C4.5 Decision Tree Implementation in Sistem Informasi Zakat (Sizakat) to Automatically Determining the Amount of Zakat Received by Mustahik

In general, Zakat Information Systems is established to manage the zakat services, so that the data can be well documented. This study proposes the existence of a feature that will determine the amount of zakat received by Mustahik automatically using C4.5 Decision Tree algorithm. This feature is expected to make the process of determining the amount of zakat be done easy and optimal. The data used in this study are the data taken from Masjid An-Nur, Pancoran, South Jakarta. The experiment results show that the proposed feature produces an accuracy rate over 85%

MEASURING QUALITY OF WIRELESS LOCAL AREA NETWORK USING QUALITY OF SERVICE FRAMEWROK

The internet has a very important role in education. Many activities are done through internet. AMIK DCC Bandar Lampung is an educational institution that uses the internet as one of the supporting facilities and infrastructures to manage and organize the data and information used by the student to find references about the lecture. There are three buildings on the main campus building A building B and C buildings, where each building using WLAN to deploy internet access. There are some complaints submitted by students related wireless network encourage researchers to study the quality of service WLAN. Thus the researchers wanted to analyze the Quality of Service WLAN networks in building A, building B, and C, in each floor. The method used in this research is Quality of Services (QoS). Where an analysis wireless network by four parameters. There are namely delay, packet lost, bandwidth, and throughput. The results of the measurement and monitoring of Quality of Service WLAN at AMIK DCC Bandar Lampung in building A, building B, C on each floor of the building can be classified in the category of good which value index is 3, and the factors that occurred in the signal range cannot cover every room in every building and have not good bandwidth management

Tinzenite: Encrypted Peer to Peer File Synchronization via the Tox Protocol

We proposed and implemented an open source, peer to peer, file synchronization software based on the Tox communication protocol. Targeted features include full secure communication between peers, an encrypted server peer, and a focus on ease of use while retaining data security. The software suite was implemented based on the Tox protocol, with Golang as the programming language, and the server client built to offer free choice of storage mechanisms, for which we implemented support for the Hadoop distributed file system. The proof of concept implementation was shown to work and further possible work discussed

Hospital Length of Stay Prediction based on Patient Examination Using General features

As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT).  In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization

Compositional Modeling: A Classical Imitative Pedagogy for the Modern Era

Students of composition develop two skills simultaneously: craft and creativity. Since students are more naturally inclined to focus on one of these skills over the other, finding the proper balance is an educational challenge. An effective pedagogical system for composition cultivates both of these necessary skills. Imitation was a preferred pedagogical approach during the Classical period. In his 1848 book School of Practical Composition, Carl Czerny instructs his readers to apply the extensive rules of composition by strictly following a compositional model. Although this methodology lost favor during the nineteenth century, the imitative techniques presented by Czerny are flexible enough for learning to compose in musical styles beyond the Classical period. Through model-centered instruction, students learn to solve compositional problems by discovering how eminent composers overcame comparable difficulties. By engaging in the process of imitation, students create and test theories of composition based on their understanding of the model works they are studying. From developmental writing to establishing thematic unity, compositional modeling provides students with a practical method to engage with the most challenging aspects of composition. This study addresses various approaches towards choosing an appropriate compositional model, including the artistic opportunities available when imitating programmatic music. Another feature of this study involves surveying multiple works that belong to the same compositional lineage and searching for a variety of possible extended family connections. Finally, this dissertation presents a tutorial on incorporating two model works into a composition with a more modern musical style. This demonstration displays compositional modeling’s ability to create a unique blend of disparate models and help the student find a distinctive compositional voice. This dissertation reveals the pedagogical power of compositional modeling by demonstrating its ability to reunite craft and creativity and unlock those mysterious aspects of artistry resistant to being analyzed and distributed in a modern academic setting. Compositional modeling creates a timeless bond between the teacher and the student, and it is this kind of enduring apprenticeship that can pass on the mastery of technique and inspire the creativity needed to lead student composers to reach for the excellence of their models and beyond

Improved Classification of Blockchain Transactions Using Feature Engineering and Ensemble Learning

Although the blockchain technology is gaining a widespread adoption across multiple sectors, its most popular application is in cryptocurrency. The decentralized and anonymous nature of transactions in a cryptocurrency blockchain has attracted a multitude of participants, and now significant amounts of money are being exchanged by the day. This raises the need of analyzing the blockchain to discover information related to the nature of participants in transactions. This study focuses on the identification for risky and non-risky blocks in a blockchain. In this paper, the proposed approach is to use ensemble learning with or without feature selection using correlation-based feature selection. Ensemble learning yielded good results in the experiments, but class-wise analysis reveals that ensemble learning with feature selection improves even further. After training Machine Learning classifiers on the dataset, we observe an improvement in accuracy of 2–3% and in F-score of 7–8%

ERROR ANALYSIS IN ENGLISH-INDONESIAN MACHINE TRANSLATE

Since the advent of the 21st century, there have been a lot of developments and new technologies have beenintroduced which have made life more convenient and simple. Although not appropriate for all situations,machine translation (MT) is now being used by many translators to ease their work. Many others use MT to get aquick grasp of foreign text that they would not understand. The quality of Google Translate depends on thenumber of human translated texts searched by Google Translate. Therefore, the quality of the translation hasbeen considered far from perfection. Thus in order to evaluate the quality of machine translation, error analysishas been suggested to be conducted. This paper presents the results of a research study focusing on the types ofGoogle translation errors found in the English translation of procedural text. The purposes of this paper are (i)the results of English-Indonesian machine translation, categorizing errors in machine translation into 3 types:semantic errors, syntax errors and morphology errors, and (ii) to describe the dominant kind of translation errorproduced by Google Translate. This study revealed that The most frequently occurring errors were form categoryof sematic (i.e., 44 errors out of 97 or 45.36%). Syntax errors ranked second (i.e. 34 errors out of 97 or 35.05%)and morphology errors ranked third (i.e., 19 errors out of 97 or 19.59%).   Keywords: machine-translate, error analysis, English, Indonesia