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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 150 Documents
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Search results for , issue "Vol 2 No 10 (2018): Oktober 2018" : 150 Documents clear
Analisis Mobilitas Node Jaringan Nirkabel Pada Software Defined Wireless Network (SDWN) Putri Rizqia Hardein; Rakhmadhany Primananda; Achmad Basuki
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Software Defined Network (SDN) has been developed over the last few years with the aim to simplify complexity in computer network. SDN's concept also developed to other kind of computer network including wireless network that really popular nowadays. The popularity of wireless network is caused by the simplicity and mobility that possible so there is no limitation for user's movement. But in the other hand, mobility also adds more challenges and really effected to the quality of wireless network performance. So, mobility is an important factor to considered when measuring wireless network performance. In this research, analysis of node mobility in SDN-wireless based or Software Defined Wireless Network (SDWN) has been done. The analysis is done by measuring network performance metrics specifically throughput, packet loss, and delay with Random Direction, Random Walk, and Random Way Point mobility model. The measurement is done at different velocity of node that is 5m/s, 10m/s, 15m/s, 20m/s, and 25m/s. The result shows that the highest throughput value 9.63 Mbps is gain in Random Direction at maximum velocity 25m/s, but it shows different result when the test is redone with 6.46 Mbps as highest throughput in Random Way Point that is caused by the instability of emulator. In the other hand, Random Way Point has the lowest packet loss 2116.2 packets at 25m/s and the lowest delay 51.056ms at 20m/s. The results show unstable value because of the emulator instability.
Optimasi Metode Extreme Learning Machine Dalam Penentuan Kualitas Air Sungai Menggunakan Algoritme Genetika Regina Anky Chandra; Edy Santoso; Sigit Adinugroho
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Seiring dengan meningkatnya jumlah populasi manusia, sumber air bersih yang ada di bumi terus berkurang. Dampak yang diberikan akibat tercemarnya sumber air juga tidak dapat diremehkan. Beberapa dampaknya antara lain adalah menurunnya kadar oksigen yang ada di bumi dikarenakan tumbuhan tidak dapat berfotosintesis dengan baik, mengganggu kesuburan tanah, mematikan hewan-hewan yang hidup di dalam air dan masih banyak dampak lainnya. Salah satu sumber air di muka bumi ini berasal dari sungai. Untuk menjaga kualitas air agar tetap pada kondisi alamiahnya, perlu dilakukan pengukuran dan analisis terhadap air sungai tentang status mutu airnya. Pada penelitian ini digunakan 7 parameter pengukuran kualitas air sungai yang kemudian akan diklasifikasikan menjadi 3 kelas berbeda. Kelas klasifikasi dibagi menjadi tercemar ringan, tercemar sedang, dan tercemar berat. Metode yang digunakan untuk pengukuran dan analisis pada penelitian ini adalah metode Extreme Learning Machine (ELM) dan Algoritme Genetika. Dalam penelitian ini, bobot awal yang digunakan pada proses training dan testing ELM akan dioptimasi menggunakan Algoritma Genetika. Data training dan data testing yang digunakan, ditentukan oleh 5 fold yang telah dibentuk dari data awal yang berjumlah 150 data. Data tiap fold akan diuji menjadi data testing secara bergantian. Berdasarkan hasil pengujian dari penelitian yang telah dilakukan, penelitian ini mampu meraih tingkat akurasi sebesar 88.0002%.
Analisis Sentimen Pada Ulasan "Lazada" Berbahasa Indonesia Menggunakan K-Nearest Neighbor (K-NN) Dengan Perbaikan Kata Menggunakan Jaro Winkler Distance Yane Marita Febrianti; Indriati Indriati; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The development of an information technology currently carries a considerable impact against the pattern of life one on purchasing power. The current purchasing power are more likely to shop online because it's considered easier. But, how does a consumer know if the items to be purchased good or otherwise. Therefore it appears there is a review or comment on any goods sold. Review on items bring considerable influence against the purchasing power of consumers to know the quality of the goods, does not be surprised if a review into one of the main goals being viewed by consumers after the price. However, not all reviews provided the consumers can be understood by other consumers due to use the word is abbreviated, it use modern languages, in typing letters, the researcher proposes the creation of a system Analysis of Sentiment on the Reviews “Lazada” Berbahasa Indonesia Using the K-Nearest Neighbor (K-NN) and Repair Word Using Jaro Winkler Distance. Testing based on the value of precission, recall, and accuracy at each analysis sentiment without repair word, or with repair word. The test result with good accuracy value is present on the analysis sentiment with repair word is 76 %, with value of precission 0,76, and recall 1.
Klasifikasi Jenis Kanker Berdasarkan Struktur Protein Menggunakan Algoritma Naive Bayes Tawang Wulandari; Marji Marji; Lailil Muflikkah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kanker adalah beberapa sel tubuh yang mulai membelah tanpa berhenti dan menyebar ke jaringan sekitarnya. Setiap tahun terdapat ribuan kasus baru kanker yang menyerang warga Indonesia. Terlambatnya deteksi dini meyebabkan banyak kasus kematian akibat kanker. Faktor penyebab kanker adalah faktor genetik dan lingkungan yang dapat merubah struktur DNA. Perubahan DNA tersebut merugikan proses pembelahan sel dan menguntungkan proses mutasi. Pada proses mutasi dapat menghasilkan gen p53, perubahan genetik tersebut paling umum ditemukan pada kanker manusia. Dari permasalahan tersebut dibutuhkan sistem untuk mengklasifikasikan jenis kanker yang diderita oleh pasien. Salah satu metode yang digunakan adalah Naive Bayes. Naive Bayes merupakan sebuah pengklasifikasian probabilitas sederhana yang mengaplikasikan Teorema Bayes dengan asumsi ketidaktergantungan yang tinggi. Algoritma tersebut diketahui telah banyak digunakan dalam bidang kedokteran. Algoritma ini diterapkan pada hal-hal yang berhubungan dengan diagnosa medis. Diagnosa dilakukan dengan cara melihat gejala-gejala yang berkaitan kemudian melihat probabilitas kemungkinan dari penyakit. Pengujian dilakukan dengan menggunakan 5 dataset yaitu 320, 400, 480, 588 dan 848 data dari data total sebanyak 848 data. Data dibagi menjadi data latih dan data uji. Data uji diambil 10% hingga 60% dari dataset. Hasil akurasi yang didapatkan pada pengujian 848 data dengan persentase data uji 60% didapatkan akurasi sebesar 79,17%.
Penerapan Klasifikasi Tweets pada Berita Twitter Menggunakan Metode K-Nearest Neighbor dan Query Expansion Berbasis Distributional Semantic Galih Nuring Bagaskoro; Mochammad Ali Fauzi; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The use of short text based on digital to date is still growing and extending to various social media. Twitter has news features in tweets to represent information representing each type. Each categorization of this type is done to make it easier for users to use it. The purpose of the use of categories in this classification, to evaluate and improve the quality of social media in grouping categories of content of the content provided. Traditional classification is still used today, but the results are sometimes not maximal, it is necessary to expand the word to add words to the text in order to improve the accuracy. Word expansion is used with a semantic-based distributional euclidean distance technique to find the closest word from an external source to be a query to be added to the test data text. Using test data 105 and training data 400, the classification using K-Nearest Neighbor can obtain 90% results with nearest neighbor K=5. These results are similar to the results of tests conducted without using word expansion techniques. While the test is done by adding the expansion of words with threshold 0.5 and the nearest immediate value K-Nearest Neighbor K=5 obtained an accuracy of 92%.
Identifikasi Penyakit Pada Kambing Menggunakan Metode Fuzzy K-Nearest Neighbor (F-KNN) Basuki Rahmat Rialdi; Nurul Hidayat; Suprapto Suprapto
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Goat (Capra Aegragus Hircus) is one of the animals raised by humans. However, goat cattle business will experience constraints when the goats are infected with the disease. In addition to causing harm, the disease can also reduce public interest to goat breeding. So the system was made to identify the disease in goat farms, so the breeder could know the type of disease that attacked and handled it appropriately. The method used is k-nearest neighbor and fuzzy. The first step of this method includes trainer data that contains symptoms of the disease. Then the classification uses k-nearest neighbor. After the implementation and testing, obtained the highest accuracy of 96% at K which is worth 9. From these results can be concluded that the results of the system and experts are aligned and have positive accuracy
Analisis Perbandingan Kinerja Protokol AOMDV, DSDV, Dan ZRP Sebagai Protokol Routing Pada Mobile Ad-Hoc Network (MANET) Fatkhurrozi Fatkhurrozi; Edita Rosana Widasari; Adhitya Bhawiyuga
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Natural disaster is an event that can cause damage to communication network's infrastructure in a region. Therefore, a network technology that is able to run independently without the infrastructure of communication and internet networks; such as BTS, router, or access point, is required. The technology is called Mobile Ad-Hoc Network (MANET). MANET is a wireless network consisting of a set of mobile nodes that communicate in a multi-hop manner inside a dynamic topology without depending on a supporting infrastructure. MANET routing protocols are generally classified into three types, such as reactive, proactive, and hybrid routing protocols. These three types of routing protocols have different algorithms in route search methods. Among the three types of routing protocols, there are AOMDV, DSDV, and ZRP. This research was propsed to compare the performance of three different types of routing protocols, such as AOMDV, DSDV, and ZRP when simulated in different scopes. Simulations were performed using Network Simulator 2 and measured by four parameters such as throughput, end-to-end delay, packet delivery ratio, and normalized routing load. Based on the tests that were conducted in different range area and number of mobile nodes scenarios, it can be concluded that AOMDV has the highest average packet delivery ratio and throughput value. On the other hand, it was found that the best average value was obtained by DSDV protocol during the measurement of end-to-end delay and normalized routing load.
Algoritme Genetika Untuk Optimasi K-Means Clustering Dalam Pengelompokan Data Tsunami Dwi Anggraeni Kuntjoro; Budi Darma Setiawan; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tsunami is one of the most deadly disaster causing damage and loss of life and wealth. It happens in a sudden and unpredictable. Lack of awareness often leads to a great damage and worsening the impact of tsunami itself. This research implements genetic algorithm optimization into K-Means method for classify tsunami data. By optimazing the initial cluster center it will used as an input on K-Means method. The method result more optimal preference than the conventional K-Means method since the central point is optimized by genetic algorithm. It was proved on this research where fitness value resulted from Silhouette Coefficient to observe how suitable data with cluster. Chromosome representation used here is real code to initialize centroid value. Extended intermediate crossover applied for crossover method. For mutation method, random mutation is run here. Also for selection method it uses elitism selection. Based on testing result, the most optimum parameter accomplished are 50 population, 70 generation, and Cr =0.9 and Mr =0.1 combination with fitness value around 0.995934
Pengembangan Sistem Informasi Pengelolaan Penjualan Keripik Buah Pada CV KAJEYE FOOD Dengan Metode Peramalan Permintaan Menggunakan Model Waterfall Arib Rahman Sutrisna; Niken Hendrakusma Wardani; Aditya Rachmadi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

CV KAJEYE FOOD is a company that engaged in fruit processing, especially chips and candied fruit that called SoKressh & Kenyil. The recap process of fruit chips sales is still done manually. It takes time and has possibility of human error that caused the error information. The company sometimes experience over-production in the fruit chips production, so it can cause harm to the company. The company needs information system that can manage the sales and can predict the demand of fruit chips in the next period. Development of information systems using waterfall method that starts from requirement analysis, design, implementation and testing. Design and implementation using structural approach (Data Flow Diagram, Entity Relationship Diagram and State Transition Diagram). Implementation of sales management information system is web-based and uses exponential smoothing and moving averages method in forecasting demand. The test results using white-box and black-box method indicate that all function of system run well according to requirement and design. Testing of forecasting method of exponential smoothing and moving averages shows that exponential smoothing method with constant 0.9 is the most accurate forecasting method, with mean absolute deviation of 924.5, mean square error of 876,752.5 and mean absolute percent error of 26.1%.
Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak Indri Monika Parapat; Muhammad Tanzil Furqon; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 10 (2018): Oktober 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Growth and development of children at an early age affect the child's personal ability in the future. Every child is unique, so growth and growth are different. Deviation of late child growth is known to result in long-term and difficult to repair. . Based on these problems, this research was conducted by using the sUPPmethod for the classification of child growth deviations. ELM method consists of training process as system learning and testing to obtain the result of classification. The parameters test are test of lambda, complexity, and maximal iteration. There are 90 data used in this research, which is divided into 3 classes. Classes in this study represent three types of diseases in growth and development are Down Syndrome, Autisme, dan Attention Deficit Hyperactivity Disorder (ADHD). Basically SVM algorithm is a method of linier classification, so there is kernel is used to overcome nonlinier data. The final result of this study produced the highest average accuracy on this research is 73,78% λ = 0,1, C = 0,1, itermax = 10 and also using polynomial kernel. The comparison of the result of the classification of child growth deviation with the help of psychologist shows that the system produces poor accuracy. This can be due to the small and unbalanced data used for the research.

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