Daniel Siahaan
Institut Teknologi Sepuluh Nopember

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SISTEM PEMBANGKIT ANOTASI PADA ARTIKEL BERGAMBAR DENGAN PENDEKATAN KONTEKSTUAL

JUTI: Jurnal Ilmiah Teknologi Informasi Vol 9, No 1, Januari 2011
Publisher : Teknik Informatika, ITS Surabaya

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Abstract

Development of E-learning sites and their materials make it is necessary to help users finding the desired materials. Context-based search engine will help users for the finding task. However that kind of searching can only be done for learning materials that have been semantically signed or annotated. Annotation is given for the article’s content or the article’s image within. There are many constraints for manually providing annotations to the learning articles such that automatic metadata or annotation generating method is needed. This paper discusses about annotation generating system with two subsystems: annotation recommender for learning material using contextual analysis and image metadata generator. The methods for contextual analysis are Latent Semantic Analysis (LSA) and WordNet-lexical dictionary usage. Our experimental results showed that subsystems can be used to generate annotation for articles and images in the articles though we have not done combination of two subsystems.

FACTORS INFLUENCING E-COMMERCE ADOPTION BY SMES INDONESIA: A CONCEPTUAL MODEL

Lontar Komputer Vol. 4, No. 2 Agustus 2013
Publisher : Teknologi Informasi Fakultas Teknik Universitas Udayana

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Abstract

E-commerce present different prospect to Small and Medium Sized Enterprises (SMEs) andprovides benefits to SMEs. At this stage, there are a number of studies focused on SMEs indeveloped countries. For developing countries, the situation is quite different. Furthermore, thereis still limited number of researches on e-commerce adoption by SMEs in Indonesia. SMEs playa vital role in reducing the rate of poverty and unemployment in Indonesian economy. In 2009,micro, small and medium enterprises in Indonesia consist of 52.7 million units or 99.99% of thetotal business enterprises, and employ 96.21 million people or 97% of the total labor forces. SMEsin Indonesia faced internal and external problems. This study explores various factors influencinge-commerce adoption by SMEs in several countries and projecting it to Indonesia. Results showsthat there are a number of perceived opportunities presented by e-commerce adoption inIndonesia, i.e. extending market-reach and even global, increasing customer personalizeservices, and improving its competitiveness. Furthermore, this study also proposes six potentialfactors influenced the adoption of e-commerce by SMEs in Indonesia, i.e. perceived usefulness,perceived ease of use, relative advantage, perceived risk, perceived trust, and compatibility.

PERBAIKAN METODE PEMERINGKATAN SPESIFIKASI KEBUTUHAN BERDASARKAN PERKIRAAN KEUNTUNGAN DAN NILAI PROYEK DENGAN MENGURANGI PERBANDINGAN BERPASANGAN

Kursor Vol 6, No 2 (2011)
Publisher : University of Trunojoyo Madura

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Abstract

PERBAIKAN METODE PEMERINGKATAN SPESIFIKASI KEBUTUHAN BERDASARKAN PERKIRAAN KEUNTUNGAN DAN NILAI PROYEK DENGAN MENGURANGI PERBANDINGAN BERPASANGAN aDaniel Siahaan, bEko Prasetyo a,b Teknik Informatika, Fakultas Teknologi Informasi, ITS Gedung T.Informatika, Jl. Raya ITS, Kampus ITS, Sukolilo, Surabaya, 60111 E-Mail: a daniel@if.its.ac.id Abstrak Pemeringkatan spesifikasi kebutuhan yang lebih diprioritaskan perlu dilakukan mengingat besarnya jumlah spesifikasi kebutuhan yang muncul diawal pengembangan perangkat lunak. Pemeringkatan juga mendekatkan relevansi keinginan pengguna dengan spesifikasi kebutuhan yang diterapkan. Metode pendekatan perkiraan keuntungan dan biaya merupakan metode multi kriteria yang mengakomodasi peringkat spesifikasi kebutuhan berdasarkan keuntungan bagi pengguna dan biaya pemgembangan bagi pengembang. Terdapat dua masalah utama dalam metode ini. Pertama, jika jumlah spesifikasi kebutuhan besar, maka perbandingan berpasangan yang harus dijawab akan semakin banyak. Kedua, keputusan peringkat akhir masih harus melalui diskusi oleh pelanggan. Dalam penelitan ini diusulkan perbaikan metode pemeringkatan pendekatan perkiraan keuntungan dan biaya dengan 100 points dan fuzzy k-means clustering untuk mengurangi perbandingan berpasangan dalam pemeringkatan spesifikasi kebutuhan berdasarkan metode AHP dan model kuadran. Hal tersebut dapat mengurangi perbandingan berpasangan, sehingga proses pemeringkatan menjadi lebih cepat. Hasil yang didapatkan menunjukkan bahwa jumlah perbandingan berpasangan yang harus dijawab oleh pelanggan dapat dikurangi 63.27% dari jumlah semula. Spesifikasi kebutuhan berhasil diperingkat dengan perbaikan metode yang diusulkan, nilai rasio konsistensi (CR) menunjukkan hasil di bawah 10% yang berarti masih berada dalam batas yang dapat dipertanggungjawabkan hasilnya. Kata kunci: 100-Points, Analitic Hierarchy Process, Fuzzy k-means, Model Kuadran, Pemeringkatan Spesifikasi Kebutuhan, Pendekatan Keuntungan dan Biaya. Abstract Predictions of benefit and cost of individual requirements are necessary for requirements prioritization methods which based on benefit and cost approach. Requirements prioritization pulls relevant requirements of user towards their implementation. A cost-value approach is a multi-criteria method for prioritizing requirements according to their relative values and costs. There were two inherited problems in the method. It requires both customers and developers to apply AHP’s pairwise comparison method to assess the relative value and estimate the relative implementation cost of candidate requirements. The problem is that this method introduces n x n number of comparisons to be assessed by customers and developers. Furthermore, the approach only provides a cost-value diagram as a recommendation for the software managers to further analyze and prioritize the requirements. This paper improves the existing approach by implementing 100p method and fuzzy kmeans to reduce the number of pairs to be compared produced by AHP, which contributed to the computational time. The experimental results show that the improved method can reduce 63.27% of the number of pairs to be compared, with consistency ratio (CR) value below the maximum acceptable threshold. Key words: 100p, Cost-Value Approach, Fuzzy k-means, Requirements Prioritization

PERBAIKAN MODEL KEBERGUNAAN PADA APLIKASI PERANGKAT BERGERAK DENGAN MENAMBAHKAN ATRIBUT GEJALA BURUK

Melek IT Information Technology Journal Vol 1, No 2 (2015): Melek IT JOURNAL
Publisher : Departement of Informatics Engineering , Faculty of Engineering, UWKS

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Abstract

AbstrakKebergunaan merupakan salah satu aspek yang sangat penting dan faktor kunci dari kesuksesan aplikasi perangkat bergerak. Kebergunaan digunakan untuk mendefinisikan kualitas dari tampilan aplikasi dan kualitas interaksi antara pengguna dan aplikasi. Model untuk mengukur kebergunaan pada aplikasi desktop tidak dapat langsung digunakan untuk aplikasi perangkat bergerak karena perbedaan karakteristik antara aplikasi desktop dan aplikasi perangkat bergerak.  Model yang digunakan untuk mengukur kebergunaan pada aplikasi perangkat bergerak sudah ada pada penelitian sebelumnya. Tetapi, Model tersebut tidak mempunyai bobot untuk masing-masing atribut serta tidak adanya rekomendasi tampilan antarmuka baru untuk evaluasi kebergunaan aplikasi perangkat bergerak yang sedang diukur. Penelitian ini menggabungkan data primer bersifat kualitatif yang diambil dari kuesioner dan data sekunder bersifat kuantitatif yang diambil dari logs aktifitas (gejala buruk) penggunaan aplikasi ketika pengguna menjalankan aplikasi perangkat bergerak. Pembobotan untuk masing-masing atribut diisi oleh pakar kebergunaan aplikasi perangkat bergerak dan menggunakan metode Analytical Hierarchy Prosess (AHP). Hasil dari penelitian ini adalah adanya model baru untuk mengukur kebergunaan pada aplikasi perangkat bergerak yang mempunyai bobot untuk masing-masing atribut kebergunaan serta dapat memberikan rekomendasi tampilan antarmuka baru untuk versi aplikasi selanjutnya.Kata Kunci: AHP, aplikasi perangkat bergerak, gejala buruk, kebergunaan. AbstractUsability is a very important aspect and one of the key factors for the successful mobile application. Usability is an attribute to define the quality of user interface and the quality of interaction between user and application. Models to measure the usability of desktop application can not directly be used for mobile applications because of the differences between the characteristics of desktop applications and mobile applications. Previous research developed a model to measure usability on a mobile application. Somehow, it lacks recommendations for new user interface usability evaluation of the mobile application that is being measured and there is no weighting for each attribute. This study combines qualitative primary data taken from questionnaires and quantitative secondary data taken from the activity logs application (Bad Symptoms) when user run mobile application. Weighting for each attribute is done by an expert of mobile applications. Analytical Hierarchy Process (AHP) Method is used to analyze experimental data. Result from this research is a novel model to measure usability for mobile application that have a weight to each usability attributes and provides recommendation for improvement of existing user interfaces.Keywords: AHP, Bad Symptoms, mobile application, usability mesurements.  

Rekomendasi Perbaikan Pernyataan Kebutuhan yang Rancu dalam Spesifikasi Kebutuhan Perangkat Lunak Menggunakan Teknik Berbasis Aturan

Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5, No 2: April 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

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Abstract

Tahap awal dalam pengembangan perangkat lunak ialah menelusuri, mengumpulkan dan menyajikan segala kebutuhan pengguna ke dalam sebuah dokumen spesifikasi kebutuhan perangkat lunak (SKPL). Latar belakang akademik yang beragam, pengalaman yang berbeda, dan keterbatasan pengetahuan yang dimiliki oleh perekayasa kebutuhan memungkinkan adanya kesalahan dalam pembuatan dokumen SKPL. Salah satu kesalahan yang sering muncul pada sebuah dokumen SKPL ialah terdapatnya penggunaan kata-kata yang rancu. Hal ini tentunya dapat menyebabkan kesalahan penafsiran dan kesulitan dalam memahami kebutuhan perangkat lunak yang hendak dibangun bagi pemangku kepentingan dalam proses pengembangan perangkat lunak. Penelitian ini bertujuan mengusulkan sebuah pendekatan untuk memberikan rekomendasi perbaikan pernyataan kebutuhan perangkat lunak yang rancu. Adapun metode yang diusulkan adalah teknik berbasis aturan dengan menggunakan model bahasa n-gram. Realibilitas metode usulan di-evaluasi menggunakan indeks statistik Gwet’s AC1. Hasil analisis metode rekomendasi yang diusulkan memiliki tingkat proporsi kesepakatan yang lebih baik dibandingkan dengan metode rekomendasi menggunakan teknik statistik berbasis frekuensi n-gram. Metode rekomendasi yang diusulkan memiliki nilai indeks statistik Gwet’s AC1 tertinggi sebesar 0.5263 dengan tingkat proporsi kesepakatan sedang. AbstractThe first stage in software development is to investigate, collect and provide all user requirements into a software requirements specification document (SRS’s). Diverse academic background, different experiences, and the limitations of knowledge possessed by the requirement engineer make possible mistakes in the creation of SRS’s documents. One of the most common mistakes in SRS’s document is the use of ambiguous words. This can certainly lead to misinterpretation and difficulties in understanding the software requirement that stakeholders to built in the software development process. The purpose of this research is to build an approach that gives recommendation improvement of ambiguous software requirement statement. The proposed method is a rule-based technique using n-gram language model. The reliability of the proposed method is evaluated using Gwet's AC1 statistical index. The analysis results of the proposed recommendation method have a better level of agreement proportion than the recommendation method using the n-gram frequency-based statistical technique. The proposed recommendation method has the highest Gwet's AC1 statistic value of 0.5263 with a moderate agreement proportion rate.

Weighted k Nearest Neighbor Using Grey Relational Analysis To Solve Missing Value

IPTEK The Journal for Technology and Science Vol 29, No 3 (2018)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

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Abstract

Software defect prediction model is an important role in detecting the most vulnerable component error software. Some research have been worked to improve the accuracy of the prediction defects of the software in order to manage human, costs and time. But previous research used specific dataset for software defect prediction model. However, there is no a generic dataset handling for software defect prediction model yet. This research proposed improvements to the results of the software defect prediction on the merged dataset, which is called generic dataset, with a number of different features. In order to balance the number of features, each dataset should be filled with a missing value. To fill the missing values, Weighted k Nearest Neighbor (WkNN) method was used. Then, after missing values were filled, Naïve Bayes was used to classify the selected features. This research needed to obtain a set of features which was relevant, then performed a feature selection method. The results showed that by using seven NASA public MDP datasets, Naïve Bayes with Information Gain (IG) or Symmetric Uncertainty (SU) feature selection presented the best balance value.Software defect, NASA public MDP, weighted KNN,Naive Bayes

Identifying Requirements Association Based on Class Diagram Using Semantic Similar;ity

Lontar Komputer Vol. 10, No. 1 April 2019
Publisher : Research institutions and Community Service, University of Udayana

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Abstract

RRequirements association depicts inter-relation between two or more requirements within a software project. It provides necessary information for developers during decision-making processes, such as change management, development milestones, bug prediction, cost estimation, and work breakdown structure generation. Modeling association between requirements became a focus of software requirements researchers. Previous studies indicate that requirements association was pre-defined by requirements engineer based on their expert judgments. The judgments require knowledge on requirements and their class realizations. This paper introduces a method to generate a mapping between a set of requirement statements and a set of classes of a given project that realized the respected requirements. The method also generates associations among requirements based on information on associations between classes and the class-requirement mapping. The method utilizes element of relational information resided in a class diagram of respected project. A semantic similarity method was used to define the requirements with their realization classes. A class is considered realizing a requirement if and only if their semantic similarity is higher than a certain threshold. A set of experimentation on four different projects was conducted. The result of the approach was compared with the output produced by human annotators using kappa statistics. The approach is considered as having a fair agreement level (i.e. with kappa value 0.37) with the human annotators to identify and model requirement associations.

English

Lontar Komputer Vol. 10, No. 1 April 2019
Publisher : Research institutions and Community Service, University of Udayana

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Abstract

The app reviews are useful for app developers because they contain valuable information, e.g. bug, feature request, user experience, and rating. This information can be used to better understand user needs and application defects during software maintenance and evolution phase. The increasing number of reviews causes problems in analysis process for developers. Reviews in textual form are difficult to understand, time-consuming, requires a lot of effort, and costly for manual analysis. Previous research shows that collection of review contains non-informative reviews because they do not have valuable information. Non-informative reviews considered as a noise and should be eliminated especially for classification process. The purpose of this research is to classify user reviews into three classes, i.e. bug, feature request, and non-informative reviews automatically. User reviews are converted into vector using word embedding. The vectors are used as input into first classifier that classify informative and non-informative reviews. The results from first classifier, that is informative reviews, then reclassified using second classifier to determine its category, e.g. bug report or feature request. The experiment using 306,849 sentences of reviews crawled from Google Play and F-Droid. The evaluation process using the following metrics: accuracy, recall, precision, and F-measure. The results show that the proposed model is able to handle large scale of imbalanced data and produces best accuracy of 0.79, precision of 0.77, recall of 0.87, and F-Measure of 0.81.