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Sosialisasi Peningkatan Pengelolaan dan Efisiensi Sistem Informasi Perpustakaan Kitab di Dayah Darul Ulum Desa Alue Awe Kota Lhokseumawe Hasdyna, Novia; Kesuma Dinata, Rozzi; Retno, Sujacka; Fajri, T Irfan; Mutasar, Mutasar
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 2 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i2.3156

Abstract

Dalam era globalisasi saat ini, teknologi informasi dan komunikasi telah mengalami perkembangan yang pesat. Perpustakaan sebagai lembaga penting dalam menyediakan akses ke pengetahuan dan informasi perlu memanfaatkan teknologi ini dengan tepat guna meningkatkan peranannya dalam masyarakat. Di Dayah Darul Ulum, Desa Alue Awe, Kota Lhokseumawe, Perpustakaan Kitab masih belum terkomputerisasi, sehingga diperlukan sosialisasi tentang pentingnya sistem perpustakaan dan pemanfaatannya secara efektif. Tujuan kegiatan ini adalah untuk meningkatkan layanan kepada pembaca dengan optimal. Melalui pengabdian kepada masyarakat, diharapkan dapat memperoleh masukan yang berguna dalam meningkatkan kualitas layanan, termasuk sumber daya manusia, fasilitas, teknologi, dan manajemen. Perpustakaan saat ini berperan sebagai pusat informasi, pengetahuan, dan layanan jasa lainnya, namun masih terdapat ruang untuk perbaikan yang signifikan. Hasil dari kegiatan pengabdian ini diharapkan dapat memberikan kontribusi yang berarti bagi Dayah Darul Ulum dalam meningkatkan kualitas layanan dengan memanfaatkan sistem informasi perpustakaan kitab.
Evaluating The Quality of K-Medoids Clustering on Crime Data in Indonesia Sujacka Retno; Rozzi Kesuma Dinata; Novia Hasdyna
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp274-280

Abstract

This study evaluates the quality of K-Medoids clustering applied to criminal incident data in Indonesia from 2000 to 2023. The analysis compares the clustering performance on both original and normalized datasets using various evaluation metrics, including the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Calinski-Harabasz Index (CH). The findings reveal that the original dataset consistently outperforms the normalized dataset across all metrics. The optimal clustering was achieved in the seventh iteration of the original data, with the lowest DBI (0.438), the highest SS (0.683), NMI (0.916), ARI (0.984), and CHI (57.418). In contrast, the normalized data exhibited higher DBI values and, in some cases, negative Silhouette Scores, indicating less distinct clusters. These results suggest that for this dataset, K-Medoids clustering performs more effectively on the original data without normalization, providing more accurate and well-defined clusters of criminal incidents. This insight is crucial for future research and practical applications in crime data analysis, emphasizing the importance of dataset preprocessing in clustering methodologies.
Implementation of Simple Additive Weighting and Profile Matching Methods to Determine Outstanding Students at Universitas Malikussaleh Nurdin, Nurdin; Fikran, Rifzan; Retno, Sujacka
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4176

Abstract

Decision support system (DSS) is a computer-based system used to support data analysis and decision modeling, with the aim of increasing the effectiveness of decisions taken. In this research, SPK is needed to determine Outstanding Students. Through this research, it is hoped that the selection process for outstanding students can be optimized by choosing the evaluation method that best suits the student's characteristics and institutional goals. The results of this research also have the potential to improve the quality of graduates by providing fairer and more objective awards to those who excel. The aim of this research is to design and implement the concept of the Simple Additive Weighting (SAW) and Profile Matching methods in a system for determining outstanding students at Universitas Malikussaleh and to find out the ranking results of the two methods (SAW and Profile Matching) in selecting outstanding students at Universitas Malikussaleh. The research methodology used was literature study, data collection, Simple Additive Weighting and Profile Matching calculations, application design, testing and evaluation. The results obtained from this research are the application of the SAW and Profile Matching methods to determine outstanding students resulting in preferences with the highest score of 1 for the SAW method and the highest score of 5 for the Profile Matching method. These two methods can be applied in selecting outstanding students to help decision making because both this method produces the same best alternative
PKM Strategi Pemanfaatan Teknologi Informasi untuk Menghadapi Cyberbullying di Kalangan Siswa Retno, Sujacka; Maida, Eka; Fhonna, Rizky Putra; Afrillia, Yesy; Fachrurrazi, Sayed; Yusuf, Edi
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 9 (2025): November
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i9.3469

Abstract

Perkembangan teknologi informasi membawa dampak positif terhadap kemajuan pendidikan, namun di sisi lain juga menimbulkan fenomena negatif seperti cyberbullying yang semakin marak di kalangan siswa. Cyberbullying sebagai bentuk kekerasan psikologis berbasis digital dapat mengganggu kesehatan mental, motivasi belajar, hingga prestasi akademik peserta didik. Pengabdian ini bertujuan untuk menganalisis strategi pemanfaatan teknologi informasi dalam mengidentifikasi, mencegah, dan menanggulangi cyberbullying di lingkungan sekolah melalui integrasi pendekatan teknologi dan edukatif. Metode pengabdian yang digunakan adalah studi literatur dengan mengkaji hasil pengabdian terdahulu, jurnal terakreditasi, serta laporan lembaga pendidikan nasional maupun internasional. Hasil kajian menunjukkan bahwa pemanfaatan artificial intelligence (AI) untuk deteksi ujaran kebencian, penggunaan learning management system (LMS) yang dilengkapi fitur pelaporan anonim, serta penerapan sistem pengawasan berbasis machine learning dapat membantu mengurangi insiden cyberbullying. Namun, efektivitas teknologi tersebut sangat bergantung pada kesadaran etika digital dan kemampuan literasi siber siswa. Oleh karena itu, kolaborasi antara sekolah, guru, dan orang tua dalam memberikan edukasi literasi digital menjadi aspek krusial. Sinergi antara inovasi teknologi dan program pendidikan karakter mampu membangun lingkungan digital yang aman, inklusif, dan mendukung kesejahteraan psikologis siswa.
A Web-Based Decision Support System Implementation for Evaluating Premier Smartphone Brands Using Weighted Product Method Novia Hasdyna; Rozzi Kesuma Dinata; Sujacka Retno
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 13 No 02 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v13i02.939

Abstract

In the current modern era, smartphones have become an indispensable part of daily life, extensively utilized across a multitude of activities, particularly through online platforms. This underscores the imperative of aiding individuals in making precise decisions regarding the smartphone that aligns most with their needs. To address this exigency, the development of a Decision Support System (DSS) employing the Weighted Product method assumes paramount significance in this research. This DSS empowers users to select the most fitting smartphone by assigning weight values to various performance metrics. The criteria used in this research are price, RAM, ROM, battery capacity, and Android version. The successful implementation of this system streamlines the smartphone selection process, enabling users to make judicious choices that perfectly cater to their requirements while optimizing performance metrics.. In this research, Poco X3 Pro has the highest Vector V value of 0.255441, making it the best-recommended smartphone.