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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) Smash: Journal of Social Management Sains and Health
M Gali Almahdi
Universitas Negeri Medan
Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Nursing Social Sciences

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Application of the K-Means Clustering Algorithm in the Analysis of Popularity and Growth Trends of Python Packages on the PyPI Dataset Muhammad Rafli Wijaya; M Gali Almahdi; Sebastian Saut Marulitua Sinaga; Benedict Sandi Pangestu Rosa
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 3 (2026): June 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i3.2233

Abstract

The rapid growth of the Python ecosystem has led to an increasing number of packages on the Python Package Index (PyPI), generating a massive volume of download data. This data can be utilized to analyze popularity levels and growth trends of libraries used by the developer community. This study aims to identify popularity patterns and growth trends of Python packages using the K-Means Clustering algorithm. The dataset was obtained from PyPI via the Google BigQuery platform with a one-year observation period using a 1% sampling technique. The pre-processing stage included a filtering process to select the 100 packages with the highest number of downloads and the formation of six main features representing the characteristics of library usage patterns. The data was then normalized using Standard Scaling, while the optimal number of clusters was determined using the Elbow Method and evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results showed that the optimal number of clusters is four, with a DBI value of 0.5534 and a Silhouette Score of 0.5748 (the highest among k = 2-10 ), representing the categories of ecosystem foundation libraries, medium-popularity libraries, libraries with concentrated download spikes, and libraries with very rapid usage growth. These results indicate that K-Means Clustering is effective for identifying popularity patterns and library growth trends in large-scale PyPI datasets.
Analisis Kebenaran dan Efisiensi Algoritma Dijkstra VS A-Star (A*) Dalam Penentuan Rute Tercepat ada Graf Berbobot Sebastian Saut Marulitua Sinaga; Benedict Sandi Pangestu Rosa; Muhammad Rafli Wijaya; M Gali Almahdi; Adidtya Perdana
SMASH: Journal of Social Management Sains and Health Vol. 3 No. 1 (2026): April 2026
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/smash.v3i1.8329

Abstract

Studi ini menganalisis kebenaran dan efisiensi algoritma Dijkstra dan A* dalam menentukan jalur terpendek pada graf berbobot. Kebenaran dibuktikan secara teoritis menggunakan pendekatan invarian loop untuk Dijkstra dan sifat admissible pada heuristik A*, serta dikonfirmasi melalui eksperimen komputasi. Efisiensi dianalisis berdasarkan kompleksitas waktu dan pengujian empiris meliputi waktu eksekusi, jumlah simpul yang dikunjungi, dan ukuran antrian prioritas. Pengujian dilakukan pada tiga skenario graf berbobot dengan ukuran berbeda menggunakan metode Random Geometric Graph. Hasil menunjukkan bahwa kedua algoritma memiliki tingkat kebenaran 100% dalam seluruh pengujian. Dari segi efisiensi, algoritma A* menunjukkan performa yang lebih cepat dan mampu mengurangi jumlah eksplorasi simpul secara signifikan dibandingkan Dijkstra. Namun, A* memiliki penggunaan memori yang lebih tinggi pada graf berukuran besar. Penelitian ini menyimpulkan bahwa A* lebih unggul dalam efisiensi waktu ketika tersedia heuristik yang valid, sementara Dijkstra lebih stabil ketika heuristik tidak tersedia.
Co-Authors Adidtya Perdana, Adidtya Benedict Sandi Pangestu Rosa Muhammad Rafli Wijaya Sebastian Saut Marulitua Sinaga