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All Journal Sistemasi: Jurnal Sistem Informasi MIND (Multimedia Artificial Intelligent Networking Database) Journal BACA: Jurnal Dokumentasi dan Informasi
SIAGIAN, AL HAFIZ AKBAR MAULANA
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Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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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
Pemanfaatan dataset ukuran efek (effect size) untuk membangun alternatif model penelitian: Studi kasus minat konsumen membeli kendaraan listrik Putri, Rizkiya Anisyah; Yoganingrum, Ambar; Febriandirza, Arafat; Asmara, Indri Juwita; Rezaldi, Muhammad Yudhi; Tohari, Amin; Prasetyadi, Abdurrakhman; Indrawati, Ariani; Siagian, Al Hafiz Akbar Maulana
BACA: Jurnal Dokumentasi dan Informasi 2024: SPECIAL ISSUE - DATA IN BRIEF FOR REPOSITORI ILMIAH NASIONAL
Publisher : Direktorat Repositori, Multimedia, dan Penerbitan Ilmiah - Badan Riset dan Inovasi Nasional (BRIN Publishing)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/baca.2024.7782

Abstract

This study presents survey data from primary research papers containing effect size data and the number of respondents for several variables influencing consumer interest in purchasing electric vehicles. The survey utilized the Meta-Analytic Structural Equation Modeling (MASEM) approach. Data were collected in June 2021 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify 11 valid papers. This survey aimed to collect effect size values, such as path coefficients and standardized regression weights, from each of the eleven papers. The effect size values collected indicate the magnitude of the relationship between the variables: Attitude, Perceived Behavior Control, Subject Norm, Charging Infrastructure, Environmental Concern, Financial Benefit, External Environment, Marketing Mix, Willingness to Pay a Premium, Incentive Policy Measures, and Perceived Value, and their impact on interest in purchasing electric vehicles. The data set stored in this repository can serve as a reference for researchers and policymakers in developing models and examining the relationship between variables influencing electric vehicles adoption.
Design and Development of Spice Image Classification Application of Zingiberaceae Family with Rapid Application Development Method Nariswari, Fawnia Avissa; Gumay, Marliza Ganefi; Riyanto, Slamet; Siagian, Al Hafiz Akbar Maulana
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3455

Abstract

In the culinary and health fields, spices are known to play a significant role. However, differentiating and identifying spices can often be challenging, especially for students or individuals with impaired sense of smell and taste. Our research builds upon our previous study, which involved designing a spice classification model using the pre-trained EfficientNetB0 model, yielding a testing accuracy of 48%. This study designs and develops an application for classifying images of spices from the Zingiberaceae family using the Rapid Application Development (RAD) method, aimed at addressing the difficulties of manual spice identification. Testing results using a black box technique indicate that the designed application performs well in basic tasks such as opening the interface without significant errors, responding promptly to functional buttons, and displaying user-inputted image results. However, in one of the testing scenarios, the application struggled to accurately classify certain spice images, indicating the need for improvements in the model used for more precise spice image classification.
Co-Authors Abdurrakhman Prasetyadi, Abdurrakhman Ambar Yoganingrum Amin Tohari Febriandirza, Arafat Indrawati, Ariani Indri Juwita Asmara Marliza Ganefi Gumay, Marliza Ganefi Muhammad Yudhi Rezaldi, Muhammad Yudhi Nariswari, Fawnia Avissa Putri, Rizkiya Anisyah Sarifuddin Madenda SHABIRA, DIYANK Slamet Riyanto