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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Matrik JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Informatika Global JSAI (Journal Scientific and Applied Informatics) Jurnal Abdimas Mandiri Jurnal Pemerintahan dan Politik Reswara: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Insyst : Journal of Intelligent System and Computation TIERS Information Technology Journal AKM: Aksi Kepada Masyarakat Jurnal Mandiri IT Indonesian Community Journal Klik - Jurnal Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) Journal of Information Systems Management and Digital Business Glow: Jurnal Pengabdian Kepada Masyarakat
Terttiaavini, Terttiaavini
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Journal : Insyst : Journal of Intelligent System and Computation

 1 
Predictive Buyer Behavior Model as Customer Retention Optimization Strategy in E-commerce Muhammad A. A. Hakim; Terttiaavini, Terttiaavini
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.379

Abstract

Lazada is one of the rapidly growing E-commerce platforms in this digital era. One of the main challenges faced by Lazada is customer retention, where customers make purchases once or a few times before switching to other platforms. Therefore, it is important to understand buyer behavior in E-commerce through customer prediction to identify factors influencing retention. This study employs the Random Forest (RF) method to analyze Lazada customer data and formulate more effective marketing strategies. The analysis is conducted by loading preprocessed datasets into the KNIME workflow and utilizing various nodes and algorithms available in KNIME to build and evaluate predictive models. The Random Forest model is trained multiple times to achieve the highest Accuracy rate, which is 72.472%, with a fairly high level of agreement and a balanced trade-off between recall and precision. Additionally, this model successfully predicts that customers purchasing electronic equipment are potentially churning at a rate of 3.85%. Subsequently, customer strategy analysis for customer retention optimization in the E-commerce industry is conducted through data visualization using Tableau. Predictive analysis of customer behavior serves as a strong foundation for formulating effective retention strategies in the E-commerce industry. With this approach, Lazada can enhance customer experience and ensure sustainability in facing the increasingly fierce competition in the digital market.
A Hybrid Approach Using K-Means Clustering and the SAW Method for Evaluating and Determining the Priority of SMEs in Palembang City Terttiaavini, Terttiaavini
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.392

Abstract

The current efforts to develop Small and Medium Enterprises (SMEs) are still facing challenges in setting appropriate targets. Although the Palembang City Cooperative and SME Agency has launched various programs and initiatives to support SME development, they have not yet successfully identified the SMEs that should be given priority for development. This study aims to apply a hybrid approach that combines the K-Means Clustering method and Simple Additive Weighting (SAW) to evaluate and prioritize SME development in Palembang City. The K-Means Clustering method is used to group SMEs based on their characteristics, while SAW provides preference values ( ). The SME data was obtained from the Palembang City Cooperative and SME Agency, covering 362 SME units. The K-Means Clustering results yielded two clusters: Cluster 0 as the High Growth Cluster and Cluster 1 as the Stability and Improvement Cluster. Validation using cross-validation showed that this model achieved an accuracy of 99.72%. The SAW analysis on Cluster 0 indicated that the Kopi Kaljo SME received the highest priority with a preference value of 45.71. This study confirms that this hybrid approach is effective in grouping SMEs based on their characteristics and prioritizing them based on data-driven evaluation. The research results are expected to help the Palembang City Cooperative and SME Agency design more effective and targeted assistance programs to optimize the contribution of SMEs to local economic growth to the maximum extent.
Co-Authors Ade Dea Doyosy Adelia Rahayu Agustina Agustina Heryati Agustina Heryati Agustina Heryati Heryati Ahmad Sanmorino Ajeng Oktatriani Akbar, M Dani Andiki Sianipar Anggi Putri Juniarti Annisa Kurnia Antoni, Darius Asmawati Asmawati Asmawati Asmawati Astuti, Lastri Widya Cahyani, Septa Candra Setiawan Cindy Destyana Putri Darmawan Susilo Derra Legiana Chintiya Dona Marcelina Eliya Berliana Endang Sri Lestari Endang Sri Lestari Ermatita - Evi Purnamasari Fadiya Faradita Fakhry Zamzam Fauziah Afriyani Fellyanus Habaora Fidya Nur Syabitha Fitriyana, Ayu Habibillah, Amri Harsi Romli Harsi Romli Harsih Rianto Hartati, Lesi Heryati, Agustina Indah Permatasari Indah Pratiwi Putri Indah Sukmawati Inessia Inessia Isabella, Isabella Iski Zaliman Iski Zaliman Jefirstson Richset Riwukore K.Ghazali Kartina, Riza Lastri Widya Astuti Lesfandra Lesfandra Lesfandra, Lesfandra Lesi Hertati Lili Syafitri, Lili Lyra Ananda Saputri M Amaruna Sahona M Ravensky Taro Danayaksa Marcelina, Dona Marnisah, Luis Martadinata, A. Taqwa Masroni Dedi Kiswanto Maya Amelia Mody Sertian Amanda Muda, Seftia Putri Muhammad A. A. Hakim Muhammad Ramadhan Mulyati Mulyati Mulyati Mulyati Mustafa Ramadhan Mustika, Suci Permata Nadila Nurhalizah Ningsih Wahyuni Nur Arminarahmah Nur Fitriyani Sahamony Oktariani, Putri Pratama, Alga Wahyu Pratiwi Putri, Indah Purnamasari, Evi Putri Tsabita Putri, Indah Pratiwi Refki Saputra Rendra Gustriansyah Resti Wulandari Roni Sumari Hutabarat Sabrina Salsabila Putri Salbani Salbani Sanawi, Fakhri Saputra, Tedy Setiawan Seftia Putri Muda Sella Oktania Septa Cahyani Silvia Kardinata Siti Komariah Hildayanti, Siti Komariah Suntana, Muhammad Yunus Thoiyibah Islamia Tri Septa Yulandari Trisna Hardianto Ulfa Fitriyani Yeti Friyani Yossy Andri Ani Yulius, Yosef Zanetti Julyah Berliana Perdana