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All Journal TEKNIK INFORMATIKA JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan)
Sudianto
Institut Teknologi Telkom Purwokerto
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering Public Health Other

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Journal : Building of Informatics, Technology and Science

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Prediksi Harga Komoditas Pangan Menggunakan Algoritma Long Short-Term Memory (LSTM) Rizki Mugi Setya Adi; Sudianto Sudianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2229

Abstract

Food is a basic need for human survival. The existence of food is influenced by production and selling prices. The problem that exists is that food producers lose out with the dynamics of selling prices. In addition, the low selling price is not commensurate with the production costs that have been spent, especially for food producers in agricultural commodities, namely local farmers. Local farmers lose money because they do not know the price of commodities when selling their agricultural products. In addition, the game of intermediaries causes local farmers to sell their crops at low prices. So from the existing problems, it is necessary to predict commodity prices to help farmers determine the commodity prices before selling their agricultural products to the market. This study aims to predict the price of food commodities, especially in Banyumas, so that local farmers can find the price of commodities before they are sold to the market. The Deep Learning method used is Long Short-Term Memory (LSTM), which can remember a collection of information that has been stored for a long time with time series data. The results obtained, the model can predict food commodity prices. Meanwhile, the prediction model with epoch 50 shows the lowest Root Mean Squared Error (RMSE) with a value of 79.19%
Analisis Kinerja Algoritma Machine Learning Untuk Klasifikasi Emosi Sudianto Sudianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2261

Abstract

Social media is a place to express or share daily activities. Various new events are often discussed on social media, such as on Twitter. Frequently, the conversations conducted by Twitter users when giving a review or opinion have various emotions, such as anger, sadness, fear, or joy. Emotions are difficult to describe the challenges that occur, sometimes leading to multiple interpretations and misunderstandings leading to debates and reporting to the authorities. So this shows that emotions in reviews and opinions are essential for classification because emotions that come from texts are difficult to understand. In addition, the classification of emotions needs to be done to speed up the identification of emotions. The purpose of this study is to find out which algorithm has optimal performance in the classification of emotions. Machine Learning methods are the Naïve Bayes algorithm, Random Forest, and Support Vector Machines; this is done to determine the dominant algorithm in classifying emotions. The results of the modeling and classification using the Random Forest algorithm obtained a dominant accuracy with an accuracy value of 81.3%, followed by the SVM algorithm with an accuracy value of 76.6% and an accuracy value of 79.1% Naïve Bayes algorithm. In addition, from the speed of time in completing the classification, the Random Forest algorithm has the fastest time of 1.27 seconds
Co-Authors Abdul Aziz Akbar Maulana Yusuf Al Fachri, Moh Aminullah Aldean, Muhammad Yusril Alif Aryaputra Alvina Tahta Indal Karim Andini Mulia Kusumaningrum Asti Dwi Sripamuji Ayuningtyas, Puji Bagas Prihatnowo Cahyo Prihantoro Chandra Ayunda Apta Soemedhy Dedy Agung Prabowo Dwi Puspa Martiyaningsih Egidya Zalsabila Hapsari Warih Utami Hasna Nur Hanifah Hermanto, Agyl Restu Hikari Adriansyah Hilmawan, Muhammad David Imada Riski Ramadhanti Jonathan Indra Chelidivano Juan Arton Arton Masheli Julian Saputra Khusnul Khatimah Lasama, Jerry LILIK BUDIPRASETYO M Yoka Fathoni Mahda Laina Arnumukti Martin Parhusip Muhammad Hilmi Kafabi Muhammad Rahaji Jhaerol Naden, Yoga Naufal, Adzan Bari Nawang Anggita Winanti Nicolaus Euclides Wahyu Nugroho Nora Trivetisia Nursatio Nugroho Puspa Wahyuningtias Rafi Wika Ananda Rumpoko Ramadhani, Rafian Reni Dyah Wahyuningrum Reni Dyah Wahyuningrum Risa Riski Amalia Rizki Mugi Setya Adi Sandhy Fernandes Satria Nur Saputro Satria, Muhammad Adhan Hady Siti Khomsah, Siti Siti Maghfiroh Surya Haganta Brema Ginting Tangguh Widodo Tavany Amalia Rizky Tiara Khumaira Putri Tri Ginanjar Laksana Tri Wulandari Utami Uli Ahda Raihan Ummi Athiyah Widi Afandi Yanuar Sabikhi Yehezkiel Nicholas Adanson Yeni Herdiyeni Zarkasih Akhmad