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All Journal STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Clean Energy and Smart Technology
Iman Ahmad Ihsannuddin
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PERBANDINGAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE UNTUK ANALISIS SENTIMEN PADA ULASAN PENGGUNA APLIKASI LINKEDIN Gishella Septania Al-Husna; Dian Asmarajati; Iman Ahmad Ihsannuddin; Rina Mahmudati
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 3 No. 2 (2024): Mei
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v3i2.3602

Abstract

Dengan semakin sedikitnya informasi lowongan pekerjaan dalam bentuk cetak, teknologi informasi yang berkembang pesat membuat lowongan pekerjaan lebih mudah ditemukan secara digital melalui aplikasi, media sosial, atau website. Namun, lowongan pekerjaan dari sumber yang tidak jelas dapat menimbulkan penipuan. LinkedIn adalah salah satu aplikasi terpercaya untuk mencari lowongan pekerjaan. Penelitian ini membandingkan dua metode dalam analisis sentimen ulasan pengguna LinkedIn dari Google Play Store, yaitu Naïve Bayes dan Support Vector Machine (SVM). Data dikumpulkan melalui web scraping dan diproses dengan text pre-processing yang mencakup data cleaning, case folding, stopword removal, tokenizing, dan stemming. Hasil menunjukkan bahwa Naïve Bayes menghasilkan akurasi 88%, precision 88%, recall 85%, dan f1-score 86%, sementara SVM menghasilkan akurasi 90%, precision 89%, recall 88%, dan f1-score 88%. SVM terbukti lebih efektif dalam analisis sentimen dibandingkan Naïve Bayes.
IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK (CNN) ALGORITHM IN MOBILE APPLICATION-BASED VOICE EMOTION CLASSIFICATION SYSTEM Naufal Ammar Raihan; Muhamad Fuat Asnawi; Iman Ahmad Ihsannuddin; Nahar Mardiyantoro; Muhammad Alif Muwafiq Baihaqy
Clean Energy and Smart Technology Vol. 4 No. 2 (2026): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v4i2.211

Abstract

The ability of machines to recognize emotions from voice is known as Speech Emotion Recognition (SER). This study developed a voice emotion classification system using a Convolutional Neural Network (CNN) and implemented it in the form of an Android mobile application. The main problem raised is how to recognize human emotions through voice signals accurately, efficiently, and in real-time on mobile devices. The study was conducted with two training stages, namely pre-training using the RAVDESS dataset and fine-tuning with the IndoWaveSentiment dataset. Audio data was converted into a 128×128×1 Mel-spectrogram to be input to the CNN. The CNN model consists of three convolution and pooling blocks, as well as dense and softmax layers. After training, the model was converted to TensorFlow Lite format and integrated with the Android application through a client-server architecture using Flask. The test results showed that the system was able to recognize neutral, happy, disappointed, and surprised emotions with a high level of accuracy both on test data but not as good on live recorded voice. The system also features a SQLite-based history feature. Test results showed 96% accuracy on external test data and 55% on live recorded audio, with an average accuracy of 75.5%. This indicates the model performs very well in structured conditions, but still needs improvement for real-world input.
Co-Authors dian asmarajati Gishella Septania Al-Husna muhamad Fuat Asnawi Muhammad Alif Muwafiq Baihaqy Nahar Mardiyantoro Naufal Ammar Raihan Rina Mahmudati