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All Journal Economics and Digital Business Review Eduvest - Journal of Universal Studies
Tasa, Tyan
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Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Neuroscience Social Sciences

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Pemanfaatan Artificial Intelligence dalam Optimalisasi Kinerja Tenaga Kependidikan di Perguruan Tinggi Mardiman , Mardiman; Rasiwan, Iwan; Taufik, Taufik; Tasa, Tyan; Ghofar, Abd; Dwinanto, Irwin
Economics and Digital Business Review Vol. 7 No. 1 (2025): Agustus - Januari
Publisher : STIE Amkop Makassar

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Abstract

Penelitian ini bertujuan mengidentifikasi bentuk pemanfaatan Artificial Intelligence (AI), menganalisis dampaknya terhadap kinerja tenaga kependidikan, serta merumuskan strategi integrasi yang efektif di Universitas Kartamulia Purwakarta. Menggunakan pendekatan kualitatif fenomenologis, data dikumpulkan melalui wawancara mendalam, observasi partisipan pasif, dan studi dokumen terhadap 27 tenaga kependidikan. Triangulasi dan analisis model interaktif digunakan untuk validasi data. Hasil menunjukkan bahwa implementasi AI terutama Robotic Process Automation (RPA) dan chatbot berbasis Natural Language Processing (NLP) masih pada tahap awal dan tidak merata antarunit. AI secara signifikan meningkatkan efisiensi waktu dan produktivitas melalui otomasi tugas repetitif, meski dampak pada akurasi data bervariasi. Hambatan utama meliputi rendahnya kompetensi digital, resistensi terhadap perubahan, dan keterbatasan anggaran. Strategi integrasi yang direkomendasikan mencakup empat pilar: capacity building berkelanjutan, penguatan infrastruktur, tata kelola AI yang robust, serta penerapan bertahap dan terukur. Temuan menegaskan bahwa kesiapan organisasi lebih menentukan keberhasilan AI daripada aspek teknis semata.
Appropriateness of Student Major Selection Using Naive Bayes and K-Nearest Neighbor Algorithms at SMK Plus Al Musyarrofah Mustofa, Kamaluddin; Tasa, Tyan; Kurniawan, Denni
Eduvest - Journal of Universal Studies Vol. 4 No. 6 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i6.1483

Abstract

The process of selecting a major is a critical stage for students because it can influence their motivation and learning outcomes while attending school, especially at Vocational High Schools (SMK). This challenge is becoming more significant with the emergence of many new schools in various cities and districts in Indonesia, especially in DKI Jakarta Province. Prospective students often choose majors not based on personal interests, which can then result in lower grades, especially in productive subjects or certain competencies. To overcome this problem, a major suitability system is needed that can provide recommendations based on student abilities through certain attributes. In this research, a department suitability classification process was carried out using the Naive Bayes and k-Nearest Neighbor methods using data from 238 tenth grade (X) students for the 2023/2024 academic year, which included 9 relevant attributes. The testing process was carried out with a composition of training data and test data in five comparisons, namely 90:10, 80:20, 70:30, 60:40, and 50:50. The research results show that the 80:20 composition provides the best results, with k-Nearest Neighbor achieving recall, accuracy and precision levels of 100%. On the other hand, the Naive Bayes Classifier produces a recall rate of 61%, with an accuracy of 73%. These results indicate that k-Nearest Neighbor is superior in predicting major suitability compared to Naive Bayes under these conditions.
Co-Authors Dwinanto, Irwin Ghofar, Abd Kurniawan, Denni Mardiman , Mardiman Mustofa, Kamaluddin Rasiwan, Iwan Taufik Taufik