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Suryadi, Sudi
Universitas Labuhanbatu
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Pelatihan Komputer dalam Peningkatan Sumber Daya Manusia Bagi Perangkat Desa Karim, Abdul; Kusmanto; Suryadi, Sudi; Febriani, Budi
Jurnal Pengabdian Harapan Bangsa Vol. 2 No. 2: Mei 2024
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jphb.v2i2.198

Abstract

Computer training plays an important role in improving human resources (HR) of village officials. This research aims to explore the impact of computer training on the ability and work efficiency of village officials. The methods used include surveys before and after training, direct observation, and in-depth interviews. The research results show that computer training significantly improves the technical skills of village officials, which contributes to increased productivity and services to the community. This training also builds self-confidence and increases technological understanding, which is important for adapting to increasingly digital work demands. Thus, computer training has proven to be an effective strategy in developing the human resource capacity of village officials, which, in turn, contributes to overall village progress and development.
Penerapan Data Mining Untuk Pengelompokan Terhadap Kualitas Kinerja Karyawan Dengan Menggunakan Algoritma K-Medoids Clustering Karim, Abdul; Esabella, Shinta; Kusmanto, Kusmanto; Hidayatullah, Muhammad; Suryadi, Sudi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7445

Abstract

HR management is recognized as a global issue and an integral part of competitiveness in the arena of globalization. The organizational structure is the placement of tasks from the very top to the placement of very basic tasks. Assessing the quality of employee performance is one of the work evaluation sessions that can provide the best for industry and citizens. In position placement, if someone does not suit the position they have, it will cause problems such as the company's operational processes not running well. Performance appraisal of employees aims to see the performance results that have been carried out or given by employees when occupying a position. Problems related to performance appraisal are important problems that must be resolved immediately. Data mining is a data processing process in the past, where data in data mining is a collection of data that has been collected over a certain period of time. Information data mining is a series of processes for exploring added value in the form of data produced by extracting and identifying patterns in an information base. Clustering is a part of data mining that aims to group based on the formation of new clusters. The K-Medoids algorithm is a partitional clustering procedure that minimizes the distance between labeled points. The K-Medoids algorithm is a classic Clustering partition technique that groups data sets of ni objects into k groups known a priori. From the results of research conducted using the K-Medoids method, 3 clusters were obtained. Where in cluster 1 there are 4 employees, in cluster 2 there are 3 employees and in cluster 3 there are 3 employees.
Analisis Klasifikasi Teks Pada Kata Slang di Media Sosial Menggunakan Pengolahan Bahasa Alami untuk Trending Topik Shabrina Rasyid Munthe; Sudi Suryadi; Fadhil Laksono
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2018

Abstract

This study aims to analyze trending topics related to the use of slang words on social media by utilizing natural language processing (NLP) techniques. The main focus of this research is to understand the patterns and trends of slang use on social media platforms, which can uncover important social and linguistic dynamics. The dataset used consisted of  tweets in Indonesia and United Kingdom containing slang words, collected from Twitter over a six-month period. The analysis process begins with data cleansing to eliminate irrelevant elements, followed by tokenization and lemmatization to normalize the text. Furthermore, the Support Vector Machine (SVM) and Random Forest classification models are applied to detect and classify slang words in the dataset. The results show that the SVM model achieves a slang detection accuracy of 88% with an F1-score of 0.87, while the Random Forest model achieves an accuracy of 85% with an F1-score of 0.84. Further linguistic analysis showed that 60% of slang words are most commonly used in informal contexts such as everyday conversation, while the other 40% are related to popular culture trends, including music, movies, and fashion. In addition, these findings indicate that there is a variation in the use of slang between Indonesian and United Kingdom-speaking Twitter users, where slang in Indonesian tends to be more creative and contextual, while in United Kingdom it is more standardized and spread globally. This study confirms the effectiveness of both models in classifying slang words as well as identifying key trends in their use on social media. The contribution of this research is important for the study of digital linguistics because it expands the understanding of the dynamics of online slang use, and shows the great potential of NLP applications in linguistic analysis in the digital age. With the results obtained, this research can be a valuable guide for researchers and practitioners interested in understanding the evolution of language on social media, while providing a foundation for the development of more sophisticated and adaptive NLP technologies in handling language variations on digital platforms.
Clusterisasi Tingkat Pengangguran Terbuka Menurut Provinsi di Indonesia Menggunakan Algoritma K-Medoids Karim, Abdul; Esabella, Shinta; Kusmanto, Kusmanto; Suryadi, Sudi; Mardinata, Erwin
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6198

Abstract

The Open Unemployment Rate (OER) in Indonesia decreased in February 2024 to 4.82%, showing an improvement compared to February 2023. Despite the decline in TPT, there are still regions with TPT reaching 7.02%, which could potentially lead to negative consequences such as increased crime. Efforts to address TPT include increasing economic growth, developing the quality of education and training. This research utilises clustering in data mining. The number of clusters formed was 3 clusters with a DBI value of -1.685. This study uses K-Medoids clustering to group 38 provinces based on TPT. Of the 38 data, there is incomplete data so preprocessing is done using the "filter example" operator in rapidminer to eliminate incomplete data so that there are 34 data that will be used in this study (after preprocessing). The results show 4 provinces with the highest TPT (Riau Islands, DKI Jakarta, West Java, and Banten) with a percentage of 11.76%.
Peningkatan Literasi Digital melalui Pelatihan Komputer untuk Pengembangan UMKM di Desa Era 5.0 Kusmanto; Febriani, Budi; Bobbi Kurniawan Nasution, Muhammad; Suryadi, Sudi
Jurnal Pengabdian Masyarakat Gemilang (JPMG) Vol. 5 No. 1: Desember 2024
Publisher : HIMPUNAN DOSEN GEMILANG INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Enhancing digital literacy is a crucial step in empowering MSMEs (Micro, Small, and Medium Enterprises) in rural areas to face challenges and seize opportunities in the 5.0 era. Digital literacy equips business actors with the ability to effectively utilize technology, such as conducting online marketing, managing digitally-based businesses, and accessing relevant information for business development. Through computer training, MSME actors can improve their skills in operating technological devices and optimizing the use of digital platforms. This training also supports MSMEs in adapting to technological changes, expanding market access, and increasing competitiveness. Thus, computer training for digital literacy serves as a strategic initiative to drive the development of rural MSMEs, ultimately contributing to local economic growth and strengthening the digital ecosystem in Indonesia..
XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis Sudi Suryadi; Masrizal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6587

Abstract

Cervical cancer continues to pose a significant global health challenge, with early detection remaining the cornerstone for effective intervention. This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms to overcome the limitations of conventional screening methodologies. An XGBoost model was developed to predict cervical cancer risk. This model incorporates demographic, behavioral, and clinical parameters. The model was developed using data from 858 patients at the Hospital Universitario de Caracas. The preprocessing pipeline was designed to address the complexities inherent in medical data, including strategic management of missing values and standardizing heterogeneous features. The model demonstrated an overall accuracy of 96.3%, with a sensitivity of 66.7% and a specificity of 97.6%. This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. The confusion matrix analysis revealed the clinical implications of the model predictions, demonstrating a promising positive predictive value of 55.0% despite the pronounced class imbalance. These findings suggest that ensemble learning approaches can effectively synthesize diverse patient data into meaningful risk assessments, potentially enhancing screening efficiency through personalized stratification. Future research directions include prospective validation across diverse populations, integration of longitudinal data, and further exploration of explainable AI techniques to bridge the gap between algorithmic predictions and clinical implementation.
Co-Authors Abdul Karim Andi Saehan Basyarul Ulya Bobbi Kurniawan Nasution, Muhammad Deci Irmayani Dodi Suryo Widodo Erlia Utami Panjaitan Fadhil Laksono Febriani, Budi Gomal J.Y Silaen Hariyanto Hariyanto Hidayatullah, Muhammad Ibnu Rasyid Munthe Ibnu Rasyid Munthe Irmayani, Deci Iwan Purnama JP, Gafar Ilyaz Kusmanto Kusmanto Kusmanto Kusmanto Kusmanto Kusmanto M Fauzi Romadhon Marpaung M Fauzi Romadhon Marpaung M. Fauzi Romadhon Marpaung Mardinata, Erwin Masrizal Masrizal Meyniar Albina Mhd Bobbi Kurniawan Nasution Muda Pardamean Nasution Muhammad Bobbi Kurniawan Nasution Muhammad Irwansyah Hasibuan, Ira Triyana Dewi, Murdani Murdani Murdani Murdani Nasution, Mhd Bobbi Kurniawan Novika Julianti Simanjuntak Purba, Elvitrianim Raden Sri Ayu Ramadhana Rizki Kurniawan Rangkuti Ronal Watrianthos Ryan Utama Tambunan Sahbuki Ritonga Samsir Samsir Sangkot Idris Ritonga Shabrina Rasyid Munthe Shabrina Rasyid Munthe Shabrina Rasyid Munthe Shinta Esabella Syafaruddin Syafaruddin Syaiful Zuhri Harahap Tatang Hidayat Pohan Taufiqqurrahman Nur Siagian Tika Sari Ramadhani Volvo Sihombing Wahyu Azhar Ritonga