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All Journal Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Computer Science Research
Muhammad Hamka
Universitas Muhammadiyah Purwokerto, Purwokerto
Computer Science & IT Control & Systems Engineering

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Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier Muhammad Hamka; Naila Alfatari; Dhani Ratna Sari
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 1 (2022): September 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4740

Abstract

The increased consumption of beauty products as a lifestyle has increased public opinion on the beauty products used. Generally, reviews are given through posts on social media. This study discusses the classification of sentiment analysis on the use of serum beauty products on Twitter using the Naïve Bayes Multinomial algorithm. Sentiment analysis of serum beauty products is carried out to provide information and preferences to the public regarding the quality of a product. The results of the information and preferences become a reference for consideration in choosing the appropriate serum beauty product. The data used in this study were 27,587 tweets using three keywords, namely "serum," "face serum", and "beauty serum". Tweet data is divided into training data and test data with the number of training data as much as 22,070 tweets and test data as much as 5,518 tweets. The data is categorized using the lexicon senticnet 7 dictionary based on polarity values. The results of the analysis of positive sentiment are 35%, negative sentiment is 63.8%, and neutral sentiment is 1.2%. The classification results using Naïve Bayes Multinomial obtain the highest accuracy value of 80%. The Confusion Matrix results get the highest precision value of 88%, the highest recall of 81%, and the highest f1-Score of 86%.
Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier Muhammad Hamka; Naila Alfatari; Dhani Ratna Sari
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4740

Abstract

The increased consumption of beauty products as a lifestyle has increased public opinion on the beauty products used. Generally, reviews are given through posts on social media. This study discusses the classification of sentiment analysis on the use of serum beauty products on Twitter using the Naïve Bayes Multinomial algorithm. Sentiment analysis of serum beauty products is carried out to provide information and preferences to the public regarding the quality of a product. The results of the information and preferences become a reference for consideration in choosing the appropriate serum beauty product. The data used in this study were 27,587 tweets using three keywords, namely "serum," "face serum", and "beauty serum". Tweet data is divided into training data and test data with the number of training data as much as 22,070 tweets and test data as much as 5,518 tweets. The data is categorized using the lexicon senticnet 7 dictionary based on polarity values. The results of the analysis of positive sentiment are 35%, negative sentiment is 63.8%, and neutral sentiment is 1.2%. The classification results using Naïve Bayes Multinomial obtain the highest accuracy value of 80%. The Confusion Matrix results get the highest precision value of 88%, the highest recall of 81%, and the highest f1-Score of 86%.
Sistem Pemantau Siklus Haid Sebagai Media Manajemen Kesehatan Reproduksi Menggunakan Metode Forward Chaining dan Certainty Factor Dimas Alva Rizki; Supriyono Supriyono; Feri Wibowo; Muhammad Hamka
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1064

Abstract

The lack of understanding regarding the normal limits of physiological menstrual parameters leads to delayed detection of reproductive health disorders. Current conventional tracking applications generally focus on date prediction without analyzing accompanying symptoms. This research provides a technical contribution in the form of an expert system design for the early diagnosis of menstrual disorders based on four basic physiological variables: menstrual duration, cycle length, blood volume, and pain symptoms. The system is built using the Forward Chaining method to map the diagnostic inference flow, and the Certainty Factor (CF) to calculate the percentage of the expert's confidence level in the initial medical conclusion. Rule base validation was conducted with a general medical expert as a reference for early-stage screening (Amenorrhea, Oligomenorrhea, Polymenorrhea, Hypermenorrhea, Hypomenorrhea, Dysmenorrhea, and Normal). Black Box functionality testing shows that the system logic runs validly according to the static rule boundaries. Evaluation using the System Usability Scale (SUS) on 30 respondents resulted in a score of 83, indicating that the application has an excellent level of usability. As an early detection prototype, this system focuses on presenting diagnostic probabilities based on expert certainty, although continuous clinical validity testing using a Confusion Matrix remains necessary to measure medical accuracy comprehensively.
Co-Authors Dhani Ratna Sari Dimas Alva Rizki Feri Wibowo Naila Alfatari Supriyono Supriyono