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All Journal Jurnal Ilmiah Matrik Jurnal Eksplora Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTEK MULTINETICS Infotech: Journal of Technology Information Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal Software Engineering and Computer Science (IJSECS) Acta Pedagogia Asiana JURNAL TEKNIK INFORMATIKA DAN KOMPUTER The Indonesian Journal of Computer Science
Kamayani, Mia
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Other

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Journal : The Indonesian Journal of Computer Science

 1 
Perbandingan Algoritma SVM Dan Naïve Bayes Pada Analisis Sentimen Penghapusan Kewajiban Skripsi Yunita, Rani; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3415

Abstract

Pada Agustus 2023 Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi membuat peraturan salah satunya menghapus kewajiban skripsi sebagai syarat kelulusan di semua perguruan tinggi di Indonesia. Pro dan kontra saling bertukar tempat di berbagai media sejak peraturan menteri tersebut diumumkan. Banyak yang mendukung kebijakan tersebut tetapi tidak sedikit yang menentang. Dari Issue tersebut, peneliti melakukan analisis sentimen di twitter tentang kebijakan yang menghapus kewajiban skripsi sebagai syarat kelulusan menggunakan 700 data. Penelitian ini membandingkan hasil evaluasi algoritma Support Vector Machine (SVM) dengan Naïve Bayes. Berdasarkan hasil yang diperoleh dari penelitian ini didapatkan 331 sentimen positif serta 369 sentimen negatif dan ditarik kesimpulan bahwa Support Vector Machine (SVM) menjadi algoritma yang terbaik dengan accuracy 80%, recall 83%, precision 76%, dan F1-Score 79%.
Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Teknik Informatika dengan Orange Data Mining Attyyatullatifah, Iqlimah; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3796

Abstract

Penyelesaian studi tepat waktu merupakan indikator penting dalam menilai kompetensi lulusan. Meskipun demikian, muncul tantangan karena tidak semua mahasiswa dapat menyelesaikan studi mereka sesuai jadwal yang telah ditentukan. Penelitian ini mengembangkan model prediksi status kelulusan mahasiswa menggunakan empat algoritma klasifikasi: Decision Tree, Naïve Bayes, K-NN, dan SVM. Data penelitian mencakup 500 data mahasiswa angkatan 2018-2020 di Universitas Muhammadiyah Prof. Dr. Hamka, dengan 60% data latihan dan 40% data uji. Analisis dilakukan menggunakan perangkat lunak Orange Data Mining, dengan evaluasi menggunakan K-Fold Cross Validation (k=5), Confusion Matrix, dan ROC. Hasil analisis menunjukkan bahwa model K-NN memiliki performa tertinggi dengan akurasi 92%, recall 90%, dan presisi 92%. Decision Tree menempati posisi kedua dengan akurasi 90%, presisi 87%, dan recall 90%. SVM mencapai akurasi sebesar 84%, dengan presisi 90%, recall 73%. Sementara itu, model Naïve Bayes menunjukkan akurasi 83%, presisi 80%, dan recall 83%.
Analis Sentimen Aplikasi Maskapai Penerbangan Lion Air Menggunakan Metode SVM dan Naïve Bayes Sulistiawati, Risa; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3836

Abstract

Lion Air App is a flight ticket purchase application launched on October 21, 2014. It can be downloaded and used anywhere, anytime. Lion Air App application is available on the Google Play Store and also the Appstore, which aims to facilitate users in the process of purchasing airplane tickets online. online. In several news articles reporting that Lion Air is the world's worst airline. in the world. However, it needs to be realized that the Lion Air application also has many users who give positive, negative and neutral reviews due to several factors. neutral due to the existence of several reviews presented in the Play Store application. This problem was researched for sentiment analysis to get a customer satisfaction rating for the Lion Air application. Lion Air application with the acquisition of 2000 data. In this research, Support Vector Machine (SVM) calculation and Naive Bayes calculation were compared using 80% training ratio and 20% test ratio. In this consideration, 795 positive opinions and 805 negative opinions were used. used, where Support Vector Machine (SVM) with Bigram features became the most superior method with 99.23% precision. method with 99.23% precision, 83.03% recall, 91.75% accuracy, F-1 score of 90.51%.         
Deteksi hate speech pada kolom komentar TikTok dengan menggunakan SVM Ariska, Amelia; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3982

Abstract

The TikTok application provides numerous features, including the comment section for users to interact with each other. Users can exchange their opinions openly through the comment section. However, as the interaction or exchange of opinions among users increases, the use of hate speech, consciously or unconsciously, remains prevalent. Hate speech refers to actions by an individual or group that can incite criminal acts, thereby harming others. This study aims to identify the use of hate speech in TikTok comment sections using the SVM algorithm and to compare two libraries used in the labeling process to observe the performance of the SVM algorithm model. The labeling process employs a lexicon-based approach. The dictionaries used in this study are the Inset lexicon and VaderSentiment. The SVM algorithm is used as the model to test the evaluation results. The results obtained using the Inset lexicon labeling show an accuracy of 82%, while the second labeling method using VaderSentiment yields an accuracy of 96.21%.
Co-Authors Al Assyam, Hafizh Dhery Arif, Zainul Ariska, Amelia Attyyatullatifah, Iqlimah Ayyubi, Reza Al Azhar, Nur Chalik Azzahra, Nabila Dewy, Avry Liyanah Eka Novianti, Eka Febriawan, Dimas Firman Noor Hasan Komarudin, Ahmad Mahendra, Ristian Muflih, Hilmy Zhafran Pangestu, Faisal Akbar Pratiwi, Anissya Agsani Rahman Malik, Luqman Abdur Rahmi Imanda Siduningrum, Estu Sinduningrum, Estu Suhendra, Renadi Fadino Sulistiawati, Risa Utami, Arneitta Dwicahya Yunita, Rani