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Tjandrasa, Benny Budiawan
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Competitive Advantage of Alfamart: A SWOT and PESTEL Analysis Yusan, Budi Oktavianus; Trisandy, Yudhi; Lu, Cen; Saragih, John Salmar; Tjandrasa, Benny Budiawan
JIPOWER : Journal of Intellectual Power Vol. 2 No. 2 (2025): JIPOWER - AGUSTUS
Publisher : Yayasan Inspirasi Merah Putih Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63786/jipower.v2i2.45

Abstract

The Indonesian minimarket sector is undergoing rapid transformation, driven by intensified competition, decentralization, and digital disruption. This study investigates the competitive advantage of Alfamart, a leading minimarket chain in Indonesia, by employing an integrative SWOT and PESTEL framework. Using a qualitative case study approach—including interviews, surveys, and observational data—the research identifies key internal strengths such as store network scale, brand loyalty, and digital initiatives like Alfagift, alongside weaknesses including operational cost rigidity and limited pricing flexibility. The analysis reveals external opportunities in scalable franchising and rising consumer demand for health-oriented retail, while highlighting threats from regulatory restrictions and digital-native competitors. The findings position Alfamart in an aggressive strategy quadrant, emphasizing the importance of aligning internal capabilities with macro-environmental dynamics. The study concludes that strategic agility is essential for sustaining competitiveness in a decentralized and digitally disrupted market. Limitations include qualitative scope and a localized sample, with future research recommended to explore broader demographics and predictive modeling.
Sistem Deteksi Pothole Menggunakan Convulutional Neural Network Dengan Squeezenet Network Lehman, Andrew Sebastian; Widjaja, Andreas; Tjandrasa, Benny Budiawan; Sanjaya, Joseph
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 3 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i3.8323

Abstract

Lubang di jalan merupakan salah satu permasalahan utama yang dihadapi pengguna jalan, karena dapat menyebabkan kerusakan serius pada kendaraan serta meningkatkan risiko kecelakaan lalu lintas. Selain berdampak pada keselamatan pengemudi, terutama mereka yang tidak familiar dengan kondisi jalan, keberadaan lubang juga dapat memicu kemacetan dan meningkatkan emisi karbon akibat perlambatan lalu lintas. Untuk mengatasi permasalahan ini, studi ini mengusulkan sistem deteksi lubang secara real-time berbasis Convolutional Neural Network (CNN) dengan arsitektur ringan SqueezeNet. Sistem dirancang untuk bekerja dengan memanfaatkan gambar yang diambil oleh kamera kendaraan yang sedang bergerak, guna mengidentifikasi lubang yang berpotensi membahayakan. Model CNN dilatih menggunakan dataset gambar yang telah diberi label berdasarkan keberadaan lubang. Kinerja sistem dievaluasi menggunakan metrik precision, recall, dan F1-score guna mengukur keakuratannya dalam mendeteksi lubang. Hasil penelitian menunjukkan bahwa pendekatan ini berpotensi tinggi untuk diterapkan dalam sistem transportasi cerdas, guna meningkatkan keselamatan jalan serta mengurangi biaya perawatan kendaraan akibat kerusakan yang disebabkan oleh lubang jalan.
Co-Authors Agus Ariwibowo, Agus Andreas Widjaja Aribowo, Agus Carolin, Angelina Cen Lu Herlina Herlina Ida Ida, Ida Iskandar, Dini Jewarut, Rofinus Lehman, Andrew Sebastian Maya Malinda Sanfa, Adiska Shabrina Sanjaya, Joseph Saragih, John Salmar Siregar, Yosua Tjandraningtyas, Jacqueline Mariae Trisandy, Yudhi Vera Intanie Dewi Wattimena, Sophia Isabella Wilson Bangun Yusan, Budi Oktavianus