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All Journal Jurnal Ilmiah Informatika Komputer Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Mantik Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika INTI Nusa Mandiri Infotek : Jurnal Informatika dan Teknologi SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Insan Journal of Innovative and Creativity Jurnal Sistem Komputer (SISKOM)
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Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Penerapan Algoritma Naive Bayes dan SVM untuk Analisis Sentimen terhadap Penggunaan True Wireless Stereo (TWS) Risca Lusiana Pratiwi; Zulia Imami Alfianti; Ahmad Fauzi; Ginabila Ginabila
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3535

Abstract

The use of wireless audio devices such as True Wireless Stereo (TWS) has become increasingly popular among Indonesian society as a solution to the limitations of wired earphones. As TWS usage continues to grow, understanding public sentiment toward these devices becomes essential to support product development and assist consumers in making informed purchasing decisions. This study aims to analyze user sentiment toward TWS on the social media platform X using the Naive Bayes and Support Vector Machine (SVM) algorithms. To improve classification performance, the Synthetic Minority Oversampling Technique (SMOTE) is applied to handle imbalanced data, while Particle Swarm Optimization (PSO) is used to optimize the model. The results show that the SVM algorithm outperforms Naive Bayes, achieving an accuracy of 80.46% and an AUC score of 0.854, with more balanced precision and recall values across both classes. Meanwhile, Naive Bayes demonstrated strength in detecting negative sentiment but with a lower accuracy of 78.00% and an AUC of 0.780
Analisis Kualitas Layanan Terhadap Kepuasan Pengguna Aplikasi Clean Hris Menggunakan Metode Webqual 4.0 Maria Fatima Jedo Tukan; Mochammad Abdul Azis; Ahmad Fauzi; Ginabila
Jurnal Sistem Komputer (SISKOM) Vol. 6 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/siskom.v6i2.1871

Abstract

This study aims to analyze the effect of Clean HRIS website service quality on user satisfaction using the WebQual 4.0 method. The independent variables in this study consist of usability, information quality, and service interaction quality, while the dependent variable is user satisfaction. This research applies a quantitative approach, with data collected through questionnaires distributed to 100 active users of Clean HRIS. The data were analyzed using validity testing, reliability testing, classical assumption testing, multiple linear regression, t-test, F-test, and coefficient of determination analysis. The results show that the three WebQual 4.0 variables, namely usability, information quality, and service interaction quality, have a positive and significant effect on user satisfaction. The coefficient of determination indicates that service quality variables are able to explain a substantial proportion of the variation in user satisfaction with the Clean HRIS website. Descriptively, users are categorized as satisfied with the aspects of usability, information quality, and service interaction quality. Therefore, the service quality of the Clean HRIS website plays an important role in improving user satisfaction. This study recommends that application managers continuously improve information clarity, ease of navigation, interface design, and service responsiveness to enhance user satisfaction in a sustainable manner.
PENANGANAN EXTREME CLASS IMBALANCE PADA ANALISIS SENTIMEN ULASAN PROVIDER INTERNET MENGGUNAKAN PENDEKATAN SMOTE DENGAN ALGORITMA RANDOM FOREST DAN SVM Risca Lusiana Pratiwi; Ginabila; Zulia Imami Alfianti
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7974

Abstract

Ketimpangan distribusi kelas yang ekstrem pada data ulasan pelanggan menjadi tantangan utama yang memicu bias prediksi pada model pembelajaran mesin. Penelitian ini bertujuan untuk menguji efektivitas penanganan extreme class imbalance pada ulasan penyedia jasa internet menggunakan pendekatan Synthetic Minority Over-sampling Technique (SMOTE) yang dikombinasikan dengan algoritma Random Forest dan Support Vector Machine (SVM). Dataset berjumlah 699 rekaman ulasan diolah melalui rangkaian pra-pemrosesan teks, ekstraksi fitur Term Frequency-Inverse Document Frequency (TF-IDF), serta diuji menggunakan skema Cross Validation. Hasil evaluasi memperlihatkan bahwa kombinasi SMOTE dan SVM mencatatkan akurasi sebesar 91,56%, nilai Area Under Curve (AUC) 0,769, presisi kelas positif 100,00%, dan recall kelas positif sebesar 4,84%. Di sisi lain, skema SMOTE dan Random Forest memperoleh akurasi 91,13%, nilai AUC 0,740, serta presisi dan recall kelas positif sebesar 0,00%. Penelitian ini menyimpulkan bahwa batas pemisah linier pada SVM lebih responsif memanfaatkan sampel sintetis SMOTE dalam ruang fitur berdimensi tinggi, serta mengonfirmasi bahwa kedua algoritma memiliki kecenderungan prediktif yang dominan dalam mengenali sampel kelas mayoritas.
ANALISIS SENTIMEN PERKEMBANGAN MOTOR LISTRIK MENGGUNAKAN SUPPORT VECTOR MACHINE DAN OPTIMASI PARTICLE SWARM OPTIMIZATION Ginabila Ginabila; Ahmad Fauzi; Risca Lusiana Pratiwi; Siti Fauziah; Zulia Imami Alfianti
INTI Nusa Mandiri Vol. 19 No. 1 (2024): INTI Periode Agustus 2024
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i1.5579

Abstract

Innovation in electric motor technology such as increased range, speed, and battery endurance can attract interest from individuals fascinated by the latest advancements. Sentiment analysis enables a profound understanding of consumer perceptions towards electric motors. In this study, Support Vector Machine (SVM) is employed as a classification tool to evaluate opinions on current developments in electric motors. SVM seeks an optimal hyperplane that maximizes the distance between sentiment categories. The development of sentiment analysis methods utilizes SVM with Particle Swarm Optimization (PSO) to successfully achieve an accuracy of 80.33% and obtain a Good Classification category based on ROC Curve results. This research provides insights into consumer perceptions of electric motor technology, offering valuable feedback for manufacturers in the development of superior electric motor products. Leveraging sentiment analysis, manufacturers can enhance product improvements, increase quality, and expand functionality to meet the evolving market demands.
Co-Authors Abdul Hamid Ahmad Fauzi Ahmad Fauzi Ahmad Hafidzul Kahfi Akmal Dirgantara Angga Kusuma Azis, Mochammad Abdul Dany, Rahmad Eko Yulianto Fauzi Ahmad Muda Fauzi, Ahamd Hafidinulloh Fitrohan Imam Nawawi Imam Nawawi, Imam Indra Chaidir, Indra Isroni Isroni Jamhari Jamhari Maria Fatima Jedo Tukan Mochammad Abdul Azis Muhammad Iqbal Prasetyo, Rizal Pratiwi, Risca Lusiana Rayhan Dharmawan Risca Lusiana Pratiwi Risca Lusiana Pratiwi Siti Fauziah Siti Fauziah Sriyadi Sriyadi, Sriyadi Zulia Imami Alfianti