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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Bulletin of Electrical Engineering and Informatics Jurnal Sarjana Teknik Informatika Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Bulletin of Electrical Engineering and Informatics Journal of Development Research Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Knowledge Engineering and Data Science Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic Journal of Digital Education, Communication, and Arts (DECA) Jurnal of Applied Multimedia and Networking International Journal of Advances in Data and Information Systems TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Walisongo Journal of Information Technology SinarFe7 Didaktika Religia Jurnal Informatika dan Teknologi Pendidikan Innovative: Journal Of Social Science Research Bulletin of Social Informatics Theory and Application Aksa : Jurnal Desain Komunikasi Visual Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Sains Komputer dan Sistem Informasi Jurnal Riset Multidisiplin dan Inovasi Teknologi Jurnal ilmiah teknologi informasi Asia
Fresy Nugroho
UIN Maulana Malik Ibrahim
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Early Detection of Phishing, Disinformation, and Extreme Opinions in Digital Text Using Transformer-Based Models Wibowo, Munif; Faisal, Muhammad; Nugroho, Fresy
ILKOMNIKA Vol 8 No 1 (2026): Volume 8, Number 1, April 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i1.863

Abstract

The rapid expansion of digital communication platforms has increased the circulation of phishing messages, disinformation, and extreme opinions, creating urgent challenges for cybersecurity and social stability. This study proposes a hybrid CNN–BiLSTM–Transformer framework for the early detection of harmful digital text. The model integrates convolutional feature extraction, sequential dependency learning, and self-attention mechanisms to capture local lexical patterns, contextual relations, and long-range semantic dependencies. Experimental evaluation was conducted using accuracy, precision, recall, F1-score, and ROC analysis, with CNN, LSTM, and RoBERTa used as baseline models. The proposed hybrid model achieved the highest classification accuracy of 95.0%, outperforming CNN (86.0%), LSTM (88.0%), and RoBERTa (91.0%). In addition, the model obtained 90.0% precision, 93.0% recall, and 91.5% F1-score, indicating a balanced ability to reduce false positives while maintaining strong detection sensitivity. Robustness testing further showed that the F1-score remained stable across normal, noisy, and adversarial text conditions, decreasing from 95.0% under normal conditions to 92.0% and 90.0% under noisy and adversarial settings, respectively. These findings demonstrate that the proposed hybrid Transformer-based architecture provides an effective and robust approach for supporting automated Cyber Early Warning Systems in detecting harmful digital content.
Visual Analysis of Customer Behavior and Churn Triggers in the Telecommunications Industry: An Implementation of a BI Dashboard Lestari, Tri Mukti; Nugroho, Fresy; Atnang, Muhammad
Journal of Development Research Vol. 10 No. 1 (2026): Volume 10, Number 1, May 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Nahdlatul Ulama Blitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/jdr.v10i1.533

Abstract

The telecommunications industry faces the challenge of high customer churn rates, which can negatively affect company revenue and long-term business sustainability. This study aims to develop a Business Intelligence (BI)-based dashboard to visualize customer behavior and identify factors contributing to customer churn. A dataset consisting of 7,043 customer records was analyzed to discover churn patterns, develop predictive models, and perform customer segmentation. The proposed dashboard was designed as a user-friendly and interactive web-based platform to support data-driven decision-making. The results showed that 26.5% of customers experienced churn. The main factors influencing churn included monthly contracts, fiber optic service usage, the absence of online security and technical support services, and electronic payment methods. The findings also revealed that customers with higher monthly charges had a greater tendency to discontinue their subscriptions. User evaluation results indicated that the developed dashboard achieved a high level of usability and effectively presented customer information in a clear and understandable format. Therefore, the proposed BI dashboard can assist telecommunications companies in identifying at-risk customers and developing more proactive, targeted, and effective customer retention strategies.
Spatial Analysis and Machine Learning Integration for Nutritional Status Mapping Using ANN and Random Forest Models Anggraini, Desi Anis; Kurniawan, Fachrul; Nugroho, Fresy; Koeshardianto, Meidya; Iqbal Bachtiar, Mohammad
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify high-risk zones. The integration of machine learning and spatial analysis proved effective in enhancing classification accuracy, improving data interpretation, and supporting data-driven nutritional policy and regional health decision-making.
PENILAIAN KINERJA PEGAWAI DENGAN METODE TOPSIS DAN BACKPROPAGATION NEURAL NETWORK Audi Bayu Yuliawan; M. Amin Hariyadi; Ririen Kusumawati; Cahyo Crysdian; Fresy Nugroho
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7826

Abstract

Transformasi digital melalui penerapan Industri 4.0 dan e-Government telah mengubah paradigma administrasi publik, sehingga menuntut sistem evaluasi kinerja pegawai yang lebih adaptif dan objektif. Penelitian ini bertujuan untuk mengklasifikasikan kinerja pegawai ke dalam lima kategori, yaitu "sangat baik", "baik", "cukup", "buruk", dan "sangat buruk", dengan menggunakan pendekatan Neural Network Backpropagation. Metodologi yang digunakan mencakup beberapa tahapan utama, dimulai dari proses preprocessing data yang menge-lompokkan kriteria penilaian ke dalam empat aspek: kualifikasi, kom-petensi, kinerja, dan disiplin. Selanjutnya, dilakukan seleksi fitur menggunakan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), dan hasilnya digunakan sebagai data pelatihan pada model Neural Network Backpropagation. Hasil pelati-han menunjukkan performa model yang cukup baik, dengan nilai loss dan Mean Squared Error (MSE) sebesar 0,000465, Mean Absolute Per-centage Error (MAPE) sebesar 19,59%, dan akurasi mencapai 80,41%. Sementara itu, hasil eksperimen dengan metode TOPSIS secara terpisah mencatat akurasi sebesar 81% dan nilai loss sebesar 0,377. Kombinasi metode TOPSIS dan Neural Network Backpropagation ter-bukti efektif dalam mengklasifikasikan kinerja pegawai secara konsis-ten. Temuan ini memberikan kontribusi terhadap pengembangan sis-tem evaluasi kinerja berbasis kecerdasan buatan yang lebih akurat dan adaptif terhadap tantangan administrasi publik modern.
Co-Authors Adnan Muhammad Taufiqulhakim Afiifah Zain Raidah Agung Teguh Wibowo Almais Ahmad Fahmi Karami Al Hamidy, Kautsar Quraisy Al Mahdi, Prayuda Zaky Alfarisi, Muhammad Firyal Alfia, Lia Alfia Aljawad, Ulil Albab Amani, Holidiyatul Anggraini, Desi Anis arfianto, farhan dzaffa Arief, Yunifa Miftachul Arif, Yunifa Mifachul Arif, Yunifa Miftahul Asyhari, Hamzah Faizal Atmaja, Gumilang Atnang, Muhammad Audi Bayu Yuliawan Azhar Affandi Azzahra, Alivia Baihaki, Achmad Fahry Biktarinanda, Arneizha Cahyo Crysdian Cahyo Crysdian Dian Maharani, Dian Eko Mulyanto Yuniarno Fachri, Moch Fachrul Kurniawan Fadilaaa, Juniardi Nur Farid Ahmad Sa’aduddin Ferdianto, Akhmad Faizal Ferelian, Muhammad Fitry Taufiq Sahary Hammad, Jehad A.H. Hammad, Jehad AH Hani Nurhayati Harfianti, Nadya Putri Harto, Sumber Hidayatullah, Fauzil I Gusti Putu Asto Buditjahjanto Ida Ayu Putu Sri Widnyani Ihsan, Afif Nuril Ikhlayel, Mohammed Iqbal Bachtiar, Mohammad Juniardi Nur Fadila Khomariyah, Aniek Nurul Koeshardianto, Meidya Lestari, Tri Mukti M. Amin Hariyadi Maharani, Elfira Putri Mahmud, Azkiya Makarim, Muhammad Abyan Marudin, Marudin Mitachul Arif, Yunifa Mochamad Hariadi Muhamad Husni Mubarok Muhammad Andryan Wahyu Saputra Muhammad Faisal Muhammad Faisal Muhammad Hasan Muhammad Ridho Mutaqin, Ghani Mutaqin, Rizal N, Alfina Nurrahma Najwa Mazaya, Nada Nadhira Nandana, Prana Wijaya Pratama Nazira, Yuzema Mala Novrindah Alvi Hasanah Nuraini, Salsabila Ramadanti Nurrahma ‘N, Alfina Nurrahma, Alfina Pebrianti, Dwi Prakasa, Aji Bagas Pratama, Dicky Arya Puspa Miladin Nuraida Safitri A. Basid Raidah, Afiifah Zain Ririen Kusumawati Rohma, Salma Ainur Roro Inda Melani RR. Ella Evrita Hestiandari Sa’aduddin, Farid Ahmad Sifaulloh, Hafizzudin Suci Wulandari Suci Wulandari Sugiharto , Tomy Ivan Suhartono Suhartono Suyanta Suyanta Syawab, Moh Husnus Tamaulina Br Sembiring Tarranita Kusumadewi Taufiqulhakim, Adnan Muhammad Utama, Isma Izha Wafiy Anwarul Hikam Wibowo, Munif Yuniar Setyo Marandy Yunifa Miftachul Arif Zainal Abidin Zidan, Muhammad Zifora Nur Baiti