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All Journal Healthy Journal International Journal Software Engineering and Computer Science (IJSECS) Agro Tatanen Computing Jurnal Informatika Competitive Jurnal Pengabdian Nasional (JPN) Indonesia BHAKTI JIVANA
Mohammad Bayu Anggara
Universitas Bale Bandung
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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TRANSFORMASI ADMINISTRASI SEKOLAH DENGAN PEMANFAATAN CANVA UNTUK PENGURUS ORGANISASI SMK NEGERI 1 CIMAHI Nistrina, Khilda; Sukiman, Sukiman; Anggara, Mohammad Bayu; Rosmalina, Rosmalina; Anzani, Elvira Aprilia
Komunikasi Vol 1 No 3 (2024): Volume 1 No 3 Desember 2024
Publisher : Forum Komunikasi Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65055/bhaktijivana.v1i3.11

Abstract

This Community Service Program (PKM) aims to enhance the digital administrative skills of student organization leaders at SMK Negeri 1 Cimahi. The main issue faced by the participants is their limited ability to manage administrative tasks using technology, which calls for a practical and relevant solution. The training was designed to introduce and train participants in using Canva, a digital application that supports professional administrative design. The method used was a participatory approach with stages of preparation, implementation, and evaluation. The training involved 30 participants and was conducted in three sessions: an introduction to Canva features, a demonstration of its use, and hands-on practice with guidance from the organizing team. Evaluation was carried out through observation and feedback surveys to assess participants' understanding and skills. The results show that 90% of participants were able to apply the training material effectively, such as creating reports and posters. Additionally, participants provided positive feedback, considering the training helpful in understanding and practicing technology-based administration. Therefore, this activity not only enhanced students' technical competencies but also provided long-term benefits for school organization management and prepared students for the digital era.
Web Attack Detection for SQLi and XSS Using Ensemble Learning Based on Character-Level N-Gram Features Yaya Suharya; Mohammad Bayu Anggara
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7193

Abstract

SQL Injection (SQLi) and Cross-Site Scripting (XSS) remain severe threats to web application security, particularly as attackers employ increasingly sophisticated obfuscation techniques to bypass conventional detection systems. This research constructs a machine learning framework using ensemble learning — specifically combining Random Forest and XGBoost — integrated with character-level n-gram feature extraction. The methodology involved rigorous data curation of a large-scale dataset, refining 156,636 raw samples into 151,783 unique entries to ensure high-quality training data. By extracting 10,000 character-level n-gram features, the model captures the intricate structural patterns of complex and obfuscated payloads. Experimental results show consistent and measurable performance: the proposed ensemble model achieved an overall accuracy of 99.67%. Stability was confirmed through a 5-fold cross-validation process, yielding a mean accuracy of 99.64% and a standard deviation of 0.0003. These findings are reinforced by ROC AUC scores of 1.0000 for XSS and 0.9999 for SQLi, indicating near-perfect discriminative capability. The combination of character-level representation and ensemble learning produces a precise and resilient solution for safeguarding modern web environments against dynamic and evolving cyber threats.
Akselerasi Daya Saing Produk Lokal Melalui Transformasi Visual Produk UMKM Menggunakan Teknologi AI Mohammad Bayu Anggara; Wini Fetia Wardhani; Inka Aqila Nurfalqi
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 7 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jpni.v7i2.1821

Abstract

Limited capability of Micro, Small, and Medium Enterprises (MSMEs) in producing appealing promotional visuals remains a major challenge in advancing local product competitiveness within the digital marketing environment. This issue is generally associated with inadequate visual design skills and restricted access to supporting technologies. This community service program aimed to improve the understanding and practical skills of MSME actors in Bandung Regency in the use of Artificial Intelligence (AI) technology as a supporting tool for creating promotional visual content. The program was conducted using a participatory approach through a seminar and workshop held on January 31, 2026, attended by 17 MSME participants. The learning methods combined lectures, group discussions, hands-on practice, and mentoring sessions. Program evaluation was carried out using pre-test and post-test instruments, accompanied by a participant satisfaction survey. The evaluation results indicated an increase in participants' understanding by 16.2%, rising from 78.7% in the pre-test to 94.9% in the post-test. Beyond cognitive improvement, the satisfaction survey results showed that most participants rated the program very positively, with average scores exceeding 4.5 on a 5-point Likert scale for material relevance, clarity of delivery, and overall program benefits. Participants were also able to produce tangible outputs in the form of AI-assisted promotional visuals, including product photographs, poster designs, and digital promotional content. These findings indicate that the application of AI technology in promotional visual training for MSMEs is effective in building business actors' capacities while achieving a high level of participant satisfaction
ANALISIS KOMPARATIF ALGORITMA MACHINE LEARNING UNTUK PREDIKSI PRODUKSI PADI KABUPATEN BANDUNG Wini Fetia Wardhiani; Mohammad Bayu Anggara; Inka Aqila Nurfalqi3
AGRO TATANEN | Jurnal Ilmiah Pertanian Vol. 8 No. 2 (2026): AGRO TATANEN Edisi Juli 2026 | Jurnal Ilmiah Pertanian
Publisher : Program Studi Agroteknologi Faperta UNIBBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55222/9bd6jc54

Abstract

Produksi padi merupakan indikator penting dalam mendukung ketahanan pangan daerah, termasuk di Kabupaten Bandung. Penelitian ini bertujuan membandingkan performa empat algoritma machine learning, yaitu Linear Regression, Support Vector Regression (SVR), Random Forest, dan XGBoost, dalam memprediksi produksi padi berdasarkan data luas panen dan produktivitas periode 2018-2025. Data penelitian diproses melalui pemeriksaan nilai kosong, pengurutan data berdasarkan tahun, serta feature engineering berupa lag produksi, rolling mean, dan interaksi antara luas panen dan produktivitas. Validasi model dilakukan menggunakan Leave-One-Out Cross Validation (LOO-CV) karena jumlah data yang digunakan terbatas, yaitu delapan observasi tahunan. Evaluasi model menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa Linear Regression memperoleh performa terbaik dengan MAE 75.277 ton, RMSE 207.530 ton, MAPE 0,24%, dan R² 0,9931. Random Forest berada pada urutan kedua dengan MAPE 4,45% dan R² 0,5508. XGBoost menghasilkan MAPE 5,08% dan R² 0,4810, sedangkan SVR menunjukkan performa terendah dengan MAPE 7,36% dan R² -0,1583. Prediksi produksi padi periode 2026-2030 menunjukkan kecenderungan stabil pada kisaran 31,28-35,37 juta ton, bergantung pada model yang digunakan. Hasil penelitian ini dapat menjadi dasar awal dalam pengembangan sistem prediksi produksi padi berbasis data di tingkat kabupaten.
Co-Authors Anzani, Elvira Aprilia Aulia Hanifa Ardita Chanra Satria Putra Fahrul Zaman Fetia Wardhiani, Wini INFORMATIKA, COMPUTING Inka Aqila Nurfalqi Inka Aqila Nurfalqi3 Iyus A Muslimin Iyus A Muslimin Khilda Nistrina Rimelke Rahmadea Febryane ROSMALINA ROSMALINA, ROSMALINA Rudi Hermawan Rudi Hermawan Septiansyah, Ridwan Sukiman Sukiman Wini Fetia Wardhani Wini Fetia Wardhiani Yaya Suharya