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All Journal Dynamic Management Journal Jurnal Media Informatika
Rambe, Hikma Gustina
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Other

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THE EFFECT OF PERCEIVED BEHAVIORAL CONTROL, SUBJECTIVE NORMS, AND QUALITY OF TAX SERVICES ON TAXPAYER COMPLIANCE WITH TAX SANCTIONS AS A MODERATING VARIABLE Duni, Duni; Kismanah, Imas; Rambe, Hikma Gustina; Supriyatno, Agus; Ranggala, Qadar
Dynamic Management Journal Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/dmj.v9i3.14531

Abstract

This study aims to examine the effects of Perceived Behavioral Control, Subjective Norms, and Quality of Tax Services on Taxpayer Compliance, with Tax Sanctions as a moderating variable. Using a quantitative approach, data were analyzed through multiple linear regression and moderation analysis using SPSS 25.0. The results indicate that Subjective Norms have a significant positive effect on taxpayer compliance, while Perceived Behavioral Control and Quality of Tax Services do not. Tax Sanctions strengthen the relationship between Subjective Norms and compliance, confirming their moderating role. However, Tax Sanctions do not moderate the effects of Perceived Behavioral Control or Quality of Tax Services on compliance. The findings highlight the importance of social influence and enforcement mechanisms in shaping taxpayer behavior. This study contributes to tax compliance literature by testing an extended Theory of Planned Behavior in a developing country context. It provides practical insights for tax authorities seeking to improve compliance through targeted policies and stronger sanction frameworks.
Analisis Citra Digital untuk Klasifikasi Kematangan Kelapa Menggunakan K-Nearest Neighbor Marpaung, Paisal Hamid; Purnamasari, Editha Dewi; Rambe, Hikma Gustina; Elisa, Elisa; Lestari, Indah
Jurnal Media Informatika Vol. 6 No. 4 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i4.6783

Abstract

Penelitian ini membahas pengembangan sistem klasifikasi tingkat kematangan kelapa berbasis pengolahan citra digital dengan algoritma K-Nearest Neighbors (KNN). Tujuannya adalah menyediakan metode penilaian kualitas kelapa yang lebih objektif, konsisten, dan efisien dibandingkan metode manual. Dataset citra kelapa diperoleh melalui proses akuisisi terkontrol, diikuti tahapan pra-pemrosesan, konversi ruang warna RGB ke HSV, dan ekstraksi fitur warna. Model KNN dioptimalkan melalui validasi silang untuk menentukan nilai k terbaik, yaitu k = 3. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, F1-score, serta analisis Receiver Operating Characteristic (ROC) dan Area Under Curve (AUC). Hasil menunjukkan akurasi rata-rata 92%, dengan nilai AUC di atas 0,90 untuk semua kelas (Muda, Setengah Matang, Matang), mengindikasikan kinerja model yang sangat baik. Kesalahan klasifikasi paling sering terjadi pada kelas Setengah Matang dan Matang karena kemiripan warna. Temuan ini menegaskan potensi KNN untuk klasifikasi kualitas kelapa berbasis warna, meskipun peningkatan dapat dilakukan melalui fitur tekstur, augmentasi data, dan pengujian pada kondisi pencahayaan yang bervariasi. Sistem ini diharapkan dapat mendukung otomasi sortasi kelapa di tingkat petani maupun industri.
Co-Authors Agus Supriyatno, Agus Duni, Duni Elisa Elisa Indah Lestari, Indah kismanah, imas Paisal Hamid Marpaung Purnamasari, Editha Dewi Ranggala, Qadar