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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Artificial Intelligence Research Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Rekomendasi Aksi Saham dengan Pendekatan Teknikal pada PT Telekomunikasi Indonesia Tbk (TLKM) menggunakan Algoritme Learning Vector Quantization (LVQ) 2.1 Tri Kurniawan Putra; Sigit Adinugroho; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stocks is a tool for buying or selling transactions in the capital market. In trading, the investor always wants profits that have low risk of failure. Therefore, an analysis is needed to get recommendations that support the stock. The results of the analysis will provide recommendations that can be used by investors to buy shares, wait, or sell their stock. Classification algorithm can used for analysis, one of them is Learning Vector Quantization. The technical approach factors that become parameters in this study consist of opening price, highest price, lowest price, closing price, volume, adj. closed, and the proportion of changes. In this study, the researcher used the Learning Vector Quantization (LVQ) 2.1 algorithm. The process starts with the initialization of data input. Then do the normalization process. Determine the winning network, update its weight and reduce the value of α, until it reaches a certain epoch or value. Tests was performed using several parameters to determine the effect of those parameters on accuracy. The best test was obtained by using training data as much as 175 training data, the value of learning rate is 0.1 and 1000 iteration produced an accuracy value of 63.64%.
Exponential Smoothing untuk Peramalan Jumlah Penjualan Hijab Vie Hijab Store Eky Cahya Pratama; Muhammad Tanzil Furqon; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the modern era like today, advances in information technology have penetrated into various fields, one of which is in the industrial sector which can assist in the decision-making process to forecasting something that will happen in the future. Vie Hijab Store is a home-based business, such as a sewing house, which is concerned in the production and sale of hijab, which has problems stocking fabric as raw material. The process of forecasting the number of sales will be very helpful in regulating the decision-making process when stocking goods. In this study, the method used for forecasting Exponential Smoothing which consists of Single Exponential Smoothing (SES), Double Exponential Smoothing (DES), Triple Exponential Smoothing (TES) methods. Referring to one of the test results on the 4th increase period data sample which represents the situation of an increase in the second year of the Hajj month obtained from the dataset, the best parameter values for khimar products in the TES method are alpha = 0,9, beta = 0,9 and gamma = 0,1 which resulted in a MAPE of 11.47%. As for pashmina products in the TES method with alpha = 0.4, beta = 0.9 and gamma = 0.8 which resulted in a MAPE of 9,22%. Based on the results of all the tests of the three methods, if a comparison is made, it is shown that the majority of the best results are obtained when using the Triple Exponential Smoothing method. Therefore, the Triple Exponential Smoothing method was chosen as the best method for forecasting the number of hijab sales.
Analisis Sentimen terhadap Opini Masyarakat mengenai Kebijakan PSBB menggunakan Metode Naive Bayes dengan Seleksi Fitur Improved Gini Index Kenza Dwi Anggita; Yuita Arum Sari; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesian governments have launched PSBB policy to emphasize the growth rate of COVID-19 cases in Indonesia. This caused a variety of responses from the public, one of them on social media Twitter. public opinion contained on social media Twitter, can help the government to know how the public opinion about psbb policy in Indonesia. This study tried to analyze the public's response about PSBB policy on social media Twitter, through sentiment analysts and classified into three classes, namely positive, negative, and neutral. By using Naive Bayes classification method and Improved Gini Index (IGI) feature selection to reduce the number of features in the classification process. The process on sentiment analysis consists of preprocessing, feature selection using the Improved Gini Index (IGI) method, and classification with Naive Bayes. The results of Naive Bayes classification test without feature selection obtained accuracy of 64%, while the results of classification accuracy test with feature selection using six different threshold values obtained the highest accuracy results at the threshold value of 30%, where there are 70% of the total terms removed and obtained accuracy of 68%.
Co-Authors Afif Musyayyidin Afrizal Aminulloh Afrizal Rivaldi Agus Wahyu Widodo Ahmad Afif Supianto Akhmad Muzanni Safi'i Alan Primandana Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Ananda Fitri Niasita Anggi Gustiningsih Hapsani Arifin Kurniawan Arrizal Amin Arrofi Reza Satria Aulia Rahma Hidayat Ayustina Giusti Bayu Rahayudi Brian Andrianto Budi Darma Setiawan Candra Dewi Cornelius Bagus Purnama Putra Dahnial Syauqy Danang Aditya Wicaksana Daris Hadyan Tisantri Dayinta Warih Wulandari Dese Narfa Firmansyah Dewan Rizky Bahari Dheby Tata Artha Diajeng Ninda Armianti Dwi Novi Setiawan Edy Santoso Eky Cahya Pratama Faizatul Amalia Felicia Marvela Evanita Fitra Abdurrachman Bachtiar Gessia Faradiksi Putri Gilang Pratama Hangga Eka Febrianto Hanson Siagian Humam Aziz Romdhoni Husein Abdulbar Ilham Firmansyah Ilham Firmansyah Imam Cholissodin Inas Hakimah Kurniasih Indah Wahyuning Ati Indriati Indriati Inosensius Karelo Hesay Irwin Deriyan Ferdiansyah Iskarimah Hidayatin Kenza Dwi Anggita Khairul Rizal Krishnanti Dewi Lailil Muflikhah Listiya Surtiningsih Lukman Hakim M. Ali Fauzi Mahendra Okza Pradhana Mayang Panca Rini Melati Ayuning Lestari Moch. Yugas Ardiansyah Mohammad Angga Prasetya Askin Muhammad Alif Fahrizal Muhammad Dio Reyhans Muhammad Dzulhilmi Rifqi Bassya Muhammad Iqbal Pratama Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Sholeh Hudin Muhammad Tanzil Furqon Muhammad Yudho Ardianto Muria Naharul Hudan Najihul Ulum Naziha Azhar Nendiana Putri Nurhana Rahmadani Putra Pandu Adhikara Putra Pandu Adikara Putra Pandu Adikara Rahman Syarif Randy Cahya Wihandika Randy Cahya Wihandika Ratna Ayu Wijayanti Regina Anky Chandra Ridho Ghiffary Muhammad Rizal Maulana Rizky Adinda Azizah Salsabila Insani Salsabila Multazam Sarah Yuli Evangelista Simarmata Shima Fanissa Siti Mutrofin Sukma Fardhia Anggraini Sulaiman Triarjo Supraptoa Supraptoa Sutrisno Sutrisno Tibyani Tibyani Tri Kurniawan Putra Tri Rahayuni Utaminingrum, Fitri Wahyu Rizki Ferdiansyah Yohana Yunita Putri Yose Parman Putra Sinamo Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari