalam penyebaran informasi, pendidikan, hingga peluang ekonomi. Namun, penggunaan yang tidak terarah dapat menimbulkan risiko, seperti kecanduan, cyberbullying, penyebaran misinformasi, dan ancaman keamanan data. Kegiatan pengabdian masyarakat ini bertujuan untuk memberikan pelatihan mengenai penggunaan media sosial secara efektif serta dampaknya, baik positif maupun negatif, kepada masyarakat dan mahasiswa di Kabupaten Sinjai. Metode pelaksanaan meliputi tahap persiapan, pelatihan interaktif, pendampingan singkat, serta evaluasi. Hasil pelaksanaan menunjukkan adanya peningkatan pengetahuan peserta mengenai risiko digital, kesadaran terhadap dampak negatif, serta keterampilan dalam memanfaatkan media sosial secara produktif, misalnya untuk promosi usaha dan pengembangan jejaring sosial. Kegiatan diskusi memperlihatkan transformasi sikap peserta dari penggunaan yang konsumtif menuju pemanfaatan yang lebih produktif. Meskipun terdapat kendala berupa perbedaan keterampilan digital dan keterbatasan waktu praktik, kegiatan ini terbukti efektif dalam meningkatkan literasi digital masyarakat. Secara keseluruhan, pelatihan ini berkontribusi dalam membentuk sikap bijak, produktif, dan aman dalam bermedia sosial serta memberikan implikasi positif bagi peningkatan kualitas interaksi sosial dan kesejahteraan masyarakat.

Ensemble-Based Clickbait Detection in Indonesian Online News Surianto, Dewi Fatmawati; Diny Anggriani Adnas
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.11251

Clickbait has become a pervasive issue in online news media, particularly in the Indonesian digital information ecosystem, where sensational headlines are frequently used to attract user attention at the expense of content accuracy. This phenomenon not only degrades information quality but also contributes to the spread of misinformation. To address this challenge, this study proposes an ensemble-based machine learning approach for detecting clickbait in Indonesian-language news articles by jointly analyzing headlines and full article content. The proposed method employs Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction with extended n-gram configurations to capture both lexical and contextual patterns characteristic of clickbait. Three baseline classifiers, Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine are integrated into a hard voting ensemble framework to leverage their complementary strengths. The experiments were conducted on the CLICK-ID dataset, consisting of annotated Indonesian news articles, using an 80:20 train–test split. Experimental results demonstrate that the proposed ensemble model outperforms all individual baseline classifiers, achieving an overall accuracy and F1-score of 93%. The ensemble approach shows notable improvements in recall for the clickbait class, indicating its effectiveness in minimizing false negatives. Furthermore, qualitative analysis using word cloud and bigram visualization reveals distinct linguistic patterns between clickbait and non-clickbait articles, supporting the discriminative capability of the extracted features. These findings confirm that combining TF-IDF with ensemble learning provides a robust and effective solution for clickbait detection in Indonesian online news. The proposed model contributes to the development of more reliable content filtering systems and supports efforts to improve information quality in digital media environments.

EduTech Acceleration: Strengthening Teacher Competencies through AI-Based Learning Media Training Surianto, Dewi Fatmarani; B, Muhammad Fajar; Hasnining, Ayu; Risal, Andi Akram Nur; Ramadhan, Haekal Febriansyah; Surianto, Dewi Fatmawati
Jurnal Sipakatau: Inovasi Pengabdian Masyarakat Vol. 3 No. 1 (2025): Jurnal Sipakatau
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/sipakatau.v3i1.265

The rapid adoption of Generative Artificial Intelligence (GenAI) in education requires teachers to strengthen their competencies in producing accurate, ethical, and safe learning media. This community engagement program was conducted in a secondary education institution to enhance teachers’ AI literacy and ability to apply structured prompting using the CRISP-EDU framework. The training emphasized fact-checking, credible source attribution, bias mitigation, and student data protection principles. Evaluation results demonstrate improvements across all competency dimensions: understanding of CRISP-EDU fundamentals (+42%), AI licensing and ethics (+32%), classroom AI-related standard operating procedures and risk awareness (+26%), generative AI utilization (+15%), and practical application of CRISP-EDU (+23.66%). These findings indicate a shift in teachers’ roles from passive users to critical and ethical content curators capable of producing safe and contextually appropriate AI-based learning materials.

Enhancing Public Speaking, Data Analysis, and Digital Financial Management Skills in the Society 5.0 Era B, Muhammad Fajar; Lestary, Fitriyanty Dwi; Ananda, Sukma Riski; Mapparenta, Muwaffiq Nurimansyah; Surianto, Dewi Fatmawati
Jurnal Sipakatau: Inovasi Pengabdian Masyarakat Vol. 3 No. 1 (2025): Jurnal Sipakatau
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/sipakatau.v3i1.271

This community service program aims to strengthen the readiness of high school students in facing higher education challenges through capacity building on public speaking, data analysis, and digital financial management. The transition toward Society 5.0 demands students to possess strong communication skills, data literacy, and the ability to manage digital-based finance responsibly. The training was conducted through interactive lectures, hands-on practices, simulations, and mentoring sessions involving high school students. Results show an increase in students’ confidence in public speaking, improvement in basic data-analysis skills using digital tools, and better understanding of digital financial planning. This training has succeeded in providing practical knowledge and strengthening students’ readiness to continue to the university level. Future programs are expected to expand similar training to broader schools and integrate more advanced modules.

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