PREFERENSI KONSUMEN BERDASARKAN KELOMPOK USIA TERHADAP VARIASI RASA, WARNA, DAN BENTUK WAFER

Wafer adalah produk makanan kering berbasis tepung terigu, yang memiliki pori-pori besar, renyah, serta penampangnya berongga jika dipatahkan. Di pasaran, ada dua jenis bentuk wafer yang umum dijumpai yaitu berbentuk persegi dan persegi panjang. Pada saat ini, wafer diproduksi dengan banyak varian rasa, di antaranya adalah coklat, vanila, stroberi, dan masih banyak lagi. Untuk membuat produk lebih disukai masyarakat daripada produk pesaing, produsen perlu membuat produk yang berbeda dari yang lain serta mempunyai keunggulan inovatif. Produsen dapat menambah atau memberikan variasi bentuk pada produk yang sudah ada serta memperluas segmen pasar dengan melayani berbagai keanekaragaman konsumen yang mempunyai selera yang berbeda-beda. Atribut dari wafer yang dapat dikembangkan antara lain varian rasa, warna krim, warna sheet, dan bentuk wafer. Varian rasa, warna krim, dan warna sheet pada wafer masih dapat diperbanyak lagi sehingga konsumen dapat lebih leluasa lagi untuk memilih varian rasa dan warna yang mereka inginkan. Banyak sekali potensi inovasi varian rasa, warna krim, dan warna sheet yang dapat diterapkan pada produk wafer. Tujuan dari penelitian ini adalah untuk mengetahui preferensi konsumen terhadap varian rasa, warna, merek, dan bentuk wafer berdasarkan kelompok usia dan mengetahui hubungan antara preferensi konsumen terhadap varian rasa, warna, dan bentuk wafer dengan produk yang dijual di pasaran. Penelitian ini dilakukan menggunakan pendekatan penelitian survei yang diawali dengan tahap pengumpulan data, data cleaning, clustering, weighting, survei produk, dan pengambilan kesimpulan. Pengumpulan data menggunakan kuesioner yang dibagikan kepada masyarakat yang berusia produktif. Variabel dalam penelitian ini adalah recency, frequency, dan monetary. Survei dibagi menjadi 2 tahap yaitu survei pendahuluan dan survei lanjutan. Dalam survei pendahuluan, kuesioner diuji validitasnya menggunakan Korelasi Pearson dan reliabilitasnya menggunakan Cronbach Alpha. Kemudian dilakukan survei lanjutan dan seluruh data yang didapatkan dilakukan clustering. Hasil penelitian terhadap merek yang disukai menunjukkan merek yang mendapatkan skor yang tertinggi oleh responden adalah wafer merek Tango. Sedangkan merek yang mendapatkan skor terendah adalah wafer merek Oreo. Hanya responden dengan kelompok usia 15 sampai 24 tahun yang menyukai bentuk wafer persegi dan persegi panjang. Sedangkan responden kelompok usia yang lain lebih menyukai bentuk wafer persegi panjang saja. Varian rasa cokelat, keju, vanila, stroberi merupakan varian rasa wafer yang paling disukai oleh semua kelompok usia. Varian rasa pisang, durian, jeruk, matcha, red velvet, pandan, manga, taro, lemon, dan apel merupakan varian rasa baru yang diinginkan oleh responden. Berdasarkan hasil survei pasaran yang telah dilakukan, sebagian besar data merek, bentuk wafer, dan varian rasa yang telah dikumpulkan dari beberapa penjual sudah sesuai dengan keinginan responden

“I Think i Discovered a Military Base in the Middle of the Ocean”—Null Island, the Most Real of Fictional Places

This paper explores Null Island, a fictional place located at 0° latitude and 0° longitude in the WGS84 (World Geodetic System 1984) geographic coordinate system. Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. Whereas it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness. The article summarizes error sources that lead to data being associated with Null Island. We identify four evolutionary phases which help explain how this fictional place evolved and established itself as an entity reaching beyond the geospatial profession to the point of being discovered by the visual arts and the general population. After providing an accurate account of data that can be found at (0, 0), geospatial, technological and social implications of Null Island are discussed. Guidelines to avoid misplacing data to Null Island are provided. Since data will likely continue to appear at this location, our contribution is aimed at academics, computing professionals and the general population to promote awareness of this error source

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

Toward relevant answers to queries on incomplete databases

Incomplete and uncertain information is ubiquitous in database management applications. However, the techniques specifically developed to handle incomplete data are not sufficient. Even the evaluation of SQL queries on databases containing NULL values remains a challenge after 40 years. There is no consensus on what an answer to a query on an incomplete database should be, and the existing notions often have limited applicability. One of the most prevalent techniques in the literature is based on finding answers that are certainly true, independently of how missing values are interpreted. However, this notion has yielded several conflicting formal definitions for certain answers. Based on the fact that incomplete data can be enriched by some additional knowledge, we designed a notion able to unify and explain the different definitions for certain answers. Moreover, the knowledge-preserving certain answers notion is able to provide the first well-founded definition of certain answers for the relational bag data model and value-inventing queries, addressing some key limitations of previous approaches. However, it doesn’t provide any guarantee about the relevancy of the answers it captures. To understand what would be relevant answers to queries on incomplete databases, we designed and conducted a survey on the everyday usage of NULL values among database users. One of the findings from this socio-technical study is that even when users agree on the possible interpretation of NULL values, they may not agree on what a satisfactory query answer is. Therefore, to be relevant, query evaluation on incomplete databases must account for users’ tasks and preferences. We model users’ preferences and tasks with the notion of regret. The regret function captures the task-dependent loss a user endures when he considers a database as ground truth instead of another. Thanks to this notion, we designed the first framework able to provide a score accounting for the risk associated with query answers. It allows us to define the risk-minimizing answers to queries on incomplete databases. We show that for some regret functions, regret-minimizing answers coincide with certain answers. Moreover, as the notion is more agile, it can capture more nuanced answers and more interpretations of incompleteness. A different approach to improve the relevancy of an answer is to explain its provenance. We propose to partition the incompleteness into sources and measure their respective contribution to the risk of answer. As a first milestone, we study several models to predict the evolution of the risk when we clean a source of incompleteness. We implemented the framework, and it exhibits promising results on relational databases and queries with aggregate and grouping operations. Indeed, the model allows us to infer the risk reduction obtained by cleaning an attribute. Finally, by considering a game theoretical approach, the model can provide an explanation for answers based on the contribution of each attributes to the risk