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All Journal STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Clean Energy and Smart Technology
Muhammad Alif Muwafiq Baihaqy
Universitas Sains Al-Qur'an
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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SISTEM INFORMASI PENJUALAN BURUNG KICAU BERBASIS WEBSITE Muhammad Alif Muwafiq Baihaqy; Muhammad Yusa Azmi; Muslim Hidayat
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 1 No. 2 (2022): Mei
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.11 KB) | DOI: 10.55123/storage.v1i2.319

Abstract

Perkembangan teknologi dan internat pada era globalisasi seperti sekarang ini sangat pesat terutama dalam bidang bisnis yang membuat persaingan antar penjual juga semakin ketat. Namun perkembangan teknologi dan internet belum secara merata memasuki berbagai bidang usaha, salah satunya adalah usaha penjualan burung online. Berdasarkan narasumber yang merupakan seorang pengusaha burung kicau, keterbatasan informasi terjadi karena minimnya terjadi komunikasi antara pecinta burung di internet. Keterbatasan lainnya berada di pemanfaatan media penjualan online sebagai akses jual beli burung kicau, dimana pengiriman burung tidak dapat melalui ekspedisi yang tersebar di seluruh Indonesia saat ini karena burung dapat mati. Berdasarkan dari permasalahan diatas, maka dilakukannya penelitian Perancangan Sistem Informasi Penjualan Burung Kicau Berbasis Website dengan menggunakan metode pengiriman Cash on Delivery (COD). Penelitian ini bertujuan untuk mempermudah pembeli dan penjual burung kicau dalam proses jual beli dengan bantuan Sistem Informasi berbasis Website. Sistem ini dapat membantu para pecinta atau pelaku usaha burung kicau untuk mencari dan membeli burung yang diinginkan sehingga para calon pembeli dapat dimudahkan dengan adanya website tersebut. Dengan adanya inovasi ini diharapkan dapat membantu kedua belah pihak yakni pembeli yang dimudahkan untuk mencari dan membeli burung kicau dan pengusaha yang dapat memperluas pasar.
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 Hidayat, Muslim Iman Ahmad Ihsannuddin Muhamad Fuat Asnawi Muhammad Yusa Azmi Nahar Mardiyantoro Naufal Ammar Raihan