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<![CDATA[IMPLEMENTASI MANAJEMEN KEARSIPAN DALAM MENINGKATKAN KUALITAS PELAYANAN ADMINISTRASI PADA KELURAHAN RAGUNAN ]]>
<![CDATA[Fattya Ariani]]>;
<![CDATA[Sumarna Sumarna]]>;
<![CDATA[Hafis Nurdin]]>;
<![CDATA[Riki Supriadi]]>
<![CDATA[ Jurnal AbdiMas Nusa Mandiri Vol 5 No 1 (2023): Periode April 2023 ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/abdimas.v5i1.4170
<![CDATA[Pengurus Dasawisma Kelurahan Ragunan merupakan bagian dari Gerakan PKK kelurahan ragunan berlamat di kantor kelurahan Ragunan di Jl. Saco No.33, RT.1/RW.4, Ragunan, Kec. Ps. Minggu, Kota Jakarta Selatan. Pelayanan Administrasi sangat diperlukan oleh dasawisma PKK sebagai bentuk pelayanan kepada masyarakat berupa penyediaan berbagai bentuk dokumen yang diperlukan oleh publik, begitu juga dengan pelayanan administrasi kelurahan Ragunan saat ini masih menggunakan Ms. Excel untuk mengolah data kependudukannya dan penggunaannya pun hanya sebagai alat pencatatan saja, sehingga sering menimbulkan berbagai masalah, diantaranya redudansi data, ketidak sesuaian data pada KTP dan Kartu Keluarga serta masalah lainnya. karena berbagai permasalahan ini, data kependudukan yang dimiliki menjadi tidak akurat. Saat diperlukan harus dilakukan proses pengecekan dan validasi ulang. Hal ini tentu membutuhkan waktu yang lama setiap kali informasi dibutuhkan. Tidak adanya sebuah sistem didalam pengurusan surat pada data kependudukan hal ini menjadikan satu kendala dalam proses administrasi pencatatan sipil. NIK dapat dijadikan identitas unik setiap penduduk dalam pengelolaan data kependudukan agar tidak terjadi redudansi dan mempermudah dalam proses pencarian serta perubahan data. Penggunaan sistem pendataan berbasis website dalam mengelola data administrasi dapat digunakan untuk memudahkan dalam sistem penginputan serta menyimpan data kedalam database yang dapat diakses dimanapun dan kapanpun sehingga lebih efisien dan efektif. Dengan akses yang mudah dan cepat hal ini bisa dimanfaatkan oleh setiap pengurus dasawisma kelurahan ragunan dalam pengurus berkas yang akan diolah. Kegiatan difokuskan pada cara penggunaan website yang telah disediakan.]]>
<![CDATA[PELATIHAN PEMBUATAN WEB COMPANY PROFILE UNIT KERJA KARANG TARUNA KELURAHAN TEGAL PARANG ]]>
<![CDATA[Ariani, Fattya]]>;
<![CDATA[Sumarna, Sumarna]]>;
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Supriadi, Riki]]>
<![CDATA[ Jurnal AbdiMas Nusa Mandiri Vol. 6 No. 1 (2024): Periode April 2024 ]]>
Publisher : <![CDATA[LPPM Universitas Nusa Mandiri ]]>
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DOI: 10.33480/abdimas.v6i1.4911
<![CDATA[Karang Taruna is an organization that exists in Indonesia. Because surely in every village there is this organization. Karang Taruna is a place for teenagers to develop themselves to be responsible and have a social spirit. Technological developments can now be enjoyed by everyone throughout the world. Looking for information, we just have to type in Google what we want, after that so many references appear so that we can read the latest information without fear of missing out on information and this is a very significant difference where in the past it was very difficult to get the latest information and You have to wait a while to get this information. Now a problem where people don't know about Karang Taruna Tegal Parang Village and the activities they carry out. The solution offered is training in creating a company profile website for Karang Taruna, Tegal Parang. The training is carried out using the Technical Assistance method in the form of Training. The results obtained from this training were that the youth organization administrators can understand how to create a company profile website and can host the website they created. So that it can produce the latest information to citizens.]]>
<![CDATA[Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization ]]>
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Sartini, Sartini]]>;
<![CDATA[Sumarna, Sumarna]]>;
<![CDATA[Maulana, Yana Iqbal]]>;
<![CDATA[Riyanto, Verry]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.12973
<![CDATA[In private universities in Indonesia, student graduation is something that is worth paying attention to, because it will be an aspect of the success of the university. Universities certainly have data on students who graduated, where student graduation data is very important to be taken into consideration by private universities, however with a lot of data it will make it difficult for private universities to find information from this data. Other researchers have previously carried out student graduation data with the same data by examining student graduation data using other methods. So we need a data mining algorithm that has never been tested on student graduation data. The method used is the neural network method with an optimization method, namely the particle swarm optimization method, to test the data, which will later produce information that is very useful for universities. After testing the student graduation data and getting accuracy results using the neural network method of 84.55% and after being optimized using the particle swarm optimization method, the accuracy results were optimal with a value of 86.94%. These results can be used by private universities to predict that students will graduate on time before they take their final semester so that the student graduation rate will be high.]]>
<![CDATA[Forward Selection as a Feature Selection Method in the SVM Kernel for Student Graduation Data ]]>
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Carolina, Irmawati]]>;
<![CDATA[Andharsaputri, Resti Lia]]>;
<![CDATA[Wuryanto, Anus]]>;
<![CDATA[Ridwansyah, Ridwansyah]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.14172
<![CDATA[In the era of information technology development, accurate graduation predictions are important to improve the quality of higher education in Indonesia. This research evaluates the effectiveness of Support Vector Machine (SVM) with various kernels, including Radial Basis Function (RBF), linear, and polynomial, as well as the application of FS as an optimization method. The dataset used consists of student graduation data which includes nine independent attributes and one label. This research aims to increase the accuracy of student graduation predictions using the SVM method which is optimized through Forward Selection (FS). The SVM method is applied using 10-fold cross validation to predict on-time graduation. The results show that the combination of SVM and FS improves prediction accuracy significantly. The SVM model with an RBF kernel optimized with FS achieved the highest accuracy of 87.06% and recall of 53.68%, showing increased sensitivity in identifying student graduation cases compared to SVM without FS. Although there is a trade-off between precision and recall, the model optimized with FS shows better performance overall. This research contributes to the development of a more efficient graduation prediction method, which can help universities in planning strategies to improve academic quality. Further studies are recommended to overcome weaknesses in the recall value by using other optimization methods or combinations of other optimization algorithms]]>
<![CDATA[Optimasi Kernel SVM dengan PSO untuk Gagal Jantung ]]>
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Sugiarto, Hari]]>;
<![CDATA[Yuliandari, Dewi]]>;
<![CDATA[Wuryanto, Anus]]>;
<![CDATA[Nawawi, Imam]]>
<![CDATA[ Jurnal Manajemen Informatika JAMIKA Vol 15 No 2 (2025): Jurnal Manajemen Informatika (JAMIKA) ]]>
Publisher : <![CDATA[Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia ]]>
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DOI: 10.34010/jamika.v15i2.14409
<![CDATA[Accurate early detection is important to improve the quality of life of patients and reduce mortality and a major burden on the public health system caused by heart failure. This study aims to improve the accuracy of heart failure prediction using Support Vector Machine (SVM). SVM is used as a strong classifier for high-dimensional data, then optimizes its kernel using Particle Swarm Optimization (PSO), which has not been widely applied in similar studies. The method used includes computational experiments with a quantitative approach based on heart failure datasets from the UCI Repository which are analyzed using SVM with three types of kernels: Dot, Radial, and Polynomial. PSO is used to optimize the selection of kernel parameters in SVM to improve classification accuracy. The results show that SVM + PSO kernel Dot gives the best performance, with an AUC of 0.865 and an accuracy of 83.97%, and this difference is confirmed significant through a paired t-test (p <0.05) compared to SVM without optimization. PSO optimization consistently improves precision and recall in the tested kernels, indicating stability and effectiveness in classification. The impact of the research is to make a significant contribution to early detection efforts for heart failure which can lead to faster treatment and improved quality of life for patients, but also adds clinical value for medical practitioners seeking efficient and accurate classification methods.]]>
<![CDATA[Pemanfaatan Artificial Intelligince Untuk Optimalisasi Kinerja Remaja Masjid Baitul Halim ]]>
<![CDATA[Ariani, Fattya]]>;
<![CDATA[Supriyadi, Riki]]>;
<![CDATA[Sumarna]]>;
<![CDATA[Nurdin, Hafis]]>
<![CDATA[ Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat Vol 6 No 1 (2025): Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat ]]>
Publisher : <![CDATA[LPPM Universitas Pamulang ]]>
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DOI: 10.32493/abdilaksana.v6i1.44449
<![CDATA[Remaja Masjid Baitul Halim menghadapi berbagai tantangan dalam menjalankan aktivitas sehari-hari. Beberapa kendala yang dihadapi termasuk keterbatasan sumber daya manusia, akses terbatas terhadap teknologi modern, dan pemahaman yang kurang mengenai manfaat serta potensi kecerdasan buatan (AI). Untuk mengatasi masalah ini, diperlukan penerapan AI yang efisien guna meningkatkan kinerja remaja masjid, termasuk pembuatan proposal dan laporan kegiatan, dan pembuatan desain flyer atau brosus kegiatan sampai dengan pembuatan presentasi. Selain itu, AI dapat memperkuat komunikasi baik di dalam maupun di luar organisasi, serta memfasilitasi kolaborasi yang lebih baik antara anggota remaja masjid dan masyarakat. Dalam rangka mendukung upaya ini, dosen dari Universitas Nusa Mandiri, Fakultas Teknologi dan Informasi, menyelenggarakan pelatihan tentang penerapan AI dengan pemanfaatan chatGPT dan aplikasi canva untuk mengoptimalkan kinerja Remaja Masjid Baitul Halim. Pelatihan ini diharapkan dapat memotivasi Remaja Masjid lainnya untuk memanfaatkan AI demi peningkatan kinerja dan memberikan dampak positif yang lebih luas bagi masyarakat, termasuk dalam pembuatan flyer dan siaran pers yang akan dipublikasikan secara elektronik.]]>
<![CDATA[NAIVE BAYES AND PARTICLE SWARM OPTIMIZATION IN EARLY DETECTION OF CHRONIC KIDNEY DISEASE ]]>
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Suhardjono, Suhardjono]]>;
<![CDATA[Wuryanto, Anus]]>;
<![CDATA[Yuliandari, Dewi]]>;
<![CDATA[Sugiarto, Hari]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.3.1750
<![CDATA[Chronic Kidney Disease (CKD) is a global health problem that requires early detection to reduce the risk of complications and disease progression. The Naïve Bayes (NB) algorithm has been proven effective in detecting CKD but its accuracy still varies. The problem with previous research is that it has not fully optimized existing algorithms in terms of accuracy and efficiency. This research aims to develop a more accurate and efficient early detection method for CKD using the NB algorithm and Particle Swarm Optimization (PSO). The NB method is known for its speed and ease of implementation, with global search capabilities and PSO for parameter optimization. Dataset from the UCI repository, which includes data pre-processing, NB implementation, performance evaluation, and enhancement with PSO. The results of NB+PSO show a significant increase in accuracy of 95.75% from 95.00% and Area Under Curve (AUC) value of 0.910% from 0.802% compared to the use of NB alone. The conclusion of this study is that the combination of NB+PSO increases effectiveness in early detection of CKD. This research opens up opportunities for further development in the medical field, especially in improving the diagnostic accuracy of other diseases.]]>
<![CDATA[PENERAPAN ARTIFICIAL INTELLIGENCE UNTUK OPTIMALISASI KINERJA KARANG TARUNA ]]>
<![CDATA[Ariani, Fattya]]>;
<![CDATA[Sumarna]]>;
<![CDATA[Nurdin, Hafis]]>;
<![CDATA[Supriyadi, Riki]]>
<![CDATA[ Jurnal Abdimas Universitas Insan Pembangunan Indonesia Vol. 2 No. 2 (2024): Agustus 2024 ]]>
Publisher : <![CDATA[LPPM Universitas Insan Pembangunan ]]>
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DOI: 10.58217/jabdimasunipem.v2i2.56
<![CDATA[Karang Taruna RW 06 in Tegal Parang Village faces challenges in carrying out community activities, including organizing events, managing data, and making strategic decisions. Some of the problems faced include limited human resources, limited access to modern technology, and minimal understanding of the benefits and potential of artificial intelligence (AI). To overcome this problem, it is necessary to apply effective AI to increase members' creativity in creating information about promotional activities, such as creating and distributing flyers and brochures. In addition, AI can strengthen internal and external communications and facilitate better collaboration between Karang Taruna members and the community. In this effort, lecturers at Nusa Mandiri University, Faculty of Technology and Information, held training on the application of AI to optimize the performance of Karang Taruna RW 06. This training inspired other Karang Taruna to utilize AI to improve performance and provide a wider positive impact on society. , including making digital brochures and flyers for promotional activities as well as making press releases that will be published electronically.]]>
<![CDATA[Prediksi Keterlambatan Pembayaran Mahasiswa untuk Mitigasi Risiko Cuti Menggunakan SVM Optimasi PSO ]]>
<![CDATA[Hafis Nurdin]]>;
<![CDATA[Imam Nawawi]]>;
<![CDATA[Anus Wuryanto]]>;
<![CDATA[Dewi Yuliandari]]>;
<![CDATA[Hari Sugiarto]]>
<![CDATA[ JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025 ]]>
Publisher : <![CDATA[Universitas Merdeka Malang ]]>
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DOI: 10.26905/jasiek.v7i1.15483
<![CDATA[Delayed tuition payments present challenges for higher education institutions, impacting both financial stability and students’ academic progress. This study proposes a predictive model using Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO) to identify students at risk of payment delays. The dataset includes academic and social attributes. A dot kernel SVM was evaluated using 10-fold cross-validation. Results show that PSO optimization significantly improved model performance, particularly in recall, which increased from 36.10% to 65.51%, indicating better identification of delayed payment cases. The analysis also reveals that social factors, such as employment and academic status, strongly influence prediction outcomes. These findings highlight the potential of the SVM-PSO model as a decision-support tool for early intervention, enabling institutions to mitigate dropout risks and enhance financial planning. By leveraging this approach, universities can better support students while maintaining administrative efficiency and institutional sustainability.]]>
<![CDATA[Pelatihan Microsoft Word dan Excel sebagai Upaya Penguatan Kompetensi Digital Masyarakat RT 007 Mampang Prapatan ]]>
<![CDATA[Ahmad Hafidzul Kahfi]]>;
<![CDATA[Jajang Jaya Purnama]]>;
<![CDATA[Hafis Nurdin]]>;
<![CDATA[Sri Rahayu]]>
<![CDATA[ Journal of Innovation and Sustainable Empowerment Vol. 4 No. 3 (2025) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas Kuningan ]]>
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DOI: 10.25134/jise.v4i3.172
<![CDATA[Perkembangan teknologi digital menuntut setiap individu memiliki keterampilan dasar dalam memanfaatkan perangkat lunak perkantoran, terutama Microsoft Word dan Excel yang berperan penting dalam penyusunan dokumen serta pengolahan data. Namun, masyarakat RT 007 Mampang Prapatan masih mengalami keterbatasan dalam keterampilan digital sehingga proses administrasi dan pelaporan belum optimal. Untuk menjawab permasalahan tersebut, tim dosen Universitas Bina Sarana Informatika melaksanakan program pengabdian kepada masyarakat melalui pelatihan Microsoft Word dan Excel bagi warga RT 007. Kegiatan dilakukan dengan pendekatan partisipatif melalui penyampaian materi, demonstrasi, dan praktik langsung. Hasil pelatihan menunjukkan peningkatan signifikan pada kemampuan peserta, yang ditunjukkan dengan rata-rata penilaian sebesar 4,57 dengan kategori Sangat Puas. Peserta mampu mengelola dokumen dan data secara lebih efektif, serta menunjukkan peningkatan kepercayaan diri dalam penggunaan teknologi digital. Program ini berdampak positif terhadap efektivitas kerja, profesionalisme warga, dan kesiapan masyarakat dalam menghadapi tantangan era transformasi digital.]]>
