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All Journal Building of Informatics, Technology and Science Journal Of Information System And Artificial Intelligence
Manurung, Syalom Kristian
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analisis dan Perancangan Website E-Business Undangan Pernikahan Digital Fauzan, Nabil; Manurung, Syalom Kristian; Pradana Saputra, Fendi; Taqwa Prasetyaningrum, Putri
Journal Of Information System And Artificial Intelligence Vol. 5 No. 1 (2024): Vol. 5 No. 1 (2024): Vol. 5 No. 1 (2024): Journal of Information System and Art
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v5i1.228

Abstract

Menyatukan dua insan dalam ikatan cinta bukanlah sekadar perayaan, tapi lambang harmoni dan kebersamaan. Di era digital ini, "Jadi Satu" hadir sebagai solusi inovatif untuk mengabadikan momen sakral tersebut melalui undangan pernikahan digital yang elegan dan terjangkau. Melalui platform "Jadi Satu", calon pengantin dapat memilih dari beragam desain berkelas, menyesuaikan kata-kata undangan sesuai keinginan, dan mengirimkannya ke seluruh penjuru dunia dengan hanya beberapa klik. Menghemat waktu, kertas, dan biaya, "Jadi Satu" turut berkontribusi terhadap langkah awal kehidupan berkeluarga yang ramah lingkungan dan efisien. Website "Jadi Satu" dirancang dengan antarmuka intuitif dan user-friendly, memungkinkan siapapun, terlepas dari keahlian teknologi, untuk berkreasi dengan mudah. Fitur unggulan seperti galeri desain tematik, editor teks fleksibel, dan RSVP online, memastikan pengalaman menyusun undangan yang menyenangkan dan tak terlupakan. Lebih dari sekadar undangan, "Jadi Satu" berkomitmen menjadi bagian dari perjalanan cinta.
Analisis Perbandingan Algoritma Random Forest dan K-Nearest Neighbors pada Klasifikasi Tingkat Stres Pekerja Manurung, Syalom Kristian; Pratama, Irfan
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7589

Abstract

Work stress has become a prominent concern in the modern professional landscape, as it can lead to reduced productivity, diminished work quality, and decreased mental well-being among employees. This study aims to evaluate and compare the performance of two machine learning algorithms, namely Random Forest and K-Nearest Neighbors (KNN), in classifying levels of work stress. The data were obtained through an online questionnaire completed by 212 respondents from various employment sectors in Indonesia. The responses were converted from Likert scale to numerical values, grouped using the K-Means clustering method, and categorized into five levels of stress, ranging from no stress to very high stress. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. The modeling process was conducted using three different data split scenarios, namely 90:10, 80:20, and 70:30, and evaluated using metrics such as accuracy, precision, recall, f1-score, and cross-validation. The findings indicate that the Random Forest algorithm consistently outperformed KNN across all scenarios. After applying SMOTE, both algorithms showed improved performance, with the Balanced Random Forest model achieving the highest accuracy and f1-score of 92 percent in the 70:30 scenario. These results suggest that combining Random Forest with SMOTE offers an effective and reliable solution for classifying work stress levels and could be developed as an objective and efficient early detection system.
Co-Authors Fauzan, Nabil Pradana Saputra, Fendi Pratama, Irfan Taqwa Prasetyaningrum, Putri