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All Journal Journal of Applied Data Sciences Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) IJCOSIN : Indonesian Journal of Community Service and Innovation
Marsally, Silvia Van
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Optimalisasi Kesadaran Merek Resto Oemah Gechok Melalui Perancangan dan Sosialisasi Brand Guideline Purbaya, Muhammad Eka; Hidayat, Chusnul Maulidina; Marsally, Silvia Van; Azzam, Abyan Naufal
Indonesian Journal of Community Service and Innovation (IJCOSIN) Vol 5 No 1 (2025): Januari 2025
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v5i1.1604

Abstract

Oemah Gechok Restaurant, located 350 meters from Saka Tunggal, is a local culinary business owned by the Village-Owned Enterprise in Cikakak, Wangon, Banyumas, which serves the traditional dish Ayam Gechok. Despite its great potential, Oemah Gechok Restaurant faced issues of low brand awareness and customer engagement, which could hinder its growth and sustainability. This service project focused on efforts to improve Oemah Gechok’s visibility and competitiveness through the design of a strong visual identity and the implementation of digital marketing strategies. The goal of this project was to strengthen the brand image and expand market reach. The methods used included the development of brand guidelines, logo creation, activation of Google Maps and Instagram, as well as training for the business managers. The results of this project showed a significant increase in customer interaction, with Instagram account reach increasing by 300% and content impressions rising by 1,720%. Satisfaction surveys indicated that the training successfully enhanced the knowledge and skills of the managers, positively impacting business management. The impact of this project not only increased brand awareness but also made a tangible contribution to supporting local economic growth through more effective and sustainable marketing strategies.
Kodein-Penetration: Recommendations of Customer Personalization Level in A CRM using Deep Learning Sudianto, Sudianto; Usman, Muhammad Lulu Latif; Prabowo, Dedy Agung; Gustalika, Muhamad Azrino; Marsally, Silvia Van; Akhmad, Fajar Kamaludin; Rakhma, Nazwa Aulia; Muna, Bunga Laelatul; Wicaksono, Apri Pandu; Rachman, Ari
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.597

Abstract

This study aims to develop a personalization-level recommendation model implemented in the Customer Relationship Management (CRM) system at PT Kodegiri, called KodeinPenetration. Personalization in CRM aims to improve customer interaction by providing more relevant recommendations based on their needs and preferences. To achieve this goal, this study tested several classification models using historical customer interaction data as the basis for analysis. The classification models tested included decision tree-based methods such as Random Forest, Gradient Boosting, and AdaBoosting, as well as deep learning models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). In addition, two main feature extraction techniques were applied to process text data, namely TF-IDF (Term Frequency-Inverse Document Frequency) and Tokenizer Padding. TF-IDF is used to represent words as numeric vectors based on their frequency of occurrence. In contrast, Tokenizer Padding is used in deep learning models to convert text into a numeric format that neural networks can process. The test results showed that the decision tree-based method using the TF-IDF feature produced the best accuracy of up to 82%. On the other hand, the deep learning model with GRU architecture utilizing Tokenizer Padding achieved the highest accuracy of 88.23%. This shows that the deep learning model has greater potential in handling sequential data and providing more accurate results compared to traditional methods. This study provides an important contribution to the development of deep learning-based personalized recommendation systems in CRM. By leveraging historical customer interactions, this system can improve user experience by offering more relevant and targeted services.
Analisis Strategi Affiliate Marketing Terhadap Keputusan Pembelian (Perspektif Industri E-Commerce) Marsally, Silvia Van; Dwiani, Yosita
Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (Jebma) Vol. 5 No. 1 (2025): Artikel Riset Maret 2025
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jebma.v5i1.5752

Abstract

Pertumbuhan e-commerce di Indonesia mendorong penggunaan strategi digital seperti affiliate marketing, di mana pihak ketiga mempromosikan produk dan mendapat komisi dari penjualan. Strategi ini makin populer berkat media sosial dan perubahan perilaku belanja online. Namun, efektivitasnya terhadap keputusan pembelian masih diperdebatkan, terutama terkait kredibilitas, transparansi, dan kualitas konten promosi. Penelitian ini menggunakan pendekatan kualitatif melalui wawancara, analisis konten promosi, dan studi literatur untuk mengeksplorasi pengaruh affiliate marketing terhadap keputusan pembelian. Hasilnya menunjukkan bahwa strategi ini efektif, khususnya jika promosi dilakukan secara jujur, transparan, dan berbasis pengalaman pribadi. Konten edukatif, visual menarik, dan insentif seperti diskon eksklusif turut meningkatkan kepercayaan dan minat beli konsumen. Temuan ini dapat menjadi acuan untuk merancang strategi affiliate marketing yang lebih relevan dan bernilai bagi konsumen
Co-Authors Akhmad, Fajar Kamaludin Azzam, Abyan Naufal Chusnul Maulidina Hidayat Dedy Agung Prabowo Muhamad Azrino Gustalika Muhammad Eka Purbaya Muna, Bunga Laelatul Rachman, Ari Rakhma, Nazwa Aulia Sudianto, Sudianto Usman, Muhammad Lulu Latif Wicaksono, Apri Pandu Yosita Dwiani Suryaningtiyas