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All Journal KORISA: Jurnal Kolaborasi Riset Sarjana
Christosie Wahyudi
Universitas Jenderal Soedirman

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Daya Saing Karet Alam Negara Pengekspor di Pasar Internasional Ely Saragih; Ady Nugroho; Hadi Jayusman; Riska Suryani; Khoirun Nisa; Sony Wibisono; Christosie Wahyudi
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 2 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 2 Mei 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Natural rubber is a plantation commodity widely cultivated in the ASEAN region, particularly in Indonesia, Thailand, and Malaysia. This study aims to analyze the export competitiveness of HS 400122 natural rubber from these three countries in the international market. The analytical model employed is the Almost Ideal Demand System (AIDS) using the Seemingly Unrelated Regression (SUR) technique. The study utilizes monthly panel data from January 2012 to December 2021, covering export values and export volumes of natural rubber, sourced from Trademap.org. The results show that Indonesia holds the largest export market share compared to Thailand and Malaysia. Own-price elasticity indicates that natural rubber is a normal good, while compensated cross-price elasticity suggests that trade relationships among these countries are characterized by substitutability. Meanwhile, uncompensated cross-price elasticity reveals both substitutive and complementary relationships among them. These findings provide an overview of the competitive positions of each country and can serve as a reference for policymakers in enhancing the export performance of natural rubber in the international market.
Analisis Perbandingan Algoritma KNN dan SVM untuk Prediksi Risiko Kesehatan Ibu Hamil Marchelya Soewarno; Rian Ardianto; Riska Suryani; Rosyid Al-Hakim; Christosie Wahyudi
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 2 No. 3 (2025): Jurnal Kolaborasi Riset Sarjana Volume 2 Nomor 3 September 2025
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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In this study, we compared the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms for predicting pregnancy risk in patients. The dataset used consisted of eight variables, such as number of pregnancies, vaccination status, blood pressure, fetal heart rate, body mass index (BMI), age, and height, which were used to classify pregnancies as high-risk or low-risk. Data preprocessing was performed by standardizing numerical features and dividing the data using a stratified split method (80% training data, 20% test data). Model evaluation results showed that KNN achieved an accuracy rate of 81%, while SVM achieved an accuracy of 75.50%. Further analysis showed that KNN was more stable in classifying data with diverse variable distributions, while SVM showed a tendency to be more sensitive to high-risk cases, with 66.7% of predictions pointing to that category. Compared with previous studies, KNN's performance was within the general accuracy range of 70–95%, while SVM's accuracy was slightly lower than the average of similar studies, which reached around 80.33%. This study concludes that both algorithms have the potential to be tools for early detection of maternal health risks, where the differences in their performance are influenced by the selected parameters, data characteristics, and class division.
Implementasi Platform CRM sebagai Upaya Peningkatan Kepuasan Pelanggan pada Pelaku UMKM Riska Suryani; Ady Nugroho; Christosie Wahyudi; Endro Sariono; Esa Putri; Hadi Jayusman; Rosyid Al-Hakim
KORISA: Jurnal Kolaborasi Riset Sarjana Vol. 3 No. 2 (2026): Vol. 3 No. 2 (2026): Jurnal Kolaborasi Riset Sarjana Volume 3 Nomor 2 Mei 2026
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LP2M) Universitas Harapan Bangsa

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Abstract

The digital transformation of Micro, Small, and Medium Enterprises (MSMEs) requires not only the adoption of digital marketing but also effective customer relationship management to enhance business competitiveness. However, many MSMEs still manage customer data manually, resulting in limited customer engagement and inconsistent service quality. This community service program aimed to implement a Customer Relationship Management (CRM) platform to improve customer satisfaction among MSMEs. The program involved ten MSMEs operating in the trade and culinary sectors through a series of activities consisting of needs assessment, CRM training, platform implementation, mentoring, and evaluation. Data were collected using questionnaires, observations, and customer satisfaction surveys. The effectiveness of the program was evaluated using descriptive statistical analysis based on participants' competency improvement and customer satisfaction scores. The results demonstrated significant improvements in participants' understanding of CRM concepts, customer database management, customer interaction recording, and the utilization of CRM features. Furthermore, customer satisfaction achieved a very good category across all evaluation indicators, indicating that CRM implementation contributed positively to service quality, communication effectiveness, and customer experience. This community service program demonstrates that implementing a digital CRM platform, supported by continuous mentoring, is an effective strategy to strengthen MSMEs' digital capabilities and improve customer-oriented business management in the digital era.
Co-Authors Ady Nugroho Ely Saragih Endro Sariono Esa Putri Hadi Jayusman Khoirun Nisa Marchelya Soewarno Rian Ardianto Riska Suryani Rosyid Al-Hakim Sony Wibisono