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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informatika dan Komputer Jurnal Pengembangan Sistem Informasi dan Informatika
Arifianto, Firman
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Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

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The Use of System Usability Scale as an Evaluation of Shopee PayLater Hasudungan, Jonlisen; Arifianto, Firman; Achsan, Harry T. Y.
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 1 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i1.5411

Abstract

The increasing popularity of e-commerce in Indonesia has led to the emergence of a variety of financial services that support online transactions, such as Pay Later. Pay Later is a feature that allows users to make purchases on credit, with the option to pay later in installments. One of the most popular e-commerce platforms in Indonesia is Shopee. Shopee offers a Pay Later feature that allows users to make purchases on credit. However, the concept of Pay Later has not yet been fully accepted by the public. Some of its functions are still confusing for ordinary users. A study conducted by the University of Indonesia investigated the usability of the Shopee Pay Later feature. The study used the System Usability Scale (SUS) to measure the usability of the feature. The SUS is a 10-item questionnaire that is used to assess the usability of a system. The results of the study showed that the System Usability Scale score for the Shopee Pay Later feature was 75.38. This score indicates that the feature has an overall usability rating of "OK." However, the study also found that there were some areas where the features could be improved. The study conducted by the student Paramadina University suggests that usability enhancements can improve the user experience of Shopee Pay Later. These enhancements can make the feature more accessible and understandable for ordinary users, which can lead to increased adoption and satisfaction.
Menerapkan Metode Klasifikasi pada Data Uji Emisi Kendaraan di Jakarta dengan Menggunakan Jupyter Notebook Muzaky, Adamara; Arifianto, Firman; Hendrowati, Retno; Darwis, Muhammad
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 5 No. 2 (2024): Jurnal Pengembangan Sistem Informasi dan Informatika
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v5i2.1722

Abstract

This research aims to apply classification methods using Jupyter Notebook on vehicle emission test data in Jakarta. The rapid growth of vehicles and urbanization in Jakarta has led to increased air pollution, triggering concerns among the government and city residents. This research aims to identify hidden patterns, relationships, and trends in emission test data through a Data Mining approach with classification analysis methods. It is hoped that this research will significantly contribute to understanding the characteristics of vehicle emissions in Jakarta, including identifying dominant pollutants and factors influencing vehicle emissions.
Segmentasi Pelanggan Berdasarkan Recency, Frequency, dan Monetary dengan K-Means Clustering: Studi Kasus Toko Pakaian Almost Famous Arifianto, Firman; Hasudungan, Jonlisen; Muzaky, Adamara; Achsan, Harry T.Y.
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 1 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i1.2096

Abstract

Penelitian ini bertujuan untuk menganalisis loyalitas pelanggan dalam konteks bisnis distro pakaian dengan menggunakan model RFM (Recency, Frequency, Monetary) dan algoritma K-Means. Data yang dianalisis berasal dari basis data membership Distro Almost Famous Clothing Store yang mencakup tiga cabang di Beji, Jagakarsa, dan Kelapa Dua. Pengumpulan data melibatkan informasi penting mengenai pelanggan terdaftar, kunjungan terakhir pelanggan, dan jumlah pembelian selama menjadi anggota membership. Setelah melalui proses pra-pemrosesan data, dilakukan segmentasi pelanggan menggunakan model RFM untuk membagi pelanggan menjadi kelompok berdasarkan tingkat recency, frequency, dan monetary value. Selanjutnya, algoritma K-Means digunakan untuk memetakan kelompok pelanggan yang serupa dengan menggunakan metode Elbow Curved, Silhouette Coefficient, dan Davies-Bouldin Index untuk menentukan jumlah cluster yang optimal. Hasil penelitian menunjukkan adanya tiga kelompok pelanggan dengan tingkat loyalitas yang berbeda: Cluster 0 (loyalitas tinggi) dengan 3225 pelanggan, Cluster 1 (loyalitas sedang) dengan 3.119 pelanggan, dan Cluster 2 (loyalitas rendah) dengan 1258 pelanggan. Implikasi dari penelitian ini adalah memberikan panduan kepada perusahaan dalam merancang strategi yang sesuai dengan karakteristik masing-masing kelompok pelanggan untuk meningkatkan retensi pelanggan dan pertumbuhan bisnis secara keseluruhan. Bagi pelanggan dengan loyalitas rendah, disarankan perusahaan untuk menyelenggarakan potongan harga atau promosi khusus, meningkatkan kualitas produk atau layanan, serta menawarkan program loyalitas guna mendorong kembali kegiatan berbelanja. Bagi pelanggan dengan loyalitas sedang, perusahaan dapat meningkatkan daya tarik program loyalitas, memperluas portofolio produk atau layanan yang relevan, dan menjalankan strategi pemasaran yang dapat meningkatkan frekuensi pembelian. Bagi pelanggan dengan loyalitas tinggi, disarankan perusahaan memberikan penghargaan tambahan, meningkatkan pengalaman pelanggan melalui personalisasi, dan terus mengembangkan produk atau layanan baru.
Co-Authors Achsan, Harry T. Y. Achsan, Harry T.Y. Hasudungan, Jonlisen Hendrowati, Retno Muh. Darwis Muzaky, Adamara