Article Per Year (5 Year)
p-Index From 2021 - 2026
2.148
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Adinugroho, Sigit
Brawijaya University, Malang
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 79 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 79 Documents
Search

 4   5   6   7   8 
Implementasi Algoritma Fuzzy K-Nearest Neighbor (FK-NN) Untuk Penentuan Kualitas Mutu Air: Implementation Of The Fuzzy K-Nearest Neighbor (FKN-NN) Algorithm For Determining Water Quality Yose Parman Putra Sinamo; Sutrisno Sutrisno; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Water has an important role in maintaining life. Without water, humans cannot carry out their daily activities, because water is an inseparable part of their daily activities. Water that is good for humans is water that has met the requirements as suitable water for daily use. For this reason, there have been many studies on water classification in determining water quality. However, in determining the results obtained, the accuracy is less than satisfactory and can be improved again. In determining the classification of water, the K-Nearest Neighbor (FK-NN) fuzzy method is used. Some of the attributes or parameters that will be used in this research are the degree of acidity (pH), TDS, NO2, NO3, hardness, chloride, manganese. There are several tests carried out including testing the k value, the distribution of data ratios, and the distribution of data classes. From this test, the accuracy value is 95.52%, with the data ratio level consisting of 70% training data and 30% test data, with a K value of 10.At the level of data class distribution, 80% accuracy is obtained with the distribution of test data classes using 20 data with class 0 is 10 and class 1 is 10. From the accuracy obtained, it is concluded that the accuracy value obtained is much greater than the accuracy of previous research which is only around 78.70% and 85.70% in the Support Vector Machine and Naive Bayes methods..
Analisis Sentimen pada Ulasan Aplikasi Mobile JKN Menggunakan Metode Maximum Entropy dan Seleksi Fitur Gini Index Text Muhammad Mauludin Rohman; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mobile JKN application is a form of BPJS Kesehatan's commitment in providing services and ease of access for BPJS Kesehatan users. BPJS Kesehatan in organizing the health insurance program since 2014, can be assessed how the people of Indonesia make use of health insurance implementation facilities through the JKN Mobile application based on user reviews of the application. Sentiment analysis needs to be done to analyze reviews provided by app user. This study used the Maximum Entropy classification method coupled with the Gini Index Text for feature selection. Sentiment analysis consists of data collection process, text preprocessing, word weighting with raw tf, followed by feature selection using Gini Index Text, then classification using Maximum Entropy with features obtained from the previous feature selection. The results of this study are that the best accuracy value is obtained when using the number of features or threshold of 80%, with a value of evaluation as an accuracy of 85,36%, a precision of 92,18%, a recall of 75,59%, and f-measure of 82,85%.
Analisis Emosional Pelajar terhadap Pembelajaran Daring Dengan Menggunakan Latent Semantic Indexing (LSI) dan N-Gram Afif Musyayyidin; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the industrial era 4.0 a lot affects human activities, especially among students. Technology applied in the world of education is online learning. Online learning is a learning method implemented in communication media both asynchronously both text and video. In order for this learning to be more effective and much better in the future, they provide a place to provide input or feedback in the form of criticism and suggestions on social media such as YouTube, Twitter and Facebook. To find out whether online learning is getting more effective, a student emotional analysis is carried out on online learning. In this study, the Latent Semantic Indexing (LSI) method was used in classifying the emotional of students and added the N-Gram method in word selection. The process in this emotional analysis includes data collection, text preprocessing which is useful in producing clean data, N-gram, weighting using the term weighting method, Single Value Decomposition (SVD), Latent Semantic Indexing, Vector Support Machine (VSM) which results in a classification process. . The data used in this study are primary data sourced from social media such as Youtube, Twitter and Facebook. The best results occur when the N-Gram is a combination or combination. From the 5 Fold, it was obtained an average accuracy of 77%, precision 76%, recall 78% and f-measure 77%.
Prediksi Volume Penggunaan Air Bulanan Kota Batu Menggunakan Metode Extreme Learning Machine (ELM) Muhammad Alif Fahrizal; Sigit Adinugroho; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Seiring bertambahnya penduduk, juga selalu beriringan dengan bertambahnya kebutuhan dalam menunjang kehidupan sehari-hari. Salah satu kebutuhan tersebut adalah air. Kota Batu, sebagai kota wisata dengan jumlah penduduk yang menetap selalu berubah-ubah yang menyebabkan volume air yang digunakan juga selalu berubah. Sehingga dari permasalahan tersebut dibutuhkan prediksi volume penggunaan air bulanan pada Kota Batu untuk menyelaraskan dengan volume air yang diproduksi. Dalam penelitian ini dilakukan beberapa proses untuk melakukan prediksi yaitu proses preprocessing pada data yang digunakan, dilanjutkan dengan perhitungan nilai prediksi menggunaan data sebelumnya pada model jaringan Extreme Learning Machine (ELM), dan terakhir dihitung nilai evaluasi hasil prediksi menggunakan Root Mean Squared Error (RMSE). Berdasarkan proses pengujian yang telah dilakukan pada model jaringan ELM, diperoleh rata-rata nilai evaluasi sebesar 16437,5 ketika digunakan 6 input neuron, 5 hidden neuron dan 80%:20% untuk pembagian data latih dan data uji. Dari nilai evaluasi tersebut dianggap belum cukup baik. Hal ini dikarenakan jumlah data yang digunakan dalam proses training pada jaringan ELM masih terlalu sedikit sehingga jaringan tersebut masih belum memahami pola data secara keseluruhan.
Analisis Sentimen Ulasan Pengunjung Simpang Lima Gumul Kediri menggunakan Metode BM25 dan Neighbor-Weighted K-Nearest Neighbor Inosensius Karelo Hesay; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Simpang Lima Gumul (SLG) is a monument that has become an iconic building as well as a tourist destination in Kediri. Visitors who come can provide reviews on Google Review SLG to help the manager know the advantages and disadvantages of existing infrastructure. However, the SLG manager does not have a system that can automatically classify positive and negative reviews. This problem can be solved by using a sentiment analysis system. The sentiment analysis system used in this study uses Neighbor-Weighted K-Nearest Neighbor (NWKNN) and BM25 methods. The stages of this system include preprocessing process, weighting TF-IDF, ranking using BM25, and classification process using NWKNN. The number of data used is 1000 data, with the division of 800 training data and 200 test data. The test was carried out using 5-fold cross validation to test the k and exponential values in the NWKNN method and the k1 and b values ​​in the BM25 method. Based on the tests carried out on each tested parameter, it was found that the best value for the parameter value k1 = 1.2, b = 0.5, k = 20, and exponent = 2. The combination of these parameter values ​​produces an average value precision of 0.9509, recall of 0.9589, accuracy of 0.93, and f-measure of 0.9548.
Penentuan Tata Letak Produk menggunakan Algoritma FP-Growth pada Toko ATK Muhammad Yudho Ardianto; Sigit Adinugroho; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stationeries are one of the basic needs in a workspace such as the office and most predominantly in education such as schools. During the beginning of the school calendar, the stationery stores are usually overcrowded by buyers. However, in these times of pandemics people tend to save money by restricting themselves from buying things. As a result, sales tend to drop as fewer people are willing to spend money on goods. One of the ways to increase sales is to observe the buyer's transactions. All of the transaction data are usually kept as an archieve in the stores. On the other hand, the transaction data of the buyers have informations which can be extracted using data mining techniques, such as information about the association rule in the consumer purchases. By understanding the habitude of the consumers, stores are able to consider on the arrangement of their goods. The FP-Growth algorithm which is being used in the shopping cart system will be able to help in developing the marketing strategy as it would observe the associations between items. The FP-Growth algorithm has a sequence of data collection, frequency counter, transaction data rearrangement, tree formation, and frequent item search. From testing the minimum support of 5%, 8 association rules are produced on which 3 of them has a confidence rate above 5%. Subsequently, there are 34 association rules with lift values above 1. The higher of the minimum support and minimum confidence values, the fewer combinations of association rules will be generated.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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 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 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 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 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 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