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All Journal Jurnal Sains dan Teknologi Conten : Computer and Network Technology The Indonesian Journal of General Medicine
Fransisca
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Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Health Professions Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Veterinary

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Aplikasi Pembayaran Uang Kuliah Pada Universitas Prima Indonesia Menggunakan Metode Fuzzy Logic Berbasis Android Jepri Banjarnahor; Aurelia Xinara Lim; Fransisca
Jurnal Sains dan Teknologi Vol. 2 No. 1 (2020): Sains dan Teknologi
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34013/saintek.v2i1.53

Abstract

< >Payment of tuition fees is an obligation for every student to acquire knowledge in college. At the University of IndonesiaIndonesia, the payment of tuition fees is still using a manual system where every student who comes is notified of the amount oftuition fees to be paid orally or in writing, as in the Universitas Prima Indonesia system. This has several drawbacks where the longpayment process is caused by a long queue system, or the number of students arriving at one time. The application uses fuzzy logicmethods in transactions so that the system can separate transactions that have been paid and transactions that have not been paid.Author-designed apps can help speed up entering transactions. The application designed by the author uses smartphone technologybased on Android to assist transactions so that it is easy to carry anywhere
SEGMENTASI PELANGGAN DAN OPTIMALISASI PENJUALAN PADA DATA RETAIL ONLINE BERBASIS MODEL RFM Gunawan Budi Sulistyo; Noor Hasan; Sri Kiswati; Fransisca; Muningsih, Elly
CONTEN : Computer and Network Technology Vol. 5 No. 1 (2025): Juni 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/nzbzre70

Abstract

Penelitian ini dilatarbelakangi oleh pentingnya segmentasi pelanggan dalam meningkatkan efektivitas strategi pemasaran dan optimalisasi penjualan pada bisnis retail online yang semakin kompetitif. Segmentasi pelanggan memungkinkan perusahaan untuk mengelompokkan konsumen berdasarkan perilaku pembelian sehingga dapat merancang pendekatan pemasaran yang lebih tepat sasaran. Metode yang digunakan dalam penelitian ini adalah model RFM (Recency, Frequency, Monetary) yang mengukur nilai pelanggan berdasarkan waktu transaksi terakhir, frekuensi pembelian, dan nilai pembelian total. Data yang digunakan berasal dari dataset Online Retail.csv yang kemudian diproses dan dianalisis menggunakan algoritma K-means clustering untuk mengelompokkan pelanggan ke dalam segmen-segmen yang homogen. Hasil penelitian menunjukkan bahwa jumlah cluster optimal yang diperoleh adalah dua, dengan karakteristik pelanggan pada Cluster 0 memiliki nilai Recency rendah dan Frequency tinggi, serta nilai Monetary yang lebih besar, sedangkan Cluster 1 terdiri dari pelanggan dengan Recency tinggi dan Frequency rendah. Segmentasi ini memberikan gambaran yang jelas mengenai perilaku pelanggan yang berbeda dan memungkinkan perusahaan untuk mengarahkan strategi pemasaran yang lebih efektif. Penerapan model RFM yang dikombinasikan dengan K-means clustering terbukti efektif dalam segmentasi pelanggan retail online, sehingga dapat mendukung optimalisasi penjualan dan peningkatan loyalitas pelanggan. Temuan ini memiliki implikasi penting bagi pengembangan strategi pemasaran berbasis data yang lebih terarah dan efisien di industri retail online.
Comparing the Effectiveness of Exercise, Antidepressants, and Their Combination in Treating Mild Depression: Systematic Review Fransisca; Evelyn
The Indonesian Journal of General Medicine Vol. 1 No. 7 (2024): The Indonesian Journal of General Medicine
Publisher : International Medical Journal Corp. Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Background: Depression is a prevalent condition affecting over 320 million people worldwide and is a leading cause of disability. Although second-generation antidepressants are commonly prescribed, their effectiveness, particularly for non-severe depression, is controversial. Alternative treatments, such as exercise, have gained attention for their potential benefits. Objectives: This systematic review aims to compare the effectiveness of exercise and antidepressants on depressive symptoms in adults with non-severe depression. Additionally, it explores the effects of combination treatment versus monotherapy and examines drop-out rates to gauge treatment acceptance. Methods: Adhering to PRISMA 2020 guidelines, studies published in English between 2014 and 2024 were reviewed. Databases searched included PubMed, Science Direct, and SagePub. Eligible studies focused on the comparative effectiveness of exercise and antidepressants for non-severe depression. Results: The review included five relevant studies. Findings suggest that exercise can be a beneficial adjunctive treatment for depression, improving both mental health and physical fitness. Conclusion: Exercise is a promising non-pharmacological treatment for non-severe depression, with evidence supporting its efficacy. Further comprehensive trials are needed to establish definitive guidelines and long-term benefits.
Co-Authors Aurelia Xinara Lim Banjarnahor, Jepri Elly Muningsih Evelyn Gunawan Budi Sulistyo Noor Hasan Sri Kiswati