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All Journal INTI Nusa Mandiri Journal of Artificial Intelligence and Digital Business
Asy’ari , Hasyim
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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Followership Dan Kepemimpinan Sukses Di Institusi Pendidikan: Pendekatan Kolaboratif Untuk Meningkatkan Efektivitas Kepemimpinan Amelia, Rizki; Saepudin, Muhammad Fahmi; Asy’ari , Hasyim
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1073

Abstract

Keberhasilan kepemimpinan di institusi pendidikan tidak hanya ditentukan oleh kemampuan pemimpin, tetapi juga oleh keterlibatan aktif pengikut (followers). Artikel ini mengkaji peran followership dalam memperkuat efektivitas kepemimpinan melalui pendekatan kolaboratif. Dengan mengacu pada literatur terbaru, penelitian ini menyoroti bagaimana pengikut yang proaktif dapat berkontribusi pada pencapaian visi dan misi organisasi pendidikan. Artikel ini juga memberikan wawasan tentang ciri-ciri kepemimpinan yang sukses, hubungan dinamis antara pemimpin dan pengikut, serta rekomendasi untuk mengintegrasikan konsep followership di lembaga pendidikan.
REKOMENDASI PEKERJAAN BIDANG EKONOMI : SISTEM REKOMENDASI MENGGUNAKAN CONTENT BASED Rouf, Abdur; Asy’ari , Hasyim; Yafi Urrohman, Maysas; Devi Rahmawati , Febriane
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6786

Abstract

The recommendation system was developed to assist students of the Institut Teknologi dan Bisnis Widya Gama Lumajang, particularly those from the Faculty of Economics and Business, in determining their preferred career options. This system helps students by providing various job references that match their individual criteria. The data was collected from a tracer study, which includes information such as academic grades, non-academic achievements, job positions, company names, salaries received. From the total dataset, 1,120 records were deemed valid and used in the research process. The aim of this research is to assist students by providing job recommendations based on similar criteria between current students and alumni. The method applied in this study is quantitative experimental research based on data mining, with the main approach being Content-Based filtering and the MLP (Multi-Layer Perceptron) Classifier algorithm. The data was split into two parts: 65% for training and 35% for testing. This division aims to allow the model to learn from most of the data while also being tested for accuracy using unfamiliar data. The recommendation model was developed using the MLP Classifier algorithm with a hidden_layer_size configuration of 100 neurons and a max_iter of 200 iterations. For the initial test, 10 sample data points were used to evaluate the model’s performance. During training, the loss value was monitored to assess how well the model understood the data and adjusted its internal weights. With this configuration, the system is expected to provide accurate job recommendations based on the user’s profile and academic history.
Co-Authors Abdur Ro’uf Devi Rahmawati , Febriane Rizki Amelia Saepudin, Muhammad Fahmi Yafi Urrohman, Maysas