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All Journal bit-Tech KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Komputer (JIK)
Napila, Ade
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Materials Science & Nanotechnology

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ANALISIS METODE MARKET BASKET ANALYSIS TERHADAP KEGIATAN PENJUALAN CATERING DENGAN ALGORITMA APRIORI DAN ALGORITMA FREQUENT PATTERN GROWTH Napila, Ade
Jurnal Ilmu Komputer Vol 6 No 1 (2023): Jurnal Ilmu Komputer (JIK)
Publisher : LPPM-STMIK Pranata Indonesia

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Abstract

Catering atau Jasa Boga merupakan sebuah bisnis yang populer di Indonesia, mulai dari industri kecil hingga industri besar. Strategi penjualan merupakan salah satu yang paling fundamental guna melariskan usaha di bidang industri ini. Salah satu yang dapat dilakukan untuk mengetahui keinginan pasar adalah dengan menggunakan teknik Data Mining yaitu Market Basket Analysis. Dengan memanfaatkan Market Basket Analysis, kita akan mendapatkan pengetahuan tentang produk apa yang dibeli pelanggan (what), produk apa saja (which) yang sering dibeli secara bersamaan, siapakah mereka (who) dan mengapa mereka melakukan suatu pembelian (why). Dalam kegiatan ini digunakan Algoritma Apriori dan Algoritma Frequent Pattern Growth untuk mengetahui frequent itemset dan asosiasi yang diharapkan membantu membuat strategi penjualan yang lebih efektif dan efisien.
Naïve Bayes Algorithm Analysis For Student Graduation Timeliness Prediction Anwar, A. Nurul; Dani, Dani; Napila, Ade
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.
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

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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.
Co-Authors ANDRIAN HIDAYAT Anwar, A. Nurul Dani Dani