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All Journal Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Telematika Computatio : Journal of Computer Science and Information Systems IJCIT (Indonesian Journal on Computer and Information Technology) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Jurnal Teknoinfo EVOLUSI : Jurnal Sains dan Manajemen Journal of Information Systems and Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Community Development Journal: Jurnal Pengabdian Masyarakat Computer Science (CO-SCIENCE) Jurnal Abdimas Ekonomi dan Bisnis Reputasi: Jurnal Rekayasa Perangkat Lunak Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Informatics and Computer Engineering Journal Indonesian Journal Computer Science (ijcs) EVOLUSI : Jurnal Sains dan Manajemen
Fabriyan Fandi Dwi Imaniawan
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Public Health

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Pelatihan Pembuatan Konten Digital Dalam Merdeka Belajar Sebagai Sarana Pendukung Promosi Desa Wisata Pangebatan Pratama, Eva Argarini; Maryani, Ina; Dwi Imaniawan, Fabriyan Fandi; Maarif, Vadlya
Jurnal Abdimas Ekonomi dan Bisnis Vol. 3 No. 2 (2023): Jurnal Abdimas Ekonomi dan Bisnis
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/abdiekbis.v3i2.3002

Abstract

Dunia pendidikan tinggi dimana di dalamnya terdapat mahasiswa yang memiliki peran besar dalam perkembangan bangsa dan negara harus memiliki kompetensi baik dari sisi hardskill maupun softskill. Kualitas softskill dapat dipersiapkan sejak dini dibangku kuliah dengan mahasiswa aktif ikut dalam berkegiatan di luar kampus. Salah satu program yang dapat di ikuti adalah dengan berorganisasi, mengikuti program merdeka belajar, membantu dalam proses pengajaran di sekolah, kegiatan sosial bermasyarakat, dan eksplorasi potensi desa. Pada kegiatan-kegiatan yang diikuti ini, dengan evaluasi, observasi, dan wawancara yang dilakukan muncul pemikiran kritis untuk dapat menemukan kebutuhan/ permasalahan dari mitra atau objek pelaksanaan kegiatan dan menemukan beberapa solusi untuk menyelesaikan masalah/ kebutuhan tersebut. Dari membantu pengajaran di sekolah dan eksplorasi desa didapatkan suatu permasalahan atau kebutuhan baik dari pihak pemerintah desa (menjadi desa wisata di wilayah Banyumas namun website desa masih kurang dalam isian konten terkait potensi desa) dan pada Sekolah Dasar Negeri Pangebatan (penerapakan Kurikulum Merdeka yang di dalamnya juga terdapat Projek Penguatan Profil Pelajar Pancasila berdasarkan karakteristik wilayah). Sehingga dengan adanya kebutuhan inovasi kegiatan P5 dan permasalahan pada konten website desa, maka perlu adanya kolaborasi untuk solusi dari kedua permasalahan tersebut yang diinisiasi dengan pelatihan pembuatan konten digital dalam merdeka belajar dan hasil dari pelatihan ini kedepannya dapat digunakan sebagai sarana pendukung promosi desa wisata Pangebatan.
Evaluasi Website Persuratan Sekretariat Lembaga Peradilan Menggunakan WebQual 4.0 & Importance-Performance Analysis (IPA) Kirso Kirso; Fabriyan Fandi Dwi Imaniawan
Evolusi : Jurnal Sains dan Manajemen Vol. 13 No. 1 (2025): Periode Maret 2025
Publisher : LPPM Universitas Bina Sarana Informatika Kampus Kabupaten Banyumas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v13i1.8262

Abstract

Penelitian ini bertujuan untuk mengevaluasi kualitas situs web persuratan lembaga peradilan di Indonesia dalam konteks pelayanan publik. Meskipun banyak aplikasi telah dikembangkan untuk memudahkan layanan, kurangnya integrasi antar situs sering kali menghambat proses birokrasi. Dengan menggunakan metode WebQual 4.0 dan Importance-Performance Analysis (IPA), kami menilai kualitas sistem, informasi, dan interaksi layanan dari sudut pandang pengguna. Hasil penelitian menunjukkan bahwa 57,14% indikator berada di Kuadran II, yang menandakan bahwa kinerja situs web sudah cukup baik. Namun, terdapat enam aspek di Kuadran I yang menunjukkan perlunya perbaikan, seperti kejelasan instruksi, daya tarik tampilan, dan penyusunan layout yang lebih efektif. Rekomendasi ini bertujuan untuk meningkatkan kepuasan pengguna dan memudahkan akses informasi bagi semua orang. Dengan demikian, penelitian ini diharapkan dapat membantu meningkatkan efisiensi pelayanan publik di era digital dan memastikan bahwa semua pengguna mendapatkan akses yang lebih baik.
Implementation of Zero-Shot DeBERTa and IndoBERT for Aspect-Based Sentiment Analysis on Reviews of Five LLM Applications Fabriyan Fandi Dwi Imaniawan; Ragil Wijianto; Vadlya Maarif; Joko Dwi Mulyanto; Mustofa Mustofa; Aprih Widayanto
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v7i1.12790

Abstract

Large Language Model (LLM) applications such as ChatGPT, Gemini, Copilot, Claude, and Perplexity have been massively adopted in Indonesia, yet user experience evaluation remains largely limited to global sentiment analysis. This study implements Aspect-Based Sentiment Analysis (ABSA) using a dual-Transformer approach: DeBERTa zero-shot for aspect extraction and IndoBERT for sentiment classification on 5,000 Indonesian-language reviews from the Google Play Store across four aspect categories. Manual validation by two annotators on 300 samples yielded Cohen’s Kappa of  (aspect) and  (sentiment), both Moderate. Evaluation against the gold standard showed aspect accuracy of 37.5% (weighted F1 = 0.42) and sentiment accuracy of 64.7% (weighted F1 = 0.61). Sensitivity analysis across five hypothesis templates revealed inter-template Kappa of 0.19–0.63, confirming template selection impact on predictions. Comparative analysis reveals Copilot achieves the highest satisfaction (mean score 4.67), while Claude records the most complaints (36.9% negative). This study contributes a validated comparative ABSA framework for Indonesian-language LLM applications
Mapping Sentiment Analysis in Educational Technology: OpenAlex Bibliometrics, Thematic Trends, and Research Gaps (2013-2025) Dany Pratmanto; Fabriyan Fandi Dwi Imaniawan
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1604

Abstract

This study aims to map the intellectual structure, publication growth, collaboration patterns, thematic evolution, and research gaps of sentiment analysis in educational technology. The study addresses the lack of an open and integrated bibliometric synthesis that connects productivity, collaboration, topic modelling, and gap detection in this field. A bibliometric and science-mapping approach was applied using OpenAlex-indexed publications from 2013 to 2025. After deduplication and eligibility screening, 977 publications were analysed, while 768 papers with sufficient abstract text were used for Non-negative Matrix Factorisation topic modelling. The analysis included publication trend analysis, country and institutional productivity, co-authorship networks, keyword burst analysis, geographic gap analysis, and platform mention analysis. The results show that annual publications increased from 22 papers in 2013 to 123 papers in 2024, with India and China as the most productive countries. Six thematic clusters were identified: Learning Analytics, Social Media Sentiment, Emotion Recognition, MOOCs and E-learning, Transformers/LLMs, and ML Classifier Ensembles. Learning Analytics was the largest cluster, while Transformers/LLMs showed the fastest recent growth. The novelty of this study lies in its reproducible OpenAlex-based bibliometric framework, which integrates performance analysis, science mapping, thematic evolution, and research gap identification for sentiment analysis in educational technology.
Detection of SQL Injection, XSS, and Command Injection Attacks in Web Payloads Using SVM, Random Forest, and XGBoost Andrian Eko Widodo; Fabriyan Fandi Dwi Imaniawan
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1655

Abstract

Web application attacks, including SQL Injection (SQLi), Cross-Site Scripting (XSS), and Command Injection (CmdI), remain major threats to digital services. This study develops and evaluates an adversarial-aware protocol for multi-class malicious payload detection, focusing on accuracy, robustness against non-adaptive mutations, and practical inference feasibility. The protocol compares LinearSVC, Random Forest, and XGBoost with character-level neural baselines, namely character CNN and BiLSTM, and a transparent rule-based comparator. Evaluation integrates stratified sampling, deduplicated validation, mutation testing, SHAP-based interpretation, and end-to-end throughput measurement. Experiments used 49,998 stratified records from the SQLi-XSS-CommandInjection dataset in Google Colaboratory. On the internal test set, XGBoost obtained the best performance, achieving 99.28% accuracy and 99.32% macro F1-score. After removing 878 exact duplicate records for stricter re-evaluation, XGBoost maintained 99.21% accuracy and 99.24% macro F1-score, indicating that the findings were not driven solely by duplicate leakage. The complete preprocessing, feature extraction, and prediction pipeline reached an average CPU inference time of 0.832 ms per sample. SHAP analysis of Random Forest highlighted injection operators, script fragments, keyword hits, and structural tokens as discriminative features. The results provide a controlled benchmark, although validation on real HTTP logs remains future work.
Optimized decision tree classification method for diabetes prediction Elly Muningsih; Fabriyan Fandi Dwi Imaniawan; Aprih Widayanto; Eva Argarini Pratama; Sutrisno Sutrisno; Sri Kiswati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25656

Abstract

Diabetes is one of the most deadly chronic diseases because most sufferers do not realize they have it. A more accurate prediction of diabetes disease must be made to reduce the risk of bad things happening to sufferers. This research will optimize the decision tree (DT) classification method for diabetes prediction. Optimization is done by splitting criteria, splitting data, particle swarm optimization (PSO), and parameter optimization to find the highest and most accurate forecast of diabetes. Splitting criteria is done by comparing the results of three criteria, namely gain ratio (GR), information gain (IG), and gini index (GI). Splitting data is done by dividing training data and testing data into three comparison groups, namely 70:30, 80:20, and 90:10. The application of PSO and parameter optimization is carried out to increase the accuracy value. The processed data is taken from the UCI machine learning repository with 520 records and 17 attributes (1 class/label attribute). From the experiments, the GI criterion with splitting data 90:10 obtained the greatest accuracy of 98.08%, and the combination with PSO resulted in an accuracy of 97.66%. Meanwhile, parameter optimization with splitting data 90:10 combined with GR criteria resulted in the highest accuracy of 97.90%.
Co-Authors Andi Dwi Riyanto Andrian Eko Widodo Angga Ardiansyah Aprih Widayanto Arifatus Zuhrina Ayu Silvia Corie Mei Hellyana Dany Pratmanto Donny Raditya Priastanto Eko Saputro, Eko Elly Muningsih Eva Argarini Pratama Fanny Fatma Wati Heru Purwanto Heru Purwanto Hidayat Muhammad Nur Hidayat Muhammad Nur - Universitas BSI Ibnu Alfarobi Ina Maryani Joko Dwi Mulyanto Kirso Kirso Maryani, Ina Muhamad Ricky Nursyamsyi Muhammad Nur, Hidayat Mustofa Mustofa Mustofa Mustofa Mustofa Mustofa Nur, Hidayat Muhammad Pradita Fadlurohman Ragil Wijianto Rian Permatasari Sardiarinto Sardiarinto Sri Kiswati Sunanto Sunanto Suripah . Sutrisno, Sutrisno Teguh Rijanandi Umi Maelani Elsa - AMIK BSI Purwokerto Vadlya Maarif Vadlya Maarif Vadlya, Vadlya Maarif Warjiyono Warjiyono Yollandhita Arum Puspa Zuhrina, Arifatus