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All Journal Aptisi Transactions on Management MEANS (Media Informasi Analisa dan Sistem) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan
Adiyarta, Krisna
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Other

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Advanced Predictive Models for the Startup Ecosystem Using Machine Learning Algorithms Febiansyah, Hidayat; Rahardja, Untung; Adiyarta, Krisna; Anderson, James; Kanivia, Aan
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2345

Abstract

The startup ecosystem, characterized by its dynamism, presents significant challenges in predicting its future trajectory. Traditional analytical methods often fall short in comprehensively addressing the myriad factors that shape this ecosystem. This research aims to enhance the predictability of trends within the startup landscape by integrating the Technology Acceptance Model (TAM) with the advanced Random Forest algorithm. While existing literature has extensively explored the challenges startups face and the nuances of stakeholder interactions, the integration of TAM's constructs with key empirical attributes, specifically Investment Dynamics, Startup Metrics, Stakeholder Interactions, Entrepreneurial Challenges, and Technological Infrastructure, is a pioneering approach. Drawing from a comprehensive dataset that spans a diverse array of startups, this study operationalizes TAM's constructs in conjunction with the specified attributes. The subsequent application of the Random Forest algorithm offers a novel predictive methodology. Initial results highlight the superior predictive capabilities of this integrated model compared to traditional approaches. The findings provide insights into the intricate relationship between technological perceptions, as framed by TAM, and the tangible realities of the startup domain. The fusion of TAM with state-of-the-art machine learning signifies a groundbreaking direction in startup ecosystem research. This innovative approach offers stakeholders an enhanced analytical tool, ensuring more informed decision-making and a deeper grasp of the multifaceted nature of startup ecosystems.
Penggunaan Metode AHP Dan TOPSIS Untuk Pemilihan Dokter Terbaik Margana, Ferry Kurniawan; Saputra, Edwin Wira; Adiyarta, Krisna
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (651.851 KB) | DOI: 10.54367/means.v5i1.739

Abstract

The hospital is the only one who handles the patients. Professional and qualified doctors can improve the quality of health services in a health institution. The problem is the existence of subjective assessments between doctors assessed with the appraiser, so that doctors who really deserve the predicate as the best doctor is often not chosen as the best doctor And the absence of information systems that can be used to determine the performance of each physician. The purpose of this research is to create a decision support system for the best performance determination doctor at the Berkah Jaya Medika Indramayu Clinic. The methods used in this acceptance decision support system use the method of Analytical Hierarcy Process (AHP) and Technique for others reference by similarity to ideal solution (TOPSIS). The results of this research is a Web application support system that is based on the decision to provide results in the form of each physician
Pemberdayaan Pencari Kerja Melalui Pelatihan Desain Grafis Berbasis Adobe Photoshop dan CorelDRAW Adiyarta, Krisna; Atmaja, R Ferry Bakti; Alkodri, Ari Amir; Saputra, Andi
Jurnal Pengabdian Masyarakat: Pemberdayaan, Inovasi dan Perubahan Vol 5, No 5 (2025): JPM: Pemberdayaan, Inovasi dan Perubahan
Publisher : Penerbit Widina, Widina Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59818/jpm.v5i5.2105

Abstract

The high unemployment rate in Pangkalpinang City, reaching 8.2% of the total workforce, requires serious attention because 65% of job seekers do not have the technical skills required by the creative industry, which affects job seekers around ISB Atma Luhur. This activity aims to improve the graphic design skills of job seekers through practice-based training using Adobe Photoshop and CorelDRAW. With 15 participants, the program was conducted over 12 days (80 hours) using a blended learning approach. The results showed an average competency increase of 75%, 100% participant satisfaction, and 64% successfully obtained employment within 3 months. This program has proven effective in improving job skills and sustainable economic opportunities.ABSTRAKPermasalahan tingginya angka pengangguran di Kota Pangkalpinang mencapai 8,2% dari total angkatan kerja memerlukan perhatian serius karena 65% pencari kerja tidak memiliki keterampilan teknis yang dibutuhkan industri kreatif yang mempengaruhi masyarakat pencari kerja di sekitar ISB Atma Luhur. Kegiatan ini bertujuan meningkatkan keterampilan desain grafis pencari kerja melalui pelatihan berbasis praktik menggunakan Adobe Photoshop dan CorelDRAW. Dengan 15 peserta, program dilaksanakan 12 hari (80 jam) menggunakan pendekatan blended learning. Hasil menunjukkan peningkatan kompetensi rata-rata 75%, kepuasan peserta 100%, dan 64% berhasil memperoleh pekerjaan dalam 3 bulan. Program ini terbukti efektif meningkatkan keterampilan kerja dan peluang ekonomi berkelanjutan.
Co-Authors Anderson, James Andi Saputra Ari Amir Alkodri Atmaja, R Ferry Bakti Febiansyah, Hidayat Kanivia, Aan Margana, Ferry Kurniawan Saputra, Edwin Wira Untung Rahardja