Computer Science

Software verification and validation is a critical phase in software development projects. But unfortunately in many software development projects, software verification and validation rarely gets sufficient attention. Lack of attention is partly due to the assumption that the software verification & validation is not a priority and there for only limited resources can be allocated. This paper presents the result of study in using forecasting analysis model to assist software test strategy preparation. This study applies forecasting analysis to accumulation of individual transaction data that have been got before the system is tested. Those forecasting results are used as comparison tool to compare with the transaction data generated by system which is being tested. This study use subsidized fuel consumption monitoring system as a case study to apply the forecasting model. Test results on the case study, with some limitations, show that forecasting analysis and simulation model can be used as one of inputs to software verification & validation strategy preparation.

Benford’s Law (aturan Bendford) menunjukkan keteraturan kemunculan angka 1 sampai dengan 9 pada sekumpulan data yang diperoleh secara alami. Benford’s Law dikenal luas dalam bidang akuntansi sebagai salah satu alat bantu untuk mendeteksi adanya kecurangan pada transaksi keuangan. Makalah ini menyajikan hasil penelitian keteraturan kemunculan angka penting pada data transaksi BBM bersubsidi di beberapa SPBU di Kota Batam pada periode Januari – Februari 2010. Dalam penelitian digunakan spreadsheet MS. Execl sebagai alat bantu. Dari hasil penelitian terhadap lebih dari 130.000 data mentah diperoleh kesimpulan bahwa ternyata pola kemunculan angka penting pada transaksi BBM bersubsidi mengikuti  Benford’s Law.  Hasil penelitian juga menunjukkan bahwa dengan memanfaatkan fungsi – fungsi yang tersedia, Microsoft Excel dapat digunakan sebagai alat bantu pengolah data yang mangkus dan dapat dijadikan sebagai alat bantu pengajaran dengan menunjukkan visualisi materi pengajaran

In the last recent years, Cloud Computing (CC) is one of well-developed information technology disciplines in terms of research and its application. Implementation of CC provides many benefits such as: efficiency, guarantee of technology handling and managing by them who are competent in their field, reduction of investment costs, and organizations that use the CC become can more focus on their business or services. Costs of investment and management of CC systems become cheaper if there are more organizations that utilize these resources sharing services. Level of the resources sharing utilization is higher when organizations that use those resources have the same interests in the process, data, and access model to the system. There are quite a lot of legal basis, procedures, data and information used jointly by Ministry Institions and Non Ministry of Government Institutions (M&NMs). This paper presents the proposed model of CC systems that can be used by those M&NMs. The proposed model is generated by performing the analysis and synthesis approach. Analysis carried out on the common service in M&NMs and synthesis is used to construct the proposed model of M&NMs CC implementation based on the analysis results.

Recently, the volume of biological data increases exponentially. Problem of utilization of this kind of data is not only concerning to the volume but also to its various format and storage distribution. To solve this kind of problems, some approaches require new methods, algorithms or tools to assist human being in getting beneficial from the biological data. This paper presents the usage of fractal dimension approach based on inter nucleotide distance to cluster DNA sequences. Inter nucleotide distance is a numerical representation of DNA sequences which is transformed to time series signal spectrum.

Many researches show that time series data is a kind of data which has biggest volume and growth compared to the others kind of data. Parallel with its huge volume and rapid growth, it is always needed new method, technique or approach to explore knowledge contained in time series data. One of many goals in data mining of time series is anomaly detection. By definition fractal is an object that has such self similarities in certain degree. This paper presents the results study of HFD (Higuchi Fractal Dimension) approach for clustering to detect the existence of an anomaly or deviation in time series data. This proposed method is applied to PT.PGN daily stock trade year 2004 to 2012 as test data. The results show that for value of discrete interval k = 5, 10, 15 their HFD tend to diverge and there are tend to converge to 2 for the greater value of k. Based on HFD time series clustering results, this approach can be used to divide the normal data and the other data that contain certain anomaly. In this study is also shown that this approach provides a better result compared to adjustment method which fill unbalance time series data with a zero value

Dari tahun ke tahun alokasi dan realisasi konsumsi BBM bersubsidi selalu menjadi permasalahan baik karena jumlahnya yang selalu meningkat dan realisasinya yang selalu melebihi perencanaan. Penimbunan, penyelundupan, dan penyelewengan merupakan permsalahan -permasalahan lain terkait konsumsi BBM bersubsidi. Data Mining (DM) telah digunakan secara luas di berbagai sektor kehidupan untuk membantu perkiraan maupun deskripsi obyek data dalam basis data. Makalah ini menyajikan studi awal penerapan DM untuk tujuan perkiraan yang akan diterapkan pada perilaku konsumsi BBM bersubsidi di tingkat Kabupaten / Kota. Metoda penelitian yang dilakukan pada studi awal ini adalah studi literatur mengenai penerapan DM untuk tujuan perkiraan, studi literatur mengenai pola konsumsi BBM bersubsidi, serta pemilihan operasi DM dan penyusuan konsep model DM untuk perkiraan konsumsi BBM bersubsidi. Hasil dari penelitian awal ini adalah identifikasi parameter yang merupakan faktor -faktor yang mempengaruhi konsumsi BBM bersubsidi di Kabupaten / Kota dan konsep model DM perkiraan perilaku konsumsi BBM bersubsidi. Kata Kunci: bbm bersubsidi, faktor pengaruh konsumsi, data mining, perkiraan

Many studies presented that among the others kinds of data have been resulted, time series data is dominant. The increment of computational capabilities make time series data volume increase very quickly. In addition to the large volume, the challenges and problems of extracting knowledge from the time series data is that time series data came from various sources in various formats. There is required new methods and techniques to explore the knowledge on the time series data so as to solve the challenges and problems. This paper presents a research proposal on a hybrid method which is used to extract knowledge in such integrated time series data. The proposed hybrid method is a combination of fractals, Bendford's Law and Finite State Automata.

Drug – drug interaction (DDI) is certain event in consuming of two or more drugs if one certain drug may influence the effects of another drug or otherwise. Drug – drug interactions may increase or decrease the effects of other drugs that affected, or could even harm the patient. The understanding of drug interactions is required both for the purposes of making a new drug or controlling its distribution. The research to understand drug interactions can be done in silo co for the efficiency. This paper presents the result study of drug – drug interaction estimation by performing the classification of the therapeutic class of FDA drug based on List of National Essential Drug 2011 (Daftar Obat Essensial Nasional /DOEN 2011) as a regulation of drug distribution released by Indonesia Government. The results study shows that from 2.384 unique FDA drugs, only 349 of them that are contained in 95 sub sub therapy classes of DOEN 2011.

There are always new challenges in the extraction of object (entity) relations contained in unstructured or semi structured text documents found in the Internet, due to the volume of the documents, the evolution of the text language, and the fast Internet growth. In this paper, the authors present the description and the experimental results of a novel role based approach in mining the entities and its relations. The proposed method defines a new concept of entity relationship which treat entities relation as the relation of the main object and its supporting object. The relation between these objects are extracted through pattern learning process that utilize the Indonesia WordNet as an external knowledge. Based on the performance evaluation of the proposed method, it can be confirmed that it is feasible to apply the method in the area. The feasibility of the method is measured by the accuracy of the extraction process in 10 experiments. The average F-score values for the experiments are 0.895 and 0.795 in main object extraction and supporting object extraction respectively.

Due many reasons, such the privacy aspects, availability, and less flexibility in parameter adjustment, the
real data set is becoming a main barrier in various researches. Facing to the difficulties to access the real data set, a
synthetic dataset is a viable alternative as a complementary to a real data set. Bi-clustering to binary categorical data
is one of the most recent challenging researches which are applied to many areas such: DNA expressions, tourism,
commerce and marketing, event sports. This paper presents the useful algorithm to generate synthetic binary matrix
data and its bi-set as the bi-clustering results ground truth. In generating this synthetic data, it uses some parameters
includes: initial size, injected bi-set size, number of overlapping size and bi-set position. Some of the generated
synthetic data sets are applied to two bi-set searching algorithms, Bibit and Bimax. The experiment result shows that
the synthetic data set has capabilities to evaluate methods or algorithm relates to binary matrix data set.

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN...

Strategi pemasaran berorientasi pelanggan memiliki peranan penting dalam mengelola hubungan baik dengan pelanggan. Agar strategi pemasaran tepat sasaran, segmentasi pelanggan dapat digunakan untuk mengelompokkan pelanggan berdasarkan karakteristik yang sama. Dalam penyusunan strategi pemasaran dapat memanfaatkan TI di bidang komputasi, salah satunya adalah data mining. Pemanfaatan teknologi komputasi untuk pengolahan data yang belum maksimal mengakibatkan penumpukan data yang miskin informasi. Pada penelitian ini dilakukan penerapan teknik clustering dengan menggunakan algoritma K-Medoids pada dataset transaksi penjualan untuk menentukan segmentasi pelanggan. Penyusunan strategi pemasaran ditentukan berdasarkan tipe dan karakteristik pelanggan pada setiap cluster atau segmen pelanggan yang terbentuk. Uji validitas cluster menggunakan Silhouette Index dan Davies Boulbin Index dilakukan untuk menentukan jumlah cluster yang paling optimal. Hasil penelitian ini menunjukan bahwa jumlah cluster optimal adalah 3 (tiga) cluster dengan nilai maksimum Silhouette Index adalah 0,375 dan nilai minimum Davies Doulbin Index adalah 1,030. Segmen pelanggan hasil penelitian adalah lost customer, core customer, dan new customer.

Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k...

The implementation of a marketing strategy requires a reference so that promotion can be on target, such as by looking for similarities between product items. This study examines the application of the association rule method and apriori algorithm to the purchase transaction dataset to assist in forming candidate combinations among product items for customer recommended product promotion. The purchase transaction dataset was collected in October and November 2018 with a total data of 1027. In the experiment, the minimum value of support is 85%, and the minimum confidence value is 90% by processing data using the Weka software 3.9 version. Apriori algorithm can form association rules as a reference in the promotion of company products and decision support in providing product recommendations to customers based on defined minimum support and confidence values.

PT. Biggy Cemerlang adalah salah satu supplier produk plastik terbaik di Indonesia yang memproduksi peralatan rumah tangga dengan menggunakan berbagai macam mold (cetakan). Mold merupakan komponen yang sangat penting dalam mencapai target produksi yang telah ditentukan oleh perusahaan. Salah satu aktivitas yang dilakukan untuk menjaga tingkat kesiapan mold supaya hasil tetap terjamin adalah perbaikan mold. Proses penentuan perbaikan sampai dengan saat ini masih dilakukan secara manual. Sistem manual mengandung kelemahan antara lain: kesulitan akses data dan kesalahan penentuan waktu perbaikan akibat kesalahan penentuan prioritas. Pada penelitian ini dibuat sistem pendukung pengambilan keputusan penentuan prioritas perbaikan mold menggunakan metode Simple Additive Weighting (SAW) yang dilakukan dengan mencari nilai bobot untuk setiap kriteria, dan kemudian membuat proses peringkat yang akan menentukan alternatif yang optimal. Hasil dari penelitian ini adalah sistem pendukung pengambi...

Sebagai upaya untuk memenangkan persaingan di pasar, perusahaan farmasi harus menghasilkan produk obat – obatan yang berkualitas. Untuk menghasilkan produk yang berkualitas, diperlukan perencanaan produksi yang baik dan efisien. Salah satu dasar perencanaan produksi adalah prediksi penjualan. PT. Metiska Farma telah menerapkan metode prediksi dalam proses produksi, akan tetapi prediksi yang dihasilkan tidak akurat sehingga menyebabkan tidak optimal dalam memenuhi permintaan pasar. Untuk meminimalisir masalah kurang akuratnya proses prediksi tersebut, dalam penelitian yang disajikan pada makalah ini dilakukan uji coba prediksi menggunakan teknik Machine Learning dengan metode Regresi Long Short Term Memory (LSTM). Teknik yang diusulkan diuji coba menggunakan dataset penjualan produk “X” dari PT. Metiska Farma dengan parameter kinerja Root Mean Squared Error (RMSE) dan MAPE (Mean Absolute Percentage Error). Hasil penelitian ini berupa nilai rata – rata evaluasi error dari pemodelan da...