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All Journal Indonesian Journal of Multidisciplinary on Social and Technology Bata Ilyas Educational Management Review
Muh. Nurfadel Hamzah
Universitas Negeri Makassar
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences

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Determinasi Kualitas Pengelolaan Keuangan UMKM: Peran Pengendalian Internal di Kota Makassar Rika Kurniawati; Muhaidir Ikram; Muh. Nurfadel Hamzah
Bata Ilyas Educational Management Review Vol. 6 No. 1: January - June
Publisher : Sekolah Tinggi Ilmu Ekonomi Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/biemr.v6i1.3833

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh pengendalian internal terhadap kualitas pengelolaan keuangan UMKM di Kota Makassar. Penelitian ini menggunakan pendekatan kuantitatif dengan desain explanatory research. Populasi penelitian adalah pelaku UMKM yang aktif beroperasi di Kota Makassar. Karena jumlah populasi tidak diketahui secara pasti, ukuran sampel ditentukan menggunakan rumus Lemeshow, sehingga diperoleh 96 responden. Teknik pengambilan sampel menggunakan purposive sampling dengan kriteria pelaku UMKM yang telah beroperasi minimal satu tahun, berdomisili atau menjalankan usaha di Kota Makassar, dan bersedia mengisi kuesioner secara sukarela. Teknik analisis data meliputi analisis statistik deskriptif dan regresi linear sederhana. Hasil penelitian menunjukkan bahwa pengendalian internal berpengaruh positif dan signifikan terhadap kualitas pengelolaan keuangan UMKM. Pengendalian internal tercermin melalui prosedur keuangan, pencatatan transaksi, penyimpanan bukti transaksi, pemantauan penggunaan dana, kesadaran terhadap risiko kesalahan atau kecurangan, serta evaluasi keuangan berkala. Temuan penelitian menunjukkan bahwa pelaku UMKM di Kota Makassar telah memiliki kesadaran yang baik terhadap pentingnya pengendalian keuangan, meskipun aspek evaluasi keuangan berkala, pemisahan fungsi, dan dokumentasi formal masih perlu diperkuat. Penelitian ini menegaskan bahwa pengendalian internal yang sederhana tetapi konsisten dapat meningkatkan ketertiban, akuntabilitas, dan kualitas pengambilan keputusan keuangan UMKM.
Analyzing User Sentiment Toward the Stimuler AI: English Fluency Application Through Google Play Store Reviews Mufidatul Azmi; Muh. Nurfadel Hamzah; Chairunnisaa M. Z. Huzaen
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 4 No. 2 (2026): Maret - Juni
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijmst.v4i2.9033

Abstract

This study aims to analyze user sentiment toward the Stimuler AI: English Fluency application based on reviews collected from the Google Play Store using the IndoBERT model. The increasing popularity of AI-based language learning applications has generated numerous user reviews containing opinions, experiences, and complaints. Therefore, sentiment analysis is needed to understand user perceptions and evaluate application quality efficiently. This research employed a quantitative approach using sentiment analysis techniques. User reviews were collected through web scraping using Python and the google-play-scraper library. A total of 3,640 reviews were initially collected, and after language filtering, 1,653 Indonesian-language reviews were selected as the final dataset. The preprocessing stages included case folding, cleaning, stopword removal, and slang normalization. Exploratory data analysis and topic analysis were also conducted to identify dominant sentiment patterns and user concerns. The sentiment classification process utilized the IndoBERT model with three sentiment classes: positive, neutral, and negative. The results showed that positive sentiment dominated the dataset, indicating that most users were satisfied with the application’s learning features and speaking practice capabilities. Meanwhile, the main negative issues included bugs, subscription problems, and voice detection issues. Furthermore, the IndoBERT model achieved an accuracy score of 90.57% and an F1-score of 89.01%, indicating effective performance in classifying Indonesian-language application reviews.
Co-Authors Chairunnisaa M. Z. Huzaen Mufidatul Azmi Muhaidir Ikram Rika Kurniawati