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All Journal Riau Journal of Computer Science JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) ARSY : Jurnal Aplikasi Riset kepada Masyarakat Bulletin of Information Technology (BIT) Jurnal Ilmiah MADIYA: Masyarakat Mandiri Berkarya International Journal of Science and Society (IJSOC) Jurnal Sains dan Teknologi Internasional Journal of Data Science, Computer Science and Informatics Technology (InJODACSIT) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Engineering Science Letter JENTIK Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat Electronic Integrated Computer Algorithm Journal JGEN : Jurnal Pengabdian Kepada Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Journal of Computer Science Advancements
Yuyun Yusnida Lase
Politeknik Negeri Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Classification Analysis of Product Sales Results at Alfamart Using the Naïve Bayes Method Lase, Yuyun Yusnida; Silaban, Citra Wasti; Sitepu, Alex Sander; Telaumbanua, Reza Kavarin
Electronic Integrated Computer Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: APRIL 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v1i2.18

Abstract

This research focuses on the analysis of the number of products sold, especially stock items from the distribution center to Alfamart stores. The main problem discussed in this study is the result of the number of unsold and sold products, which causes overstocking in the warehouse area. To overcome this problem, it will be solved using the Naive Bayes classification method. This research uses sample data of 100 products and uses data collection techniques such as observation and interviews. The collected data is analysed through a classification approach. This research aims to predict goods that sell and do not sell using Rapidminer using the NaïveBayes method. And to produce more accurate data for the product sales process. The reason for using this naïve bayes algorithm in the process of processing and analysing data is because the way this algorithm works uses statistical methods and probability in predicting future results. The validation results show that the Naive Bayes classification method implemented through Rapidminer provides a significant explanation with a fairly high accuracy and a positive effect on the prediction of sales of goods based on consumer demand and needs.
Analysis of Regression and Neural Network Models in Predicting Patient Visit Volume Harizahahyu; Friendly; Fathoni, Muhammad; Lase, Yuyun Yusnida; Prayudani, Santi; Harfita, Nur Laily
International Journal of Science and Society Vol 7 No 4 (2025): International Journal of Science and Society (IJSOC)
Publisher : GoAcademica Research & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/ijsoc.v7i4.1561

Abstract

Predicting patient visit volume plays a crucial role in supporting decision-making and resource allocation in healthcare services. This study aims to compare the performance of Multiple Linear Regression and an Artificial Neural Network (ANN) in forecasting patient visits at a dental clinic, using daily patient visit data and predictor variables such as holidays and promotional activities. Multiple regression was used to capture the linear relationship between the predictor and response variables, while ANN was applied to explore potential non-linear relationships. The results indicate that multiple regression outperformed the ANN, demonstrated by lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values, and provided clearer interpretability, making it more beneficial for healthcare practitioners, particularly in the context of a limited dataset. In contrast, the ANN tended to produce overestimates and was less responsive to short-term variations. Therefore, multiple regression can still be considered a reliable, efficient, and interpretable prediction method for clinical data with a moderate sample size, while future research is recommended to use larger datasets and test other machine learning algorithms to improve the accuracy and generalizability of the results.
Optimization of Convolutional Neural Network for Classification of Hydroponic Vegetable Cultivation Using Machine Learning Lubis, Arif Ridho; Prayudani, Santi; Putra, Purwa Hasan; Lase, Yuyun Yusnida
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7231

Abstract

In an effort to apply applied product innovation and support the improvement of hydroponic vegetable cultivation, it is based on several things. Among them are changes in the texture of the year, stems and vegetable quality. At this time the problems faced by hydroponic vegetable pickers, especially banyumas village youth organizations who have UMKM hydroponic vegetable cultivation. This situation will have an impact on problems and losses that result in a lack of yield and quality of harvested vegetables if not resolved quickly. The results of this study resulted in optimal accuracy performance in the classification of hydroponic vegetables with CNN, this study also successfully classified normal vegetables with vegetables affected by disease. This research produces accuracy in the first test 73% and the second test 92%.
Perbandingan Kinerja Isolation Forest dan AutoEncoder untuk Deteksi Anomali Cuaca Fatmi, Yulia; Lase, Yuyun Yusnida; Hamdi , Khairil
Jurnal Manajemen Teknologi Informatika Vol. 3 No. 3 (2025): Jurnal Manajemen Teknologi Informatika
Publisher : JENTIK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70038/jentik.v3i3.196

Abstract

Deteksi anomali cuaca berperan penting dalam mendukung sistem pemantauan dan peringatan dini cuaca, terutama pada data cuaca multivariat yang bersifat kompleks dan nonlinier. Penelitian ini bertujuan membandingkan kinerja algoritma Isolation Forest dan AutoEncoder dalam mendeteksi anomali cuaca. Metode penelitian menggunakan pendekatan eksperimen dengan tahapan prapemrosesan data, pemodelan deteksi anomali, serta evaluasi kinerja menggunakan metrik statistik dan analisis kurva ROC serta distribusi skor anomali. Hasil penelitian menunjukkan bahwa kedua metode memiliki performa yang baik dalam mendeteksi anomali cuaca. Isolation Forest unggul dalam efisiensi komputasi dan kestabilan model, sedangkan AutoEncoder menunjukkan sensitivitas yang lebih tinggi terhadap anomali cuaca yang bersifat kompleks, ditunjukkan oleh nilai AUC yang lebih tinggi dan pemisahan skor anomali yang lebih jelas. Pemilihan metode deteksi anomali cuaca perlu disesuaikan dengan kebutuhan sistem pemantauan dan analisis risiko cuaca.
PENGEMBANGAN APLIKASI BASIS INFORMASI PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT PADA P3M POLITEKNIK NEGERI MEDAN Putra, Purwa Hasan; Lase, Yuyun Yusnida; Asmara, Wira Bayu
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5832

Abstract

Abstract: This study aims to analyze the utilization of the Simlibtamas system in managing research proposal submissions at the Medan State Polytechnic. Based on observations on the system dashboard, there are 1,299 registered users, 21 of whom are active, who have submitted proposals, and a total of 800 proposals that have been processed. This system is able to display information in a structured manner through a submission table containing the name of the proposer, the proposal title, the original scheme, document files, assessments, and the amount of proposed and recommended funding. In addition, a recapitulation graph of the amount of funding shows the existence of funding submissions within a certain time period, reflecting the dynamics of research activity. The results show that the Simlibtamas system has functioned well in supporting the administration and evaluation process of research proposals. However, the involvement of proposer users still needs to be improved for more optimal system utilization. In conclusion, Simlibtamas is able to facilitate monitoring, transparency, and management of research, and can be further developed to increase participation and effectiveness of research fund distribution. Keywords: Information Systems, Applications, Simlibtimnas, Research, Community Service Abstrak: Penelitian ini bertujuan untuk menganalisis pemanfaatan sistem Simlibtamas dalam pengelolaan pengajuan proposal penelitian di Politeknik Negeri Medan. Berdasarkan hasil pengamatan pada dashboard sistem, tercatat sebanyak 1299 pengguna terdaftar dengan 21 pengguna aktif yang mengajukan proposal, serta total 800 proposal yang telah diproses. Sistem ini mampu menampilkan informasi secara terstruktur melalui tabel pengajuan yang memuat nama pengusul, judul proposal, skema pendanaan, file dokumen, penilaian, serta jumlah dana yang diusulkan dan direkomendasikan. Selain itu, grafik rekapitulasi jumlah dana menunjukkan adanya fluktuasi pengajuan dana dalam rentang waktu tertentu, yang mencerminkan dinamika aktivitas penelitian. Hasil penelitian menunjukkan bahwa sistem Simlibtamas telah berfungsi dengan baik dalam mendukung proses administrasi dan evaluasi proposal penelitian. Namun, keterlibatan pengguna pengusul masih perlu ditingkatkan agar pemanfaatan sistem menjadi lebih optimal. Kesimpulannya, Simlibtamas mampu memudahkan monitoring, transparansi, dan pengelolaan penelitian, serta dapat dikembangkan lebih lanjut untuk meningkatkan partisipasi dan efektivitas distribusi dana penelitian. Kata kunci: Sistem Informasi, Aplikasi, Simlibtimnas, Penelitian, Pengabdian
Perbandingan TextRank Berbasis TF-IDF dan Word2Vec dalam Peringkasan Teks Berita Bahasa Indonesia Yohannes Christian Gurning; Samuel Cristian Saragih; Yuyun Yusnida Lase; Julham Julham
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.552

Abstract

Automatic text summarization has become an essential solution for processing massive textual information, particularly in lengthy news articles. This study compares two variants of the TextRank algorithm using different weighting schemes: TF-IDF and Word2Vec, for summarizing Indonesian news texts. The dataset comprises 160 news articles from Kompas.com, which underwent preprocessing. Evaluation was conducted using ROUGE metrics (ROUGE-1, ROUGE-2, ROUGE-L), manual readability assessment, and execution runtime. The results indicate that TextRank with Word2Vec outperforms TF-IDF in both ROUGE scores (ROUGE-1 F1: 0.7033 vs 0.6454) and processing speed. These findings suggest that incorporating semantic representations into graph-based algorithms like TextRank significantly improves summary quality and runtime efficiency.
Multi-Modal Deep Learning Approach for Waste Management: Integrating Image Classification and Text Mining for Environmental Awareness Santi Prayudani; Ainul Hizriadi; Yuyun Yusnida Lase
Engineering Science Letter Vol. 5 No. 02 (2026): In Press - Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.002036

Abstract

Environmental degradation caused by inefficient waste management remains a major global challenge, largely due to the limitations of conventional systems that rely on manual waste sorting and limited utilization of heterogeneous data sources. This study proposes a novel multi-modal deep learning framework that integrates visual and textual information to enhance waste classification performance while simultaneously providing insights into environmental awareness. The proposed framework combines convolutional neural networks (CNNs) for waste image classification and a recurrent neural network with long short-term memory (LSTM) architecture for text analysis. Visual and textual feature representations are integrated through a feature-level fusion strategy using vector concatenation before final classification. The image dataset consists of six waste categories, cardboard, glass, metal, paper, plastic, and trash, while the textual dataset contains waste management descriptions, community feedback, and environmental discourse collected from public and field sources. Environmental awareness was assessed through text mining by identifying dominant themes related to recycling practices, waste sorting behavior, environmental responsibility, and public concern regarding pollution and sustainability issues. Experimental results demonstrate that the proposed multimodal framework achieves an accuracy of 88.9% and an F1-score of 0.89, outperforming image-only and text-only models with accuracies of 78.4% and 81.2%, respectively. This corresponds to absolute performance improvements of 10.5% over the image-based model and 7.7% over the text-based model, while reducing the classification error rate by 40.96%. Furthermore, the multimodal model exhibits superior robustness under degraded data conditions, with only a 4.7% reduction in accuracy compared to larger performance declines observed in unimodal approaches. The main contribution of this study lies in the integration of waste image recognition and environmental-awareness extraction within a unified multimodal learning framework, enabling not only accurate waste categorization but also the generation of behavioral and sustainability-related insights that support more intelligent and sustainable waste management systems.
Pemberdayaan Dharma Wanita Politeknik Negeri Medan Melalui Pelatihan Desain Digital Canva dalam Mendukung Literasi Digital dan Pembuatan Konten Visual Andam Lukcyhasnita; Meryatul Husna; Yuyun Yusnida Lase; Santi Prayudani
Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 4 No. 1 (2026): Edisi Juni
Publisher : Jurusan Teknik Sipil, Politeknik Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51510/komposit.v4i1.3067

Abstract

Kegiatan ini bertujuan untuk meningkatkan literasi digital Dharma Wanita Politeknik Negeri Medan melalui pelatihan penggunaan aplikasi Canva. Pelaksanaan pelatihan dilakukan untuk menjawab permasalahan rendahnya kemampuan desain visual ibu Dharma wanita, ketergantungan pada pihak luar dalam membuat media publikasi, serta rendahnya keterampilan membuat konten usaha rumahan. Metode pelatihan meliputi observasi awal, penyusunan modul Canva, pelaksanaan praktik, pendampingan teknis, dan evaluasi kemampuan peserta. Hasil kegiatan menunjukkan terjadi peningkatan pengetahuan, kemampuan menggunakan fitur Canva, keterampilan membuat poster, konten usaha, dan bahan publikasi organisasi secara mandiri. Peserta juga mengalami peningkatan kepercayaan diri terhadap pemanfaatan teknologi digital. Dampak pengabdian terlihat dari terbentuknya Tim Kreatif Dharmawanita, pemanfaatan Canva secara rutin, dan meningkatnya produktivitas konten organisasi maupun usaha rumahan anggota. Kegiatan ini membuktikan bahwa pelatihan digital Canva efektif dalam meningkatkan pemberdayaan perempuan dan literasi teknologi dalam komunitas organisasi.
Prediksi Dampak Pembelajaran Hybrid Learning Menggunakan Naive Bayes Yuyun Yusnida Lase; Yulia Fatmi; Haryadi; Santi Prayudani
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.968

Abstract

This research use to predict the impact of hybrid learning on Medan State Polytechnic students. This algorithm was chosen because it has excellent performance in classification compared to other algorithms. Statistical and probabilistic methods are used in the operation of this algorithm to make predictions about what will happen in the future. Technology mastery, level of teacher-student interaction, and mastery of teaching materials are the variables used in this study. The sample data used came from students of the Software Engineering Technology Study Program of Medan State Polytechnic. The prediction results carried out manually with naïve bayes, with training data of 100 (one hundred) students and test data of 1 (one) student, produced a result of 0.012, which indicates an increase in student academic results. The test results were proven using the phyton programming language. The first test results, with 20% test data, resulted in an increase in academic results by 86% around 13 students with an accuracy value of 80%, and the second test, with 40% test data, resulted in an increase in academic results by 92% around 29 students with an accuracy value of 88%.
THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN PROCESSING HEALTH DATA IN BIOMEDICAL INFORMATION Santi Prayudani; Yuyun Yusnida Lase; Meryatul Husna; Hikmah Adwin Adam
Journal of Computer Science Advancements Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v3i2.2245

Abstract

The increasing complexity and volume of health data in modern biomedical systems have necessitated advanced technologies for effective data processing and analysis. Traditional methods often fall short in managing real-time, multidimensional data generated from various biomedical sources, such as electronic health records (EHRs), wearable devices, and genomic data. This research investigates the application of artificial intelligence (AI) in optimizing the processing and interpretation of biomedical health data. The objective of this study is to explore how AI-based technologies, including machine learning and deep learning algorithms, enhance the efficiency, accuracy, and predictive capabilities in biomedical information systems. By identifying patterns, anomalies, and correlations in large datasets, AI offers potential improvements in disease diagnosis, patient monitoring, and treatment personalization. This research employs a qualitative systematic review method, analyzing peer-reviewed literature published between 2015 and 2024 from major databases such as PubMed, IEEE Xplore, and Scopus. The analysis focuses on case studies, comparative evaluations, and implementation outcomes of AI in various biomedical domains. The findings reveal that AI applications significantly improve data processing speed and accuracy, enable early diagnosis of diseases such as cancer and diabetes, and support predictive analytics for patient outcomes. However, challenges remain in areas such as data privacy, ethical compliance, and algorithm transparency. In conclusion, the integration of AI into biomedical data systems holds transformative potential for healthcare delivery, though further interdisciplinary collaboration is required to address its limitations and ensure equitable access and ethical use.
Co-Authors Adwin Adam , Hikmah Ainul Hizriadi Al Khowarizmi Alex Sander Sitepu Andam Lukcyhasnita Asmara, Wira Bayu Citra Wasti Silaban F. Friendly Fatmi, Yulia Fildzah Nadiah Friendly Friendly, Friendly Gabriel Ardi Hutagalung Hamdi , Khairil Harfita, Nur Laily Harizahahyu Harizahayu Harizahayu, Harizahayu Harris Aminuddin Haryadi Haryadi - Hikmah Adwin Adam Husna, Meryatul Julham Julham Lampson Pindahaman Purba Lubis, Arif Ridho Mahulae, Doni Ganda Marbungaran Muhammad Fathoni Muhammad Luthfi Hamzah Muharman Lubis Nauli, Arif Aryaguna Prayudani, Santi Putra, Purwa Hasan Ramdani Safitri, Habibi Reza Kavarin Telaumbanua Samuel Cristian Saragih Silaban, Citra Wasti Sitepu, Alex Sander Syafli, Sekar Arini Syamsul Arifin Telaumbanua, Reza Kavarin Yohannes Christian Gurning Yulia Fatmi Yulia Fatmi