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All Journal Jurnal Teknologi Industri Pertanian E-jurnal Agro-Industri Indonesia Jurnal Manajemen dan Agribisnis Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Agrointek Indonesian Journal of Business and Entrepreneurship (IJBE) Sinergi Journal of Regional and City Planning Journal of ICT Research and Applications agriTECH Scientific Journal of Informatics Industria: Jurnal Teknologi dan Manajemen Agroindustri Jurnal Sistem dan Manajemen Industri Faktor Exacta JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknologi Agro-Industri International Journal of Supply Chain Management Kilat Indonesian Journal of Electrical Engineering and Computer Science Airlangga International Journal of Islamic Economics and Finance SEAS (Sustainable Environment Agricultural Science) JURNAL TEKNIK INDUSTRI Jurnal Sistem Informasi Buletin Ilmiah Litbang Perdagangan
Taufik Djatna
Departemen Teknologi Industri Pertanian, Fakultas Teknologi Pertanian, Institut Pertanian Bogor, Kampus Institut Pertanian Bogor Darmaga Bogor 16680
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences Transportation Other

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework Ahmad Luky Ramdani; Taufik Djatna; Heru Sukoco
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.4624

Abstract

The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessibility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users according to values of each characteristic. Thus, user influence was indicated as user that was not influenced by other users in any combination of skyline objects. The use of MapReduce framework model in identification and selection stage, support whole operation where Twitter had big size data and rapid changes. The results in identification and selection of user influence exhibited that MapReduce framework minimized the execution time, whereas in parallel skyline query could reveal user influence on the data.
Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes Aisah Rini Susanti; Taufik Djatna; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.4284

Abstract

Telecommunication users are rapidly growing each year. As people keep demanding a better service level of Short Message Service (SMS), telephone or data use, service providers compete to attract their customer, while customer feedbacks in some platforms, for example Twitter, are their souce of information. Multinomial Naïve Bayes Tree, adapted from the method of Multinomial Naïve Bayes and Decision Tree, is one technique in data mining used to classify the raw data or feedback from customers.Multinomial Naïve Bayes method used specifically addressing frequency in the text of the sentence or document. Documents used in this study are comments of Twitter users on the GSM telecommunications provider in Indonesia.This research employed Multinomial Naïve Bayes Tree classification technique to categorize customers sentiment opinion towards telecommunication providers in Indonesia. Sentiment analysis only included the class of positive, negative and neutral. This research generated a Decision Tree roots in the feature "aktif" in which the probability of the feature "aktif" was from positive class in Multinomial Naive Bayes method. The evaluation showed that the highest accuracy of classification using Multinomial Naïve Bayes Tree (MNBTree) method was 16.26% using 145 features. Moreover, the Multinomial Naïve Bayes (MNB) yielded the highest accuracy of 73,15% by using all dataset of 1665 features. The expected benefits in this research are that the Indonesian telecommunications provider can evaluate the performance and services to reach customer satisfaction of various needs.
Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means Irfan Wahyudin; Taufik Djatna; Wisnu Ananta Kusuma
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2385

Abstract

In Small Medium Enterprise’s (SME) financing risk analysis, the implementation of qualitative model by giving opinion regarding business risk is to overcome the subjectivity in quantitative model. However, there is another problem that the decision makers have difficulity to quantify the risk’s weight that delivered through those opinions. Thus, we focused on three objectives to overcome the problems that oftenly occur in qualitative model implementation. First, we modelled risk clusters using K-Means clustering, optimized by Pillar Algorithm to get the optimum number of clusters. Secondly, we performed risk measurement by calculating term-importance scores using TF-IDF combined with term-sentiment scores based on SentiWordNet 3.0 for Bahasa Indonesia. Eventually, we summarized the result by correlating the featured terms in each cluster with the 5Cs Credit Criteria. The result shows that the model is effective to group and measure the level of the risk and can be used as a basis for the decision makers in approving the loan proposal. 
Sentiment Mining of Community Development Program Evaluation Based on Social Media Siti Yuliyanti; Taufik Djatna; Heru Sukoco
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.4633

Abstract

It is crucial to support community-oriented services for youth awareness in the social media with knowledge extraction, which would be useful for both government agencies and community group of interest for program evaluation. This work provided to formulate effective evaluation on community development program and addressing them to a correct action. By using classification based SVM, evaluation of the achievement level conducted in both quantitative and qualitative analysis, particularly to conclude which activities has high success rate. By using social media based activities, this study searched the sentiment analysis from every activities comments based on their tweet. First, we kicked off preprocessing stage, reducing feature space by using principle of component analysis and estimate parameters for classification purposes. Second, we modeled activity classification by using support vector machine. At last, set term score by calculating term frequency, which combined with term sentiment scores based on lexicon.The result shows that models provided sentiment summarization that point out the success level of positive sentiment.
A Mobile Ecotourism Recommendations System Using Cars-Context Aware Approaches Neny Rosmawarni; Taufik Djatna; Yani Nurhadryani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 4: December 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i4.1209

Abstract

The requirements to fullfill mobility of ecotourism activities have been urgent to support each traveler with the mobile gadget application. The objective of this research is to develop an application of recommendation system based on online user personalization. This application provided features to recommendation of ecotourism based on profile user and current location, then supplied information about distance and facilities in each ecotourism place. The main of computation worked online which was based on approach called as CARS (Context Aware Recommender Systems) algorithm. The result showed that the application framework succeeded to give appropriate recommendations and explaination on a mobile platform both in the form of profile based spatial data and user preferences.
A Sentiment Knowledge Discovery Model in Twitter’s TV Content Using Stochastic Gradient Descent Algorithm Lira Ruhwinaningsih; Taufik Djatna
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.2671

Abstract

The use of social media that the explosive can be a rich source for data mining. Meanwhile, the development of television programs become increased and varied so motivate people to make comments on it’s via social media. Social network contains abundant information which is unstructured, heterogeneous, high dimensional and incremental in nature. Abundant data can be a rich source of information but it is difficult to identify manually. The contributions of this research are to perform preprocessing to address unstructured data, a lot of noise and heterogeneous; find patterns of information and knowledge of social media user activities in the form of positive and negative sentiment on twitter TV content. Some methodologies and techniques are used to perform preprocessing. They are eliminates punctuation and symbols, eliminates number, replace numbers into letters, translation of Alay words, eliminate stop word and Stemming Porter Algorithm. Methodology of this study was used Stochastic Gradient Descent (SGD).The text that has been through preprocessing produces a more structured text, reducing noise and reducing the diversity of text. So, preprocessing affect to the correctly classified istances and processing time. The experiment results reveal that the use of SGD for discovery of the positive and negative sentiment tends to be faster for large data or stream data. Correctly classified instance with a maximum of 88%.
A Cellular Automata Modeling for Visualizing and Predicting Spreading Patterns of Dengue Fever Puspa Eosina Hosen; Taufik Djatna; Helda Khusun
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i1.2404

Abstract

A Cellular Automata (CA) model is used for visualizing and predicting spreading pattern of the disease. The main problem of this model is how to find a function that represents an update rule that changes the state of a cell in time steps affected by neighborhood. This research aims to develop visualization and prediction model of the spreading patterns of Dengue Hemorrhagic Fever. The contribution of our study is to introduce a new approach in defining a probabilistic function that represents CA transmission rule by employing Von Neumann neighborhood and the Hidden Markov Model (HMM). This study only considered an infective state which dedicated particular attention to the spatial distribution of infected areas. The infected data were devided into four categories and change the definition of a cell as an area. The evaluation was conducted by comparing the results of the proposed model to that of one yielded by a Susceptible-Infected-Recovered (SIR) model. The evaluation result showed that the CA model was capable of generating patterns that similar to the patterns generated by SIR models with a similarities value of 0.95.
Agent Based Modeling on Dynamic Spreading Dengue Fever Epidemic Heti Mulyani; Taufik Djatna; Imas Sukaesih Sitanggang
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 3: September 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i3.4511

Abstract

Agent based model (ABM) is a computational model for simulation, behavioral representation and interaction of autonomous agents. The main problem definition related to how to make a dynamic model of dengue fever with consideration of their behavioral and interaction agent. This paper aims to develop interactive behavioral agents and related simulation models for such dynamic spreading dengue fever epidemic. This model construction consists of two agents, namely a human agent as a host and mosquito as a vector, where temperature and humidity are the environmental parameters. These environmental parameters deployed data and information from National Meteorology Climatology and Geophysics agency and supported by recent community health data of Bogor region. The verification stage evaluated model performance of two periods between January to June and between July to December 2015 showed the fitness of the model. During simulation stage where 100 humans agent and 10 mosquitoes agent were observed, indicating the decreasing of mosquito by 26.3% and the number of infected human decrease to 16% from the period of January until June to July until December 2015 respectively. These evaluation results showed that the agent based model results succeeded to follow a similar trend of decreasing pattern as actual data.
Co-Authors Afifah Nur Arfiana Agus Buono Agus Mulyana Aisah Rini Susanti Akhmad Fatikhudin Andri Agung Riyadi Annisa Annisa Asep Herman Suyanto Basrum Basrum Betaubun, Kamilius D Dadan Kusdiana Delfitriani Dianawati Dianawati Dianawati Dinar Ajeng Kristiyanti Ditdit N Utama E Gumbira Said Eddy Prasetyo Nugroho Erliza Hambali Erliza Noor Erna Rusliana Muhamad Saleh Erni Krisnaningsih Fadly Akbar Saputra Faqih Udin Galih Kurniawan Sidik Galih Kurniawan Sidik Hasbi Rahma Yani Helynda Mulya Arga Retha Hendra Utama Heru Sukoco Hidayat, Agriananta Fahmi Ifri Handi Lubis Imas Sukaesih Sitanggang Indah Yuliasih Irfan Wahyudin Irman Hermadi Irwansyah Saputra Irwansyah Saputra Irzaman, Irzaman Kamilius Deleles Betaubun Khusun, Helda Laksana Tri Handoko Lira Ruhwinaningsih M Amirul Ghiffari M. Syamsul Maarif Machfud Machfud Marimin , Ma’ruf Pambudi Nurwantara Muhammad Romli dan Suprihatin Andes Ismayana Muslich Muslich Nastiti S Indrasti Nastiti S.I. Nastiti Siswi Indrasti Neny Rosmawarni Nina Hairiyah Paduloh Paduloh Petir Papilo Puspa Eosina Rahmat Fadhil Rahmat Wahyudi Nasution Ramdani, Ahmad Luky Riki Ruli A. Siregar Riki Ruli A. Siregar, Riki Ruli A. Rina Fitriana Rina Fitriana Ruhul Amin Sambas Sundana Sapta Rahardja Sarinah Sarinah Sergius Sarmose Manggara Putra Sarmose Manggara Putra Silmi Azmi Sitanggang, Imas S. Siti Yuliyanti, Siti Sony Hartono Wijaya Sukardi Sukardi Sukardi Sukardi Sukardi Sukardi Suprihatin Suprihatin Tanti Novianti Teniwut, Wellem Anselmus Thabed Tholib Baladraf Ummi Safrianti Vonny Setiaries Johan Windi Habsari Winnie Septiani Wisnu Ananta Kusuma Yandra Arkeman Yandra Arkeman Yani Nurhadryani Yogi Purna Rahardjo Yusianto Rindra Zulfikar Dabby Anwar