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All Journal Jurnal Ilmiah Ekonomi dan Bisnis Al Ishlah Jurnal Pendidikan Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini IT JOURNAL RESEARCH AND DEVELOPMENT INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL TEKNOLOGI DAN OPEN SOURCE Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Zonasi: Jurnal Sistem Informasi Jurnal Pengabdian Masyarakat Multidisiplin Jutsi: Jurnal Teknologi dan Sistem Informasi J-Com (Journal of Computer) Jurnal Ekonomika Dan Bisnis J-COSCIS : Journal of Computer Science Community Service CONSEN: Indonesian Journal of Community Services and Engagement Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Indonesian Research Journal on Education JURNAL KOMUNITAS SAINS MANAJEMEN SATIN - Sains dan Teknologi Informasi Jurnal Testing dan Implementasi Sistem Informasi Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi INOVTEK Polbeng - Seri Informatika Proceedings of the Siliwangi Annual International Conference on Guidance and Counselling
Nurliana Nasution, Nurliana
Universitas Lancang Kuning
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Phishing Detection Model on Social Media Enhanced With CNN and BERT Nurliana Nasution; Wenni Syafitri; Feldiansyah Feldiansyah
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2194

Abstract

Phishing on social media has become an increasingly serious cyber threat because attackers exploit persuasive language, conversational context, and dynamic interaction patterns to deceive users. This study proposes a hybrid CNN-BERT model for detecting phishing content in Indonesian social media text by combining BERT’s contextual semantic representation with CNN’s ability to capture locally relevant textual patterns. The dataset was preprocessed to remove noise, normalize writing variations, and prepare the text for deep learning analysis; class proportions were also examined to support fairer evaluation. Model performance was assessed under multiple data-splitting scenarios and cross-validation to examine robustness and consistency. The experimental results indicate that the proposed hybrid model achieves strong and stable performance across accuracy, precision, recall, and F1-score, and outperforms the baseline model when the BERT backbone is frozen. However, when BERT is fully fine-tuned, the performance gain from the CNN layer becomes marginal, suggesting that strong contextual representations are already highly effective for this task. These findings indicate that integrating CNN and BERT is effective for phishing detection on social media, although domain adaptation challenges, overfitting risk, and real-world deployment constraints remain important considerations. The novelty of this work lies in systematically comparing frozen versus fully fine-tuned IndoBERT backbones with and without a CNN head for Indonesian short-message phishing detection.
Penerapan Algoritma K-Nearest Neighbor (Knn) Untuk Klasifikasi Status Gizi Balita di Kecamatan Rumbai Timur Marshanda Marshanda; Nurliana Nasution
J-Com (Journal of Computer) Vol. 6 No. 1 (2026): MARET 2026
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v6i1.4412

Abstract

Abstract: This study aims to classify the nutritional status of toddlers based on anthropometric data using the K-Nearest Neighbor (KNN) algorithm. Data were obtained from 20 Integrated Health Posts (Posyandu) in Rumbai Timur District, including Lembah Sari Village and Limbungan Village with a total of 1,000 toddler data. After cleaning and preprocessing, 782 data were obtained ready for use. The preprocessing stages include data cleaning and transformation, outlier removal, minority class handling, and data normalization. Next, data balancing was carried out using the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. The data was divided into 80% training data and 20% test data, then the K parameter was tested from 1 to 15 using 5-fold cross-validation. The results showed that the value of K = 1 provided the best performance with a macro recall of 0.8827 and an accuracy of 86.26%. These results indicate that the combination of the KNN algorithm with the SMOTE method and Min-Max normalization is effective in improving classification performance on imbalanced data and producing accurate and balanced predictions of toddler nutritional status between classes. Keywords: k-nearest neighbor; toddler nutritional status; SMOTE; min-max scaling; classification; anthropometric data Abstrak: Penelitian ini bertujuan untuk mengklasifikasikan status gizi balita berdasarkan data antropometri menggunakan algoritma K-Nearest Neighbor (KNN). Data diperoleh dari 20 Posyandu di Kecamatan Rumbai Timur, meliputi Kelurahan Lembah Sari dan Kelurahan Limbungan dengan total 1.000 data balita. Setelah melalui proses cleaning dan preprocessing, diperoleh 782 data yang siap digunakan. Tahapan pra-pemrosesan meliputi pembersihan dan transformasi data, penghapusan outlier, penanganan kelas minoritas, serta normalisasi data. Selanjutnya dilakukan penyeimbangan data menggunakan Synthetic Minority Oversampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas. Data dibagi menjadi 80% data latih dan 20% data uji, kemudian dilakukan pengujian parameter K dari 1 hingga 15 menggunakan 5-fold cross-validation. Hasil penelitian menunjukkan bahwa nilai K = 1 memberikan performa terbaik dengan recall macro sebesar 0,8827 dan akurasi 86,26%. Hasil ini menunjukkan bahwa kombinasi algoritma KNN dengan metode SMOTE dan normalisasi Min-Max efektif dalam meningkatkan kinerja klasifikasi pada data tidak seimbang serta menghasilkan prediksi status gizi balita yang akurat dan seimbang antar kelas. Kata kunci: k-nearest neighbor; status gizi balita; SMOTE; min-max scaling; klasifikasi; data antropometri
Analisis Karakteristik Deret Waktu dan Peramalan Harga Kartu Pokémon Charizard sebagai Aset Koleksi Menggunakan Model ARIMA Asrul Puadi; Eddissyah Putra Pane; Nurliana Nasution
Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Vol. 3 No. 3 (2026): SINTA: JULI
Publisher : Berkah Tematik Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61124/sinta.v3i3.300

Abstract

Kartu Pokémon Charizard Base Set #4 merupakan salah satu aset koleksi dengan nilai ekonomi terbilang tinggi dan dapat di perdagangkan secara aktif pada pasar sekunder. Perubahan harga yang terjadi dari satu periode ke periode lainnya menimbulkan ketidakpastian bagi kolektor maupun investor sehingga diperlukan analisis untuk memahami pola pergerakan harga dan menghasilkan prediksi pada periode mendatang. Penelitian ini bertujuan untuk menganalisis karakteristik deret waktu harga kartu Pokémon Charizard Base Set #4 serta membangun model peramalan menggunakan metode Autoregressive Integrated Moving Average (ARIMA). Data penelitian yang digunakan yakni data harga bulanan dengan periode Januari 2021 hingga Mei 2026 yang diperoleh melalui proses web scraping dari situs PriceCharting. Tahapan pada penelitian ini meliputi preprocessing data, analisis deskriptif, uji stasioneritas Augmented Dickey-Fuller (ADF), proses differencing, analisis Autocorrelation Function (ACF) dan Partial Autocorrelation Function (PACF), pemodelan ARIMA, serta evaluasi model dengan menerapkan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil yang didapat pada penelitian ini menunjukkan bahwa ARIMA sebagai model pilihan mampu menangkap pola historis harga kartu Pokémon Charizard dan menghasilkan prediksi harga untuk enam bulan berikutnya. Evaluasi model menghasilkan nilai MAE sebesar 52,42, RMSE sebesar 72,17, dan MAPE sebesar 15,86%. Hasil tersebut menunjukkan bahwa metode ARIMA dapat digunakan untuk melakukan peramalan harga aset koleksi, meskipun tingkat akurasinya masih dipengaruhi oleh karakteristik pasar koleksi yang dinamis dan adanya faktor eksternal yang dapat mempengaruhi data historis harga.
Optimization of The Use of Togaf Adm in The Design of Information Systems For Islamic Boarding Schools Nurliana Nasution; Mhd Arief Hasan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i2.1910

Abstract

Pesantren is an educational institution that stands traditionally where students live in one place to live with each other and study under the guidance of a teacher who is also known as a Kiai. For its implementation, many things must be managed by a boarding school, starting from student registration, student placement, student learning, and evaluation of student learning at the boarding school. So far, there is no adequate system for all of these administrative needs. The purpose of this study is to build an information system framework for Islamic boarding schools using the TOGAF FRAMEWORK. Miftahul Huda Islamic Boarding School Pekanbaru City uses the TOGAF-ADM methodology as the standard tool used. The use of TOGAF can bring a consistent enterprise architecture, based on stakeholder requirements, and bring some considerations. . In designing this blueprint, it will rely on the work steps of the TOGAF ADM Framework, in which this enterprise architecture framework is divided into (four) categories, namely: business architecture, data, applications, and technology.
Application of Sales Forecasting Using The Least Square Method in Web-Based Information Systems Nurliana Nasution; Dadang Rukmana Sitompul; Walhidayat Walhidayat
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.2580

Abstract

Technology has become an important role in life, causing the role of computers to be indispensable in various aspects. The presence of technology today is not only in the field of technology but computational methods are also developing. The use of the internet in the aspect of E-commerce (electronic commerce) also plays an important role in the business process cycle. Sale is a major aspect of supporting survival in an industry. Because the high level of sales in an industry/service can compensate let alone provide benefits for the industry. The LEAST Square method is used as an analytical tool for forecasting / predicting sales at the ABDS Store Pekanbaru store. The level of accuracy of the calculation will have an impact on the availability of stock in the store. This method is often used in finding the best parameters of a mathematical model that describes observational data. By using the least squares method, it is expected that the resulting mathematical model can provide a better description of the observation data.
Synthetic Minority Oversampling Technique for Efforts to Improve Imbalanced Data in Classification of Lettuce Plant Diseases Nurliana Nasution; Feldiansyah Feldiansyah; Ahmad Zamsuri; Mhd Arief Hasan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.2883

Abstract

In this study we classified lettuce plant diseases. These plant diseases are available in the form of images that have been converted in .csv format to be classified. These plant diseases are available in the form of images that have been converted in .csv format to be classified. Image These plant diseases have been divided into several classes or categories. Then we determine the features of each row and column of the dataset. Each line in the CSV file represents one image, and each column represents one feature Each line in the CSV file represents one image, and each column represents one feature. Then a label is made for each line in the CSV file, namely the class or category where the images are grouped. Thus, so that we get datasets that are ready to be processed with machine learning. However, in processing the dataset, we get imbalanced data. So we added the Synthetic Minority Over-sampling Technique (SMOTE) method to overcome the imbalance that occurs. So that the data can be classified using several algorithms to find the best accuracy.
Phishing Detection Model on Social Media Enhanced With CNN and BERT Nurliana Nasution; Wenni Syafitri; Feldiansyah Feldiansyah
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2194

Abstract

Phishing on social media has become an increasingly serious cyber threat because attackers exploit persuasive language, conversational context, and dynamic interaction patterns to deceive users. This study proposes a hybrid CNN-BERT model for detecting phishing content in Indonesian social media text by combining BERT’s contextual semantic representation with CNN’s ability to capture locally relevant textual patterns. The dataset was preprocessed to remove noise, normalize writing variations, and prepare the text for deep learning analysis; class proportions were also examined to support fairer evaluation. Model performance was assessed under multiple data-splitting scenarios and cross-validation to examine robustness and consistency. The experimental results indicate that the proposed hybrid model achieves strong and stable performance across accuracy, precision, recall, and F1-score, and outperforms the baseline model when the BERT backbone is frozen. However, when BERT is fully fine-tuned, the performance gain from the CNN layer becomes marginal, suggesting that strong contextual representations are already highly effective for this task. These findings indicate that integrating CNN and BERT is effective for phishing detection on social media, although domain adaptation challenges, overfitting risk, and real-world deployment constraints remain important considerations. The novelty of this work lies in systematically comparing frozen versus fully fine-tuned IndoBERT backbones with and without a CNN head for Indonesian short-message phishing detection.
Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Ahmad Zamsuri; Nurliana Nasution; Maya Ramadhani
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
PERANCANGAN DAN IMPLEMENTASI SISTEM PEMBUATAN DAN PEMINDAIAN QR CODE UNTUK PENDATAAN TANAMAN DIPTEROCARPACEAE Sri Rahayu Prastyaningsih; Didik Siswanto; Nurliana Nasution; Ridho Alfanandi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3309

Abstract

Abstract: QR Code-based information systems are increasingly being used to facilitate access to information. In this study, a system consisting of the dipterocarpaceae.my.id website and the Diptero Check application was designed and implemented for plant data collection from the Dipterocarpaceae family. The website allows users to enter plant descriptions and generate downloadable QR Codes. The Diptero Check application is used to scan the QR Code to obtain plant information instantly. The system model was developed using UML (Unified Modeling Language) which includes Use Case Diagrams, Sequence Diagrams, and Activity Diagrams. The system implementation was tested by displaying the website and application interfaces and observing the workflow of creating and scanning QR Codes. The results of the study indicate that this system can improve efficiency in managing Dipterocarpaceae plant information. Keywords: QR Code, Information System, Dipterocarpaceae Abstrak: Sistem informasi berbasis QR Code semakin banyak digunakan untuk mempermudah akses informasi. Penelitian ini merancang dan mengimplementasikan sistem yang terdiri dari Website Dipterocarpaceae.my.id dan aplikasi Diptero Check untuk pendataan tanaman dari famili Dipterocarpaceae. Website memungkinkan pengguna untuk memasukkan deskripsi tanaman dan menghasilkan QR Code yang dapat diunduh. Aplikasi Diptero Check digunakan untuk memindai QR Code tersebut guna memperoleh informasi tanaman secara instan. Model sistem dikembangkan menggunakan UML (Unified Modeling Language) yang mencakup Use Case Diagram, Sequence Diagram, dan Activity Diagram. Implementasi sistem diuji dengan menampilkan tampilan antarmuka website dan aplikasi serta mengamati alur kerja dari pembuatan dan pemindaian QR Code. Hasil penelitian menunjukkan bahwa sistem ini dapat meningkatkan efisiensi dalam pengelolaan informasi tanaman Dipterocarpaceae. Kata kunci: QR Code, Sistem Informasi, Dipterocarpaceae 
ANALISIS SENTIMEN PERUNDUNGAN TERHADAP GURU DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN NAÏVE BAYES Ahmad Zamsuri; Nurliana Nasution; Susandri Susandri; Novia Putri Bimby
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4916

Abstract

Abstract: This study discusses sentiment analysis of bullying experienced by teachers on social media. The research employs the Support vector machine (SVM) and Naïve Bayes methods to classify sentiments into positive, negative, or neutral categories. The data were collected from various social media platforms and analyzed using text mining techniques. The results show that the SVM method achieved a higher accuracy rate compared to Naïve Bayes in detecting negative sentiments related to bullying toward teachers. These findings contribute to a better understanding of digital bullying patterns targeting educators and provide a foundation for developing more effective policies to address bullying cases in the educational environment. Keywords: Sentiment Analysis, Bullying, Teachers, Support Vector Machine, Naïve Bayes, Text Mining. Abstrak: Penelitian ini membahas analisis sentimen terhadap perundungan yang dialami oleh guru di media sosial. penelitian ini menggunakan metode support vector machine (svm) dan naïve bayes untuk mengklasifikasikan sentimen menjadi positif, negatif, atau netral. data yang digunakan berasal dari berbagai platform media sosial dan dianalisis menggunakan teknik text mining. hasil penelitian menunjukkan bahwa metode svm memiliki tingkat akurasi yang lebih tinggi dibandingkan dengan naïve bayes dalam mendeteksi sentimen negatif terkait perundungan terhadap guru. temuan ini dapat membantu dalam memahami pola perundungan digital terhadap tenaga pendidik serta memberikan dasar untuk kebijakan yang lebih efektif dalam menangani kasus perundungan di dunia pendidikan. Kata Kunci: Analisis Sentimen, Perundungan, Guru, Support Vector Machine, Naïve Bayes, Text Mining.
Co-Authors Adek Irawan Agung Pratama Ahmad Zamsuri, Ahmad Ahmad, Rafik Alfajar, Muhammad Alfanandi, Ridho Alkindi, Aditia Fikri Alwan, Hiba Basim Anto Ariyanto arizal N, arizal Asrul Puadi Bayu Febriadi, Bayu Bimby, Novia Putri Budi Cahya Dadang Rukmana Sitompul darmayunata, yuvi David Setiawan David Setiawan Deni Iskandar Desnelita, Yenny Didik Siswanto Eddis Syahputra Pane, Eddis Syahputra Erlin Fachreza Akbar Fadhlillah, Il Fajar, Muhammad Al Fajrizal Fajrizal, Fajrizal Feldiansyah Feldiansyah Bakri Nasution Feldiansyah Feldiansyah Feldiansyah Feldiansyah Feldiansyah Nasution Firman Hidayat Fransiskus Zoromi Herlinawati Herlinawati Ilfan Setiawan Ilzi Adrolis SNR Jeni Wardi Khairani Djahara, Khairani Laili Suryati Mariza Devega Marshanda Marshanda Martahan, Sutan Marwa, Marwa Maya Ramadhani Mhd. Arief Hasan, Mhd. Arief Muhammad Al Fajar MUHAMMAD RIZAL Nasution, Feldiansyah Novendra, Rizki Novia Putri Bimby nurhayani lubis Nurli Indra Sari Nurul Aien Binti Abd Aziz Putra, Rudi Julian Eka Ramadhani, Maya Ria Saputri Ria Saputri, Ria Ridho Alfanandi Ririn Handayani Roki Hardianto Roliah, Roliah Safrini, Safrini Siswanto, Didik Sri Rahayu Prastyaningsih Sri Utami Lestari Sri Wahyuni Susandri Susandri Susandri, Susandri Sutan Martahan Syofyan Syofyan Tumilah, Tumilah Wahyuni Sri Walhidayat Walhidayat Wenni Syafitri Yayi Suryo Prabandari Yogo Turnandes Yoyon Efendi Yummastian Yummastian, Yummastian Yuvi Darmayunta Zamzami, Zamzami