Khabib Mustofa

Modularity Index Metrics for Java-Based Open Source Software Projects

Open Source Software (OSS) Projects are gaining popularity these days, and they become alternatives in building software system. Despite many failures in these projects, there are some success stories with one of the identified success factors is modularity. This paper presents the first quantitative software metrics to measure modularity level of Java-based OSS Projects called Modularity Index. This software metrics is formulated by analyzing modularity traits such as size, complexity, cohesion, and coupling of 59 Java-based OSS Projects from sourceforge.net using SONAR tool. These OSS Projects are selected since they have been downloaded more than 100K times and believed to have the required modularity trait to be successful. The software metrics related to modularity in class, package and system level of these projects are extracted and analyzed. The similarities found are then analyzed to determine the class quality, package quality, and then combined with system architecture measure to formulate the Modularity Index. The case study of measuring Modularity Index during the evolution of JFreeChart project has shown that this software metrics is able to identify strengths and potential problems of the project.

Evaluation of Selection Policy with Various Virtual Machine Instances in Dynamic VM Consolidation for Energy Efficient at Cloud Data Centers

Various VM instances in Cloud Infrastructure provide flexibility for user to meet their computation requirements. However, this condition leads to the complex infrastructure that require numerous resources and consumes massive electricity due to the flexibility of VM instances. This paper concern in evaluate VM selection policy in Dynamic VM Consolidation. The study would evaluate our proposed method Constant Position Selection Policy (CPS) that compared with other VM Selection Policy such as Minimum Migration Time (MMT),Random Choice(RC) and Maximum Correlation (MC).Evaluation process of this study, measured the performance of Energy Consumption, SLAV, SLATAH, and PDMwith real workload trace data from PlanetLab VMs in various VM instances. Result the proposed method able to minimizing energy consumption of cloud data center in various VM instances with acceptable SLA

A Review: Clearing Oil Palm Plantation with Multi-Stakeholder Model

The clearing land or clearing of oil palm plantations needs stakeholders‟ involvement in decision making, such as the role of government group, environmentalists, investors and the agricultural community groups from non-governmental organizations (NGO). This paper discusses about the Group Decision Support (GDS) that can be used for Oil Palm Plantation Land Clearing cases involving various stakeholders. Problem solution of Group Decision Support Clearing Oil Palm Plantation (GDS-COPP) can be categorized based on the stakeholder‟s model. The grouping results showed that Multi-Criteria Decision Analysis (MCDA) methods most widely used in previous papers. This paper aims to provide an overview that MCDA models and Analytic Hierarchy Process (AHP) method can be used in cases involving stakeholders in decision-making groups for plantation land clearing cases

Vector Autoregression (Var) Model for Rainfall Forecast and Isohyet Mapping in Semarang - Central Java - Indonesia

Agricultural and plantation activities in Indonesia, especially in Semarang, Central Java, Indonesia rely on water supply from the rainfall. The rainfall in the future is basically influenced by rainfall patterns, humidity and temperature in the past. In this case, Vector Autoregression (VAR) multivariate model is applied to forecast the rainfall in the future, in which all along Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) generally uses ARIMA model (Autoregressive Integrated Moving Average) to carry out the same thing. The study applied the data, comprising the data of rainfall, humidity and temperature taken on a monthly basis during 2001-2013 periods from 5 measurement stations. Plotting of rainfall forecast result with VAR method is portrayed in the form of isohyet contour map to see the correlation between rainfall and coordinates of the area of the rainfall. The forecast result shows that VAR method is quite accurate to use for rainfall forecast in the study area as well as better than ARIMA method to forecast the same thing as having smaller Mean Absolute Error (MAE) and Mean Absolute Percentage Error(MAPE).

Predicting Size of Forest Fire Using Hybrid Model

This paper outlines a hybrid approach in data mining to predict the size of forest fire using meteorological and forest weather index (FWI) variables such as Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), temperature, Relative Humidity (RH), wind and rain. The hybrid model is developed with clustering and classification approaches. Fuzzy C-Means (FCM) is used to cluster the historical variables. The clustered data are then used as inputs to Back-Propagation Neural Network classification. The label dataset having value greater than zero in fire area size are clustered using FCM to produce two categorical clusters,i.e.: Light Burn, and Heavy Burn for its label. On the other hand, fire area label with value zero is clustered as No Burn Area. A Back-Propagation Neural Network (BPNN) is trained based on these data to classify the output (burn area) in three categories, No Burn Area, Light Burn and Heavy Burn. The experiment shows promising results depicting classification size of forest fire with the accuracy of confusion matrix around 97, 50 % and Cohens Kappa 0.954. This research also compares the performance of proposed model with other classification method such as SVM, Naive Bayes, DCT Tree, and K-NN that showed BPNN have best performance.

Semantic search with rule reasoning for scholarship information search

Tendency to pursue graduate study has become a positive trend in Indonesia. This is supported by the growing number of agency or institution that organizes a postgraduate scholarship program with a wide range of requirements. Generally, finding information through the Internet is done by a keyword-based search engine which possibly gives several irrelevant results and relies on users ability in selecting keywords. This research aims to provide an alternative by building semantic search system for finding scholarship information that allows natural language sentence instead of keywords. It implements rule reasoning to obtain implicit knowledge by defining the rules from basic knowledge that explicitly defined. Among the rules that are used in this system, data range restriction is used to determine data based on certain thresholds. The experiment results show that the system has recall rate 98.51% and precision rate 100% from 90 of 105 input sentences that are able to be processed by the system.

Early-Detection system for cross-language (translated) plagiarism

The implementation of internet applications has already crossed the language border. It has, for sure, brought lots of advantages, but to some extent has also introduced some side-effect. One of the negative effects of using these applications is cross-languages plagiarism, which is also known as translated plagiarism. In academic institutions, translated plagiarism can be found in various cases, such as: final project, theses, papers, and so forth. In this paper, a model for web-based early detection system for translated plagiarism is proposed and a prototype is developed. The system works by translating the input document (written in Bahasa Indonesian) into English using Google Translate API components, and then search for documents on the World Wide Web repository which have similar contents to the translated document. If found, the system downloads these documents and then do some preprocessing steps such as: removing punctuations, numbers, stop words, repeated words, lemmatization of words, and the final process is to compare the content of both documents using the modified sentence-based detection algorithm (SBDA). The results show that the proposed method has smaller error rate leading to conclusion that it has better accuracy.

IMPLEMENTASI KOMPUTASI AWAN MENGGUNAKAN TEKNOLOGI GOOGLE APP ENGINE (GAE) DAN AMAZON WEB SERVICES (AWS)

Interoperabilitas, dalam arti cara bagaimana suatu sistem yang memiliki platform perangkat keras dan perangkat lunak tertentu dapat berkomunikasi dengan sistem-sistem yang memiliki platform yang berbeda, mungkin merupakan bagian dari ‘masa lalu’. Di masa-masa yang akan datang, interoperabilitas yang selama ini ditangani secara manual oleh organisasi-organisasi/perusahaan-perusahaan akan ditangani langsung oleh vendor-vendor penyedia komputasi awan (cloud computing) yang memang memiliki sumberdaya-sumberdaya manusia (analis sistem, pemrogram, pakar jaringan), perangkat keras (komputer-komputer server yang berjumlah sangat banyak dan berkemampuan raksasa), serta perangkat lunak (sistem operasi, server aplikasi, server Web) yang memang memenuhi syarat untuk itu. Di masa yang akan datang, untuk mendapatkan layanan-layanan (service) dan tempat penyimpanan tertentu, organisasi-organisasi/perusahaan-perusahaan tidak perlu berinvestasi terlalu tinggi untuk menyediakannya sendiri; mereka bisa saja menyewanya dari vendor-vendor komputasi awan yang saat ini mulai bermunculan. Google dan Amazon adalah para pendahulu dari teknologi komputasi awan (cloud computing) ini. Melalui tulisan ini, kita tidak akan membahas struktur internal keduanya secara rinci, melainkan kita akan mencoba membahas kelebihan serta kekurangan kedua vendor komputasi awan ini dari sudutpandang para manajer di bidang Teknologi Informasi yang akan melakukan investasi yang bermanfaat bagi organisasi/perusahaannya. Kata kunci : Cloud Computing, Google App Engine, Amazon Web Service.