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All Journal Jurnal Teliska Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Journal of Technology Research in Information System and Engineering
Andi Yulia Muniar, Andi Yulia
Program Studi Sistem Informasi, STMIK AKBA, Makassar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Penerapan Jaringan Syaraf Tiruan dan Sistem Pakar untuk Mengidentifikasi Penyakit Pencernaan dengan Pengobatan Herbal Ashari, Ashari; Muniar, Andi Yulia
Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) 2015
Publisher : Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (642.746 KB)

Abstract

Penerapan model integrasi jaringan syaraf tiruan (JST) dan sistem pakar untuk mengidentifikasi penyakit pencernaan dengan pengobatan cara herbal. Metode yang digunakan yaitu model backpropagation dan forward chaining. Backpropagation merupakan metode sistematik untuk pelatihan multiplayer, sedangkan forward chaining merupakan metode inferensi untuk penalaran dari suatu masalah kepada solusinya. Penelitian ini sebagai produk ilmu pengetahuan dan teknologi yang diharapkan dapat memberikan manfaat sebagai konsultan atau instruktur dalam masyarakat umum, dokter dan paramedis pada klinik, puskesmas, rumah sakit maupun dokter praktek, sebagai bahan acuan dan perbandingan untuk pengembangan aplikasi JST dan sistem pakar yang lebih baik dalam pengembangan khazanah keilmuan. Perancangan sistem dilakukan melalui 4 bagian meliputi pengumpulan data, perancangan rules, perancangan proses dan pengujian sistem. Output yang dihasilkan berdasarkan gejala penyakit pencernaan yang kemungkinan terjadi dengan memberikan solusi pencegahan terhadap penyakit berdasarkan hasil inference yang ada dengan pengobatan cara herbal.
APLIKASI VIDEO KONFERENSI BERBASIS WEB DENGAN FITUR FACE RECOGNITION UNTUK VERIFIKASI IDENTITAS PESERTA Fathurrahman; Muniar, Andi Yulia; Abdul Kadir Parewe, Andi Maulidinnawati; Angriawan, Randy; Yusa Wardhani, Dyah Utari Yusa Wardhani
JURNAL TELISKA Vol 17 No III (2024): Teliska November 2024
Publisher : Teknik Elektro Polsri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14241599

Abstract

This research aims to develop a website-based application that makes it possible to automatically verify the identity of participants in video conferences using facial recognition technology. This application was created to make the participant identification process easier compared to conventional methods which often take longer. The process of creating this application includes several stages, namely literature study to understand relevant technology, user interface design, application design, implementation of the face recognition feature, and application testing using black box and questionnaire methods. Based on the results of this research, the results obtained show that this application is able to verify participant identity automatically through its facial recognition feature and shortens the time for verifying participants. This was obtained through tests that have been carried out with various scenarios to ensure its performance. Testing was carried out with several participants and different conditions, such as the use of face masks and filters, low video quality, and inappropriate participant faces.
EVALUASI PERFORMA METODE LONG SHORT TERM MEMORY (LSTM) DAN RECURRENT NEURAL NETWORK (RNN) PADA ANALISIS SENTIMEN KOMENTAR PENGGUNA APLIKASI KITALULUS Nurzaenab, Nurzaenab; Maslihatin, Tatik; Halid, Agus; Muniar, Andi Yulia; Sabir, Fitriana M.; Parawewe, Andi Maulidinnawati Abdul Kadir; Awaliah, Neneng; Halfiani, Halfiani
JTRISTE Vol 12 No 1 (2025): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/jtriste.v12i1.614

Abstract

Sentiment analysis is one of the essential techniques in natural language processing for identifying user opinions toward a product or service. This study aims to evaluate the performance of the Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN) methods in sentiment analysis of user comments on the Kitalulus application. A total of 2,500 comments collected from user reviews were utilized and underwent several preprocessing stages, including case folding, tokenization, stopword removal, stemming, and padding. The processed data were then trained using both RNN and LSTM models with similar architectural configurations. The experimental results show that the LSTM method outperforms RNN, achieving the highest accuracy of 91.51%, while RNN attained 88.48%. These findings demonstrate that LSTM is more effective in capturing long-term dependencies in textual data, making it more suitable for sentiment analysis of user comments on the application.
Co-Authors Abdul Kadir Parewe, Andi Maulidinnawati Agus Halid, Agus ANGRIAWAN, RANDY Awaliah, Neneng Dyah Utari Yusa Wardhani Fathurrahman Halfiani, Halfiani Maslihatin, Tatik Mochammad Imron Awalludin Muhajirin NURZAENAB, NURZAENAB Parawewe, Andi Maulidinnawati Abdul Kadir Rani, Rahmat Sabir, Fitriana M. Suryadi Syamsu, Suryadi Utami, Pertiwi Nurul