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INDONESIA
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Published by Universitas Brawijaya
ISSN : -     EISSN : -     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering
Jurnal Pengembangan Teknlogi Informasi dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya merupakan jurnal keilmuan dibidang komputer yang memuat tulisan ilmiah hasil dari penelitian mahasiswa-mahasiswa Fakultas Ilmu Komputer Universitas Brawijaya. Jurnal ini diharapkan dapat mengembangkan penelitian dan memberikan kontribusi yang berarti untuk meningkatkan sumber daya penelitian dalam Teknologi Informasi dan Ilmu Komputer.
Arjuna Subject : -
Articles 6,923 Documents
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Pengembangan Sistem Informasi Pengelolaan Klinik Gigi Berbasis Website Menggunakan Prinsip Point of Sale (Studi Kasus: Klinik Gigi Senyum Sehat Dental Care) Rafiqah Majidah; Denny Sagita Rusdianto; Komang Candra Brata
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Senyum Sehat Dental Care is a dental clinic which has been operating for 3 years. The number of patients recorded by the dental clinic is numbered approximately at 500. Along with the development and the increasing number of patients of this dental clinic, various problems arise, one of the problems that arise is lack of work efficiency in terms of time and exerted effort. The next problem is lack of information on the amount of dental care ingredients availability. Based on these problems, it is necessary to develop an efficient system to assist the dental clinic to better save time and exerted efforts. In order to test whether the system that has been developed is correct according to the needs, unit, integration, and validation testing with white-box and black-box method used in this case. Non functional testing takes efficiency aspects by testing the system performance to see the load time, then comparing the time needed before and after using the system. The results of validation, unit, integration, and performance testing are 100% valid. This system can increase time efficiency 30 times faster than before using the system.
Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Haralick Feature Extraction dengan Naive Bayes Classifier Gabriel Mulyawan; Yuita Arum Sari; Muhammad Tanzil Furqon
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

As living things, humans need to survive. One of the basic need human's bodies require to survive is food. Foods provide nutrients that contain carbohydrates, protein, minerals, fats, and vitamins for boosting endurance. Basically, foods can be easily identified with human's eyes. But it is not like the brain-computer that require the introduction or features extraction from food objects for classification. The features extraction used are HSV Color Moment for color features and Haralick for texture features. Then, the results of the features extraction will be classified using the Naive Bayes classifier method. The data set used are based of the primary data that contains pictures and the pictures were taken by the smartphone camera consist of 276 foods images.. This research uses 2 testing processes, that are the comparison of the amount of the training data and testing data, and the testing of the used features. Based on the testing of the comparison of the amount of the training data and the training data using K-Fold Cross Validation, it showed that the best accuracy is 61,95% that using 166 training data images and 110 training data images. Then, the accuracy from the features test that was just using the HSV Color Moment feature is about 57,66%. The accuracy from test that using the Haralick feature is 36,67%. The accuracy from the combination of 2 features of the HSV Color Moment and Haralick are better than only using the texture features with the 56,33% accuracy. The image processing technique using HSV Color Moment and Haralick features extraction can be used for foods image classification using the Naive Bayes classifier method.
Prediksi Rating Otomatis Berdasarkan Review Restoran pada Aplikasi Zomato dengan menggunakan Extreme Learning Machine (ELM) Diajeng Tania Ananda Paramitha; Imam Cholisoddin; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this modern culture, technology advancement are growing better than we ever discovered before. One of the apps we use to search for information about restaurant in Jakarta are known as Zomato. Zomato is an application that provides various information about a restaurant from it facility, price, review, and rating. Users of The Zomato App can input various information that people haven't aware of about the restaurant into the app. Besides of inputting information into the app, Users of The Zomato App can also input a review and rating of a specific restaurant. The data review is used as an information about the restaurant for the potential customer from The Zomato App but sometimes the data review doesn't yet include a restaurant rating. This lack of misinformation will surely make the restaurant owner to occure some difficulties such as improving the restaurant services status for future outcomes. This research helps to classifying the review into the rating. Test protocol of this research are using a prediction with Extreme Learning Machine (ELM) Methods as it core. The prediction process however are build from a several steps such as pre-processing, word weighting with TF-IDF, and Extreme Learning Machine (ELM) Method calculations. Test result of The ELM parameter provides accuracy result 80,01% with k=10 amount hidden neuron 25 Interval weights -0.5 until 0,5 using function activation Sigmoid biner. We have come to conclusion were ELM method could positively solve the prediction problem exquisitely.
Peringkasan Review Konsumen Restoran Menggunakan Weighted Frequent Itemset Mining Moh Iqbal Yusron; Fitra Abdurrachman Bachtiar; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Review written by customer toward a restaurant can be useful for prospective customer or owner of the restaurant to knows the others opinion about the restaurant. However, this can cause a problem if customer review comes in large number. Automatic text summarization system can be a good solution to this problem. One of the best known method for automatic text summarization is TF-IDF weighting. Yet, this method also has a weakness for having tendency to extract long sentences as summary which has high score for contaning many words. In this research, writer propose an approach to use automatic text summarization which not only extract sentences based on its weight but also the ones which covered some words. This is because in the sentences which considered as summary, exist some words which appear together frequently (frequent itemset). Therefore, in this research Weighted Frequent Itemset method is used to summarize customer review for restaurant. This method summarize text by extracting sentences which covered many frequent itemsets and has high sentence relevance score. The result from the test shows that summarization using Weighted Frequent Itemset Mining method archieve average F-measure 0.279.
Sistem Pengukuran Tinggi dan Berat Badan Berdasarkan Perhitungan Body Surface Area (BSA) Menggunakan Boundingbox Berbasis Raspberry Pi Mohammad Isya Alfian; Hurriyatul Fitriyah; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

BSA (Body Surface Area) is used as a way to get information on a person's body condition. Paramedics use these calculations in patient data collection and require a long time. In previous studies using image processing techniques to speed up data retrieval. Data retrieval is carried out front and side view then processed using pixels by cropping the image. From these studies, researchers have the idea of using a mini pc to further speed up data retrieval and be efficient. BSA calculations are included in the mini pc Raspberry Pi and assisted by webcam cameras as image takers. This tool is useful to help paramedics to collect data on body weight and height, faster and more efficiently. The object of the research that was sampled was 20 people, male and female, consisting of children and adults. Data collection was carried out 22 times each in children and adults. The data obtained is calculated by the Bounding Box accuracy value. The results of height accuracy in boys were 91.4%, girls were 87.8%, male adults were 98.34%, and female adults were 98.2%. While the results of accuracy for weight are based on the k value of each object. The k value for boys is 1.34 with an accuracy of 75.32%, girls 1.34 with an accuracy of 79.76%, male adults 1.26 with an accuracy of 95.6%, and adult women 1 , 22 with an accuracy of 92.38%.
Evaluasi dan Perbaikan Proses Bisnis dengan Quality Evaluation Framework (QEF), Root Cause Analysis (RCA), dan Teknik Esia (Studi Kasus: Pelayanan Pasien BPJS Rawat Jalan Rumah Sakit Islam Aisyiyah Malang) Tirta Saraswati; Ismiarta Aknuranda; Nanang Yudi Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Islam Aisyiyah Hospital Malang is the one of private hospitals that acccepted inpatient and outpatient BPJS services. The business process of outpatient BPJS patient services Islam Aisyiyah Hospital is a service for BPJS patients by providing examinations and medication in one day without hospitalization. The BPJS outpatient services can be done in two ways, through the UGD if the patient condition emergency and through the Polispesialis if the patient is referral from a clinic or health centre. During the implementation of the BPJS outpatient services there were still problems about the detection of the referral number, the poly destination, and the patient's hospital destination in the referral letter at BPJS application and the patient's file was exchanged during the registration process. Based on these problems, it is necessary to evaluate business processes using Quality Evaluation Framework (QEF) to knows the suitability of achieving the company's by the business processes that have been modeled using Business Process Modeling and Notation (BPMN). The next step is to find the root problems with Fishbone Analysis. After that, the next step is to improve business processes using ESIA techniques (Eliminate, Simplify, Integrate, and Automation). And then, the results of business process simulation the outpatient services of BPJS at Polispesialis increased by 16.69% and at the UGD the Islam Aisyiyah Hospital increased by 4.01%.
Implementasi Mekanisme Load Balancer dan Failover pada IoT Middleware berbasis Publish-Subscribe Ahmad Naufal Romiz; Eko Sakti Pramukantoro; Widhi Yahya
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Previously, IoT middleware was built to overcome the problem of syntactical interoperability (silo). The IoT middleware was implemented on the Raspberry Pi. In addition, to make IoT middleware functions more scalable, cluster message edge storage was developed which can increase memory capacity on the IoT middleware node (Raspberry Pi engine). In that system, the entire message delivery process was limited to one IoT middleware node which function is as a broker. That results in unbalanced loads by stacking on one IoT middleware node. The problem of unbalanced loads, can be overcome by adding a load balancer on the system, with multiple IoT middleware as the aim of traffic load balancing. Round robin algorithm is used in the research as a traffic distribution method by load balancer. Load balancers are developed as a single entry point on a system. Two devices are used as load balancer. Keepalived is also implemented so that a failover mechanism in the node load balancer can be occured. Testing was carried out to determine the time process done by IoT middleware on publish and subscribe messages. In addition, the testing was also used to determine the number of messages per second which IoT middleware can handle. From the testing result, the average concurrent publish value of CoAP is 62 messages/second on a system without a load balancer and 63 messages/second on a system with a load balancer. The concurrent publish average value of MQTT is 41 messages/second on systems without load balancers and 73 messages/second on systems with load balancers. The concurrent subscribe average value is 37 messages/second on the system without a load balancer and 68 messages/second on the system with a load balancer.
Pembangunan Kakas Bantu untuk Memprediksi Failure Rate dari Perangkat Lunak menggunakan Musa's Algorithm Dindy Fitriannora; Bayu Priyambadha; Fajar Pradana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Software development cycle has many phases and software will undergo one of these phases, namely the testing phase where unit of program or program will be tested before being given to client to ensure the software already has things that match the needs that have been defined and each unit is in accordance with its specifications. Moreover, purpose of the testing phase is to find program defects before use. Ability of software to maintain its performance level under specified conditions for a time period is called reliability. Measurement of software reliability is centered on failures and failure intensity where failure intensity is the number of occurrences of failure in a certain time period. Failure intensity can be seen from predictable failure rates with many techniques, one of which is Musa 's algorithm that can be used at the start of testing. This system has been tested using the white box testing method with basis path testing techniques in unit testing and integration testing and black box testing methods in validation testing. This system has an accuracy rate of 100% and the duration to calculate the failure rate using the system is an average of 2000 times faster than manual calculation.
Prediksi Harga Saham menggunakan Metode Backpropagation dengan Optimasi Ant Colony Optimization David Bernhard; Muhammad Tanzil Furqon; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 5 (2019): Mei 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stocks are a sign of a person's or party's investment contribution to a company or limited liability company. Movement of stock prices affects the profits and losses that will be obtained by the investor. The obstacle is stock prices can change in every minute on weekdays. It takes a method that is able to predict stock prices accurately and consistently, so that it can minimize the risk of stock investment. Besides its advantages, BPNN has shortcoming, such as slow convergence time, easy convergence to local minimum points, and poor generalization capabilities. ACO has advantages in distributed computing, positive feedback, and metaheuristic properties that can improve the weaknesses of BPNN. This study uses time series data from the stock price of Bank Rakyat Indonesia (Persero) Tbk. period 1 January 2018 until 31 December 2018. ACO serves to optimize the value combination of learning rate, momentum, and number of hidden nodes for BPNN training phase. Best combination of ACO parameter values was obtained, namely the ant cycle constant worth 0.8, the control constant of pheromone intensity worth 0.1, the visibility control constant worth 0.1, the local pheromone evaporation constant worth 0.5, global pheromone evaporation constant worth 0.1, number of ants 5, and number of iterations 7. That combination produces an average of MAPE 1.745, while BPNN only reached 3.024.
Pengembangan Sistem Aplikasi Manajemen Distribusi Pupuk Berbasis Web (Studi Kasus: PT Petrokimia Gresik) Moh. Wahyu Dwi Ridiansyah; Fajar Pradana; Nurudin Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 6 (2019): Juni 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PT Petrokimia Gresik is a state-owned enterprise that produces subsidized and non-subsidized fertilizers. In the distribution activities, there were several problems found. The first problem felt by a staff of the Department of Regional 1 was having problems in processing reporting data from the buffer warehouse because of the large amount of data available. The second problem was felt by the Regional 1 Manager who complained of the difficulty of monitoring the condition of the stock in the area quickly and efficiently. The third problem was complained by the Department of Regional 1 staff regarding the reporting of damaged fertilizer in Warehouse Buffer, the process that occurs now is inefficient and there are no media that can monitor the delivery of damaged fertilizer to the central warehouse for recycling. The fourth problem complained by the staff in the inventory section complained that the process was not efficient because the results of the stock inventory must be manually adjusted to the stock listing. Based on these problems, the system developer will do this research that can solve the problem. The waterfall method is used in the software development process in this study. The stages carried out in this study are literature study, needs analysis, system design and implementation, testing, taking conclusions and suggestions. The development of this system is web-based using the CodeIgniter framework. To create a map of the condition of the regional stock used the Google Maps API is equipped with KML layering. Unit and integration testing is done using the white box testing method with 100% valid results. Validation testing uses the black box testing method with 100% valid results. Compatibility testing is done with three different browsers with 100% valid results.

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