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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

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Pengelompokan Wilayah berdasarkan Penyandang Masalah Kesejahteraan Sosial (PMKS) dengan Optimasi Algoritme K-Means menggunakan Self Organizing Map (SOM) Iskarimah Hidayatin; Sigit Adinugroho; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Persons with Social Welfare Problems are people, families, groups or someone who cannot function socially because there is a spiritual, physical or social difficulty. Grouping regions based on PMKS is very important to do to provide an overview of PMKS problems with the policy objectives taken right on target. Self Organizing Map (SOM) algorithm for determining the number of clusters and initial centroids while the K-Means algorithm for determining the cluster end result. The research flow, that is, the data is normalized, then the SOM process then to K-Means, then testing and analysis are carried out. SOM parameter testing using silhouette coefficient obtained the best parameter is the learning rate value of 0.2, beta at 0.8, r (neighboring) of 0, the number of clusters by 2, and epoch by 50. K-Means algorithm optimization using SOM is better than algorithm K-Means based on the silhouette coefficient value. The silhouette coefficient value in SOM is 0.21882702 while K-Means has a value of 0.201911102. Analysis of the results obtained K-Means algorithm optimization with SOM is cluster 1 with a total of 26 districts / cities by having similarities in the variable Social Problem Migrant Workers, Families with social psychological problems, and children who are victims of violence / who are treated wrongly have an average value high average and cluster 0 with the number of 12 districts / cities with similarities in variables other than cluster 1.
Pengelompokan Wilayah Berdasarkan Kesejahteraan Sosial Menggunakan Algoritme Self-Organizing Maps Dengan Perbaikan Missing Value K-Nearest Neighbors Dese Narfa Firmansyah; Sigit Adinugroho; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 7 (2019): Juli 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Persons with Social Welfare Problems (PMKS) are social groups that live below the community welfare line and are one of component for determining policies in East Java. The study aim to find out the characteristics of the region in East Java based on the PMKS dataset. The method proposed in this study is clustering with the Self-Organizing Maps algorithm and K-Nearest Neighbors (KNN) missing value imputation. KNN used to overcome the amount of missing value in PMKS dataset. First, missing value is filled using KNN imputation. Furthermore, the clustering done with training in SOM network and the result of cluster is evaluated using Silhouette Coefficient. The best parameters for SOM are learning rate=0.1; neighborhood coefficient=0.2; max epoch=160 and neuron size=2x2. The best parameter for KNN is K=2. K=2 gives an increase in Silhouette Coefficient value of 3.4% compared to clustering without missing value imputation KNN. Using best parameter, the highest Silhouette Coefficient obtained is 0.351 which categorized as weak structure. The shape of the cluster produced is a cluster with a proportion of 1:37. The five attributes with the highest difference between the two clusters were Neglected Elderly, Homeless and Psychotic Homeless, Scavengers, Beggars and Minority Groups.
Klasifikasi Teks Pengaduan Suara Warga Kabupaten Pasuruan menggunakan Metode Maximum Entropy Mayang Panca Rini; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Suara Warga is a website that it provided by the government of Pasuruan Residence to receive complain from the society. In the application, the admin must pass on the complain to related official manually. To increase the efficient of time, it is needed a classification of the text, Maximum Entropy is a method that is used in this research with Confusion Matrix evaluation method which will count the evaluation from the equal-wont data and the unequal-wont data, with the complain as much 200 data. Before doing the classification, the first step which is done is pre-processing and the next is process of word quality. Classification is done through looking for the opportunity of every word in every document and the result of classification is got based on the higher opportunity result from document class. The result of equal-wont data evaluation produce better result than the result of the unequal-wont data evaluation with the accuration: 89,27%, precision: 92,49%, recall: 89,27% and f-measure: 89,44%.
Rekomendasi Lagu berdasarkan Lirik dan Genre Lagu menggunakan Metode Word Embedding (Word2Vec) Melati Ayuning Lestari; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Listening to songs has become a norm in society, serving many different purposes, and songs are released frequently nowadays, especially by media-service providers. Users need to overcome the struggle of selecting specific songs because of the enormous information provided by media-service providers. The song recommendation model can play an important part in this puzzlement as an automatic song selector, thus improving the user's experience. In this research, the song recommendation model uses Word2Vec Skip-Gram that functions as a query expansion for the sole purpose of finding the desired lyrics by producing a weight for query expansion. TF-IDF is first used to select the words in the lyrics that will be expanded. The model will give a list of 10 recommended songs. The evaluation results of the recommended song list shows the highest average of precision@10 score of 0.584 and the highest Mean Average Score (MAP) score of 0.7278.
Klasifikasi Genre Lagu dengan Fitur Akustik Menggunakan Metode K-Nearest Neighbor Husein Abdulbar; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Song cannot be separated from humans daily activities. When listening to songs humans can focus more on their activities. The rapid development of information on multimedia and electronic devices has led to a dramatic increase in music appreciation and creation. On the one hand this increase encourages people to enjoy songs more. But on the other hand, this increase forced the development of new technologies for the convenience of listening to songs. An example is how someone wants to find a song based on a song that has been heard. Genres classification is one of machine learning techniques that can group songs based on their usefulness. This technique can be used as a function in a system to support other functions, such as song recommendations, special word, or similar song searches. This study will use the K-Nearest Neighbor (K-NN) method as a genre classification technique for songs. To measure the similarity of two songs, a normalized cross correlation (NCC) equation is used to replace the distance calculation equation in the K-NN method. The features that extracted from a song are zero crossing rate, spectral centroid, spectral rolloff, and energy. Data obtained from feature extraction will be normalized using the z-score equation. The test results show that the best evaluation is obtained when the duration is 10, the offset is 120, and K in K-NN is 10. Precision, recall, and f-measure that obtained in this study are precision with a value of 0.637, recall with a value of 0.633, and f-measure with a value of 0.635.
Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dan Lexicon Based Features Arifin Kurniawan; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid development of information technology has resulted in many people writing their opinions on social media as in the KASKUS forum. KASKUS is an online forum site that provides a place to find information and share hobbies. One is called Movies forum which contains discussions about a movie that has been watched. Users writes their opinion about a film whether the film is good or bad. These opinions can be analyzed to determine how the user feedback about the film in order to produce useful output for the filmmaker by perform sentiment analysis to classify opinions into positive or negative classes. The process of sentiment analysis was performed using methods Naive Bayes for classification and Lexicon Based Features to weight the sentiment value of a word. The process starts from text preprocessing, term weighting, Naive Bayes training, and Naive Bayes testing with Lexicon Based Features weighting using Barasa's lexicon. Based on the results of tests performed, using Naive Bayes and Lexicon Features Based method, the values of accuracy, precision, recall, and f-measure were 0.8, 0.8, 0.8 and 0.8. While using the Naive Bayes method without Lexicon Based Features, the values of accuracy, precision, recall, and f-measure are 0.95, 1, 0.9 and 0.9474. So, the use of Naive Bayes and Lexicon Based Features methods still cannot provide better results.
Klasifikasi Hoaks Menggunakan Metode Maximum Entropy Dengan Seleksi Fitur Information Gain Albert Bill Alroy; Putra Pandu Adikara; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 9 (2019): September 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In 2016, Indonesia has 132 million internet users. This number increase to 143 million users in 2017. Internet user can access many things such as chatting services, social media, and e-commerce. There are many people who intentionally make false information known as Hoax. Hoax are information or news that contains uncertain facts or events that have not occured. The problem of spreading Hoax can be reduced by making a system that can classify whether a news is a Hoax or not. The method used in this research is Maximum Entropy with Information Gain Fiture Selection. The amount of data used in this research is 600 articles in Indonesian. There are 372 news articles classified as facts and 228 news articles classifed as Hoax. The amount of best results accuracy in this research is 0,8 with information information gain fiture selection (threshold = 50%), 1 precision, 0,8 recall, and 0,89 f-measure.
Penentuan Waktu Terakhir Penggunaan Ganja Menggunakan Multidimensional Hierarchical Classification Khairul Rizal; Sigit Adinugroho; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Drug abuse in Indonesia increases every year. One type of narcotics that often consumed freely without obtaining permission from the pharmaceutical industry is cannabis which can make the user experience euphoria (excessive joy without cause). The handling of drug addicts can be done through government rehabilitation services and one of the services is giving medical rehabilitation to aburses based on their level of dependency. Therefore, classification of determining last time use of cannabis is carried out which can help in determining the right type of rehabilitation service for abusers. The method that used by researcher is Multidimensional Hierarchical Classification (MHC) because this method focuses on determining the best path in the classification process and using the Naive Bayes Classifier to find probabilities that have high values ​​from the data. Data that used were 1885 secondary data form UCI Machine Learning with the title Drug Consumption which is divided into 7 classes based on the last time use of cannabis. Steps of this research conducting MHC training process and testing process using MHC. Testing process were carried out using 3 testing process, K-Fold Cross Validation with k = 5, testing with overall data and with balanced data. Testing results shows that the highest accuracy value is 42,86% using testing with balanced data.
Klasifikasi Emosi Lagu Berdasarkan Lirik pada Teks Berbahasa Indonesia Menggunakan K-Nearest Neighbor dengan Pembobotan WIDF Diajeng Ninda Armianti; Indriati Indriati; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In a song-making, one of the main component which must be considered is lyric. Lyric in a song play a main part to deliver the emotion or meaning from the songwriter to the listener. Sometimes, the emotion means to delivered by the writer is misinterpreted by the listener. To avoid the misinterpreted song-lyric meaning manually, an automatic classification is needed. Classification is also needed to gain information about the emotion from the songs accurately. One of the method used is K-Nearest Neighbor. Before classifications process, there are several steps need to be done such as text pre-processing and weighting using WIDF method. 108 data used in this research with the ratio 1:5; in which, 18 data used for testing and 90 data used for training with the same amount of data each class. The result from 6 attempts of testing based on random K value shown the best average precision is 0,49 and the best recall is 0,53. Songs classification with WIDF weighting method shown a poor accuracy results for 66%. Ambiguity of the words and amount of data training cause the less optimal result.
Prediksi Keputusan Pelanggan Menggunakan Extreme Learning Machine Pada Data Telco Customer Churn Daris Hadyan Tisantri; Randy Cahya Wihandika; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this modern era many company and institution compete to sell their services like internet and telecommunication that use subscription system to sell their services. Because of that, company must compete via marketing strategy. Main factor for customer to continuously extend their subscription is loyalty. Loyalty have directly proportional with business performance. Because of marketing factor and customer loyalty, many customers changed or stopped their subscription from one and another similar company and makes some company lost their customer and revenue. If company or institution can predict churn, they can anticipate so that customer didn't churn. In this research, the dataset that used for this research is from Kaggle sourced from IBM Sample Data Sets. This dataset consists of 7043 data that have 20 features with two classes yes if the customer churn and no if the customer is not churn. After that, the feature on the dataset that not used will be eliminated with Pearson correlation. After that the data will be trained on Extreme Learning Machine to predict customer will churn or not. Result of this research is the system can get accuracy 76,96%, precision churn 65,45%, precision non churn 78,65%, recall churn 29,38%, recall non churn 94,19%
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