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All Journal JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi) JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Akbar Firdaus
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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EMPOWERING STARTUP COMPANIES WITH ARTIFICIAL INTELLIGENCE TECHNOLOGY Akbar Firdaus; Winasis, Shinta
JMBI UNSRAT (Jurnal Ilmiah Manajemen Bisnis dan Inovasi Universitas Sam Ratulangi). Vol 12 No 1 (2025): JMBI UNSRAT Volume 12 Nomor 1
Publisher : FEB Universitas Sam Ratulangi Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35794/jmbi.v12i1.60511

Abstract

Startup companies have emerged as a crucial pillar of Indonesia's economy, owing to their ability to rapidly adapt to various situations and their role in attracting foreign investment. Currently, the number of startups in Indonesia ranks among the top six globally and contributes approximately 10% to the country's Gross Domestic Product (GDP). Nevertheless, the number of companies that have achieved unicorn and decacorn status still lags significantly behind neighboring countries such as Singapore and India. To enhance investor interest in startups, it is essential to highlight various aspects that startups offer in comparison to competitors, including products or services, market opportunities, growth potential, business models, and innovations. In this context, many startups are leveraging Artificial Intelligence (AI) technology to improve their business performance. This research aims to analyze the functions and advantages of utilizing AI within the startup ecosystem, as well as its impact on enhancing attractiveness and business performance, thereby drawing greater attention from investors. This study employs a literature review method, with the expectation that the findings will provide insights into the use of AI for empowering startups in enhancing their business performance. Keywords : Startup, Artificial Intelligence, Business Performance
PENERAPAN METODE K-MEANS CLUSTERING UNTUK STOK PENJUALAN SPAREPART SEPEDA MOTOR Akbar Firdaus; Sriani Sriani; Ali Darta
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4792

Abstract

 Abstract: Availability of motorcycle spare parts in CV. Solid Mandiri Cemerlang must be monitored to avoid product shortages. The problem that occurs in reporting regarding Most of the items purchased by Most of the customers is under control. Processing incoming and outgoing goods that are not processed by the system requires goods management techniques. The more complete the types of spare parts, customer needs will be met. The collection of available spare parts will be divided into several groups to get the spare parts that customers have purchased the most for each transaction. Data mining is sourced from raw database. This causes problems in databases which tend to be dynamic, complete and large. The K-means Clustering algorithm is capable and effective for finding clusters in data. This calculation will determine the number of clusters at the calculation center and the maximum iteration of data that has been entered into the system. The purpose of implementing the K-means algorithm is to find the value of the goods purchased by the majority of customers so that it makes it easier to provide spare parts. The results of the k-means calculation: C1 (high) has 9 items, C2 (low) has 1 items. Keyword: Motorcycle Parts, K-Means, Cluster Abstrak: Ketersediaan suku cadang sepeda motor di CV. Solid Mitra Cemerlang harus dimonitor untuk menghindari kekosongan barang. Masalah yang terjadi dalam pelaporan mengenai Sebagian besar barang yang dibeli oleh Sebagian besar pelanggan menjadi kendali. Mengolah barang masuk dan keluar yang tidak diproses dengan sistem membutuhkan teknik mengelola barang. Semakin lengkap jenis-jenis suku cadang, kebutuhan pelanggan akan terpenuhi. Pengumpulan suku cadang yang tersedia akan dibagi menjadi beberapa kelompok untuk mendapatkan suku cadang yang paling banyak dibeli pelanggan untuk setiap transaksi. Penambangan data bersumber dari basis data mentah. Hal ini menyebabkan masalah dalam database yang cenderung dinamis, lengkap dan besar. Algoritma K-means Clustering mampu dan efektif untuk menemukan cluster dalam data. Pada perhitungan ini akan menentukan jumlah cluster pada pusat perhitungan dan iterasi maksimum data yang telah dimasukkan kedalam sistem. Tujuan dari penerapan algoritma K-means adalah untuk menemukan nilai dari barang yang dibeli oleh Sebagian besar pelanggan sehingga memudahkan untuk menyediakan suku cadang. Hasil perhitungan k-means: C1 (tinggi) ada 9 barang, C2 (rendah) ada 1 barang. Kata kunci: Suku Cadang Sepeda Motor, K-Means, Cluster 
Co-Authors Ali Darta Shinta Winasis Sriani Sriani