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All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH The Journal of Information Technology, Computer Science, and Electrical Engineering
Maida Indrayani
Universitas Pembangunan Panca Budi
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Analysis of User Satisfaction in the Development of ICT Learning Media Using AR-Based Assemblr Edu with the PIECES Framework Method Zulham Sitorus; Ami Abdul Jabar; Maida Indrayani; Sipra Barutu; Meiarni Situkkir
Journal of Information Technology, computer science and Electrical Engineering Vol. 1 No. 2 (2024): June-September 2024
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v1i2.56

Abstract

This study analyzes user satisfaction in the development of Augmented Reality (AR) learning media using the Assemblr Edu application. The method employed is the PIECES Framework, which includes the variables of Performance, Information, Economics, Control and Security, Efficiency, and Service. The sample consists of 30 respondents, including 1 teacher and 29 students from class X TKJ 1 at SMKS Panca Budi Medan. Data were collected through a questionnaire utilizing a Likert scale to measure satisfaction levels. The analysis results indicate that the respondents' satisfaction level is VERY SATISFIED. This research is expected to contribute to the development of AR-based learning media in education, enhance understanding of user satisfaction with the Assemblr Edu application, and encourage the use of similar technologies in other educational institutions.
Application of Data Mining on Mobile Phone Sales Data Using the Apriori Algorithm (Case Study: Sentral Phone Store) Maida Indrayani; Muhammad Iqbal
Journal of Information Technology, computer science and Electrical Engineering Vol. 2 No. 2 (2025): June-September 2025
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v2i2.201

Abstract

Mobile Phone is an electronic device used to communicate, transact digitally and as a medium for exchanging data and information. Mobile Phone is one of the secondary needs that must be owned by the community. In its development, there are various types of mobile phone brands that are sold and the prices offered also vary. The community can buy mobile phones according to their needs and financial capabilities. To see the mobile phone brands that are most in demand by the community, data mining is used using the apriori algorithm to analyze the sales of the most sold mobile phones. So that with the use of the apriori algorithm, it avoids the accumulation of unsold mobile phones.
PEMETAAN PILIHAN LULUSAN SMK PANCA BUDI MEDAN MENGGUNAKAN ALGORITMA K-MEANS DAN VISUALISASI DATA Maida Indrayani; Muhammad Iqbal; Darmeli Nasution
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3543

Abstract

Abstract: This research maps the career choices of SMK Panca Budi Medan graduates using the K-Means algorithm and data visualization. The study included 219 graduates from 2024 across eight study programs. The majority (44.7%) chose to work, followed by 32.0% who pursued higher education, 16.4% were undecided, and 6.8% became entrepreneurs. Graduates with higher average report card scores tended to continue their studies, while those with lower scores often opted to work or were undecided. The K-Means algorithm successfully clustered graduates, with Cluster 1.0 showing the highest academic potential (average score: 94.60). The findings provide strategic recommendations for the school, including intensifying career guidance for undecided graduates, strengthening higher education pathways for high-achievers, accelerating entrepreneurship incubators, and implementing personalized alumni coaching based on clustering analysis. Keywords: Tracer Study, K-Means, Data Visualization, SMK, Alumni Outcomes Abstrak: Penelitian ini memetakan pilihan karier lulusan SMK Panca Budi Medan menggunakan algoritma K-Means dan visualisasi data. Studi melibatkan 219 lulusan tahun 2024 dari delapan program studi. Sebagian besar lulusan (44,7%) memilih langsung bekerja, diikuti oleh 32,0% yang melanjutkan kuliah, 16,4% "belum tahu", dan 6,8% berwirausaha. Alumni dengan rata-rata nilai rapor tertinggi cenderung melanjutkan kuliah, sedangkan yang lebih rendah umumnya memilih bekerja atau belum memiliki rencana. Algoritma K-Means berhasil mengelompokkan lulusan, dengan cluster 1.0 merepresentasikan potensi akademik tertinggi (rata-rata nilai: 94,60). Temuan ini menghasilkan rekomendasi strategis bagi sekolah, meliputi pengintensifan program bimbingan karier, penguatan jalur kuliah bagi siswa berprestasi tinggi, akselerasi pengembangan inkubator wirausaha, serta implementasi pembinaan alumni berbasis personalisasi dari hasil clustering. Kata kunci: Tracer Study, K-Means, Visualisasi Data, SMK, Outcome Alumni
Co-Authors Ami Abdul Jabar Darmeli Nasution Meiarni Situkkir Muhammad Iqbal Sipra Barutu Sitorus, Zulham