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All Journal Indonesian Journal on Software Engineering (IJSE) Jurnal Teknoinfo AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT MULTINETICS Jurnal Teknik Informatika
Faqih, Husni
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Computer Science & IT Engineering

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Journal : MULTINETICS

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Studi Komparatif Metode Naive Bayes dan Support Vector Machine dalam Menganalisis Sentimen Ulasan Ask-AI Faqih, Husni; Aji, Sopian; Suseno, Kheri Agus
MULTINETICS Vol. 11 No. 1 (2025): Vol. 11 No. 1 (2025): MULTINETICS Mei (2025)
Publisher : POLITEKNIK NEGERI JAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32722/multinetics.v11i1.7534

Abstract

The development of Artificial Intelligence (AI) has brought significant changes in the field of information and communication. The Ask-AI application is popular and has many reviews on the Google Play Store platform. The purpose of this study is to analyze user review sentiments towards the Ask-AI application and compare the performance of two text classification algorithms, namely Naive Bayes and Support Vector Machine (SVM) in classifying reviews into positive and negative sentiment categories. A total of 628 reviews were used as a dataset consisting of 314 positive reviews and 314 negative reviews. The dataset has gone through a text preprocessing stage including letter transformation (transform cases), tokenize, common word removal (stopword removal), and dictionary-based stemming. Data analysis using RapidMiner software and for model performance evaluation using the k-fold cross-validation approach which can provide more stable and representative results for the entire data. The evaluation results produce a performance value of the SVM algorithm which has very good performance. SVM produces an accuracy of 94.08%, a precision of 96.23%, a recall of 92.31%, and an Area Under Curve (AUC) value of 0.981. Meanwhile, the Naive Bayes algorithm provides an accuracy of 78%, a precision of 85.23%, a recall of 68.37%, and an AUC of 0.801. The results of the study indicate that the SVM method is superior to Naïve Bayes in classifying the sentiment of Ask-AI application user reviews because it can provide more accurate, consistent, and more sensitive classification results to variations in text data. It is hoped that this study can be a reference for choosing the optimal sentiment classification algorithm for AI-based application user review data.
“Seeker” Platform LMS Pengembangan dan Penyediaan Tenaga Kerja Kompeten Hasirun; Prakosa, Herjuna Ardi; Rachindratama, Joda; Zahbika, Putra Maulana; Rifai, Zanuar; Faqih, Husni; Nur Afiana, Fiby
MULTINETICS Vol. 11 No. 02 (2025): Vol. 11 No. 2 (2025): MULTINETICS Nopember (2025)
Publisher : POLITEKNIK NEGERI JAKARTA

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Abstract

The Industrial Revolution 4.0 is estimated to eliminate 23 million jobs but create 46 million new types of jobs based on technology such as IoT, AI, and Big Data. This change demands the readiness of human resources, including in the MSME sector, as well as increased digital competency. Many companies now require skills certification in addition to formal diplomas. LMS (Learning Management Systems) are present as digital learning solutions that manage materials, assignments, and online learning interactions. However, most LMSs still focus on formal education, not on developing skills for high school/vocational school or university graduates. The aim of this research is to combine elements of LMS and job seekers in an effort to strengthen the advantages of future generations through competitive qualifications, skills, and technology in the industry. This platform allows collaboration with educational institutions, industry, and MSMEs to build a strong cooperative ecosystem in the socio-environmental field. Using the Prototype method as a software development method to support the success of the research. The LMS "Seeker" is present as a platform for providing competent workers through certified skills classes at affordable prices. Its advantages are a curriculum relevant to industry needs, increased workforce competitiveness, and access to broad job opportunities
Co-Authors Aji, Sopian Akbar, Muhammad Taufan Ery Suryanti Fandhilah , Fandhilah Hasirun Husni Mubarok Nur Afiana, Fiby Pangestu, Andika Tulus Prakosa, Herjuna Ardi Puspa Fortuna Zulfa Rachindratama, Joda Rifai, Zanuar Rousyati, Rousyati Suci, Adillah Bulan Suseno, Kheri Agus Yulia, Erika Zahbika, Putra Maulana