Article Per Year (5 Year)
p-Index From 2021 - 2026
2.338
P-Index
This Author published in this journals
All Journal bit-Tech JAMAIKA: Jurnal Abdi Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Abdi Jurnal Publikasi Journal of Research and Publication Innovation Jurnal Ilmu Komputer (JIK) OKTAL : Jurnal Ilmu Komputer dan Sains Journal of Innovative and Creativity
Ade Napila
Universitas Pamulang
Astronomy Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Analisis Sentimen pada Ulasan Aplikasi FinTech di Indonesia: Studi Komparatif Model Machine Learning dan Deep Learning Ahmad Fauzi; Achmad Lutfi Fuadi; Agus Heri Yunial; Andrian Hidayat; Ade Napila
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.6939

Abstract

Pertumbuhan sektor Teknologi Finansial (FinTech) telah menjadikan umpan balik pelanggan dari platform digital sebagai sumber penting untuk pengambilan keputusan strategis. Namun, volume dan ketidakstrukturan data, khususnya dalam bahasa informal seperti Bahasa Indonesia, menimbulkan tantangan analitis yang signifikan. Penelitian ini bertujuan untuk mengidentifikasi pipeline optimal untuk klasifikasi sentimen pada ulasan pengguna Livin' by Mandiri, super-app perbankan digital Indonesia. Kami melakukan analisis komparatif menggunakan dataset dunia nyata berisi 117.471 ulasan yang tidak seimbang (55% negatif, 31% positif, 14% netral) yang dibersihkan dari Google Play Store. Dua teknik vektorisasi teks, Bag-of-Words (BoW) dan TF-IDF, diuji pada empat classifier machine learning: Random Forest, Logistic Regression, Decision Tree, dan SVM, serta dibandingkan dengan model Deep Learning berbasis Long Short-Term Memory (LSTM). Hasilnya menunjukkan bahwa model LSTM unggul dengan akurasi 98,02% dan weighted F1-score 0,99, sementara model machine learning terbaik, Logistic Regression dengan TF-IDF, menghasilkan weighted F1-score 0,92. Temuan ini menegaskan bahwa meskipun machine learning tradisional efektif, LSTM lebih unggul dalam menangkap konteks dalam data sekuensial yang kompleks dan tidak seimbang. Penelitian ini menawarkan kerangka kerja yang berguna bagi institusi keuangan untuk menerapkan sistem analisis sentimen otomatis yang akurat dan efektif.
Cultivating Digital Media Ethics Awareness: The Dangers of Deepfakes and Disinformation for Teenagers (MTSS AL-Hidayah) hidayat, andrian; Napila, Ade
KOMMAS: Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2026): KOMMAS: JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : KOMMAS: Jurnal Pengabdian Kepada Masyarakat

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

Abstract

Deepfakes are fake digital content (images, videos, or audio) that are realistically synthesized using artificial intelligence (AI) and machine learning techniques, such as artificial neural networks. This technology is capable of manipulating media, for example by swapping faces or forging voices, creating the illusion that a person has performed actions they never actually did. In Indonesia, deepfake has evolved from an entertainment tool into a serious threat, causing various cases involving the general public, public figures, and stakeholders. Given the high risk, understanding the identification and response to deepfake content needs to be instilled early on, especially among adolescents, to minimize negative impacts in the digital era. This Community Service (Pengabdian Kepada Masyarakat/PKM) activity aims to increase the knowledge and awareness of Madrasah Tsanawiyah (MTs) students regarding the deepfake phenomenon and its impact on daily digital security. The PKM implementation uses educational methods through structured counseling, interactive discussions, and simple practices for detecting the characteristics of deepfake content. The results of the activity show a significant increase in students' knowledge and critical attitude towards digital security issues. Students now have the basic foundation to detect manipulative content, making them more vigilant and selective in filtering information circulating on social media. Keywords: Deepfake, Digital Security, Adolescents, Community Service.
Naïve Bayes Algorithm Analysis For Student Graduation Timeliness Prediction A. Nurul Anwar; Dani Dani; Ade Napila
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2825

Abstract

This study developed a student graduation prediction system using the Naïve Bayes algorithm, using PS1-PS4 scores, PK, and SKS as indicators of academic progress. This model achieved 88.33% accuracy and an ROC value of 0.900, indicating superior predictive ability. These results outperform other common models such as logistic regression and the C4.5 decision tree, which have approximately 85% accuracy in predicting student graduation. These results also outperform previous research in the same field, which had ROC values of approximately 0.85.Graduation predictions were categorized as "ON TIME" and "LATE" with high precision. The Naïve Bayes algorithm has proven effective in predicting student graduation, particularly in identifying factors that influence graduation timeliness, such as poor academic performance, difficulty completing final assignments, poor personal conditions, and lack of motivation and interest.By designing a graduation prediction system using the Naïve Bayes algorithm, this research aims to help educational institutions predict student graduation timeliness and provide appropriate interventions. This system can improve educational quality and reduce dropout rates, making it an important tool for educational institutions to improve graduate quality and achieve their academic goals.This research demonstrates that the Naïve Bayes algorithm can be an effective and accurate graduation prediction method, thus helping educational institutions develop strategies to improve educational quality and reduce dropout rates. Therefore, this research has the potential to significantly impact higher education institutions and assist them in achieving their academic goals.
Co-Authors A. Nurul Anwar A. Nurul Anwar Achmad Lutfi Fuadi Agus Heri Yunial Ahmad Fauzi Andrian Hidayat Dani Dani Raihan Hari Dwiguna Yuliana Yuliana