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All Journal Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Marthanu, Indra Wiguna
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Law, Crime, Criminology & Criminal Justice Nursing

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Sosialisasi Profesi Auditor untuk Meningkatkan Minat dan Motivasi Mahasiswa Program Studi Akuntansi UCIC dalam Menghadapi Persaingan Dunia Kerja Marthanu, Indra Wiguna; Sanjani, M Rafikaraf; Maghfiroh, Siti
Abdimas Galuh Vol 7, No 1 (2025): Maret 2025
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v7i1.17717

Abstract

Profesi auditor memiliki peran strategis dalam memastikan transparansi dan akuntabilitas laporan keuangan, yang menjadi fondasi kepercayaan di dunia bisnis. Namun, minat mahasiswa akuntansi terhadap profesi ini masih memerlukan dorongan lebih lanjut. Kegiatan pengabdian kepada masyarakat yang dilaksanakan di Universitas Catur Insan Cendekia (UCIC) bertujuan untuk meningkatkan minat dan motivasi mahasiswa Program Studi Akuntansi dalam menghadapi persaingan dunia kerja, khususnya sebagai auditor. Materi yang disampaikan meliputi pengenalan profesi auditor eksternal, gambaran pekerjaan di Kantor Akuntan Publik (KAP) beserta persyaratan yang dibutuhkan, serta langkah-langkah persiapan menuju akuntan publik. Metode yang digunakan dalam kegiatan ini adalah ceramah interaktif, yang dilengkapi dengan sesi diskusi dan tanya jawab. Hasil dari kegiatan menunjukkan peningkatan antusiasme mahasiswa terhadap profesi auditor, yang terlihat dari tingginya partisipasi mereka dalam sesi diskusi serta banyaknya pertanyaan yang diajukan. Temuan ini mengindikasikan bahwa kegiatan pengabdian masyarakat semacam ini efektif dalam memperkuat pemahaman dan minat mahasiswa terhadap profesi auditor, sehingga mereka lebih siap untuk menghadapi persaingan di dunia kerja.
FP-Growth for Data-Driven Purchase Pattern Analysis and Product Recommendations at Flanetqueen Store Marwah, Sopa; Rahaningsih, Nining; Ali, Irfan; Marthanu, Indra Wiguna; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1850

Abstract

The advancement of information technology has encouraged the use of data analytics to support data-driven business decision-making. This study aims to analyze purchasing patterns of hoodie products and provide product recommendations for customers at Flanetqueen Store using the FP-Growth (Frequent Pattern Growth) algorithm. The research applies the Knowledge Discovery in Database (KDD) framework, consisting of five stages: data selection, preprocessing, transformation, data mining, and interpretation/evaluation. The dataset comprises hoodie sales transactions recorded from January to December 2024. Data analysis was conducted using RapidMiner Studio version 10.3 with a minimum support of 0.2 and minimum confidence of 0.4. The analysis produced 26 itemsets and 11 association rules indicating product correlations. The strongest rule, Bloods → Champion, achieved a confidence of 0.414, revealing that customers who purchased Bloods hoodies were also likely to buy Champion hoodies. These findings were used to design cross-selling strategies and generate relevant product recommendations. The study demonstrates that FP-Growth effectively extracts frequent purchase patterns and contributes to the development of data-driven recommendation systems in the local fashion retail industry.
Mitigating Imbalanced Citrus Disease Image Datasets with Oversampling Gunawan, Arya; Suarna, Nana; Bahtiar, Agus; Marthanu, Indra Wiguna; Kaslani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1862

Abstract

Dataset imbalance is a critical challenge in plant disease image classification because it causes bias towards the majority class. This study evaluates the effectiveness of augmentation-based oversampling techniques on the classification performance of citrus leaf images using the MobileNetV2 architecture. The four leaf disease classes classified include Greening, Fresh, Canker, and Blackspot. The dataset was obtained from a public repository and processed through preprocessing (resize, normalization) and augmentation (rotation, flipping, zoom) stages. The model was trained and tested in two scenarios: baseline (unbalanced data) and mitigation (data balanced through augmentation). The experimental results show that the mitigation approach was able to increase accuracy from 91.92% to 93.94%. The F1-score, precision, and recall values also increased significantly, especially in the minority class. Evaluation using a confusion matrix reinforced the finding that augmentation-based oversampling is effective in reducing classification errors. This study shows that the integration of augmentation techniques and MobileNetV2-based transfer learning can significantly improve classification performance and contribute to the development of early detection systems for plant diseases in precision agriculture.
Co-Authors Arya Gunawan Irfan Ali, Irfan Kaslani Marwah, Sopa Nining Rahaningsih Sanjani, M Rafikaraf Siti Maghfiroh Suarna, Nana