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Streamed Sampling on Dynamic data as Support for Classification Model Silvanie, Astried; Djatna, Taufik; Sukoco, Heru
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 | Full PDF (51.295 KB)

Abstract

Data mining process on dynamically changing data have several problems, such as unknown data size and skew of the data is always changing. Random sampling method commonly applied for extracting general synopsis from very large database. In this research, Vitter’s reservoir algorithm is used to retrieve k records of data from the database and put into the sample. Sample is used as input for classification task in data mining. Sample type is backing sample and it saved as table contains value of id and priority. Priority indicates the probability of how long data retained in the sample. Kullback-Leibler divergence applied to measure the similarity between population and sample distribution. Result of this research is showed that continuously taken samples randomly is possible when transaction occurs. Kullback-Leibler divergence is a very good measure to maintain similar distribution between the population and the sample with interval from 0 to 0.0001. Sample results are always up to date on new transactions with similar skewnes. In purpose of classification task, decision tree model is improved significantly when the changing occurred.
Pembandingan Stabilitas Algoritma Seleksi Fitur menggunakan Transformasi Ranking Normal Djatna, Taufik; Morimoto, Yasuhiko
Jurnal Ilmiah Ilmu Komputer Vol 6, No 2 (2008): Jurnal Ilmiah Ilmu Komputer
Publisher : Jurnal Ilmiah Ilmu Komputer

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Abstract

We address the need for evaluating the ranking robustness on  different classifiers in feature selection algorithm.  We propose a normalized rank transformation metric to compare the stability on classifying target class on training and testing dataset.  Using stability comparison is a promising effort to improve the choice decision on deploying any feature selection algorithms.  We also show relationship between stability and scalability.
A Mobile Ecotourism Recommendations System Using Cars-Context Aware Approaches Rosmawarni, Neny; Djatna, Taufik; Nurhadryani, Yani
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 | Full PDF (51.295 KB)

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.
Performance Improvement Strategy of Supply Chain Management in SEI Galuh Palm Oil Mill, PT Perkebunan Nusantara V Lubis, Ifri Handi; Djatna, Taufik; Novianti, Tanti
Jurnal Manajemen & Agribisnis Vol 15, No 3 (2018): JMA Vol. 15 No. 3, November 2018
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jma.15.3.280

Abstract

Palm oil becomes Indonesia's leading agricultural commodity which is inseparable from competition with CPO producers from other countries. PT Perkebunan Nusantara V (PTPN-V) is a state-owned company engaged in palm oil plantation and processing palm oil fresh fruit into crude palm oil (CPO) and palm kernel (palm kernel). One of the 12 palm oil processing units of PTPN-V is the Sei Galuh Palm Oil Mill (PKS SGH), in the past 5 years the plant has the lowest performance compared to 12 other factories. Therefore, the purpose of this research is to analyze the supply chain of PKS SGH and performance improvement strategy of supply chain management at the PKS SGH through SCOR-AHP approach. The result shows that supply chain strategy in this research is to run lean supply chain strategy because the output is a functional product. So the strategy applied was should low cost strategy (efficient). The result of performance measurements obtained matrix order fulfillment, quality conformity, processing cycle time, and employee costs is ?good?. Then, matrix order fulfillment time, flexibility of capacity increase and cash to cash cycle is ?average?; while matrix supply flexibility and cost of processing were obtained ?poor? results.Keywords: supply chain, SCOR, AHP, crude palm oil (CPO), PTPN-V
A Mobile Ecotourism Recommendations System Using Cars-Context Aware Approaches Rosmawarni, Neny; Djatna, Taufik; Nurhadryani, Yani
Journal of Telematics and Informatics Vol 1, No 2: September 2013
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jti.v1i2.69-77

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.
IDENTIFIKASI DAN EVALUASI RISIKO MENGGUNAKAN FUZZY FMEA PADA RANTAI PASOK AGROINDUSTRI UDANG Nasution, Syarifuddin; Arkeman, Yandra; Soewardi, Kadarwan; Djatna, Taufik
Jurnal Riset Industri Vol 8, No 2 (2014): Teknologi Pengendalian Pencemaran Lingkungan untuk Industri Hijau
Publisher : Badan Penelitian dan Pengembangan Industri

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Abstract

Agroindustri udang dihadapkan pada berbagai masalah yang kompleks dan rentan terhadap gangguan.Untuk dapat mengenali risiko masing-masing pelaku rantai pasok dan memilih tindakan berdasarkan prioritas diperlukan suatu model identifikasi dan evaluasi risiko.Tujuan penelitian ini adalah menghasilkan modelidentifikasidan evaluasirisikorantai pasok udang. Identifikasi risiko akan dilakukan dengan pendekatan what-if analysis dan evaluasi risiko yang dikembangkan menggunakan model fuzzy FMEA, dengan input data dari beberapa ahli dan pelaku rantai pasok udang. Hasil penelitian menunjukkan bahwa pelaku petani mempunyai risiko yang paling tinggi dengan probabilitas sebesar 0,45. jika dibandingkan risiko pada tingkat pedagang pengumpul (0,29) dan risiko agroindustri (0,18). Risiko dominan pada tingkat petani disebabkan oleh kegagalan panen akibat serangan hama dan penyakit. Pada tingkat pengumpul risiko dominan adalah keberadaan dan loyalitas pemasok.Sedangkan pada tingkat prosesor risiko dominan adalah keragaman mutu pasokan dan kontaminasi antibiotik pada komoditi udang. Secara keseluruhan model ini dapat digunakan untuk mengidentifikasi faktor-faktorrisiko dan variabel pada tiap tingkatan rantai pasok serta memilih tindakan prioritas sehingga akan diperolehrekomendasi berupa tindakan yang tepat untukmengantisipasinya. Kata kunci: identifikasi dan evaluasi risiko, rantai pasok udang, fuzzy FMEA
Rancangan Model Performansi Risiko Rantai Pasok Agroindustri Susu dengan Menggunakan Pendekatan Logika Fuzzy Septiani, Winnie; Djatna, Taufik
Agritech Vol 35, No 1 (2015)
Publisher : Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (257.21 KB) | DOI: 10.22146/agritech.9423

Abstract

The critical point of performance and risk supply chain in a dairy product agro-industries was found in the product’s perishable characteristics. Bacterial and antibiotic contaminations have been identified as the major risks. This risks arise from a series of activities strarting from farms, cooperatives and Milk Processing Industry that will affect the entire supply chain performance. This paper aimed to design performance and risk supply chain model for agroindustrial dairy product supply chain risks by using Fuzzy Associative Memories (FAMS) approach. The approach is used to translate a quantity that is expressed using the language (linguistic). The fuzzy logic system provides four basic elements, namely : (i) rule base; (ii) inference engine; (iii) fuzzification; (iv) defuzzification. There are three proposed components in the model, namely : (i) performance profile; (ii) risk profile and (iii) risk exposure, which were expressed in time, cost and quality. The initial stage began with the analysis of invetible exposure risk exposure, including analysis of environmental and configuration characteristics, as well as dairy agro-industries supply chain. The second stage was analysis of evitable exposure risk, while the third stage was to change, risk exposure into time, cost and quality performance units. The second stage generateds risk magnitude of risk as a function of probability and severity, the two value that were measured with Fuzzy Associative Memories (FAMS). The Model, therefore  showed the  impact of emerging risks damage to the agro-industry supply chain, which could be measured and be minimized in order to improve the robustnesss of the supply chain.ABSTRAKTitik kritis dari performansi dan risiko rantai pasok agroindustri susu terletak pada karakteristik produknya yang mudah rusak. Risiko tertinggi yang teridentifikasi pada rantai ini adalah risiko susu terkontaminasi bakteri dan antibiotik. Risiko ini muncul dari rangkaian aktivitas yang terjadi mulai dari peternakan, koperasi dan Industri Pengolahan Susu (IPS) yang akan mempengaruhi performasi rantai pasok keseluruhan. Paper ini bertujuan untuk merancang model performansi dan risiko rantai pasok agroindustri susu dengan menggunakan pendekatan Fuzzy Assosiated Memories (FAMs). Logika fuzzy digunakan untuk menerjemahkan suatu besaran yang diekspresikan menggunakan bahasa (linguistic). Secara umum dalam sistem logika fuzzy terdapat empat buah elemen dasar, yaitu: basis kaidah (rule base), mekanisme pengambilan keputusan (inference engine), proses fuzzifikasi (fuzzification) dan proses defuzzifikasi (defuzzification). Ada tiga komponen yang dipertimbangkan dalam rancangan model yaitu profil performansi, profil risiko dan eksposur risiko dalam ukuran waktu, biaya dan kualitas. Tahap pertama dimulai dengan menganalisis eksposur risiko yang tidak terhindarkan yang meliputi analisis karakteristik lingkungan dan konfigurasi serta karakteristik rantai pasok agroindustri susu. Tahap kedua adalah menganalisis eksposure risiko yang dapat dihindari. Tahap ketiga adalah mengubah eksposur risiko ke dalam ukuran performansi waktu, biaya dan kualitas. Pada tahap kedua dihasilkan magnitude risiko, yang merupakan fungsi dari nilai probabilitas dan severity yang dilakukan dengan menggunakan Fuzzy Assosiated Memories (FAMs).Dengan model ini diharapkan dampak kerusakan dari risiko yang muncul pada rantai pasok agroindustri susu dapat terukur dan dapat diminimasi sehingga dapat meningkatkan ketangguhan (robustnes) dari rantai pasok.
Cluster Analysis for SME Risk Analysis Documents Based on Pillar K-Means Wahyudin, Irfan; Djatna, Taufik; Kusuma, Wisnu Ananta
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 | Full PDF (317.373 KB) | 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. 
A Sentiment Knowledge Discovery Model in Twitter’s TV Content Using Stochastic Gradient Descent Algorithm Ruhwinaningsih, Lira; Djatna, Taufik
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 | Full PDF (242.205 KB) | 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%.
Twitter’s Sentiment Analysis on Gsm Services using Multinomial Naïve Bayes Susanti, Aisah Rini; Djatna, Taufik; Kusuma, Wisnu Ananta
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 | Full PDF (422.528 KB) | 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.