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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURTEKSI JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Sistem Informasi Kaputama (JSIK) MULTINETICS 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) Journal Software, Hardware and Information Technology Bulletin of Information Technology (BIT) International Journal of Science and Society (IJSOC) Journal of Computer Science and Informatics Engineering Jurnal Sains dan Teknologi Journal International of Lingua and Technology Technologica SWARNA Engineering Science Letter Komposit : Jurnal Pengabdian Dan Pemberdayaan Masyarakat Electronic Integrated Computer Algorithm Journal JGEN : Jurnal Pengabdian Kepada Masyarakat SWARNA: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat dan Desa (PASSA) AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal Journal of Computer Science Advancements
Prayudani, Santi
Politeknik Negeri Medan
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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.
Optimization Performance of Extreme Gradient Boosting and Random Forest for Child Stunting Classification Based on Economic Factors Lase, Yuyun Yusnida; Putra, Purwa Hasan; Lubis, Arif Ridho; Prayudani, Santi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5864

Abstract

Stunting remains a major health concern in Indonesia due to its impact on children’s physical growth and cognitive development. One of the factors influencing the incidence of stunting is family economic status, which is linked to access to nutrition, sanitation, and a healthy environment. This study aims to optimize the performance of the XGBoost and Random Forest algorithms in classifying stunting in children based on economic factors and to compare the performance of the two models. The methods used in this study involve a machine learning approach, including data preprocessing, model training, hyperparameter optimization, and performance evaluation using a confusion matrix, accuracy, precision, recall, F1-score, and ROC-AUC curves. The results indicate that both algorithms perform well in classification, with an accuracy rate of approximately 70%. The Random Forest model demonstrated better performance than XGBoost with an AUC value of 0.7655, while XGBoost had an AUC value of 0.75. Additionally, the feature importance results indicated that economic and environmental factors, such as housing conditions and sanitation, have a significant influence on the incidence of stunting.
The Community Partnership Service Utilization of Social Media for Promoting Binjai Batik Products at UMKM Tan Collection, Binjai District, Binjai City, North Sumatra Yuyun Yusnida Lase; Andam Lukcyhasnita; Santi Prayudani; Harizahayu; Friendly
GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): GANDRUNG: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Olahraga dan Kesehatan, Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/gandrung.v7i1.6332

Abstract

The creative economy serves as a vital platform for promoting and preserving local cultural heritage. UMKM Tan Collection, a creative enterprise producing Binjai's signature "rambutan batik," faces significant marketing challenges as the sole producer of handcrafted rambutan batik in the city. Digital visibility remains particularly low, with minimal online information and motif documentation compared to other batik varieties, resulting in unstable market demand. This community service program aims to enhance the digital promotion and sales of rambutan batik through social media content creation training. The methodology includes problem identification, CapCut video editing workshops, content development assistance, and implementation evaluation. Results demonstrate improved digital literacy among artisans, creation of engaging social media content, and enhanced online brand visibility. The program successfully established a sustainable digital marketing framework, enabling continuous product promotion and cultural preservation through digital platforms.
Analisis Deteksi Penyakit Daun Pisang Menggunakan Ekstraksi Fitur CNN (MobileNetV2) dan Klasifikasi SVM Yuyun Yusnida Lase; Lampson Pindahaman Purba; Santi Prayudani; Arif Ridho Lubis; Hikmah Adwin Adam
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 6 (2025): Desember 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i6.6590

Abstract

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.
Sistem Pendaftaran Santri Berbasis Web Di Pondok Pesantren Tahfizh Darul Qur’an: Indonesia Santi Prayudani; Nadiyah Hikmah Butar Butar
Technologica Vol. 5 No. 2 (2026): Technologica
Publisher : Green Engineering Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55043/technologica.v5i2.486

Abstract

Selama ini proses penerimaan santri di Pondok Pesantren Tahfizh Darul Qur’an masih bertumpu pada cara manual dan formulir Google Form, sehingga pengelolaan datanya belum tertata dengan baik dan rentan keliru saat penginputan. Penelitian ini berupaya merancang sekaligus membangun sistem informasi pendaftaran santri berbasis web yang menyatukan setiap tahap pendaftaran, mulai dari pembuatan akun, pengisian formulir, pengunggahan dokumen, hingga pengumuman hasil seleksi. Pembangunan sistem mengikuti model Waterfall yang melingkupi analisis kebutuhan, perancangan, implementasi, pengujian, serta pemeliharaan. Sistem dibangun memakai bahasa pemrograman PHP berbasis framework Laravel dengan MySQL sebagai pengelola basis datanya. Beberapa layanan yang tersedia di antaranya autentikasi pengguna, verifikasi berkas oleh admin, penyampaian jadwal seleksi, publikasi hasil seleksi, sampai ekspor data ke format PDF dan Excel. Keunggulan utama sistem ini terletak pada penyatuan seluruh alur pendaftaran dalam satu wadah, sehingga data menjadi lebih teratur dan potensi kesalahan dapat ditekan dibanding cara sebelumnya. Kontribusi penelitian berupa tersedianya sistem siap pakai yang dapat langsung dimanfaatkan pesantren untuk mendigitalkan administrasi pendaftaran. Uji coba dilakukan dengan teknik black box, dan hasilnya memperlihatkan bahwa seluruh fungsi sistem berjalan sebagaimana mestinya. Melalui sistem ini, proses pendaftaran terbukti lebih ringkas, rapi, dapat diakses kapan saja, sekaligus menyokong digitalisasi administrasi di lingkungan pesantren
IMPLEMENTASI SISTEM PREDIKSI GAYA BELAJAR MAHASISWA MENGGUNAKAN NAÏVE BAYES BERBASIS WEB Yuyun Yusnida Lase; Sekar Arini Syafli; Yulia Fatmi; Santi Prayudani; Arif Ridho Lubis; Haryadi Haryadi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 7 No. 4 (2024): November 2024
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v7i4.2327

Abstract

Aplikasi ini dibuat untuk memprediksi  gaya belajar mahasiswa, menggunakan algoritma naïve bayes, dibandingkan dengan algoritmanya naïve bayes sangat baik dalam  proses klasifikasi, untuk melakukan prediksi dimasa depan algoritma ini menggunakan probabilitas dan statistik. Data yang digunakan berupa data demografis mahasiswa seperti semester/tingkat studi, data gaya belajar seperti visual, kinestetik, auditori, dan data preferensi belajar seperti preferensi belajar visual, preferensi belajar auditori, dan preferensi belajar kinestetik. Metode pembelajaran yang diamati untuk menentukan gaya belajar metode synchoronous.  Sampel data yang digunakan adalah mahasiswa program studi teknologi rekayasa perangkat lunak. Bahasa yang digunakan dalam membuat aplikasi ini menggunakan  PHP dan database MySQL. Aplikasi ini nantinya dapat membantu tenaga pendidik dapat menyusun strategi pembelajaran yang sesuai dengan gaya belajar mahasiswa sehingga proses pembelajaran dapat berjalan dengan efektif dan efesien.
Co-Authors Adha, Lilis Tiara Ahmad Hidayat Ainul Hizriadi Ainul Hizriadi Al Khowarizmi Andam Lukcyhasnita Ariyani, Tika F. Friendly Fatmi, Yulia Friendly Friendly, Friendly Gabriel Ardi Hutagalung Ginting, Heri Sanjaya Harfita, Nur Laily Harizahahyu Harizahayu Harizahayu, Harizahayu Harris Aminuddin Haryadi Haryadi - Haryadi Haryadi Hidayah, Rizki Hikmah Adwin Adam Husna, Meryatul Ivan Jaya Jaya, Ivan Julham Julham Julham Julham Kadri Yusuf Lampson Pindahaman Purba Lubis, Arif Ridho Muhammad Fathoni Muhammad Luthfi Hamzah Muhammadiyah, Mas’ud Muharman Lubis Munthe, Joy Lousia Brigitha Na, Li Nadiyah Hikmah Butar Butar Nishida, Daiki Nugroho, Muhammad Rivan Putra, Purwa Hasan Radea Shiddiq Rahayu Mayang Sari Salam, Azrizal Sekar Arini Syafli Sembiring, Zakaria Shiddiq, Radea Sibarani, Yous Sinaga, Kristin Sinuraya, Junus Situmorang, Dita Rouli Basa Suadi Zainal Syafli, Sekar Arini Syamsul Arifin Tazkiyatun Nisa Tumanggor, Orli Virdyra Tasril Wiwin Sry Adinda Banjarnahor Xiang, Yang Yulia Fatmi Yulia Fatmi Yusuf, Kadri Yuyun Yusnida Lase Zakaria Sembiring Zakaria Sembiring