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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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Sistem Monitoring Volume Dan Gas Sampah Menggunakan Metode Real Time Operating System (RTOS) Tugar Aris Andika Prastiyo Raharjo; Sabriansyah Rizqika Akbar; Rakhmadhany Primananda
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rubbish is things that can't be used anymore and it is usually dirty and smelly. A pack of rubbishes in trash bin produce gasses that can give bad impacts to health, like methane (CH4), ammonia (NH3), and hydrogen sulphide (H2S). If the methane concentration is too high, then it will cause asphyxiation and fire. Besides that, rubbish volume also should be noticed because it can cause uncomfortable environment condition if the rubbish has exceeded the volume of trash bin. This research will design a technology system which can help the owner of trash bin to know the trash bin condition in real time and continuously. That system observes the rubbish volume and gases produced by sensors of NH3, CH4, H2S and infrared E18-DN80NK, by using Real Time Operating System (RTOS). RTOS is used as one of the sensor readings and delivery tasks that are handled by the microcontroller in real-time. From the results of 10x testing done on the rotten garbage is known to read the sensor MQ-135 has an average ammonia level of 35.71 PPM, MQ-4 has a methane content of 293.5 PPM, TGS2602 has 9.738 PPM of hydrogen sulphide and Infrared E18-DN80NK sensor can detect the height of the waste by output above the threshold (waste exceeds the sensor limit) and below threshold when the waste has not exceeded the sensor limit. So the level of gas produced by waste is methane gas.
Implementasi Perangkat Mobile Publisher Subscriber Sebagai Perantara Pengiriman Data Sensor Dari Lapangan Ke Pusat Data Sukma Alamsyah Budianto; Adhitya Bhawiyuga; Dany Primanita Kartikasari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sensor devices implemented in the field facing a problem when sending data on unstable network conditions to the data center. To solve these problems, we develop an application based on the Android operating system implementing publish/ subscribe communication as an intermediary for sending sensor data in the field to the data center using the MQTT protocol. To prove the application can operate as an intermediary for sending sensor data in the field to the data center, three tests are carried out. There are functional testing, scenario testing and performance testing. In functional tests, five functional requirements designed can be fulfilled by the application as an intermediary for sending sensor data to the data center. In scenario tests, from the three test scenarios performed the results of the application can be carried out without any application errors. In performance testing by calculation application performances and get the results of Subscriber Throughput is equal to 161.55 message/s, Publihser Throughput is equal to 2.13 message/s, the latency message average Subscriber apps are 1488.28 ms and the publisher message latency average app is 5208.86 ms.
Evaluasi Pada Variasi Proses Bisnis Jenis Pembayaran Tagihan Air dengan Menerapkan Process Mining dan Quality Evaluation Framework (QEF) Pada Perusahaan Daerah Air Minum Kota Malang Kartika Utami; Nanang Yudi Setiawan; Djoko Pramono
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PDAM Malang City is one of Regional Owned Enterprises (BUMD) which provides clean water in Malang. As an organization in the service sector, PDAM Malang City is expected to give the best service for Malang's citizen in various business process viewpoint. A dominant service in PDAM is water payment which defined in three payment status based on an interview to resolve different customer characteristic. It is necessary to do an evaluation of the business process to guaranty best service in PDAM and understand business process quality which is currently running. The water payment business process is modeled by Yet Another Workflow Language (YAWL). Furthermore, the business process is simulated to acquire event log from the models. The event log is used to find business process variant using process mining method and ProM Tools. Evaluating the business process has been implemented by the Quality Evaluation Framework (QEF) method. There are 6 Quality Factors which doesn't suit with organization target. It shows that there are gaps between targets and performances in the organization. Further evaluation related to the business process variant so it can be concluded that the emergence of nonconformance in Quality Factor business process is not influenced by business process variant existence. And the existence of business process variant is not influenced by business process evaluation type of water payment in PDAM Malang City.
Pengembangan Aplikasi E-Learning Dengan Menerapkan Metode Gamification Irwan Suprianto; Fajar Pradana; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Online learning platform, also known as e-learning is a very popular media for education these days. the lack of interest from the students to the media used by the school are the main cause for the ineffective of the e-learning. interest and student satisfaction are influenced by some factor such as the feature and content does not help the student finishes their assignment so they decided not to use the application again. in order to combat these problems, a new e-learning app are developed implementing game method for the feature and content. This method are called Gamification. interest is the main target for gamification
Klasifikasi Dokumen SAMBAT Online Menggunakan Metode Naive Bayes dan Seleksi Fitur Berbasis Algoritme Genetika Tony Faqih Prayogi; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 3 (2019): Maret 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Integrated Community Asking Application System (SAMBAT) Online is one of application that becomes an eGov system in Malang City to provide a place for the people of Malang City to voice their aspirations towards problems that exist for the good of the city itself. All complaints that enter through SAMBAT Online have been grouped based on the existing parts and later will be sorted manually and forwarded to the respective Regional Work Unit (SKPD) so that they can be immediately followed up. But because of the number of complaints received so long enough to be processed by each SKPD. Therefore a system was created for the classification of SAMBAT Online documents. In this study implemented a naive bayes method and genetic algorithm-based feature selection for the SAMBAT Online document classification. The implementation process itself consists of preprocessing, term weighting, Feature Selection using genetic algorithms and the classification process using naive bayes method. The results of the tests that have been done, obtained the highest accuracy of 89.79% in the test of 49 data test with the parameter value of generations 70, population size 20, crossover rate 0.8 and mutation rate 0.2.
Diagnosis Penyakit Ikan Koi Menggunakan Metode Naive Bayes Classifier Yudo Juni Hardiko; Nurul Hidayat; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Koi fish (Cyprinus carpio) is a type of freshwater ornamental fish that is widely cultivated because it has an attractive body shape and color. Koi morphology is almost similar to other fish species, koi body covered by two layers of skin, the outer skin (epidermis) and the skin (dermis). Epidermis is useful as a protective skin from the outside environment or as protection such as impact, dirt, and pest. Disease attacks and parasitic infections are a common problem faced by fish farmers. Diseases that often attack koi caused by pathogens in the form of bacteria, fungi, or viruses. The pathogens that live in the body of koi is very harmful because it will indirectly affect the color of koi fish. Koi fish diseases generally have some common symptoms that are almost the same as excessive mucus, punctured wounds or lumps on the body of fish and koi fish so menyediri. With so many diseases that have the same symptoms it makes fish farmers difficult to diagnose diseases in koi fish. Many methods can be used to create an system one of them is by using the method of Naive Bayes Classifier. In this system receive input in the form of data koi fish disease symptoms and the data is then processed using the method of Naive Bayes the results of system output in the form of diagnosis of diseases and treatment of disease outcomes that are diagnosed. Based on the accuracy testing of 20 data yields an accuracy of 90%.
Implementasi Kerangka Kerja Cobit 4.1 Domain Acquire and Implement (AI) Terhadap Tata Kelola Teknologi Informasi (Studi Kasus: Dinas Komunikasi dan Informatika Kota Bukittinggi) Fandy Adityo; Suprapto Suprapto; Admaja Dwi Herlambang
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Department of Communication and Information of Bukittinggi is one of the government institutions that have the authority to handle the government affairs on the field of communication and information. It facing some problems on governing and implementing their own assets. Hence, the IT governance evaluation was required using the COBIT 4.1 frameworks to all of the processes on Acquire and Implement (AI) domain. The purpose of this research was aimed to get know how is the existing condition of IT governance on Department of Communication and Information of Bukittinggi by assessing the maturity level and giving the recommendation in the form of the draft of the documents. The data was collected by doing several methods such as questionnaires, interviews, and observation. Respondents are defined by analyzing the RACI Chart. The result for the maturity level has an average of 1.57 and the gap analysis was 1.00. The target level was defined to be one level ahead of the existing conditions. To achieve that targets, the recommendation that given was by making Standard Operational Procedure (SOP) on all of the processes on Acquire and Implement (AI) domain and completing the documentation by doing it with a formal and standardized format. It will be later implemented on Department of Communication and Information of Buktttinggi so, the IT governance can be good and controlled.
Implementasi Sistem Klasifikasi Sampah Organik dan Anorganik dengan Metode Jaringan Saraf Tiruan Backpropagation Fungki Pandu Fantara; Dahnial Syauqy; Gembong Edhi Setyawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Organic and inorganic waste have different decomposition time. Organic waste has longer decomposition time than inorganic waste. So, organic and inorganic waste have a different ways of handling recycling. Sorting garbage before being accommodated to landfills (TPA) is very important to reduce the amount of garbage dump that keeps increasing every year. This research examines the implementation of classification system of organic and inorganic waste by using artificial neural network method backpropagation. Artificial neural network architecture applied is 3 neurons in the input layer, 1 layer hidden with 4 neurons, and 1 neurons on the output layer. Data training is not performed on built systems but on additional systems to search for weights, so the built system only predicts data directly from sensor readings. Based on this research, the system can be built using 3 sensors which are used as input data, they are: Light Dependent Resistor (LDR), inductive proximity, and capacitive proximity and a servo output which can open the lid automatically based on the classification result done by system. The system has 90% accuracy with the performance of each prediction takes 42.9ms average time.
Sistem Diagnosis Penyakit Kelinci Menggunakan Metode Fuzzy Tsukamoto Gustian Ri'pi; Nurul Hidayat; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rabbit is one of the many pets maintained by the general public in Indonesia. Like others pet, rabbits are also susceptible to various diseases. This will cause harm to rabbit farmers if not treated properly. Most rabbit farmers have difficulty identifying the type of rabbit disease and the way of treatment, so they should consult directly with the veterinarian to get the right solution. To fix this problem, then in this research a system was created to help rabbit farmers in identifying diseases in rabbits quickly and precisely. The system is made in the platform android application so users can diagnose diseases flexibly whenever and wherever. This research using Fuzzy Tsukamoto method to calculate recommendations for diseases detected. In application, begins with the formation of fuzzy sets. Then rule formation in the inference machine using the MIN implication function. The final step is calculating the z value of each rule using a weighted average. The biggest z value is used as a recommendation for a detected disease. The data used were 16 types of rabbit disease with 49 symptoms of the disease obtained from interviews with one of the veterinarians in Malang City. The results of the implementation and testing of accuracy in this research amounted to 95% of the 20 test data indicating that the system was working properly.
Pemanfaatan Ciri Gray Level Co-Occurrence Matrix (GLCM) Citra Buah Jeruk Keprok (Citrus reticulata Blanco) untuk Klasifikasi Mutu Restu Widodo; Agus Wahyu Widodo; Arry Supriyanto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The most important thing of production result of fruit is quality. Especially in citrus, it is related to selling value. 92% production of citrus is “Keprok”. But now the quality classification in the fruits industry is still done manually, so it becomes subjective. Information technology is needed to speed up the process of quality classification and make it an objective. This research utilizes the extraction feature of gray level co-occurrence matrix (GLCM) citrus image for quality classification. Begins with collecting data of citrus. There are 100 image data, 60 as training data and 40 as test data. Of each training data, obtained one 64x64 pixels good and bad data image. Do pre-processing on the image and GLCM matrix is formed in direction 0°, 45°, 90° and 135°, feature extraction are contrast, homogeneity, energy and entropy. Support vector machine (SVM) is used for good and bad image identification, to get the percentage of fruit defects. The quality classification is Super Grade, Grade A and Grade B. The result shows that the best classification accuracy is 82.5%, with the amount of training data is 20, distance=2 at 45° GLCM.

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