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All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal on Education Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Jurnal Review Pendidikan dan Pengajaran (JRPP) INTI Nusa Mandiri Jurnal Sistem informasi dan informatika (SIMIKA) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Indonesian Community Journal Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Optimization of Random Forest Algorithm with Backward Elimination Method in Classification of Academic Stress Levels Amalia, Salsabila Dani; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9280

Abstract

Stress is a phenomenon experienced by all individuals as a natural response to pressure, which can impact mental and physical health. In an academic setting, the stress experienced by students is known as academic stress, which can affect their performance and mental well-being. Therefore, there is a need for effective prediction methods to aid in the management and prevention of academic stress. Therefore, there is a need to predict the level of academic stress to aid more effective management and prevention. This study uses a public dataset categorized based on the Student-life Stress Inventory (SSI), which includes psychological, physiological, social, environmental, and academic factors. Data mining is often used to detect diseases, one of which is the Random Forest algorithm. The Random Forest algorithm is applied as a classification technique for academic stress levels, with optimization using the Backward Elimination method for feature selection to improve model accuracy. The results showed that the accuracy of the Random Forest algorithm without feature selection obtained an accuracy of 86%, compared to the random forest algorithm with feature selection using the Backward Elimination method obtained a higher accuracy of 88%. This increase shows that the feature selection method can optimize model performance by selecting more relevant features. Thus, this research is expected to contribute to the management of student academic stress against the risk of academic stress.
A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique Maulani, Vicka Rizqi; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9481

Abstract

Basic human needs include a house that serves as a place to live and a shelter from everything. In Indonesia, owning a house is still a challenging aspect due to its high price. Information on house prices is needed for prospective buyers or consumers, so that buyers can adjust their needs and finances, and for producers or sellers it is used as a way to determine the segmentation of targeted market groups. House prices are influenced by several factors including, building area, number of bedrooms, number of bathrooms, location, condition and the presence of a garage. This research aims to improve the quality of house price clustering with K-means and the application of one-hot encoding in the data pre-processing process in representing categorical data. The dataset used has two types of data, namely numeric and categorical. The cluster evaluation is based on the silhouette score matrix and the determination of k is based on the elbow graph. The results showed an increase in the silhouette score value after applying one-hot encoding 0.15 which was previously 0.09, with the number of k = 3. The 0.15 matrix result is relatively low, which is caused by the overlap of house price values in the dataset, but it has been shown that one-hot encoding can represent categorical data well in the data pre-processing process so that the data can be processed with the k-means algorithm.
Co-Authors Abdul Aziz Amalia, Salsabila Dani Andiyani, Putri Aprillia Dwi Ardianti Deni Reskianto Deni Denny Nurdiansyah Dina, Intan Rachma Dwi Irnawati Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Edi Noersasongko Efendi, Ervina Putri Fina Indri Silfana Hidayah, Alvinatul Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa'ida Jauhar Vikri, Muhammad Laili, Elok Fathiyatul Levia, Zachdyna Aurelya Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Munir, Ach Sirojul Muzakka, Moch. Arifuddin Nasirudin, M. Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur’aini, Zulfiana Panigoro, Buyung Pelangi Eka Yuwita Pratiwi, Fannisa Salsabila Purwanto Purwanto Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Sahri Sahri Sahri Santi, Nirma Ceisa Saputra, Agus Bima Silfana, Fina Indri Sinta Ningrum Usman Nurhasan Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi