Publication Information

{'id': 'https://openalex.org/W173448675', 'doi': None, 'title': 'Model fuzzy untuk data fuzzy time series dan aplikasinya di bidang finansial', 'display_name': 'Model fuzzy untuk data fuzzy time series dan aplikasinya di bidang finansial', 'publication_year': 2010, 'publication_date': '2010-01-01', 'ids': {'openalex': 'https://openalex.org/W173448675', 'mag': '173448675'}, 'language': 'en', 'primary_location': {'is_oa': False, 'landing_page_url': 'https://repository.ugm.ac.id/91602/', 'pdf_url': None, 'source': None, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}, 'type': 'dissertation', 'type_crossref': 'dissertation', 'indexed_in': [], 'open_access': {'is_oa': False, 'oa_status': 'closed', 'oa_url': None, 'any_repository_has_fulltext': False}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5053065137', 'display_name': 'Agus Maman Abadi', 'orcid': 'https://orcid.org/0000-0002-5488-3043'}, 'institutions': [], 'countries': [], 'is_corresponding': True, 'raw_author_name': 'Agus Maman Abadi', 'raw_affiliation_strings': [], 'affiliations': []}], 'institution_assertions': [], 'countries_distinct_count': 0, 'institutions_distinct_count': 0, 'corresponding_author_ids': ['https://openalex.org/A5053065137'], 'corresponding_institution_ids': [], 'apc_list': None, 'apc_paid': None, 'fwci': None, 'has_fulltext': False, 'cited_by_count': 0, 'citation_normalized_percentile': {'value': 0.0, 'is_in_top_1_percent': False, 'is_in_top_10_percent': False}, 'cited_by_percentile_year': {'min': 0, 'max': 64}, 'biblio': {'volume': None, 'issue': None, 'first_page': None, 'last_page': None}, 'is_retracted': False, 'is_paratext': False, 'primary_topic': {'id': 'https://openalex.org/T13373', 'display_name': 'Data Mining and Machine Learning Applications', 'score': 0.9811, 'subfield': {'id': 'https://openalex.org/subfields/1710', 'display_name': 'Information Systems'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}, 'topics': [{'id': 'https://openalex.org/T13373', 'display_name': 'Data Mining and Machine Learning Applications', 'score': 0.9811, 'subfield': {'id': 'https://openalex.org/subfields/1710', 'display_name': 'Information Systems'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}, {'id': 'https://openalex.org/T11734', 'display_name': 'Decision Support System Applications', 'score': 0.9742, 'subfield': {'id': 'https://openalex.org/subfields/1404', 'display_name': 'Management Information Systems'}, 'field': {'id': 'https://openalex.org/fields/14', 'display_name': 'Business, Management and Accounting'}, 'domain': {'id': 'https://openalex.org/domains/2', 'display_name': 'Social Sciences'}}, {'id': 'https://openalex.org/T13559', 'display_name': 'Edcuational Technology Systems', 'score': 0.97, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}], 'keywords': [{'id': 'https://openalex.org/keywords/defuzzification', 'display_name': 'Defuzzification', 'score': 0.81296974}, {'id': 'https://openalex.org/keywords/fuzzy-associative-matrix', 'display_name': 'Fuzzy associative matrix', 'score': 0.5492329}, {'id': 'https://openalex.org/keywords/fuzzy-mathematics', 'display_name': 'Fuzzy Mathematics', 'score': 0.48058915}], 'concepts': [{'id': 'https://openalex.org/C170260401', 'wikidata': 'https://www.wikidata.org/wiki/Q5251735', 'display_name': 'Defuzzification', 'level': 5, 'score': 0.81296974}, {'id': 'https://openalex.org/C148671577', 'wikidata': 'https://www.wikidata.org/wiki/Q5511133', 'display_name': 'Fuzzy set operations', 'level': 4, 'score': 0.787717}, {'id': 'https://openalex.org/C127385683', 'wikidata': 'https://www.wikidata.org/wiki/Q1475696', 'display_name': 'Fuzzy classification', 'level': 4, 'score': 0.7842803}, {'id': 'https://openalex.org/C1883856', 'wikidata': 'https://www.wikidata.org/wiki/Q3407463', 'display_name': 'Fuzzy number', 'level': 4, 'score': 0.71131223}, {'id': 'https://openalex.org/C17350324', 'wikidata': 'https://www.wikidata.org/wiki/Q7860605', 'display_name': 'Type-2 fuzzy sets and systems', 'level': 5, 'score': 0.6475841}, {'id': 'https://openalex.org/C58166', 'wikidata': 'https://www.wikidata.org/wiki/Q224821', 'display_name': 'Fuzzy logic', 'level': 2, 'score': 0.6069979}, {'id': 'https://openalex.org/C33923547', 'wikidata': 'https://www.wikidata.org/wiki/Q395', 'display_name': 'Mathematics', 'level': 0, 'score': 0.58336306}, {'id': 'https://openalex.org/C144170203', 'wikidata': 'https://www.wikidata.org/wiki/Q5511099', 'display_name': 'Fuzzy associative matrix', 'level': 5, 'score': 0.5492329}, {'id': 'https://openalex.org/C42011625', 'wikidata': 'https://www.wikidata.org/wiki/Q1055058', 'display_name': 'Fuzzy set', 'level': 3, 'score': 0.5209559}, {'id': 'https://openalex.org/C29470771', 'wikidata': 'https://www.wikidata.org/wiki/Q4165150', 'display_name': 'Neuro-fuzzy', 'level': 4, 'score': 0.5166474}, {'id': 'https://openalex.org/C166586474', 'wikidata': 'https://www.wikidata.org/wiki/Q5511129', 'display_name': 'Fuzzy mathematics', 'level': 5, 'score': 0.48058915}, {'id': 'https://openalex.org/C124101348', 'wikidata': 'https://www.wikidata.org/wiki/Q172491', 'display_name': 'Data mining', 'level': 1, 'score': 0.45912775}, {'id': 'https://openalex.org/C17212007', 'wikidata': 'https://www.wikidata.org/wiki/Q5511111', 'display_name': 'Fuzzy clustering', 'level': 3, 'score': 0.45258036}, {'id': 'https://openalex.org/C5263885', 'wikidata': 'https://www.wikidata.org/wiki/Q1967302', 'display_name': 'Membership function', 'level': 4, 'score': 0.4282271}, {'id': 'https://openalex.org/C154945302', 'wikidata': 'https://www.wikidata.org/wiki/Q11660', 'display_name': 'Artificial intelligence', 'level': 1, 'score': 0.42199954}, {'id': 'https://openalex.org/C41008148', 'wikidata': 'https://www.wikidata.org/wiki/Q21198', 'display_name': 'Computer science', 'level': 0, 'score': 0.32254392}, {'id': 'https://openalex.org/C195975749', 'wikidata': 'https://www.wikidata.org/wiki/Q1475705', 'display_name': 'Fuzzy control system', 'level': 3, 'score': 0.28640366}], 'mesh': [], 'locations_count': 1, 'locations': [{'is_oa': False, 'landing_page_url': 'https://repository.ugm.ac.id/91602/', 'pdf_url': None, 'source': None, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}], 'best_oa_location': None, 'sustainable_development_goals': [{'id': 'https://metadata.un.org/sdg/8', 'display_name': 'Decent work and economic growth', 'score': 0.52}], 'grants': [], 'datasets': [], 'versions': [], 'referenced_works_count': 0, 'referenced_works': [], 'related_works': [], 'abstract_inverted_index': {'Fuzzy': [0, 3, 17], 'Model': [1], 'for': [2, 129, 203, 417, 446], 'Time': [4], 'Series': [5], 'Data': [6], 'and': [7, 58, 104, 146, 153, 162, 251, 258, 270, 385, 395, 426], 'Its': [8], 'Application': [9], 'in': [10, 199], 'Finance': [11], 'By': [12], 'Agus': [13], 'Maman': [14], 'Abadi': [15], '06/09-I/2021/PS': [16], 'time': [18, 38, 65, 131, 419, 448], 'series': [19, 39, 66, 132, 420, 449], 'is': [20, 80, 94, 107, 112, 229, 260, 276, 298, 311, 341, 351], 'a': [21, 102, 204], 'dynamical': [22], 'process': [23], 'of': [24, 36, 55, 91, 98, 139, 177, 226, 231, 245, 262, 264, 273, 283, 287, 292, 306, 316, 319, 335, 343, 388, 398, 404, 406, 429], 'linguistic': [25, 32], 'variables': [26, 275, 310], 'whose': [27], 'fuzzy': [28, 37, 64, 71, 84, 99, 110, 119, 128, 130, 141, 160, 165, 168, 178, 186, 192, 205, 209, 216, 223, 227, 232, 238, 246, 265, 329, 336, 360, 364, 369, 375, 407], 'sets': [29], 'as': [30], 'its': [31], 'values.': [33], 'The': [34, 136, 167, 243, 377], 'uniqueness': [35], 'model': [40, 44, 93, 228, 450], 'are': [41, 175, 188, 290, 331, 371, 379], 'that': [42, 413, 442], 'the': [43, 62, 92, 307, 314, 324, 367], 'can': [45], 'formulate': [46], 'problems': [47], 'based': [48, 86, 143, 148, 173, 180, 201, 248, 253], 'on': [49, 87, 144, 181, 249, 323], 'expert': [50, 56], 'knowledge': [51, 57], 'only': [52], 'or': [53, 118], 'hybrid': [54], 'empirical': [59], 'data': [60, 117, 133, 214], 'In': [61, 121], 'modeling': [63, 127], 'data,': [67], 'previous': [68], 'researchers': [69], 'define': [70], 'set': [72, 193, 206, 217, 224, 233], 'using': [73, 150, 195, 219, 240, 255, 402, 414, 433, 443], 'discrete': [74], 'membership': [75], 'function.': [76], 'Then': [77, 184, 222, 346, 366, 422], 'Mamdani': [78], 'composition': [79, 145, 172, 250], 'applied': [81, 352, 380], 'to': [82, 101, 114, 126, 190, 300, 313, 353, 357, 373, 381], 'construct': [83], 'relations': [85, 100, 142, 169, 179, 187, 247, 266, 330, 361, 370], 'training': [88, 182, 213], 'data.': [89, 183], 'Optimization': [90], 'done': [95, 277], 'by': [96, 171, 268, 278, 333], 'clustering': [97], 'group': [103], 'then': [105], 'defuzzification': [106], 'applied.': [108], 'This': [109, 296, 339], 'relation': [111, 210, 239, 337, 408], 'used': [113, 189, 299, 372], 'predict': [115, 191], 'real': [116], 'sets.': [120], 'this': [122, 185, 288], 'research,': [123], 'new': [124], 'procedures': [125, 137], 'were': [134], 'established.': [135], 'consist': [138], 'constructing': [140, 163], 'individual': [147, 200, 252], 'inferences': [149, 174, 254], 'operator': [151, 196, 220, 241, 256], 's-norm': [152, 257], 't-norm,': [154], 'selecting': [155], 'input': [156, 274, 294, 303, 309, 326], 'variables,': [157, 327], 'designing': [158], 'complete': [159, 328, 363], 'relations,': [161], 'optimal': [164, 359, 368], 'relations.': [166, 365], 'designed': [170, 332], 'union': [176, 230], 'output': [194, 218, 225, 234], 'sup-t.': [197, 221], 'Furthermore,': [198], 'inferences,': [202], 'input,': [207], 'every': [208, 237], 'resulted': [211, 235], 'from': [212, 236, 362], 'determines': [215], 'tnorm.': [242], 'construction': [244, 263], 't-norm': [259], 'generalization': [261, 342], 'introduced': [267], 'Song': [269], 'Chissom.': [271], 'Selection': [272], 'singular': [279, 347, 434], 'value': [280, 348, 435], 'decomposition': [281, 349, 436], 'method': [282, 297, 340, 350, 403, 416, 437, 445], 'sensitivity': [284, 291], 'matrix.': [285, 321], 'Columns': [286], 'matrix': [289, 356], 'each': [293], 'variable.': [295], 'determine': [301], 'significant': [302, 308, 325], 'variables.': [304], 'Position': [305], 'equivalent': [312], 'position': [315], 'entry': [317], '“1”': [318], 'permutation': [320], 'Based': [322], 'degree': [334, 405], 'method.': [338, 345], 'Wang’s': [344], 'firing': [354], 'strength': [355], 'choose': [358], 'design': [374], 'model.': [376, 421], 'methods': [378], 'forecast': [382], 'inflation': [383, 393, 424], 'rate': [384, 387, 394, 397, 425, 428], 'interest': [386, 396, 427], 'Bank': [389, 399, 430], 'Indonesia': [390, 400, 431], 'certificate.': [391], 'Forecasting': [392], 'certificate': [401, 432], 'gives': [409, 438], 'better': [410, 439], 'accuracy': [411, 440], 'than': [412, 441], 'standard': [415, 444], 'conventional': [418, 447], 'forecasting': [423]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W173448675', 'counts_by_year': [], 'updated_date': '2024-12-07T01:21:07.255385', 'created_date': '2016-06-24'}