Peranan Tablet dalam Implementasi Paperless Office

Produksi kertas mencapai 331 juta ton pada tahun 2002 (Holik,2006). Angka ini naik tujuh kali lipat sejak tahun 1950. Usaha untuk mengurangi kenaikan ini dengan mengurangi penggunaan kertas telah dilakukan. Salah satu alternatif adalah mengaplikasikan kantor tanpa kertas, merubah yang tadinya menggunakan kertas menjadi bentuk digital. Tetapi, semenjak kantor digital dikumandangkan sejak 30 tahun yang lalu, penggunaan kertas malah naik seiring dengan propaganda adanya kantor tanpa kertas (York,2006).Pada awal pengenalan kantor tanpa kertas ada permasalahan yang muncul yaitu pada akses data. Dengan menggunakan kertas, data dapat dipindahkan dan dibawa dengan mudah kapan saja dibandingkan dengan harus mengakses data melalu computer. Tetapi dengan berkembangnya teknologi secara signifikan, dengan adanya tablet mempermudah pengguna untuk dapat mengakses data dan memodifikasi data kapan saja pengguna tersebut inginkan. Tablet ini mudah untuk dibawa dan memudahkan pengguna untuk bekerja secara mobile. Dapatkah tablet menjadi solusi untuk implementasi kantor tanpa kertas? Penelitian ini akan membahas mengenai peranan tablet dalam implementasi kantor tanpa kertas

Pairwise Preferences Learning for Recommender Systems

Preference learning (PL) plays an important role in machine learning research and practice. PL works with an ordinal dataset, used frequently in areas such as behavioural science, medical science, education, psychology and social science. The aim of PL is to predict the preference for a new set of items based on the training data. In the application area of Recommender Systems (RSs), PL is used as an important element to produce good recommendations. Many ideas have been developed to build better recommendation techniques. One of the challenges in RSs is how to develop systems that are proactive and unobtrusive. To address this problem, we have studied the use of pairwise comparisons in preference elicitation as a very simple way of expressing preferences. Research in PL has also discovered this kind of representation and considers it to be learning from binary relations. There are three contributions in this thesis: The first and the most significant contribution is a new approach based on Inductive Logic Programming (ILP) in Description Logics (DL) representation to learn the relation of order. The second contribution is a strategy based on Active Learning (AL) to support the inference process and make choices more informative for learning purposes. A third contribution is a recommender system algorithm based on the ILP in DL approach, implemented in a real-world recommender system with a large used-car dataset. The proposed approach has been evaluated by using both offline and online experiments. The offline experiments were performed using two publicly available preference datasets, while the online experiment was conducted using 24 participants to evaluate the system. In the offline experiments, the overall accuracy of our proposed approach outperformed the other 3 baseline algorithms, SVM, Decision Tree and Aleph. In the online experiment, the user study also showed some satisfactory results in which our proposed pairwise comparisons interface in a recommender system beat a common standard list interface

Learning from Ordinal Data with Inductive Logic Programming in Description Logic

Here we describe a Description Logic (DL) based Inductive Logic Programming (ILP) algorithm for learning relations of order. We test our algorithm on the task of learning user preferences from pairwise comparisons. The results have implications for the development of customised recommender systems for e-commerce, and more broadly, wherever DL-based representations of knowledge, such as OWL ontologies, are used. The use of DL makes for easy integration with such data, and produces hypotheses that are easy to interpret by novice users. The proposed algorithm outperforms SVM, Decision Trees and Aleph on data from two domains

On the benefit of logic-based approach to learn pairwise comparisons

In recent years, many daily processes such as internet web searching, e-mail filtering, social media services, e-commerce have benefited from Machine Learning (ML) techniques. The implementation of ML techniques has been largely focused on black box methods where the general conclusions are not easily interpretable. Hence, the elaboration with other declarative software models to identify the correctness and completeness of the models is not easy to perform. On the other hand, the emerge of some logic-based machine learning approaches that can overcome such limitations with their white box methods has been proven to be well-suited for many software engineering tasks. In this paper, we propose the use of a logic-based approach to learn user preference in the form of pairwise comparisons. APARELL as a novel approach of inductive learning is able to model the user’s preferences in Description Logic(DL) and then build a model by generalising the concept for all examples given. This offers a rich, relational representation beyond the usual propositional domain, which is then can be used to produce a set of recommendations. A user study has been performed in our experiment to evaluate the implementation of pairwise preference recommender system when compared to a standard list interface. The result of the experiment shows that the pairwise interface was significantly better than the other interface in many ways

Learning Binary Preference Relations : Analysis of Logic-based versus Statistical Approaches

It is a truth universally acknowledged that e-commerce platform users in search of an item that best suits their preferences may be offered a lot of choices. An item may be characterised by many attributes, which can complicate the process. Here the classic approach in decision support systems – to put weights on the importance of each attribute – is not always helpful as users may find it hard to formulate their priorities explicitly. Pairwise comparisons provide an easy way to elicitate the user’s preferences in the form of the simplest possible qualitative preferences, which can then be combined to rank the available alternatives. We focus on this type of preference elicitation and learn the individual preference by applying one statistical approach based on Support Vector Machines (SVM), and two logic-based approaches: Inductive Logic Programming (ILP) and Decision Trees. All approaches are compared on a dataset of car preferences collected from human participants. While in general, the statistical approach has proven its practical advantages, our experiment shows that the logic-based approaches offer a number of benefits over the one based on statistics

A genetic‐based pairwise trip planner recommender system

The massive growth of internet users nowadays can be a big opportunity for the busi- nesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user prefer- ence elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study

A Tree-based Mortality Prediction Model of COVID-19 from Routine Blood Samples

COVID-19 has been declared by The World Health Organization (WHO) a global pandemic in January, 2020. Researchers have been working on formulating the best approach and solutions to cure the disease and help to prevent such pandemics in the future. A lot of efforts have been made to develop a fast and accurate early clinical assessment of the disease. Machine Learning (ML) has proven helpful for research and applications in the health domain as a way to understand real-world phenomena through data analysis. In our experiment, we collected the retrospective blood samples data set from 1,000 COVID-19 patients in Jakarta, Indonesia for the period of March to December 2020. We report our preliminary findings on the use of common blood test biomarkers in predicting COVID-19 patient mortality. This study took advantage of explainable machine learning to examine the data set. The contribution of this paper is to explain our findings on predicting COVID-19 mortality, including the role of the top 11 biomarkers found in our dataset. These findings can be generalized, especially in Indonesia, which is now at its highest peak of the epidemic. We show that tree-based AI models performed well on predicting COVID-19 mortality, while also making it easy to interpret the findings, as they lend themselves to human scrutiny and allow clinicians to interpret them and comment on their viability

Analysis of Indonesia’s Fish Consumption with Regression Method using Go Language

The study is made to predict the amount of fish consumption in Indonesia throughout the years 1960 to current year. The amount of fish production and catches will be used as supplementary information to help validate the fish consumption rate. This study is conducted using the Go programming language to prove that even though Go is a general programming language that is rarely being used for data science, it can still be used to perform analytics and machine learning while out-performing other languages that are usually used to do data science like Python and R. There are two primary datasets that are being used in this study, them being the fish captures dataset and the fish consumption dataset. These two datasets will later be parsed and processed to a single file before being fed to the linear regression and decision tree models to achieve the objective of predicting Indonesia’s fish consumption. The Linear Regression model created from our Go Program has predicted a successful model that has a very low R2 score of the predicted regression value vs the true value. Additionally using Go a Decision Tree model has also been created to further strengthen the results of our models given they agree with each other. Both models actually show very high correlation with their final predictions which is 92%. The result of this study solidifies 3 points and that is that Go is a very capable language to be used for data science, linear regression performs better than decision tree in this given scenario that is being used, and finally the fish consumption rate of Indonesia is rising at a much greater rate the world has seen in 1900s