Article Per Year (5 Year)
p-Index From 2021 - 2026
0.947
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH International Journal of Management Science and Information Technology (IJMSIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Computer Science Advancements
Parini Parini
Universitas Royal
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Consumer Segmentation With K-Means at Lucky Shop Tanjungbalai Reza Ahmad Fauzi; Masitah Handayani; Parini Parini
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7213

Abstract

Consumer segmentation is an important strategy for improving marketing effectiveness and inventory management in retail businesses. Lucky Shop Tanjungbalai faces challenges in understanding diverse customer purchasing patterns, making it difficult to develop targeted marketing strategies. This study aims to apply the K-Means Clustering method to classify consumers based on purchasing behavior patterns. The data used consisted of 15 customer transaction records collected from Lucky Shop Tanjungbalai, with attributes including purchase frequency, quantity of purchased products, and product categories. This research adopted a qualitative approach combined with data mining techniques using the CRISP-DM framework, which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The system was developed using PHP and MySQL. The results indicate that K-Means Clustering successfully segmented customers into Loyal Customers and Occasional Customers based on their purchasing characteristics. These segmentation results provide practical benefits for Lucky Shop by enabling more targeted promotional programs, improving customer relationship strategies, optimizing inventory planning, and supporting data-driven business decision-making. Therefore, the implementation of K-Means Clustering can serve as an effective solution for customer segmentation in local retail businesses.
Implementation of the Apriori Algorithm for Product Recommendation Analysis at Asyifa Serba 35.000 Retail Store in Kisaran Ardiansyah Putra Tambunan; Adi Prijuna Lubis; Parini Parini
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7289

Abstract

This study aims to implement the Apriori algorithm to analyze sales transaction data and generate product recommendations at Toko Asyifa Serba 35.000. The research addresses the problem of underutilized transaction data, where sales records are only used for administrative purposes without further analysis to support marketing strategies and decision-making. The significance of this study lies in its contribution to enhancing data-driven decision-making in retail businesses, particularly in improving product promotion strategies, inventory management, and customer satisfaction. The research adopts an applied quantitative approach with an experimental design. Data were collected through observations, interviews, and documentation of sales transactions, and analyzed using data mining techniques, specifically the Apriori algorithm, to identify frequent itemsets and association rules based on support and confidence values. The results indicate that the implementation of the Apriori algorithm successfully uncovers patterns of consumer purchasing behavior, revealing combinations of products frequently bought together. The generated recommendations provide practical benefits for retail management, including more effective product bundling strategies, optimized shelf arrangement, targeted promotional campaigns, and improved inventory planning. These improvements can contribute to increased sales opportunities and better customer shopping experiences. These findings enable the development of a recommendation system that provides accurate and relevant product suggestions. The study concludes that the application of Apriori-based recommendation systems improves sales effectiveness, optimizes product placement, and enhances customer satisfaction. It is recommended that retail businesses adopt data mining techniques to maximize the value of transaction data and further develop integrated recommendation systems for better decision support.
Implementation of a Cloud-Based E-Learning System for Integrated Learning in Higher Education Parini Parini; Sri Nur Rahmi; Fransiskus Ghunu Bili; Ahmya Ayaka
Journal of Computer Science Advancements Vol. 2 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i6.1625

Abstract

The integration of technology in higher education has gained significant momentum, with cloud-based e-learning systems emerging as a transformative approach to support integrated and flexible learning environments. Traditional learning systems often face limitations in scalability, accessibility, and resource-sharing, prompting the need for innovative solutions. Cloud-based e-learning systems offer a centralized platform that enhances collaboration, resource management, and learning continuity. This research explores the implementation of a cloud-based e-learning system in higher education institutions, focusing on its impact on learning outcomes and system efficiency. The study employs a mixed-method approach, combining quantitative surveys and qualitative interviews. Data were collected from 300 students and 50 faculty members across three universities that recently adopted cloud-based e-learning platforms. The research assessed system usability, learner engagement, and academic performance, alongside implementation challenges and benefits. The findings reveal that cloud-based e-learning systems significantly improve accessibility, resource-sharing, and collaboration among students and educators. Survey results indicated a 40% increase in learner engagement and a 35% improvement in resource utilization. Faculty interviews highlighted reduced administrative burdens and enhanced flexibility in course delivery. However, challenges such as data security concerns and the need for technical support were noted. The study concludes that cloud-based e-learning systems are a valuable tool for modernizing higher education. Addressing implementation challenges and ensuring continuous technical support are critical for maximizing their potential. Future research should explore long-term impacts and integration with emerging technologies to further enhance learning experiences.
Konsep Lean Supply Chain dalam Meningkatkan Efisiensi Operasional: The Lean Supply Chain Concept in Improving Operational Efficiency Rian Refanza Lumbantoruan; Jeperson Hutahaean; Parini Parini
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2606

Abstract

Penelitian ini dilatarbelakangi oleh permasalahan pengelolaan persediaan dan rantai pasok pada Toko Leni Love Hijab yang belum berjalan optimal, sehingga sering terjadi kelebihan stok pada beberapa produk dan kekurangan stok pada produk yang memiliki permintaan tinggi. Kondisi ini berdampak pada menurunnya efisiensi operasional serta pelayanan kepada pelanggan. Tujuan penelitian ini adalah untuk menganalisis penerapan konsep Lean Supply Chain dalam meningkatkan efisiensi operasional pada Toko Leni Love Hijab. Metode penelitian yang digunakan adalah metode kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan studi dokumentasi. Penelitian ini juga melakukan analisis sistem serta perancangan sistem menggunakan pendekatan Supply Chain Management berbasis teknologi informasi dengan dukungan pemodelan sistem seperti UML dan perancangan basis data. Hasil penelitian menunjukkan bahwa penerapan konsep Lean Supply Chain dapat membantu mengidentifikasi aktivitas yang tidak memberikan nilai tambah, mengoptimalkan pengelolaan persediaan, mempercepat aliran informasi antara toko dan supplier, serta meminimalkan keterlambatan pengiriman produk. Kesimpulan dari penelitian ini adalah bahwa penerapan Lean Supply Chain mampu meningkatkan efisiensi operasional, memperbaiki manajemen stok, serta meningkatkan kualitas pelayanan kepada pelanggan pada Toko Leni Love Hijab
METODE MFEP PADA PEMILIHAN PENERIMA BANTUAN MAKANANAN TAMBAHAN BALITA STUNTING Febby Madonna Yuma; Parini Parini; Mustika Fitri Larasati Sibuea; Salwa Yuma Annisa
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.4234

Abstract

Abstract: Stunting is a long-term nutritional disorder that negatively impacts children's physical development. In Indonesia, stunting rates among toddlers remain alarming. The government responded by implementing a Supplementary Feeding (PMT) program, but its distribution is not always accurate. Therefore, a support system is needed in selecting PMT recipients for stunted toddlers. The purpose of this study was to design a recommendation system using the MFEP method for users to avoid errors in aid recipients. This research method used interviews with Posyandu (Integrated Service Post) officials and the community, and data collection methods were carried out through observation and interviews. The results showed that the application of the MFEP method can help community health centers (Puskesmas) in resolving the problem of inaccurate targeting of aid recipients, so that the results are more accurate and meet community expectations. The implementation of MFEP for PMT recipients for stunted toddlers at Posyandu with an accuracy value of more than 80% indicates that this method is working well. Keywords: toddler; MFEP method; aid recipient; stunting;                     Abstrak: Stunting merupakan gangguan gizi jangka panjang yang berdampak negatif terhadap perkembangan fisik anak. Di Indonesia, angka stunting pada balita masih memprihatinkan. Pemerintah merespons dengan menyelenggarakan program Pemberian Makanan Tambahan (PMT) namun penyalurannya belum selalu tepat. sehingga dibutuhkan sistem penunjang dalam pemilihan penerima PMT balita stunting. Adapun tujuan peneltian ini untuk mendapatkan rancangan dan mengevaluasi sistem penunjang keputusan menggunakan metode MFEP kepada pengguna agar tidak ada kesalahan pada penerima bantuan. Metode penelitian ini menggunakan metode  wawancara kepada pihak posyandu dan masyarakat dan metode pengumpulan data dilakukan melalui observasi dan wawancara. Hasil penelitian menunjukan bahwa penerapan metode MFEP dapat membantu puskesmas dalam menyelesaikan masalah ketidaktepatan sasaran penerima bantuan tersebut sehingga hasilnya lebih akurat dan sesuai harapan masyarakat. Implementasi MFEP  pada penerima bantuan PMT Balita Stunting di posyandu dengan nilai ketepatan lebih dari 80 % menunjukan bahwa metode ini berjalan dengan baik. Kata kunci: balita;metode MFEP; penerima bantuan; stunting;
Co-Authors Adi Prijuna Lubis Ahmya Ayaka Ardiansyah Putra Tambunan Febby Madonna Yuma Fransiskus Ghunu Bili Jeperson Hutahaean Masitah Handayani Mustika Fitri Larasati Sibuea Reza Ahmad Fauzi Rian Refanza Lumbantoruan Salwa Yuma Annisa Sri Nur Rahmi