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Analisis Prediksi Customer Repeat Order menggunakan Algoritma Decision Tree pada Perusahaan Transportasi: Analysis of Customer Repeat Order Prediction using Decision Tree Algorithm in Transportation Company Syafii, Imam; Ribhi, Ahmad Aufar; Astutik, Liya Yuni; Budiono, Graceilla Kristia Seraphim; Pamela, Alberta Silvia
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1538

Abstract

Data merupakan inti yang tidak terpisahkan dalam pengelolaan pengembangan pelayanan yang dimiliki perusahaan. Banyak perusahaan yang tidak memanfaatkan data untuk keberlangsungan perusahaan atau meningkatkan mutu dari pelayanan perusahaan. Minimnya pemanfaatan data membuat data kurang memberikan nilai tambah terhadap suatu jasa atau pelayanan perusahaan. Oleh sebab itu, diperlukan adanya pemanfaatan data-data tersebut agar menghasilkan sebuah pengetahuan yang bermanfaat bagi perusahaan. pemanfaatan teknik data mining ini memberikan manfaat dalam pengolahan data dengan menggunakan model klasifikasi, khususnya algoritma decision tree, untuk memprediksi pelanggan yang berpotensi melakukan pemesanan ulang. Algoritma decision tree ini banyak digunakan untuk melakukan analisis prediksi karena menghasilkan keakuratan yang tinggi. Tujuan dari penelitian adalah untuk memahami layanan perusahaan yang paling diminati dan meningkatkan peluang pelanggam untuk melakukan pemesanan ulang layanan perusahaan sehingga mendapatkan manfaat dalam menaikan pendapatan perusahaan dengan menekan pengeluaran untuk pemasaran. Hasil dari penelitian menggunakan algoritma decision tree ini menghasilkan pohon keputusan dengan pengukuran accuracy sebesar 83,33%, pengukuran precision sebesar 100%, dan pengukuran recall sebesar 70%. Hasil pengukuran accuracy, precision dan recall menunjukan hasil yang baik sehingga penggunaan algoritma decision tree ini dapat dijadikan solusi untuk perusahaan guna melakukan prediksi customer repeat order.
OPTIMALISASI DAYA SAING UMKM MELALUI PELATIHAN FOTOGRAFI PRODUK RUMAHAN BERBASIS ESTETIKA VIRTUAL SHOP: STRATEGI BRANDING VISUAL DENGAN PERALATAN SEDERHANA UNTUK TRANSFORMASI DIGITAL USAHA MIKRO: Strategi Branding Visual dengan Peralatan Sederhana untuk Transformasi Digital Usaha Mikro Tamtomo, Agatha Pricillia Sekar; Budiono, Graceilla Kristia Seraphim; Mutia Ulfa; Fauzi, Muhammad Anwar; Ahmad Aufar Ribhi
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3: Agustus 2025
Publisher : Bajang Institute

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Abstract

Transformasi digital menuntut pelaku UMKM untuk memiliki kemampuan memasarkan produk secara daring dengan tampilan visual yang menarik. Namun, banyak pelaku usaha mikro menghadapi keterbatasan pengetahuan dan fasilitas dalam menghasilkan foto produk yang estetis. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kapasitas branding visual UMKM melalui pelatihan fotografi produk rumahan berbasis estetika virtual shop, menggunakan peralatan sederhana yang mudah diakses. Metode yang digunakan adalah pelatihan partisipatif, demonstrasi teknik fotografi DIY, serta pendampingan praktik langsung oleh peserta. Subjek kegiatan ini adalah kelompok UMKM Paroki St. Antonius Padua Purbayan. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan peserta mengambil foto produk yang layak unggah, serta peningkatan kesadaran akan pentingnya tampilan visual dalam membangun daya saing di platform digital. Program ini mendorong perubahan sosial menuju kemandirian promosi dan peningkatan nilai jual produk UMKM secara digital.
GENERATION Z'S BEHAVIOR IN INVESTING : UNIQUE AND ODD Tamtomo, Agatha Pricillia Sekar; Setiawan, Doddy; Budiono, Graceilla Kristia Seraphim; Ulfa, Mutia
JOURNAL OF ADVANCED STUDIES IN MANAGEMENT Vol. 1 No. 2 (2024): November 2024
Publisher : Magister Manajemen of Universitas Islam Nahdlatul Ulama Jepara

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Abstract

This study was conducted with the aim of determining the influence of investor behavioral factors on risk perception and investment decisions in generation Z stock investors. These behavioral factors consist of overconfidence and emotion and use financial literacy as a moderator. The respondents in this study were 361 generation Z investors in Indonesia. Sampling used purposive sampling technique, while data analysis used SEM (Structural Equation Modeling) method using smartPLS software. The results of this study indicate that overconfidence and emotion influence the risk perception of generation Z investors. Financial literacy has also been shown to moderate the relationship between overconfidence and investment decisions. Unlike other similar studies, financial literacy was found to be a very strong driving factor in influencing the existence of the relationship between overconfidence and investment decisions. One of the reasons is the rapid development of technology and the differences in the hierarchy of needs in this generation. This study can help brokerage and securities companies to plan in dealing with young investors, especially generation Z, to better direct and introduce risks in stock investors.
Artificial Intelligence in Digital Marketing Performance Optimization for Micro Small and Medium Enterprises in Surakarta Ribhi, Ahmad Aufar; Budiono, Graceilla Kristia Seraphim; Rizki, Muhammad Ircham
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 4 (December 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i4.1322

Abstract

Digital marketing has become a necessity for Micro, Small, and Medium Enterprises (MSMEs) to increase their competitiveness in the digital economy era. Artificial Intelligence (AI) plays a central role in this revolution, particularly in the field of digital marketing, by reducing targeting complexity, enabling predictive analytics, and personalizing content, which is crucial for businesses with limited resources like MSMEs. The research method used is a qualitative approach with data collection techniques through interviews, observations, and documentation, selecting Surakarta MSME respondents who had never used AI in their digital marketing. This study focuses on proving the effectiveness of using AI, which is currently a trend, in improving MSME digital marketing performance. The results of the testing documentation comparing Meta Advantage+ (AI-based) with a prospecting set up or structured control (without AI) showed a good improvement in digital marketing performance. The use of AI in digital marketing was proven to provide a substantial increase in key ad campaign performance parameters such as Click Through Rate and Conversion Rate. This optimization occurred thanks to AI's ability to analyze consumer behaviour, perform hyper-personalization targeting, and automate effective ad allocation. generally, AI assists MSMEs in product and market research, formulating marketing strategies, and increasing operational efficiency. Although AI offers great opportunities to optimize MSME performance, its implementation in Surakarta still faces a number of multidimensional challenges. The main obstacles include low technological literacy and a lack of human resources with expertise in technology within the MSME sector. Furthermore, ethical issues, such as privacy risks and the security of sensitive data in AI systems, are also concerns for business owners when creating ad campaigns. The implementation of AI through Meta Advantage+ is proven to increase efficiency, service personalization, and digital promotion effectiveness. To maximize AI potential and achieve competitive advantage, MSMEs are advised to adopt a strategic approach that prioritizes collaboration between AI and humans, accompanied by increased digital literacy and human oversight to overcome ethical and technical obstacles.