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PENDAMPINGAN PENGELOLAAN ADMINISTRASI DAN LOGISTIK LAYANAN PUBLIK DI PT POS INDONESIA KANTOR CABANG BATU Survival Survival; Mulyono Mulyono; Wiwin Purnomowati; Hartini Prasetyaning Pawestri; Untung Wahyudi; Vernanda Septia Putri
Prosidia Widya Saintek Vol. 5 No. 1 (2026)
Publisher : Universitas Widyagama Malang

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Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan efektivitas pengelolaan administrasi dan logistik layanan publik di PT Pos Indonesia (Persero) Kantor Cabang Batu. Mitra kegiatan menghadapi tantangan berupa tingginya volume pekerjaan administratif, pengelolaan dokumen layanan publik seperti: Taspen dan Bantuan Subsidi Upah (BSU), serta kebutuhan monitoring aktivitas logistik dan mitra korporat yang menuntut ketelitian dan ketepatan data. Metode pelaksanaan pengabdian dilakukan melalui pendampingan langsung di unit kerja Bidang Operasi dan Layanan serta Bidang Kurir Logistik dan Korporat, yang meliputi kegiatan pengelompokan dan pengarsipan dokumen, input dan verifikasi data penerima layanan, pendampingan distribusi dokumen, rekapitulasi transaksi dan tagihan mitra, pencetakan resi, pengemasan kiriman, serta penyusunan data monitoring kinerja Oranger. Hasil kegiatan menunjukkan adanya peningkatan kerapian administrasi, ketepatan distribusi dokumen layanan publik, tersedianya data rekapitulasi yang lebih sistematis, serta meningkatnya efisiensi proses pelayanan dan logistik. Kegiatan ini berkontribusi dalam memperkuat tata kelola administrasi dan mendukung peningkatan kualitas pelayanan publik di lingkungan PT Pos Indonesia Kantor Cabang Batu.
Application of Data Mining with Apriori Algorithm and FP Growth on Cafe Bread Sales to Support Business Intelligence Muhammad Auzhar Rafli Ramadhani; Shaifany Fatriana Kadir; Niken Paramita; Wiwin Purnomowati; Tshering Peldon; Farrel Muhammad Raihan Akhdan
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): June
Publisher : Lumina Infinity Academy Foundation

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Abstract

This study aims to apply the Apriori and FP-Growth algorithms in analyzing sales transaction patterns in a bakery Cafe, with a focus on developing a business intelligence strategy. The data used includes 20,507 transactions from January 11, 2016 to December 3, 2017. The results of the analysis show that items (coffee and bread) are the most frequently purchased, with the highest support values of 26.67% and 32.72%, respectively. In addition, several significant association rules were found, such as a positive relationship between (hot chocolate and coffee). This study provides insights that can be used to design more effective marketing strategies, including bundling promotions and more efficient stock management, so as to increase sales and customer satisfaction.