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All Journal Jurnal Ilmiah Wahana Akuntansi Clean Energy and Smart Technology TECHNOMEDIA : Informatics and Computer Science
Nur Fitriyanto
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mechanical Engineering Social Sciences

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Journal : technomedia informatics and computer science

 1 
SISTEM PENDUKUNG KEPUTUSAN KINERJA DOSEN MENGGUNAKAN SISTEM KECERDASAN BUATAN BERBASIS ALGORITMA K-MEANS CLUSTERING Asnawi, Muhamad Fuat; Nur Fitriyanto; M. Agoeng Pamoengkas
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 1 No 2 (2024): Juli
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v1i2.99

Abstract

This study aims to analyze the teaching performance quality of lecturers in the Faculty of Engineering and Computer Science at Universitas Sains Al-Qur'an using an artificial intelligence system based on the K-Means Clustering algorithm. Evaluating lecturer performance is a crucial step in ensuring the effectiveness and efficiency of the teaching process which includes pedagogical, professional, personality, and social indicators. Conventional approaches are often complex and ineffective in identifying in-depth performance patterns. The K-Means Clustering algorithm enables the grouping of evaluation data into clusters based on feature similarities, thus revealing patterns that are difficult to discern manually. The data used in this study comes from lecturer evaluation questionnaires completed by students during the even semester of the 2022/2023 academic year. The results showed that the clustering process yielded good separation with a Silhouette Score of 0.593 and a Davies-Bouldin Index of 0.606. These findings indicate that the data within the clusters are more similar to each other than to data in different clusters, suggesting effective clustering. The insights gained from this study are expected to be valuable for the management and development of teaching quality and to promote the use of artificial intelligence technology in higher education evaluations. The findings of this study are also expected to be adaptable by other educational institutions to improve academic excellence standards and produce high-quality graduates.
Tinjauan Pustaka Sistematis tentang Teknologi Keamanan Data: Tren dan Tantangan Asnawi, muhamad Fuat Asnawi; Nur Fitriyanto; M. Agoeng Pamoengkas
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 2 No 2 (2025): Juli
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v2i2.151

Abstract

Penelitian ini merupakan tinjauan pustaka sistematis (Systematic Literature Review/SLR) yang bertujuan mengidentifikasi tren dan tantangan utama dalam teknologi keamanan data selama periode 2020–2025. Sebanyak 91 artikel dari basis data IEEE Xplore dan ScienceDirect telah dikaji secara mendalam dengan fokus pada inovasi teknologi enkripsi, penerapan kecerdasan buatan (AI) dan machine learning dalam keamanan data, serta tantangan dalam perlindungan data pribadi di lingkungan cloud computing dan Internet of Things (IoT). Hasil kajian menunjukkan bahwa tren utama teknologi keamanan data meliputi adopsi Advanced Encryption Standard (AES), homomorphic encryption, searchable encryption, hingga enkripsi berbasis blockchain, serta integrasi AI untuk deteksi dan mitigasi ancaman secara proaktif. Meskipun demikian, penelitian ini juga menyoroti tantangan besar berupa keamanan data pribadi, kompleksitas pengelolaan data di cloud, serta implementasi regulasi seperti GDPR yang masih menghadapi berbagai kendala teknis dan hukum. Studi ini memberikan kontribusi penting dengan memetakan perkembangan terbaru sekaligus mengidentifikasi gap riset yang relevan untuk penguatan keamanan data di masa mendatang.
IMPLEMENTASI BIG DATA ANALYTICS DALAM KLASIFIKASI KUALITAS UDARA MENGGUNAKAN ALGORITMA GRADIENT-BOOSTED TREE CLASSIFIER PADA PYSPARK Muhamad Fuat Asnawi; Nur Fitriyanto; M. Agoeng Pamoengkas
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 2 No 1 (2025): Januari
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v2i1.124

Abstract

This study aims to classify air quality based on PM1.0, PM2.5, and PM10 parameters using a Big Data Analytics approach with the Gradient-Boosted Tree Classifier (GBT) algorithm implemented on the PySpark framework. The dataset used was downloaded from OpenAQ, covering the period from April 14, 2021, to April 16, 2023, with a total of 1,048,154 entries, representing a large and complex volume of data. The research process includes data preprocessing to address data imbalance, dataset splitting for training and testing, and hyperparameter tuning using grid search and cross-validation to optimize model performance. By leveraging PySpark’s advantage in parallel processing of large data, the GBT model achieved an accuracy of 98.87%, precision of 99.00%, recall of 98.87%, and an F1-Score of 98.90%. This study demonstrates how Big Data Analytics can enhance efficiency and accuracy in air quality classification, contributing significantly to the development of real-time monitoring systems that support air pollution mitigation and data-driven policy-making.
Co-Authors Asnawi, Muhamad Fuat Asnawi, Muhamad Fuat Asnawi M. Agoeng Pamoengkas muhamad Fuat Asnawi Slamet Haryono