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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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Prediction of Cyberbullying in Social Media on Twitter Using Logistic Regression Prayudani, Santi; Adha, Lilis Tiara; Ariyani, Tika; Lubis, Arif Ridho
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9842

Abstract

As cases of cyberbullying on social media increase, there is a need for efficient measures to detect the vice. This research aims to establish the application of machine learning algorithms in analyzing text on social media to determine potentially harmful comments using logistic regression. The first and most important research question of this study is to assess the extent to which the model is capable of correctly identifying the comments that contain features of cyberbullying and those that do not. The data set included comments from different social media sites and was preprocessed before further analysis was conducted on it. Exploratory Data Analysis was applied in the study to establish relationships and textual features with bullying behavior. As with any other model, after training and testing the model, the results were analyzed using parameters like precision, precision, gain, and F1 statistics. The outcomes of this study revealed that the use of logistic regression models can give a fairly satisfactory level of accuracy in identifying cyberbullying. In light of this, this study underscores the need to use machine learning algorithms to minimize negative actions in cyberspace.
Peningkatan Kapasitas Serapan Pakan Hijauan Guna Mereduksi Biaya Pengadaan Pakan Kambing Di Desa Tanjung Gusta Aminuddin, Harris; Suadi; Hidayat, Ahmad; Prayudani, Santi
Jurnal Pengabdian Kepada Masyarakat dan Desa Volume 2, Nomor 2, Januari 2025
Publisher : Politeknik Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51510/passa.v2i2.2645

Abstract

Mitra memiliki 56 ekor kambing yang terdiri dari 35 kambing domba (gibas) dan 21 kambing kacang. Dua jenis kambing ini diberi pakan rumput yang didapat dari lahan kosong milik warga yang tidak produktif. Dalam sehari mitra harus mendapatkan 6 ikat rumput yang beratnya ±30 kg/ikat. Biaya pengadaan pakan rumput Rp 15.000/ikat atau Rp 90.000/hari. Selain rumput diberikan pakan tambahan berupa konsentrat secara terpisah untuk menambah daya tahan kambing terhadap penyakit. Mitra menyediakan konsentrat sebanyak 50 kg yang harganya Rp 180.000 untuk 10 hari. Jika dihitung, biaya untuk konsentrat sebesar Rp 18.000/hari, sehingga biaya pembelian pakan sebesar Rp Rp 108.000/hari. Biaya sebesar itu cukup berat, sementara rumput yang diberikan tidak semua dimakan disebabkan sistem pemberian pakan yang tidak tepat. Pemberian rumput tanpa dicacah menyebabkan banyak rumput jatuh ke tanah, terinjak-injak dan bercampur dengan kotorannya yang jumlahnya mencapai 50%. Jika dikonversikan, mitra kehilangan Rp 45.000/hari atau Rp 1.350.000/bulan. Oleh sebab itu, untuk mengurangi atau bahkan meniadakan kerugian tersebut dengan mencacah rumput yang akan diberikan ke kambing. Mesin pencacah rumput adalah solusi pilihan, karena adanya mesin semua rumput yang dberikan termakan tanpa sisa. Dengan mengkonsumsi rumput cacah secara maksimal kambing akan cepat tumbuh kembang gemuk, sehingga masa tunggu layak jual tidak terlalu lama.
Tree Triple Exponential Smoothing Analysis in Forecasting of Fertilizer Sales Prayudani, Santi; Banjarnahor, Wiwin Sry Adinda; Nugroho, Muhammad Rivan; Tazkiyatun Nisa
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: DECEMBER 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v1i2.49

Abstract

The majority of Indonesia's population relies on the agricultural sector, making fertilizer an essential raw material to increase productivity. PT. Pupuk Iskandar Muda (PIM), faces challenges in maintaining the balance of urea fertilizer production and demand. In 2021, PIM's urea fertilizer production was unable to meet demand, while in 2019, 2020, and 2022 there was overproduction. This inventory non-optimization can lead to productivity bottlenecks and increased storage costs. One solution to this problem is forecasting. This research uses the Triple Exponential Smoothing (TES) forecasting method in forecasting urea fertilizer sales for the next period. The data used is fertilizer sales data from PT PIM for the 2019-2023 period. Evaluation of the accuracy value is done using the MAD, MSE, and MAPE matrices. The results of this study indicate that the TES method with a smoothing weight value of Alpha = 0.4, Beta = 0.2, and Gamma = 0.4 produces a MAD value of 22,017.75, MSE of 990,752,983.08, and MAPE of 22.3% which can be categorized as quite feasible to use in forecasting the demand for urea fertilizer at PT PIM seen from the MAPE value.
Multimodal Sentiment Analysis in Indonesian: A Comparative Study of Deep Learning Models for Hate Speech Detection on Social Media Muhammadiyah, Mas’ud; Xiang, Yang; Na, Li; Nishida, Daiki; Prayudani, Santi
Journal International of Lingua and Technology Vol. 4 No. 1 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiltech.v4i1.824

Abstract

With the rapid expansion of social media, the prevalence of hate speech has become a critical issue, particularly in the context of Indonesian language and culture. The detection of hate speech in social media platforms is a complex task due to the multimodal nature of online communication, where text, images, and videos are often combined to express sentiments. This study aims to explore and compare deep learning models for multimodal sentiment analysis, focusing on their effectiveness in detecting hate speech in Indonesian social media content. By analyzing both textual and visual data, the study seeks to enhance the accuracy of sentiment classification, specifically identifying instances of hate speech. The research employs several state-of-the-art deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-based models, to perform sentiment analysis on a multimodal dataset. The dataset includes text and images from Indonesian social media posts, labeled for hate speech detection. The results show that multimodal models outperform text-only models, with the Transformer-based model yielding the highest accuracy and F1-score in detecting hate speech. The inclusion of visual data significantly improved the model’s ability to classify complex and subtle expressions of hate speech. This study concludes that multimodal deep learning models offer a promising solution for detecting hate speech in Indonesian social media, with implications for better content moderation and online safety.
Pengaruh Penerapan Metode Student Centered Learning (SCL) Dan Discovered Learning (DL) Mengenai Pemahaman Mahasiswa Pada Pembelajaran Teknologi Informasi Prayudani, Santi
MULTINETICS Vol. 5 No. 1 (2019): MULTINETICS Mei (2019)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v5i1.1464

Abstract

Model pembelajaran saat ini sangat bervariasi, mahasiswa dapat mengeksplorasi berbagai ide maupun wawasan yang dimiliki dan mampu mengembangkan wawasan berpikir secara bebas tanpa pernah merasa terpenjarkan pola pikirnya dengan menggunakan metode pembelajaran Student Centered Learning (SCL). Selain itu, mahasiswa juga diharuskan untuk mengeksplorasi dan mengidentifikasi masalah yang muncul sehingga mereka dapat menemukan pengetahuan sendiri. Pembelajaran ini dikenal sebagai Discovery Learning (DL) dimana model pembelajaran ini merupakan model penyelesaian masalah yang akan sangat berguna bagi siswa dalam menghadapi kehidupan nyata di masa depan. Penelitian ini bertujuan untuk mengetahui pengaruh penerapan metode Discovery Learning (DL) pada pemahaman siswa tentang pembelajaran teknologi informasi. Hasil studi dari kedua model pembelajaran tersebut diharapkan dapat meningkatkan kualitas pendidikan di perguruan tinggi khususnya di bidang teknologi informasi dan dapat mengembangkan bidang keilmuan Model pembelajaran saat ini sangat bervariatif, mahasiswa dapat mengeksplorasikan ide maupun gagasan yang dimiliki serta mampu mengembangkan wawasan berpikir secara bebas tanpa pernah merasa terpenjarakan pola pikirnya dengan menggunakan metode pembelajaran Student Centered Learning (SCL) selain itu mahasiswa juga dituntut untuk dapat menggali serta mengidentifikasikan permasalahan yang muncul sehingga mereka dapat menemukan pengetahuan dengan sendirinya. Pembelajaran ini dikenal dengan pembelajaran penemuan atau Discovery Learning (DL) dimana model pembelajaran ini merupakan suatu model pemecahan masalah yang akan sangat bermanfaat bagi mahasiswa dalam menghadapi kehidupan nyata di kemudian hari. Penelitian ini bertujuan untuk mengetahui pengaruh penerapan metode Discovery Learning (DL) mengenai pemahaman mahasiswa pada pembelajaran teknologi informasi. Hasil penelitian dari kedua model pembelajaran tersebut diharapkan dapat meningkatkan mutu pendidikan di perguruan tinggi khususnya di bidang teknologi informasi serta dapat mengembangkan bidang keilmuan tersebut.
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%.
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): 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.
A COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS AND USER EXPERIENCE FOR ACADEMIC PERFORMANCE PREDICTION Virdyra Tasril; Santi Prayudani; J. Prayoga; Rahayu Mayang Sari
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 3 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i3.4534

Abstract

This study aimed to compare the performance of machine learning algorithms and user experience in predicting students’ academic achievement. The research is motivated by the need for prediction systems that are not only highly accurate but also easily interpretable by users. The proposed methodology involved the implementation of two algorithms, namely Decision Tree and Random Forest, using an academic dataset that included grade point average, attendance, and assessment scores. Model performance was evaluated using accuracy, precision, recall, and F1-score, while user experience was assessed through the System Usability Scale (SUS) based on a simple user interface. The findings revealed that Random Forest achieved higher predictive accuracy, whereas Decision Tree provided better interpretability and ease of understanding for users. These results indicated a trade-off between model performance and user experience, suggesting that algorithm selection should consider both aspects in order to develop an effective and user-friendly academic prediction system
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.
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