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PENELITIAN DI BIDANG TEKNOLOGI INFORMASI DAN KOMUNIKASI

PENELITIAN DI BIDANG TEKNOLOGI INFORMASI DAN KOMUNIKASI. Zainal A. Hasibuan, PhD ( zhasibua@cs.ui.ac.id). PELATIHAN METODOLOGI PENELITIAN BAGI MAHASISWA PASCA UNIV ERSITAS DIAN NUSWANTORO. Characteristics of Scientific Method.

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PENELITIAN DI BIDANG TEKNOLOGI INFORMASI DAN KOMUNIKASI

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  1. PENELITIAN DI BIDANG TEKNOLOGI INFORMASI DAN KOMUNIKASI Zainal A. Hasibuan, PhD (zhasibua@cs.ui.ac.id) PELATIHAN METODOLOGI PENELITIAN BAGI MAHASISWA PASCA UNIVERSITAS DIAN NUSWANTORO

  2. Characteristics of Scientific Method • Based on facts • Objective Consideration • Analytical • Quantitative in Nature • Deductive Logic-Hypothesis • Inductive Logic-Generalization

  3. Masalah-Masalah yang DitelitiDalam Bidang Teknologi Informasi • Implementasi penggunaan sistem core banking agar penerimaan oleh pengguna akhir dapat meningkat. • Penggunaan sistem informasi yang sesuai dengan kebutuhan organisasi diharapkan dapat meningkatkan koordinasi antara unit yang terdapat di BSI dan dapat mencegah terjadinya kesimpangsiuran implementasi suatu sistem pada unit yang ada di BSI • Bagaimana proses bisnis operasional di industri Penyedia Layanan TI • Bagaimana proses bisnis yang terdapat pada modul Distribution perangkat lunak ERP dari Industrial dan Financial System AB (IFS)

  4. Contoh Penelitian yang Dilakukan • Studi Kepuasan Pengguna akhir terhadap Sistem CORE Banking pada Bank XYZ • Perencanaan Strategis Sistem Informasi studi kasus: Akademi BSI • Pemetaan dan perbaikan proses bisnis pada kegiatan operasional di Industri Penyedia Layanan TI studi kasus: PT. XYZ • Pemetaan Proses Bisnis Perangkat Lunak Enterprise Resource Planning studi kasus: Modul IFS Distribution

  5. Berbagai Metodologi yang digunakan dalam Penelitian Bidang TI • Metode yg digunakan adalah Technology Acceptance Model sebagai model dasar yang dikombinasikan dengan model Computer Self-Efficacy dan End-User Computing Satisfaction. • Metodologi yang digunakan dalam penelitian ini adalah SISP (Strategic Information System Planning) dengan menggunakan langkah-langkah seperti pengumpulan data, analisis kondisi dan interpretasi. Alat bantu yang diguanakan dalam penulisan tesis ini adalah value chain, PEST Analysis, Porter's five forces analysis, critical success factors, SWOT analysis, dan matriks portofolio McFarlan

  6. Berbagai Metodologi yang digunakan dalam Penelitian Bidang TI • Model proses bisnis disimulasikan dengan menggunakan aplikasi Pro Vision dari Proforma Corp. sebagai alat bantu • Proses bisnis dipetakan dengan menggunakan perangkat lunak pemodelan proses bisnis ProVision 4.2

  7. Tema Penelitian Ilmu Komputer • Tema dalam Pemprosesan Teks • Tema dalam Sistem Informasi • Tema dalam Temu Kembali Informasi • Tema dalam Grafika Komputer • Tema dalam Pengolahan Citra • Tema dalam Teknik Perangkat Lunak

  8. Masalah-Masalah yang Diteliti • Bagaimana mengembangkan sistem temu kembali citra yang mampu merepresentasikan salah satu atribut tingkat tinggi, yaitu sensasi yang ditimbulkan citra • Bagaimana menghasilkan klasifikasi pengenalan pola dari citra yang lebih akurat untuk mengatasi data yang redundant • Bagaimana penyusunan bahasa spesifikasi (lingu) sebagai alternatif solusi dalam bahasa pemrograman yang dipakai untuk mengimplementasi sistem perangkat lunak

  9. Contoh Penelitian yang Dilakukan • Sistem temu kembali citra untuk representasi sensasi berbasis teori fuzzy • Perbandingan reduksi data citra hyperspectral dengan projection pursuit dan principal component • Pengembangan penerjemah lingu ke java dengan Attribute Grammar

  10. Berbagai Metodologi yang digunakan dalam Penelitian Bidang Ilmu Komputer • Metodologi yang digunakan berupa teknik penghitungan histogram dan juga menggunakan rumusan sensasi menurut Teori Itten dimodelkan dengan teori fuzzy • Untuk optimasi pemilihan data tereduksi berdasarkan nilai maksimum projection indeks yang dihasilkan, maka digunakan metode skewness dan kurtosiss sebagai Projection indeksnya • Metodologi yang digunakan adalah dengan menggunakan sistem attribute grammar (UUAG) yang merupakan hasil pengembangan Universitas Utrecht dengan berbasis bahasa pemrograman Haskell

  11. Contoh Penelitian Bidang Ilmu Komputer The Use of Maximal Frequent Sequences to Improve Document Ranking in Information Retrieval System for Indonesian Language

  12. Background • The amount of textual information available through the World Wide Web has increased dramatically in recent years • Web users need effective search mechanism in order to find useful information from the enormous quantities of available text data • Very often, users are precision-oriented, they prefer a small set of documents containing a good proportion of useful documents to a large set of documents that contains a lot of useful information, but a fair amount of irrelevant information as well. Thus, there has been a growing interest in high-precision IR systems in recent times.

  13. Related Works (Previous Research) • One approach that has traditionally been regarded as a tool for increasing precision is the use of phrases for indexing and retrieval of document [SAL83, SAL89]. • The use of phrases for indexing is better than the use of single words for indexing. Single words are often ambiguous and not specific enough for accurate discrimination of documents • Syntactic phrase (i.e., phrase with certain syntactic relations) are always more spesific than single words and thus are intuitively attractive for indexing.

  14. Related Works (Previous Research) • Despite the intuitive rationality of using phrases for indexing, syntactic phrases have been reported to show no significant improvement of retrieval performance [BEL87, MIT 87, ZHA97]. • Maximal Frequent Sequences (MFS) can handle all these problems. MFS are sequences of words that are frequent in the document collection and that are not contained in any other longer frequent sequence. MFS are able to account for word ordering in the modeling of textual documents

  15. Method and Implementation • Data Sets • The data sets used in this experiment were comprised of 2 Indonesian corpuses, a news corpus and a scientific corpus. • The news corpus is a collection of on-line news from famous Indonesian newspapers, consists of 3000 documents, and is formatted in the TREC-like format. The corpus comes with 20 set of queries and their relevancies [ASI04]. • Programming Language and Libraries • We implemented most of the method and algorithm in Python, a high-level, interpreted object-oriented programming language well-known within the UNIX and internet community (http://www.python.org).

  16. Method and Implementation • Collection Preprocessing • The reindexed collection was then subjected to word filtering to remove punctuation, one-letter words, numbers, word repetition, and about 250 stop words • The filtered collection then underwent stemming using an Indonesian stemmer [ASI03] • Term-Frequency Processing • Use a tf-normalized version of the tfc (term frequency cosine) term-weighted components with the following formula [SAL88]:

  17. Method and Implementation • MFS Processing • Pair Discovery • MFS Discovery • MFS Indexing • Retrieval System • Boolean Term Frequency Model • Term Frequency Cosine Model • MFS Model • Hybrid (Term Frequency Cosine/MFS) Model • Evaluation Strategy

  18. Method and Implementation • Collection Preprocessing • Term Frequency Construction • The Process of MFS Discovery • Pair Discovery • MFS Discovery • MFS Indexing • Evaluation of Retrieval System • Boolean Term Frequency Model • Term Frequency Cosine Model • MFS Model • Hybrid (Term Frequency Cosine/MFS) Model • Evaluation Strategy

  19. Result • Collection Preprocessing The characteristics of each document collections before and after filtering and stemming were summarized on table 1 and 2

  20. Characteristic of documents in news collection before filtering, after filtering and after stemming, for 2999 documents.

  21. Characteristic of documents in scientific collection before filtering, after filtering and after stemming for 1162 documents

  22. Result • Term Frequency Construction For each collection sets, both filtered and stemmed collections were subjected to frequency counting process and term-weighting calculation. The results were saved in the BsdDB database

  23. Algorithm 1. MFS Discovery Input : Pf : frequent pairs S : the document collection Output : Max : the set of maximal frequent sequences • Pfreq = { p | p E frequent pairs in S } • G = Pfreq; P = Pfreq; Cmax := 0 • Loop A: • Pused := 0; Gnext = 0 • For every g E G • (Gjoin, Pused) := Join(g, Pfreq, Pused) • If Gjoin is empty • Cmax := Cmax U g • else • Gnext := Gnext U Gjoin • If Gnext is empty • exit from loop A • P = Pused • G = Gnext • Max := Reduce(Cmax) • Return Max

  24. Algorithm 2: Join Input : g : gram to be joined P : pairs used for joining Pused : pairs that have been used for joining Output : Gnew : the newly joined grams • Pused : pairs that have been used for joining • Gnew := 0 • For every p E P = { p | p starts with g[-1] } • pos = FindPosition(g, p) • If len(pos) > threshold: • gnew := g + p[1] • Pused := Pused U p • Gnew := Gnew U gnew • For every p E P = { p | p ends with g[0] } • pos = FindPosition(p, g) • If len(pos) > threshold: • gnew := p[0] + g • Pused := Pused U p • Gnew := Gnew U gnew • Return Gnew, Pused

  25. Algorithm 3: Reduce Input : Cmax: candidate MFS kmax: the length of the longest candidate Output : Max: maximum frequent sequences • Max := 0 • For k from kmax to 2 • For every c E {g| g E Cmax and length g = k} • If c is not a subsequence of some m E Max • Max := Max U c • Return Max

  26. Result • The Process of MFS Discovery • The results of searching frequent word pairs (2-grams) between stemmed and non-stemmed version of each collection(figure 1 and 2) • The results of MFS discovery for different threshold were shown in figure 3 and 4. Because of space limitation, we cannot provide all results from different combination of threshold, parameter g and collection version (stemmed and non-stemmed)

  27. Figure 1. Result Of Word Pair Searching For News Collection

  28. Figure 2. Results Of Word Pair Searching For Scientific Collection

  29. Result Figure 3.The distribution of MFS for different threshold of non-stemmed news collection

  30. Figure 4.The distribution of MFS for different threshold of stemmed scientific collection

  31. Result • Evaluation of Retrieval System The precisions of each models of the retrieval system for both news and scientific collection are shown in table 3 and 4, respectively

  32. Table 3. Comparison of the result of each method for news document collection. The Precision@10 indicates the precision of the method for the first 10 documents, while R-Precision indicates the precision of the method for the first n-documents, where n is the number of the relevant document of that particular query

  33. Table 4. Comparison of theresult of each method for scientific document collection. The Precision@20 indicates the precision of the method for the first 20 documents

  34. Discussion • Collection Preprocessing • Some documents in the news collection contains unrelated terms that needs to be cleaned up manually • The filtering process affects (impacts) scientific collections more than news collection • The effect of the stemming process in reducing unique words is roughly the same for both document collections • The stemmer can receive more improvement and enhancement • Document encoding may contribute in less memory usage and execution time

  35. Discussion • Term Frequency Construction • The Discovery of MFS • The process of discovering MFS for each collection started with the search of frequent word pairs (or 2-gram) • Using a bigger parameter g results in more pairs being constructed • The search for frequent pairs proved to be a major challenge for BsdDB component of the retrieval system • The discovery of MFS proved to be cpu and memory intensive process • Smaller threshold spreads the distribution of MFS • Smaller threshold increases the processing time substantially

  36. Discussion • Evaluation of Retrieval System • Term frequency method with tfc (term frequency cosine) scoring scheme gave a respectable results • A modified tfc with correction factor (tfc2) improves the precision of the term frequency cosine model for the scientific collection • More MFS representing document usually improves the precision of the retrieval algorithm • Stemming improves the precision of all retrieval models

  37. Conclusion And Suggestion • An improved method for utilizing MFS in calculating the rank of document should be formulated • To be practical in the real-word application, a new algorithm for doing incremental MFS discovery needs to be developed

  38. Contoh Penelitian Bidang Teknologi Informasi Penggunaan Cobit dan IT-IL Sebagai Alat Analisa Dan Cobit dan ITBSC Sebagai Alat Ukur Kinerja Manajemen TI Perusahaan

  39. Pendahuluan • Tindakan-tindakan perusahaan yang tidak sesuai dengan visi dan misinya. • Strategi yang tidak selaras dengan tujuan-tujuan dari departemen, team dan individu. • Strategi yang tidak selaras dengan alokasi sumber daya jangka panjang maupun jangka pendek. • Umpan balik yang taktis bukan strategis.

  40. Permasalahan • Penerapannya masih cenderung sebagai sistem manajemen operasional saja yang seharusnya juga digunakan sebagai sistem manajemen strategi. • Kurangnya cause and effect relationship dan performance driver. • Penerapannya dikomunikasikan hanya sampai pada level manajemen IT saja. • Pengembangan dan penerapan IT BSC terlihat sebagai suatu proyek

  41. Manajemen TI IT-IL sudah membaginya kedalam beberapa kelompok dan disiplin, yaitu : • Penyampaian layanan. Layanan-layanan apa yang harus pusat data sediakan untuk cukup mendukung: • Manajemen Keuangan IT • Manajemen Kapasitas • Manajemen Ketersediaan • Manajemen Tingkatan Layanan • Manajemen Kesinambungan TI

  42. Manajemen TI 2. Dukungan layanan. Bagaimana cara pusat data memastikan bahwa pelanggan mempunyai akses kepada layanan yang sesuai untuk: • Manajemen perubahan • Manajemen Release • Manajemen masalah • Manajemen incident • Manajemen konfigurasi • Sevice Desk

  43. Manajemen TI 3. Perencanaan untuk Menerapkan Manajemen Layanan. Bagaimana cara memulai perubahan sistem kerja ke ITIL. Hal ini menjelaskan langkah-langkah yang perlu untuk mengidentifikasi bagaimana suatu organisasi mungkin mendapatkan manfaat dari ITIL. • Manajemen Keamanan. • Perspektif Bisnis. Hal ini menjelaskan kebutuhan dan prinsip kunci bisnis organisasi dan operasi serta bagaimana hal ini berhubungan dengan pengembangan, penyampaian dan dukungan layanan TI.

  44. Manajemen TI 6. Manajemen Aplikasi. Bagaimana cara mengatur pengembangan daur hidup software, pengembangan isu yang menyangkut Pengembangan daur hidup software dan uji coba layanan TI. 7. Manajemen Asset Perangkat lunak.

  45. Start Identifikasi dan interpretasi bisnis untuk TI Dokumen Perusahaan Identifikasi dan interpretasi tujuan TI COBIT Identifikasi proses TI berdasarkan Tujuan TI A Tahapan Analisa Manajemen TI

  46. Membagi proses TI dan Control Objective agar sesuai dengan area manajemen TI A COBIT dan IT-IL Melakukan komparasi dan interpretasi data Terhdap control objective terpilih Dokumen Perusahaan End Analisa dan Interpretasi Manajemen TI

  47. Start COBIT Identifikasi KGI dan KPI pada proses TI Pemetaan pada IT BSC Memberikan interpretasi terhadap analisa Melakukan cause –effect analysis End Pengukuran Kinerja Manajemen TI

  48. CONTOH STUDI KASUS PT. BANK XYZ Tujuan bisnis dari TI pada PT BANK XYZ yaitu : • Tercapainya Kepuasan Nasabah. • Tersedianya produk teknologi unggulan dengan daya saing tinggi. • Terwujudnya otomasi penuh pada proses internal bank. • Terwujudnya teknologi sistem informasi dengan ketersediaan dan security yang tinggi. • Terwujudnya pengelolaan teknologi sistem informasi mengikuti standard nasional dan internasional.

  49. Proses-proses TI Dan Control Objectivenya

  50. KPI, KGI dan pemetaannya kedalam IT BSC (PO1)

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