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All Journal Mutiara : Jurnal Ilmiah Multidisiplin Indonesia CHAIN: Journal of Computer Technology, Computer Engineering and Informatics
Singgih Yulizar Ma'ruf
Universitas Islam Negeri Raden Intan Lampung
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Analisis Topik dan Karakteristik Kelayakan Pengajuan Judul Skripsi Mahasiswa Program Studi Sistem Informasi Menggunakan TF-IDF dan K-Means Clustering Singgih Yulizar Ma'ruf; David Naista
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.296

Abstract

Pengajuan judul skripsi merupakan tahap awal yang penting dalam penyusunan tugas akhir mahasiswa. Seiring meningkatnya jumlah usulan judul setiap tahun, program studi memerlukan analisis yang mampu mengidentifikasi pola topik penelitian serta karakteristik kelayakan usulan judul secara sistematis. Penelitian ini bertujuan untuk menganalisis topik dominan dan karakteristik kelayakan pengajuan judul skripsi mahasiswa Program Studi Sistem Informasi menggunakan pendekatan text mining dan clustering. Dataset yang digunakan terdiri dari 950 data pengajuan judul skripsi yang kemudian dilakukan proses deduplikasi sehingga diperoleh 757 judul unik. Tahapan penelitian meliputi preprocessing teks yang terdiri dari case folding, tokenisasi, stopword removal, dan stemming, dilanjutkan dengan pembobotan kata menggunakan Term Frequency–Inverse Document Frequency (TF-IDF). Selanjutnya dilakukan pengelompokan dokumen menggunakan algoritma K-Means Clustering dengan jumlah cluster optimal sebanyak enam cluster berdasarkan evaluasi silhouette score. Hasil penelitian menunjukkan bahwa enam cluster yang terbentuk merepresentasikan kelompok topik utama, yaitu sistem informasi dan manajemen, aplikasi mobile dan layanan digital, sistem informasi desa dan pengelolaan data, sistem pendukung keputusan, sistem monitoring dan dashboard, serta sistem informasi pariwisata. Nilai silhouette score sebesar 0,0314 menunjukkan bahwa data memiliki tingkat kemiripan antar topik yang cukup tinggi, namun masih mampu menghasilkan kelompok topik yang dapat diinterpretasikan. Analisis skor kelayakan menunjukkan adanya variasi karakteristik antar cluster, di mana cluster sistem monitoring dan dashboard memiliki rata-rata skor kelayakan tertinggi sebesar 92,36. Hasil uji Kruskal–Wallis menghasilkan p-value sebesar 1,51×10⁻⁹ yang menunjukkan adanya perbedaan signifikan skor kelayakan antar cluster. Penelitian ini dapat membantu program studi dalam memetakan tren penelitian mahasiswa dan mendukung proses evaluasi usulan judul skripsi secara lebih objektif.
Implementasi Generative Artificial Intelligence sebagai Media Pembelajaran Adaptif untuk Meningkatkan Literasi Digital Mahasiswa Sistem Informasi David Naista; Ghifar Javad H. Aziz; Singgih Yulizar Ma'ruf
MUTIARA: Jurnal Ilmiah Multidisiplin Indonesia Vol. 4 No. 1 (2026): JIMI - JANUARI
Publisher : PT. PENERBIT TIGA MUTIARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61404/mutiara.v4i1.550

Abstract

The development of Generative Artificial Intelligence (GenAI) has created new opportunities to develop more adaptive learning while enhancing students' digital literacy. This study aims to analyze the implementation of GenAI as an adaptive learning medium, its effect on improving digital literacy, and Information Systems students' perceptions of its use. This research employed a quantitative approach using a pre-experimental method with a one-group pre-test and post-test design. The results showed that the average digital literacy score increased from 68.8 to 85.0, representing an improvement of 16.2 points after the implementation of GenAI. The highest improvement was found in the Digital Safety and Ethics dimension with 19 points, followed by Digital Content Creation with 17 points, Information Literacy and Digital Technology Utilization with 16 points each, and Digital Communication and Collaboration with 13 points. The paired sample t-test produced a t-value of 8.45 with a significance level of 0.000, indicating that the implementation of GenAI had a statistically significant effect on improving students' digital literacy. The students also expressed positive perceptions of GenAI because it facilitated their understanding of learning materials, increased learning motivation, and supported more effective completion of academic tasks. This study concludes that GenAI is an effective adaptive learning medium for improving students' digital literacy. The novelty of this study lies in the development of a GenAI implementation model that integrates adaptive learning with the five dimensions of digital literacy among Information Systems students in a comprehensive manner.
Co-Authors David Naista Ghifar Javad H. Aziz