cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota malang,
Jawa timur
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 40 Documents
 1   2   3   4 
Search results for , issue "Vol 1 No 11 (2017): November 2017" : 40 Documents clear
Sistem Pendeteksi Kebocoran Gas LPG Menggunakan Metode Fuzzy yang Diimplementasikan dengan Real Time Operating System (RTOS) Lavanna Indanus Ramadhan; Dahnial Syauqy; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1137.291 KB)

Abstract

LPG is an alternative fuel that people use for daily purposes, in addition, LPG has cheaper prices and is also easier to use. However, LPG is flammable and has heavier characteristics that are bigger than air, making it difficult to detect the gas in case of leakage. Based on the problem, a system capable of detecting leakage of LPG gas and its leakage level based on gas content and temperature on LPG. In this research there are 2 sensors that are gas module MQ-6 and LM35 temperature sensor connected with arduino uno microcontroller and can be used as a feature to classify gas condition using fuzzy sugeno method. The output of the system is displayed using the LCD and marked with a buzzer sound. In arduino uno embedded RTOS that served as scheduling task on the system. The RTOS used is already provided in the Arduino Uno library ie FreeRTOS. From the test results, the system can determine the various conditions on the gas leak with accuracy reaches 100%. Scheduling tasks performed in accordance with the priorities made. The average system execution time using RTOS is 1.8976 ms, while the system without RTOS is 1,7304 ms. System with RTOS takes longer time, because in system there is take-and-give semaphore function which need execution time for ± 0,05 ms.
Prediksi Jumlah Follower Official Account Line Menggunakan Regresi dan Algoritma Genetika Nizar Riftadhi Prabandaru; Rekyan Regasari Mardhi Putri; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1005.91 KB)

Abstract

This research used a case study of LINE IKI MALANG official account. The result of the interview showed that LINE IKI MALANG did not have a basis of its marketing service price change. Therefore, the author tried to provide assistance by giving a basis for any price change of the marketing service based on the predicted number of its followers on each month. The method used in this research was a regression method built with genetic algorithm. Regression was used to predict the followers, while the genetic algorithm was used to optimize the variables that influenced the predicted result. To find optimal predictive results, every optimized variable will be tested on a particular vulnerable numbers. But from the experiment, the researcher got a non-optimal result which was 7,801E-03 because of the minimum data so that the prediction was not close to the training data.
Optimasi Komposisi Makanan untuk Penderita Hipertensi Menggunakan Algoritma Genetika dan Simulated Annealing Agustin Kartikasari; Dian Eka Ratnawati; Titis Sari Kusuma
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (831.735 KB)

Abstract

Hypertension ranks third largest as a disease that causes early death (Depkes, 2006). One way to prevent and treat hypertension is to modify food intake. But for the layman, arranging the composition of everyday food is still considered difficult. The problem is then solved by a combination of genetic algorithm and simulated annealing. The combination of these two algorithms aims to improve the solutions generated by genetic algorithms and avoid the occurrence of early convergence. At this problem solving used one-cut crossover method, reciprocal exchange mutation, elitism selection, and neighborhood move on simulated annealing. Based on the parameters test, the best parameter values ​​are population size of 1000, the number of generations is 200, the combination value of cr and mr is 0.6 and 0.4, the final temperature (Tn) is 0.2, and the cooling rate of 0.9. While based on system testing conducted can be seen that the combination of both algorithms able to solve this problem because the resulting nutritional content is within the limit of tolerance given by nutritionists is ± 10%
Prediksi Waktu Panen Tebu Menggunakan Gabungan Metode Backpropagation dan Algoritma Genetika Dwi Ari Suryaningrum; Dian Eka Ratnawati; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (708.75 KB)

Abstract

Before sugar cane was milled by the factory, the first process is analysis of sugar cane maturity. The best sugar cane condition to be ground is mature cane that can be seen from several factors such as garden area, age, stem diameter, the average segment per stem and the average length per stem. These factors are used as attributes in the research conducted. To simplify the process, then we proposed this research on the prediction of sugar cane harvest time. With so much data being used and repeated processes, it will be difficult to process manually and takes a long time. In addition, the manual process does not close the possibility of an increasing error. This research uses a combination of genetic algorithm and backpropagation in the process of predicting the harvest time. Genetic algorithms are the best solution used to optimize prediction results by weight selection and bias. Backpropagation method is used to calculate Mean Square Error (MSE) value, which will be used in calculation of fitness value and also on prediction of data test. In this research will be done five kinds of testing, as follows generation test, population size test, test combination of crossover rate and mutation rate, testing of learning rate and testing of Average Forecasting Error Rate (AFER). The result of this research are predictions of harvest time, the value of fitness and AFER. The best result is result of AFER value is 0,0205%.
Implementasi Algoritma Genetika Pada Metode AHP dan SAW untuk Rekomendasi Varietas Unggul Tanaman Tebu (Studi Kasus: Pusat Penelitian Gula PTPN X Jengkol) Nilna Fadhila Ganies; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (810.171 KB)

Abstract

Sugarcane varieties are one of the main factors that determine the yield of quality cane productivity. In this study used several criteria to determine the recommendation of superior varieties of sugar cane that is the number of stems, stem diameter, stem height, brix, nirs1, nirs2, nirs3, and nirs4. In order to get the recommendation of superior varieties of cane that is optimal, then in this research used genetic algorithm. The genetic algorithm will optimize the weight value on the AHP and SAW methods. In the process of genetic algorithm the representation of the chromosome used is real code, with a gene length of 28, which is adjusted by the number of varieties criteria. In the reproduction process, the crossover method used is the extended intermediate crossover, and the mutation method used is random mutation. For fitness value obtained from the calculation of accuracy on SAW. While the selection method used is elitism selection, by choosing the highest fitness value as much as the population size. From the test results obtained optimal parameters that the population size of 50 and many generations of 50, with the average fitness of 0.912 so that the obtained accuracy of 91.2%
Optimasi Scheduling untuk Proses Extract, Transform, Load (ETL) pada Data Warehouse Menggunakan Metode Round Robin Data Partitioning (Studi Kasus: Universitas XYZ) Agung Yudha Berliantara; Satrio Agung Wicaksono; Aryo Pinandito
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (959.04 KB)

Abstract

The data in the data warehouse can be extracted from many sources through the ETL (extract, transform, and load) processes. The scheduling process in ETL data warehouse is a process that must be passed for the establishment of data warehouse. Problems that arise when performing the scheduling process for the ETL process are to perform the ETL process for very large data, it will require a very large time cost as well. If it is not handled properly, it will probably take a very long time for execution and inconsistency data. The ETL process in the conventional way (not using the Round-Robin method) will be tested and compared with the ETL process using the Round-Robin method to obtain test results for this research. The difference between these two processes is the number of partition tables used in the ETL process. In the ETL process of the Round-robin method, the table target will be partitioned into sections, whereas for the conventional way there is no need to partitioning the table target. After the trial is complete, the difference between the two methods is found in terms of execution time. Through the experiment, it is concluded that the Round-robin method gives a more efficient execution time up to 60,1% depending on the amount of data and the number of partitions used.
Implementasi Naive Bayes pada Embedded System untuk Menentukan Status Gizi Bayi Fauzi Rivani; Dahnial Syauqy; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (748.82 KB)

Abstract

A baby is a stage of human development from 0-12 months where brain development and physical development are the main focus interest to see the baby growth. Nutrition status is one of the essential components that is always under supervision for baby growht. Baby's nutrition system is not only measured in terms of whether they are good or bad nutrition, but also being measurd through baby's height and baby's weight. Based on these problems, needed research on the classification system to detecting nutrition of baby and baby's parents can monitor the development nutrition from their baby. Furthermore, there are other parameters such as gender and age that serve as contributing factor when measuring baby's nutrition, A censor with microcontroller has already been developed to provide specific number of the baby's gender and age which then will be input to a system using 4x4 keypad. On the other hand, ultrasonic censor and load cell are used as a measurement system for baby's height and weight. Then all these data will be input and compared to arduino uno classification that will determine the baby's nutrition. The method use for this classification is called Naive Bayes. All the development stages will be put on 16x2 LCD to provide an assessment and analysis regarding the accuracy of this system. Based on the reading the censor provides on 10 objects, the heihgt (lenght) censor has 98.28% accuracy and weight with 71.02% accuracy. The classification test with 30 data that is picked randomly has 100% accuracy for the weight and height. Then average of time classification test are 0.026 second and 0.032 second respectively. The test and analysis of the overall system has 96.66% for ist accuracy for the height and 60% accuracy for the weight system.
Peringkasan Teks Otomatis Pada Artikel Berita Kesehatan Menggunakan K-Nearest Neighbor Berbasis Fitur Statistik Rachmad Indrianto; Mochammad Ali Fauzi; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.774 KB)

Abstract

Now days, information about healthy has been widely scattered and very easily obtained through the online website. But, within largest information that contain in the text of article make the reader can't understand about contents of the text. So, we need a system that can summarize a text to make easy the reader in understanding the contents of the text. Automatic text summary using k-nearest neighbor based on statistical features can be solution about the problem. Statistical features such as position of a sentence in a paragraph, overall sentence position, numerical data, inverted commas, the length of the sentence and keyword has important influence become parameter in summarization. From testing of statistical features that have been done by using k = 3, this method get result the best value of precision, recall and f -measure on feature set 9 with values 0.75, 0.71 and 0.72. From the test can concluded that the features that have a significant influence on the rise and fall of precision and recall values are position of a sentence in paragraph and sentence overall position. And then, from the test of k variation on the best feature set, we get maximum feature set value when k = 1 with the average value of precision, recall and f-measure of 0.89, 0.74 and 0.81.
Sistem Rekomendasi Bahan Makanan Bagi Penderita Penyakit Jantung Menggunakan Algoritma Genetika Elisa Julie Irianti Siahaan; Imam Cholissodin; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1111.691 KB)

Abstract

Lack of public awareness in regulating the consumption of food based on nutrition can cause several diseases including heart disease. Heart disease is caused from blockage of cholesterol and fat in the coronary artery. It is very important for people with heart disease to regulate food intake in order to reduce the blockage. Managing the food for the heart diet is difficult because heart diet is different from the other diets, because the amount of protein and fat is reduced. Genetic algorithms can solve the problem of managing food by computation process. The data that are used in this research are diet food ingredients data that consist of 8 kinds of food ingredients, carbohydrate, animal protein, vegetable protein, vegetable, fruit, milk, sugar and oil. In converting food into chromosome, chromosome real code representation is used. The crossover method that is used is extended intermediate crossover, the mutation method that is used is random mutation and the selection method is elitism selection. From the results of the testing, the optimal parameter scores of the genetic algorithm are the population number of 280 with the average fitness score of 103.7, Cr and Mr scores are 0.5 and 0.5 with the average fitness score of 103.3 and for the generations score is 100 with average fitness score of 111.2. Output of the system is food ingredients recommendation with 5 times a day meal time, which consists of breakfast, snack, lunch, snack and dinner with number of days based on user choice.
Peramalan Dosis Pupuk Berdasarkan Karakteristik dan Lingkungan Tanaman Jeruk Siam Menggunakan Metode Backpropagation Muhammad Najmi Ridhani; Rekyan Regasari Mardi Putri; Sutopo Sutopo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 11 (2017): November 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1244.744 KB)

Abstract

Citrus is one of the horticultural plants which are popular in Indonesia but the citrus production from year to year has fluctuated. There are some main causes that affected to the fluctuation of national production of citrus which are climate, environment, and diseases. One way to overcome the climate, environment, and diseases of citrus production is to provide fertilizer at the right dose and proportional to that matched with the environment and its characteristics. This study aims to forecast the dosage of citrus fertilizer according to the characteristics and environment. This study uses Artificial Neural Network (ANN) backpropagation. The architecture a network of 3 nueron input layer that represents the related parameters is width of the canopy, soil texture and rainfall, one hidden layer, and 3 nueron output layer that represents the composition of the fertilizer that is nitrogen, phosphorus, and potassium. The best network architecture design for forecasting doses of citrus fertilizer are 3 input neurons, 5 nueron hidden layer and 3 output neurons. The value of the learning rate used is 0.3 with the maximum iteration of 500 and the training data is 56 and the test data 8. The Mean Absolute Precentage Error (MAPE) evaluation value of the composition data of the fertilizer dose is 9.178% obtained from average error of dose of nitrogen, phosphorus, and potassium fertilizer.

Page 3 of 4 | Total Record : 40

 1   2   3   4 

Filter by Year

2017 2017


Reset
Filter By Issues
All Issue Vol 10 No 13 (2026): Publikasi Khusus Tahun 2026 Vol 10 No 01 (2026): Januari 2026 Vol 10 No 4 (2026): April 2026 Vol 10 No 3 (2026): Maret 2026 Vol 10 No 2 (2026): Februari 2026 Vol 9 No 13 (2025): Publikasi Khusus Tahun 2025 Vol 9 No 12 (2025): Desember 2025 Vol 9 No 11 (2025): November 2025 Vol 9 No 10 (2025): Oktober 2025 Vol 9 No 9 (2025): September 2025 Vol 9 No 8 (2025): Agustus 2025 Vol 9 No 7 (2025): Juli 2025 Vol 9 No 6 (2025): Juni 2025 Vol 9 No 5 (2025): Mei 2025 Vol 9 No 4 (2025): April 2025 Vol 9 No 3 (2025): Maret 2025 Vol 9 No 2 (2025): Februari 2025 Vol 9 No 1 (2025): Januari 2025 Vol 8 No 13 (2024): Publikasi Khusus Tahun 2024 Vol 8 No 10 (2024): Oktober 2024 Vol 8 No 9 (2024): September 2024 Vol 8 No 8 (2024): Agustus 2024 Vol 8 No 7 (2024): Juli 2024 Vol 8 No 6 (2024): Juni 2024 Vol 8 No 5 (2024): Mei 2024 Vol 8 No 4 (2024): April 2024 Vol 8 No 3 (2024): Maret 2024 Vol 8 No 2 (2024): Februari 2024 Vol 8 No 1 (2024): Januari 2024 Vol 7 No 13 (2023): Publikasi Khusus Tahun 2023 Vol 7 No 9 (2023): September 2023 Vol 7 No 8 (2023): Agustus 2023 Vol 7 No 7 (2023): Juli 2023 Vol 7 No 6 (2023): Juni 2023 Vol 7 No 5 (2023): Mei 2023 Vol 7 No 4 (2023): April 2023 Vol 7 No 3 (2023): Maret 2023 Vol 7 No 2 (2023): Februari 2023 Vol 7 No 1 (2023): Januari 2023 Vol 7 No 14 (2023): Antrian Publikasi Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022 Vol 6 No 12 (2022): Desember 2022 Vol 6 No 11 (2022): November 2022 Vol 6 No 10 (2022): Oktober 2022 Vol 6 No 9 (2022): September 2022 Vol 6 No 8 (2022): Agustus 2022 Vol 6 No 7 (2022): Juli 2022 Vol 6 No 6 (2022): Juni 2022 Vol 6 No 5 (2022): Mei 2022 Vol 6 No 4 (2022): April 2022 Vol 6 No 3 (2022): Mei 2022 Vol 6 No 2 (2022): Februari 2022 Vol 6 No 1 (2022): Januari 2022 Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021 Vol 5 No 12 (2021): Desember 2021 Vol 5 No 11 (2021): November 2021 Vol 5 No 10 (2021): Oktober 2021 Vol 5 No 9 (2021): September 2021 Vol 5 No 8 (2021): Agustus 2021 Vol 5 No 7 (2021): Juli 2021 Vol 5 No 6 (2021): Juni 2021 Vol 5 No 5 (2021): Mei 2021 Vol 5 No 4 (2021): April 2021 Vol 5 No 3 (2021): Maret 2021 Vol 5 No 2 (2021): Februari 2021 Vol 5 No 1 (2021): Januari 2021 Vol 5 No 13 (2021) Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020 Vol 4 No 12 (2020): Desember 2020 Vol 4 No 11 (2020): November 2020 Vol 4 No 10 (2020): Oktober 2020 Vol 4 No 9 (2020): September 2020 Vol 4 No 8 (2020): Agustus 2020 Vol 4 No 7 (2020): Juli 2020 Vol 4 No 6 (2020): Juni 2020 Vol 4 No 5 (2020): Mei 2020 Vol 4 No 4 (2020): April 2020 Vol 4 No 3 (2020): Maret 2020 Vol 4 No 2 (2020): Februari 2020 Vol 4 No 1 (2020): Januari 2020 Vol 3 No 12 (2019): Desember 2019 Vol 3 No 11 (2019): November 2019 Vol 3 No 10 (2019): Oktober 2019 Vol 3 No 9 (2019): September 2019 Vol 3 No 8 (2019): Agustus 2019 Vol 3 No 7 (2019): Juli 2019 Vol 3 No 6 (2019): Juni 2019 Vol 3 No 5 (2019): Mei 2019 Vol 3 No 4 (2019): April 2019 Vol 3 No 3 (2019): Maret 2019 Vol 3 No 2 (2019): Februari 2019 Vol 3 No 1 (2019): Januari 2019 Vol 2 No 12 (2018): Desember 2018 Vol 2 No 11 (2018): November 2018 Vol 2 No 10 (2018): Oktober 2018 Vol 2 No 9 (2018): September 2018 Vol 2 No 8 (2018): Agustus 2018 Vol 2 No 7 (2018): Juli 2018 Vol 2 No 6 (2018): Juni 2018 Vol 2 No 5 (2018): Mei 2018 Vol 2 No 4 (2018): April 2018 Vol 2 No 3 (2018): Maret 2018 Vol 2 No 2 (2018): Februari 2018 Vol 2 No 1 (2018): Januari 2018 Vol 2 No 8 (2018) Vol 2 No 6 (2018) Vol 1 No 12 (2017): Desember 2017 Vol 1 No 11 (2017): November 2017 Vol 1 No 10 (2017): Oktober 2017 Vol 1 No 9 (2017): September 2017 Vol 1 No 8 (2017): Agustus 2017 Vol 1 No 7 (2017): Juli 2017 Vol 1 No 6 (2017): Juni 2017 Vol 1 No 5 (2017): Mei 2017 Vol 1 No 4 (2017): April 2017 Vol 1 No 3 (2017): Maret 2017 Vol 1 No 2 (2017): Februari 2017 Vol 1 No 1 (2017): Januari 2017 More Issue