Article Per Year (5 Year)
p-Index From 2021 - 2026
5.997
P-Index
This Author published in this journals
All Journal JURNAL INTEGRASI PROSES RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Informatika Kaputama (JIK) Industrial Engineering Journal (IEJ) Jurnal Informasi dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) International Journal of Engineering, Science and Information Technology Journal of Childhood Development TECHSI - Jurnal Teknik Informatika Jurnal MathEducation Nusantara Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Jurnal Pendidikan Matematika Malikussaleh Jurnal Teknologi Terapan and Sains 4.0 Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Solusi Masyarakat Dikara Jurnal Ilmiah Kesehatan Keperawatan Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Maryana Maryana
Universitas Malikussaleh, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

Published : 49 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 49 Documents
Search

 1   2   3   4   5 
Penerapan Teknologi Aplikasi Google Classroom Untuk Meningkatkan Pembelajaran Siswa di Dayah Nurul Iman Sahputra, Ilham; Fuadi, Wahyu; Sofyan, Diana Khairani; Muthmainnah, Muthmainnah; Maryana, Maryana; Zuraida, Zuraida
Jurnal Malikussaleh Mengabdi Vol. 3 No. 1 (2024): Jurnal Malikussaleh Mengabdi, April 2024
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v3i1.16770

Abstract

Penerapan Google Classroom sebagai platform pembelajaran daring telah menjadi solusi efektif dalam mengatasi tantangan pendidikan. Teknologi ini memungkinkan pendidik dan siswa untuk tetap terhubung, memastikan proses pembelajaran berlangsung tanpa hambatan meskipun terpisah secara fisik akan tetapi tetang berjalan pembelajaran. Dengan fitur yang mendukung interaksi dua arah, penugasan, penilaian, dan akses materi pembelajaran dapat terlaksana, Google Classroom telah membantu menjaga kontinuitas Pendidikan dalam mengajar. Selain itu, platform ini juga mendukung adaptasi terhadap perkembangan teknologi dan metode pembelajaran yang inovatif, yang pada akhirnya meningkatkan kualitas pembelajaran dan mempersiapkan siswa untuk menjadi lebih mandiri dalam belajar. Dari hasil pengabdian ini menunjukkan bahwa teknologi memiliki banyak sekali peran dan manfaatnya dalam dunia pendidikan terlebih saat pembelajaran jarak jauh seperti pembelajaran online seperti google classroom hasil pengabdian ini untuk mengetahui penggunaan Penggunaan teknologi dalam pendidikan, khususnya selama pembelajaran jarak jauh, telah menunjukkan peran pentingnya dalam memastikan kontinuitas proses belajar mengajar. Google Classroom, sebagai salah satu platform Learning Management System (LMS), telah menjadi alat yang sangat berharga bagi dosen dan siswa. hasil pengabdian ini dengan adanya teknologi Google Classroom telah terbukti menjadi sumber daya yang sangat berguna dalam Pendidikan dan pembelajaran siswa
Comparative Analysis of K-Nearest Neighbor and Support Vector Machine Methods for Assessing Quality Standards of Palm Oil Bunches Siti Hajar; Rozi Kesuma Dinata; Maryana
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

Oil palm (Elaeis guineensis Jacq) is a crucial crop in the agricultural sector, particularly in Indonesia, as it produces various economically valuable products. The quality of oil palm fruit bunches (TBS) significantly influences the production process of crude palm oil (CPO), making accurate quality assessments essential for maintaining industry standards. This study aims to compare the effectiveness of two machine learning methods, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM), in determining the acceptable quality of TBS. Using TBS data from the years 2019 to 2023, the research analyzes several variables, including maturity level and yield percentage, to develop a web-based system for classifying TBS. The classification process involves preprocessing the data, applying the algorithms, and evaluating their performance based on key metrics such as accuracy, recall, and precision. The results indicate that the K-NN method outperforms SVM, achieving an accuracy of 100%, a recall of 100%, and a precision of 100%. In contrast, the SVM method demonstrates an accuracy of 91%, a recall of 100%, and a precision of 91%. These findings highlight the effectiveness of K-NN in classifying TBS quality while also demonstrating the reliability of SVM. This research is expected to provide valuable insights and effective solutions for decision-making regarding the acceptance of TBS quality, ultimately benefiting stakeholders in the palm oil industry and serving as a reference for future studies in data mining classification.
Application of the K-Medoids Clustering Method for Grouping High-Risk Areas of Violence Against Women and Children Annisa Afrilia Zahra Annisa; Rozzi Kesuma Dinata; Maryana
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

Violence against women and children has been increasing in both quantity and variety, necessitating special attention. This study aims to cluster areas prone to violence against women and children in North Aceh using the K-Medoids Clustering method. The data used includes physical, sexual, exploitation, and neglect violence, obtained from 542 villages sourced from Unit II PPA Polres North Aceh for the period of 2021-2023. The clustering is categorized into three clusters: very prone, prone, and not prone. The results show that in 2021, there were 16 very prone villages, 22 prone villages, and 506 not prone villages, with the smallest DBI value of 0.12263 from 8 trials. In 2022, there were 22 very prone villages, 18 prone villages, and 502 not prone villages, with a DBI value of 0.10517 from 10 trials. In 2023, there were 15 very prone villages, 11 prone villages, and 516 not prone villages, with a DBI value of 0.21408 from 6 trials. The developed web-based system, using PHP and UML, is expected to assist authorities in preventing and addressing violence in prone areas, thereby reducing the incidence of violence in North Aceh.
Implementation Clustering Diabetes Suffering Areas Using Web-Based Dbscan Algorithm North Aceh District Ahmad Fauzi Abdillah; Rozzi Kesuma Dinata; Maryana
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.622

Abstract

Diabetes has shown a significant increase in Indonesia, including in the North Aceh District. This research implements the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise) web-based method to map diabetes distribution patterns in 27 North Aceh sub-districts. This system was built using the PHP programming language and database MySQL. Proses clustering utilizing data on population, number of sufferers, and number of deaths from 2021-2023 obtained from Prima Inti Medika Hospital and Cut Meutia RSU, with parameters epsilon = 0.5 and MinPts = 3. Results clustering shows an increase in high-risk areas from year to year. In 2021, 2 high-risk sub-districts were identified, Dewantara and Lhoksukon, increasing to 3 sub-districts in 2022 Dewantara, Lhoksukon, and Nisam, in 2023 to 4 sub-districts Dewantara, Lhoksukon, Nisam and Muara Batu. The resulting web-based system succeeded in visualizing diabetes distribution patterns and can be used to plan more effective and targeted health programs.
Public Sentiment Analisys on the Phenomenom of Body Shaming on Social Media X Using the Extreme Gradient Boosting Algorithm Nadya Raudathul Sofa; Dahlan Abdullah; Maryana Maryana
International Journal of Engineering, Science and Information Technology Vol 6, No 2 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i2.1818

Abstract

The phenomenon of body shaming on social media platform X (Twitter) has become increasingly widespread and has caused various psychological impacts on its victims. The high level of social media activity has made the spread of negative comments related to body shape more difficult to control. Therefore, a system capable of automatically performing sentiment analysis is needed to identify public opinions regarding this phenomenon. This study aims to implement the Extreme Gradient Boosting (XGBoost) algorithm in classifying public sentiment toward the body shaming phenomenon on social media X and to determine the sentiment analysis results obtained. The research data were collected using a web scraping technique through Tweet Harvest, resulting in 1,383 Indonesian-language tweets which were manually classified into three sentiment classes: positive, negative, and neutral. The text preprocessing stage included case folding, cleansing, tokenizing, normalization, and filtering without applying stemming, as it was proven to reduce model performance on social media text data. Feature weighting was carried out using the TF-IDF method, while the data were divided using an 80:20 ratio with the implementation of Random Over Sampling (ROS) to address class imbalance. The XGBoost model was built using parameters of n_estimators = 300, learning_rate = 0.05, and max_depth = 5. The evaluation results using a confusion matrix showed an accuracy value of 80.87%, with F1-scores of 0.85 for the negative class, 0.71 for the neutral class, and 0.81 for the positive class. The results indicate that the XGBoost algorithm is capable of classifying public sentiment toward the body shaming phenomenon with fairly good performance. In addition, a web-based sentiment analysis system was successfully implemented to facilitate the automatic and structured sentiment classification process.
CLUSTERING TINGKAT KECANDUAN GAME MOBILE LEGENDS TERHADAP KEHARMONISAN KELUARGA MENGGUNAKAN METODE K-MEANS Muhammad Fadhil; Wahyu Fuadi; Maryana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6439

Abstract

Penelitian ini bertujuan untuk menganalisis tingkat kecanduan game Mobile Legends dan dampaknya terhadap keharmonisan keluarga menggunakan pendekatan machine learning dengan algoritma K-Means Clustering. Metode penelitian menggunakan pendekatan kuantitatif dengan mengumpulkan data dari 297 responden melalui kuesioner yang mencakup 10 variabel, terdiri dari 5 variabel addiction dan 5 variabel keharmonisan. Data yang terkumpul kemudian diproses menggunakan preprocessing dengan teknik encoding dan scaling, selanjutnya dianalisis menggunakan algoritma K-Means Clustering dengan optimasi jumlah cluster melalui kombinasi Elbow Method, Silhouette Analysis, dan Davies-Bouldin Index. Hasil penelitian menunjukkan bahwa algoritma K-Means berhasil mengidentifikasi tiga cluster optimal (K=3) dengan kualitas clustering yang memadai, ditunjukkan oleh Davies-Bouldin Index sebesar 1.580, Silhouette Score 0.233, dan Inertia 2089. Distribusi cluster menunjukkan bahwa 60.3% responden berada dalam kategori kecanduan ringan dengan keharmonisan tinggi (Cluster 1), 32.7% dalam kategori sedang-sedang (Cluster 0), dan 7.1% dalam kategori kecanduan berat dengan keharmonisan rendah (Cluster 2). Temuan utama penelitian mengkonfirmasi hipotesis adanya hubungan invers yang signifikan antara tingkat kecanduan game Mobile Legends dengan keharmonisan keluarga, dimana semakin tinggi tingkat kecanduan semakin rendah keharmonisan keluarga. Principal Component Analysis menunjukkan bahwa dua komponen utama mampu menjelaskan 60% varians data, memberikan validasi visual terhadap hasil clustering. Penelitian ini memberikan kontribusi penting dalam memahami dampak psikologis gaming addiction terhadap dinamika keluarga dan dapat menjadi dasar pengembangan strategi intervensi yang tepat sasaran untuk meningkatkan keharmonisan keluarga.
IMPLEMENTASI METODE CONTENT-BASED FILTERING DALAM REKOMENDASI KEDAI KOPI DI KOTA LHOKSEUMAWE Muhammad Arrayyan; Rozzi Kesuma Dinata; Said Fadlan Anshari; Fadlisyah; Maryana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6641

Abstract

Lhokseumawe a city known for its numerous coffee shops, serves as the focus of this study, which aims to develop a coffee shop recommendation system using a content-based filtering approach based on Google Maps review analysis. A total of 54 coffee shops were collected through web scraping and filtered to 32, as only these shops provided sufficient and relevant reviews according to the selected keywords. User reviews were processed through preprocessing, TF-IDF weighting, and cosine similarity to measure the alignment between user preferences and shop characteristics. A scenario-based evaluation was conducted by using keywords such as “noodles,” “parking,” “spacious,” “toilet,” and “watching together” to represent user preferences. The results show that the system generates recommendations consistent with the presence and relevance of these keywords, with shops such as AN Coffee and Arabica Kopi frequently appearing as top suggestions. Although the evaluation is limited to scenario-based testing, the system demonstrates potential in assisting users in selecting suitable coffee shops. Future work may include hybrid filtering, machine learning methods, automated keyword extraction through topic modeling, and user-based evaluation to improve recommendation quality.
Teknologi Informasi dalam Melihat Peluang Bisnis dan Pengembangan Wirausaha dalam Dunia Industri Defi Irwansyah; Cut Ita Erliana; Maryana Maryana; Effan Fahrizal; Ezwarsyah Ezwarsyah
Jurnal Solusi Masyarakat Dikara Vol 3, No 2 (2023): Agustus 2023
Publisher : Yayasan Lembaga Riset dan Inovasi Dikara

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

Abstract

Perkembangan teknologi informasi terutama dengan tersedianya berbagai jenis media digital untuk melakukan kegiatan ekonomi sangat membantu siswa dalam melihat peluang wirausaha untuk meningkatkan penjualan online. Tujuan dari pengabdian ini memberikan Pengaruh teknologi informasi kepada siswa untuk berwirausaha dan siswa dapat bimbingan teknis dalam membuka lapangan pekerjaaan. Selanjutnya pengabdian ini juga bertujuan untuk mengetahui pengaruh wirasuha dalam dunia industri bagi siswa dan peluang mana yang banyak terbuka dalam membuka wirausaha baru. Metodelogi yang dilakukan observasi, kuesioner, dan dokumentasi untuk melihat bagaimana faktor-faktor yang mempengaruhi teknologi informasi dalam melihat peluang bisnis dan pengembangan wirausaha dalam dunia industri untuk siswa. Adanya pengabdian ini  dapat meningkatkan motivasi minat bagi siswa dalam tersedia lapangan kerja yang lumayan fleksibel dan dapat membantu siswa untuk mendapatkan biaya tambahan sehari-hari serta membangkitkan jiwa-jiwa ekonomi kreatif dan inovatif yang dimiliki siswa. Selanjutnya hasil dari pengabdian ini adalah untuk mengetahui Teknologi Informasi dalam melihat peluang Bisnis dan pengembangan wirausaha dalam Dunia industri serta mengetahui sistem seperti apa yang bisa digunakan untuk menjalankan suatu wirausaha secara online. hasil pengabdian juga diharapkan dapat dibangun dan membantu siswa membuka wirausaha online secara fleksibel tanpa meninggalkan kewajiban seorang siswa.
Comparison of the K-Nearest Neighbor and Random Forest Methods in Classifying the Best Selling Medicines at Khan Pharmacy Matang Glumpang Dua Anya Regina Putri; Rozzi Kesuma Dinata; Maryana
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25183

Abstract

Khan Matang Glumpang Dua Pharmacy faces difficulties in analyzing drug sales patterns that affect inventory efficiency and customer satisfaction. The need to anticipate demand and reduce the risk of stockouts or excess stock requires an effective classification system for best-selling drugs. This study aims to test the K-Nearest Neighbor (KNN) and Random Forest methods to perform and find the best classification model. The data used in this study consisted of 382 data points. This study compared two classification models on pharmacy sales data. The K-Nearest Neighbor (KNN) model was tested using the parameter k=3, while the Random Forest model was tested with 100 trees and a max depth of 5. The results showed that the KNN and Random Forest (RF) algorithms. The Random Forest (RF) model outperformed KNN on all metrics: RF achieved an Accuracy and F1-Score of 94.81%, while KNN recorded an Accuracy of 93.51% and an F1-Score of 93.44%.
Co-Authors Ahmad Fauzi Abdillah Aklimawati Aklimawati, Aklimawati Angga Pratama Annisa Afrilia Zahra Annisa Anya Regina Putri Arief Rahman Aryandi, Aryandi Ayuni, Novita Cut Ita Erliana Dahlan Abdullah darnila eva Daud, Eva Darnila Defi Irwansyah Dema Yulianto E. Elwina EDI YUSUF, EDI Effan Fahrizal Eka Yuliana Fatimah Erna Isfayani Eva Darnila Ezwarsyah Ezwarsyah Ezwasyah, Ezwasyah Fadlisyah Fadlisyah Fadlisyah Fajriana, Fajriana Fardiansyah, T. fatimah Fatimah Faturrahman, Puja Fauzi, Alfi Fuadi, Wahyu Haves Qausar Hayatun Nufus Ilyana, Anis Ismiaton Fitria Iwan Pahendra Jasiah Khairunnisa Khairunnisa Khalid Mawardi Lis Ayu Widari, Lis Ayu Listiana, Yeni Marhami, Marhami Marlina Sari Martunis Maulida, Leni Meriatna Meriatna Muhammad Arrayyan Muhammad Azmi, Muhammad Muhammad Daud Muhammad Fadhil Muhammad Muhammad Muhammad Ridhwan Muhammad Zakaria muli ani Munawarah Munawarah, Munawarah Mursalin . Muthmainnah Muthmainnah mutiara mutiara Nadya Raudathul Sofa Ndari, Wulan Novi Sylvia Nur Elisyah Nurdin Nurdin Nurdin Nurdin Olanda, T.M.Riski Raihan Putri Rini Meiyanti Rohantizani, Rohantizani Rozi Kesuma Dinata Rozzi Kesuma Dinata Safwandi Safwandi Sahputra, Ilham Said Fadlan Anshari salamah salamah Sari, Riska Kumala Sinambela Marzuki Siti Hajar Sofyan, Diana Khairani Sri Meutia Sugeng - Sujacka Retno Sumarni Rumfot Syibral Malasyi, Syibral Syukriah Syukriah, Syukriah Taufiq Taufiq Ulfi Zahara Ulia, Rahmatul Wahyu Fuadi Wahyu Fuadi Wanrice, Mabrur Wulandari Wulandari Yazid Bindar Yovi Chandra Zalfie Ardian Zara Yunizar Zarkasyi Zarkasyi Zuraida Zuraida