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All Journal Jurnal Bina Desa Recursive Journal of Informatics
Shantika, Febryan Surya
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Computer Science & IT Social Sciences

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Adab Kebiasaan Bertamu dalam Lingkungan Masyarakat pada Masa Pandemi Covid-19 Shantika, Febryan Surya; Widyasningrum, Rosalia Ika; Damayanti, Melliana; Irawan, Fajar Awang
Jurnal Bina Desa Vol 3, No 2 (2021): Juni
Publisher : Universitas Negeri Semarang

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Abstract

Pandemi Covid-19 merupakan pandemi infeksi penyakit baru yang disebabkan oleh coronavirus. Program pencegahan telah dilaksanakan oleh pemerintah untuk menekan penyebaran Covid-19. Salah satunya adalah Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM) berupa protokol kesehatan yaitu 5M, memakai masker, mencuci tangan, mengurangi mobilitas, menjaga jarak, dan menghindari kerumunan. Untuk menentukan keefektifannya maka dilakukan uji dan analisis kepustakaan mengenai aturan pemberlakuan dan tata cara yang secara umum berlaku di masyarakat. Dari analisis tersebut didapatkan hasil perubahan langkah yang dapat dilakukan ketika bertamu dimasa pandemi. Dengan melakukan pengujian data dengan menggunakan kuesioner didapatkan dari 30 responden, sebanyak 25 orang (83,3%) mengikuti seluruh prosedur yang berlaku selama proses bertamu dari awal hingga akhir, dan 21 orang (70%) menganggap protokol 5M efektif untuk menangani  penyebaran Covid-19 di lingkungan masyarakat. Sehingga dapat disimpulkan protokol kesehatan 5M telah dilaksanakan di kegiatan bertamu masyarakat dengan sedikit mengubah tata cara yang berlaku.
Sentiment Analysis of Jobstreet Application Reviews on Google Play Store Using Support Vector Machine Algorithm with Adaptive Synthetic Shantika, Febryan Surya; Abidin, Zaenal
Recursive Journal of Informatics Vol. 3 No. 2 (2025): September 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/rji.v3i2.11891

Abstract

Abstract. Purpose: This research aims to test the performance result of the Support Vector Machine (SVM) classification algorithm using the help of Adaptive Synthetic (ADASYN) oversampling to analyze sentiment in Jobstreet application reviews on the Google Play Store. Sentiment analysis is a significant method to understand the market needs and application improvement. Methods/Study design/approach: The dataset originates from Google Play reviews gained using the scrapping method, comprising 5,174 reviews with 11 attributes. The process begins with data scrapping, data labeling, and data preprocessing, including casefolding, tokenizing, filtering, and stemming using Python programs. The data is then weighted and split using an 80:20 ratio. Then applying oversampling ADASYN on a clean dataset before using SVM classification to produce the performance result. Result/Findings: Both scenarios are conducted on SVM classification to classify the dataset. The evaluation results indicate that using SVM classification without ADASYN produces an accuracy result of 89.08%. Other scenarios by using SVM classification with the ADASYN sampling approach produce an accuracy result of 89.95%. The performance in accuracy result by using the ADASYN sampling approach on SVM classification shows an increasing result of 0.87%. Novelty/Originality/Value: This study employs two result scenarios of SVM classification by using the ADASYN sampling approach. It contributes to the literature by demonstrating the usability of the ADASYN oversampling approach to optimalize the SVM classification result used for sentiment analysis in Jobstreet application reviews on the Google Play Store.
Co-Authors Damayanti, Melliana Fajar Awang Irawan Widyasningrum, Rosalia Ika Zaenal Abidin