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All Journal Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan MIND (Multimedia Artificial Intelligent Networking Database) Journal
SHABIRA, DIYANK
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Computer Science & IT Medicine & Pharmacology

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Deteksi Seksisme Online menggunakan Support Vector Machine dan Naïve Bayes SHABIRA, DIYANK; MADENDA, SARIFUDDIN; SIAGIAN, AL HAFIZ AKBAR MAULANA; RIYANTO, SLAMET
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 8, No 2 (2023): MIND Journal
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v8i2.254-266

Abstract

AbstrakSeksisme online menjadi topik penting di media sosial yang mempengaruhi perkembangan internet, menimbulkan efek negatif dan menjadi ancaman serius bagi wanita yang menjadi target. Penelitian ini menggunakan machine learning untuk mendeteksi seksisme pada kalimat bahasa Inggris. Algoritma yang digunakan adalah Support Vector Machine dan Naive Bayes. Grid search diterapkan pada model untuk mencari kombinasi hyperparameter terbaik sehingga menghasilkan skor terbaik. Pelatihan dibagi menjadi dua tugas, yaitu (1) pelatihan model menggunakan data tanpa penanganan imbalanced dan (2) pelatihan model menggunakan data yang telah dilakukan SMOTE. Hasil dari pelatihan model menunjukkan model SVM+SMOTE menghasilkan rata-rata skor F1 terbaik paling tinggi yaitu sebesar 0,96. Pengujian menggunakan data uji menunjukkan model SVM+SMOTE menghasilkan skor F1 tertinggi, yaitu sebesar 0,90 dengan 1467 kalimat diklasifikasikan benar 'not sexist’, 47 kalimat ‘not sexist’ diklasifikasikan sebagai ‘sexist’, 189 kalimat ‘sexist’ diklasifikasikan benar dan 297 kalimat ‘sexist’ diklasifikasikan sebagai ‘not sexist’.Kata kunci: Seksisme, Deteksi, SVM, Naive Bayes, SMOTEAbstractOnline sexism has become a significant issue on social media, impacting internet progress and posing a serious threat to targeted women. This research uses machine learning to detect sexism in English sentences. The algorithms used are Support Vector Machine and Naive Bayes. Grid search is applied in the model to find the best combination of hyperparameters to produce the best score. The training is divided into two tasks: (1) training the model using unhandle the imbalanced data and (2) training the model using data with SMOTE. The training results show that the SVM+SMOTE model produces the highest average best F1 score is 0.96. The testing results show that the SVM+SMOTE model produces the highest F1 score is 0.90 with 1467 sentences correctly classified as 'not sexist', 47 'not sexist' sentences classified as 'sexist', 189 sentences classified as 'sexist' correctly and 297 'sexist' sentences were classified as 'not sexist'.Keywords: Sexism, Detection, SVM, Naive Bayes, SMOTE
Development of Health Educational Game Application “Worm Free” Based on Android Guspianto, Guspianto; Humaryanto; Yun Nina , Ekawati; Mohamad , Ilhami; Shabira, Diyank
Jambi Medical Journal : Jurnal Kedokteran dan Kesehatan Vol. 11 No. 2 (2023): Jambi Medical Journal: Jurnal Kedokteran dan Kesehatan Special Issues: Jambi M
Publisher : FAKULTAS KEDOKTERAN DAN ILMU KESEHATAN UNIVERSITAS JAMBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jmj.v11i2.24993

Abstract

Background: Strategies for preventing and controlling worms are carried out by increasing PHBS behavior from an early age. Health promotion efforts such as health education for student have been carried out, one of which was through the game method (gamification) which was able to stimulate various senses, attract and be liked by children. In line with the development of communication and information technology, digital-based educational models were very necessary and helpful in improving children's thinking patterns, creativity, and ability to obtain health information. This study aims to develop a digital game application "worm-free" based on Android. Method: This studied conducted Research and Development (R&D) design with five stages namely needs analysis, product design development, validation, revision, limited trial and field test. Result: The validation results of media experts and material experts as well as limited trials concluded that the digital game product was informative, easy to access and use, and interesting so it was very feasible to use and proven to increase PHBS knowledge for Helminthiasis prevention in primary school students. Conclusion: It is recommended that digital game health education "Worm Free" based on Android can be widely utilized by the community, especially elementary school children to increase PHBS knowledge and behavior to prevent worms from early age.
Co-Authors Guspianto Guspianto Humaryanto Mohamad , Ilhami Sarifuddin Madenda SIAGIAN, AL HAFIZ AKBAR MAULANA Syamsiar, Syamsiar Yun Nina , Ekawati