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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Impresi Indonesia
Aryuda, Aryuda
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Religion Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Social Sciences

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LEGAL PROTECTION OF CONSUMERS REGARDING ILLEGAL ONLINE COSMETIC DISTRIBUTION IS REVIEWED FROM LAW NO. 8 OF 1999 CONCERNING CONSUMER PROTECTION Indah, Auchia; Aryuda, Aryuda
Jurnal Impresi Indonesia Vol. 3 No. 1 (2024): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v3i1.4516

Abstract

The writing of this scientific paper entitled "Legal Protection of Consumers Regarding the Distribution of Illegal Cosmetics Online given Law no. 8 of 1999 concerning Consumer Protection" aims to find out generally and broadly about cosmetics, why illegal cosmetics can circulate online, the distribution of illegal cosmetics according to applicable law. We need to know what illegal cosmetics mean. Considering the large number of Indonesian women's interest in using beauty products, online business people are flocking to produce their products in various ways, including online marketing. However, on the other hand, due to tight business competition, online business actors act in bad faith by illegally marketing cosmetic products. Just like women, men also use cosmetic products to increase their self-confidence. Many factors cause consumers to use beauty products without knowing the consequences they will get. One of the factors is the need for more consumer education and knowledge regarding illegal cosmetics sold online and sold freely through online sites based on the provisions of the Consumer Protection Law. This research aims to examine and analyze the problem of legal violations for naughty business actors regarding the distribution of cosmetics. This research was carried out normatively, namely library legal research, which refers to consumer legislation. This research focuses on determining legal protection for consumers who use illegal cosmetics sold online. Cosmetics are produced to beautify a person's appearance. However, recently, many cosmetics sold online do not have a circular from BPOM, thus endangering consumers who buy beauty products online.
KOMPARASI BERBAGAI MODEL KLASIFIKASI TEKS UNTUK ANALISIS SENTIMEN KINERJA PELATIH TIMNAS INDONESIA Aryuda, Aryuda; Suryono, Ryan Randy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6535

Abstract

Proses Pelatihan timnas Indonesia mengalami perubahan signifikan dalam beberapa tahun terakhir, terutama sejak kedatangan pelatih Shin Tae-yong pada akhir 2019. Sebelumnya, timnas sering mengalami fluktuasi performa akibat metode pelatihan yang kurang terstruktur dan kurangnya inovasi dalam strategi permainan. Dengan Shin Tae-yong, timnas mulai menerapkan pendekatan pelatihan yang lebih modern, dengan fokus pada teknik dasar, taktik permainan, dan kebugaran fisik pemain. Penelitian ini menganalisis 4.476 data sentimen publik dari pengguna X mengenai kinerja Shin Tae-yong. Peneliti membandingkan tiga model klasifikasi teks, yaitu Naive Bayes, SVM, dan Logistic Regression. Melalui perbandingan ini, penelitian diharapkan dapat menentukan model klasifikasi mana yang lebih baik dalam menganalisis komentar publik terkait kinerja Shin Tae-yong. Dengan menggunakan teknik optimasi SMOTE, data yang digunakan dapat diseimbangkan, di mana pelabelan menghasilkan 4.128 data mayoritas dan 344 data minoritas. Dengan optimasi SMOTE, data sentimen positif dan negatif disesuaikan agar model algoritma dapat bekerja lebih baik. Hasil komparasi menunjukkan bahwa model SVM dan Logistic Regression menghasilkan akurasi yang sama, yaitu 99%, sedangkan Naive Bayes menghasilkan akurasi sebesar 91%. Meskipun demikian, Logistic Regression menunjukkan sedikit keunggulan dalam Confusion Matrix, dengan True Positive (TP) sebesar 1.229 dan True Negative (TN) sebesar 1.222, dibandingkan dengan SVM yang memiliki TP 1.220 dan TN 1.221. Ini menunjukkan bahwa Logistic Regression sedikit lebih baik dalam mengklasifikasikan sentimen dibandingkan dengan Naive Bayes dan SVM.
Co-Authors Indah, Auchia Ryan Randy Suryono