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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Systemic: Information System and Informatics Journal Community Empowerment
Putra Pandu Adikara
Universitas Brawijaya
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Prediksi Nilai Harga Patokan Batu Bara (HPB) Untuk Merek Dagang Gunung Bayan I dengan Metode Extreme Learning Machine (ELM) Evilia Nur Harsanti; Muhammad Tanzil Furqon; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Coal is a fossil fuel that is often used by industrial companies as a source of energy and power as a raw material for steelmaking. Coal is obtained by industrial companies through a sale and purchase transactions conducted with coal mining companies. Price is a major factor in the transaction process, because industrial companies need to design an expenditure budget every month before making a transaction. Budget design is done to maximize the company's money to meet all the needs of the company. Therefore, the prediction of coal price will be very beneficial for industrial companies that will buy coal products to know the estimated price in the future. The method used to make the prediction process is the method of Extreme Learning Machine (ELM). ELM has the advantage of fast computing time and small error rate, so ELM does not require a long time in the learning process. Based on the result of research, the best Means Absolute Percentage Error (MAPE) score is 3,926804% for training process and 7,360343% for testing process.
Sistem Pencarian Jurnal Ilmiah Cross Language dengan Metode Vector Space Model (VSM) Indah Mutia Ayudita; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Scientific journals are periodical publications that contain scientific papers with data and information written in accordance with the rules of scientific writing. Scientific journals used widely as a reference to make a new research or continue the previous research. As the usage is growing, scientific journals also easier to find digitally and available in a digital library such as Science Direct and IEEE, but the searching process is still limited to Monolingual Information Retrieval, in which the search results have the same language as the query inputted, even though the relevant documents also available in other languages. This research is done to observe the result of implementing Cross Language Information Retrieval that can do the searching process in one language for the input and retrieved document in two languages. The final result is 8 out of 10 queries have a higher precision up to 74,5% and recall up to 41,5%. Generally, system can retrieved the relevant documents in average for 84%.
Klasifikasi Hoax Pada Berita Kesehatan Berbahasa Indonesia Dengan Menggunakan Metode Modified K-Nearest Neighbor Andre Rino Prasetyo; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

News is a source of information about current events which can be found in newspapers, television, the internet and other media. Currently the news that is disseminated often without writing the source clearly, especially the type of news about health, it can lead to misinterpretation because the news is not necessarily true or wrong so it takes a smart system to classify health news is whether included in the category of hoax or fact. The hoax classification process use several stages ranging from preprocessing consisting of tokenisasi and filtering. Continued with word-weighting process and cosine similarity to classification process using Modified K-Nearest Neighbor method. The results obtained based on the implementation and testing resulted in the best value of k amounted to 4, precision of 0,83 recall of 0,75 f-measure of 0,79 and the accuracy of 75%. The test results obtained because the health news content used is still too common, many non-standard words and the determination of k-values ​​used are very influential on whether or not the process of classification of health news documents.
Analisis Sentimen Konten Radikal Di Media Sosial Twitter Menggunakan Metode Support Vector Machine (SVM) Ferdi Alvianda; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Lately, there are many terrorist threats by the radicals in Indonesia. Radicals keep growing by numbers each day as they share their radical beliefs to other people. These radical beliefs can be shared through social media, such as Twitter. Therefore, a research regarding that problem is conducted. Documents of Twitter that contain radical tweets are classified to two categories, positive radical content and negative radical content. The method used for this research is Support Vector Machine (SVM) with Polynomial Degree Kernel. The highest accuracy rate achieved from this research is 70% with the parameter value of λ is 0,1, constant value of γ is 0,1, maximum iteration of 5 with training data sets of 80 documents (60 negative documents and 20 positive documents) as training data sets and 20 documents (15 negative documents and 5 positive documents) as testing data sets.
Optimasi Penjadwalan Ujian Semester Menggunakan Algoritme Genetika (Studi Kasus: STMIK Kadiri) Mayang Arinda Yudantiar; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Scheduling is an important issue in the implementation of activities, so that absence of such activities will not run smoothly. One example of scheduling is the scheduling of semester exam is performed on a STMIK Kadiri. Scheduling tests done still manually (conventional) so it may take longer computation. This is because the difficulty of putting slots schedule to avoid clashing occurs and there are lots of class but the test room which can be used a bit. So it needs optimization scheduling that is able to minimize conflicting schedules and activities the test can run well. Genetic algorithm is one of the most common optimization methods is used to solve the problems of scheduling. The data used in this study using the test schedule data will be represented in chromosomes, in the form of code exam schedule. Crossover method used is onecut point while mutase method using reciporal exchange mutation and elitism selection method and roulette wheel. The optimal parameter values ​​obtained based on the test result are population size 60, generation size as much as 850, with cr and mr value is 0,5 and 0,5. So the fitness value that is gained is 0.000574..
Penentuan Jumlah Kendaraan Menggunakan Blob Detection dan Background Subtraction San Sayidul Akdam Augusta; Yuita Arum Sari; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Traffic is an important problem because traffic is medium to move from one place to another. When there is a traffic problem or become stuck, then people's mobility will have problem too. Traffic density data is an important role to understand traffic condition. Currently, in order to obtain traffic density still do it in conventional way, that is with some people to count each vehicles passing by at certain time. The purpose of this research is to apply algorithm Background Subtraction and Blob Detection to determine total vehicles and test the result of the vehicle counter system. Background Subtraction is used to process segmentation to separate an object with the background by counting difference between each pixel and use a threshold to make two dominant group of pixel. The method used to determine object position and total vehicles by Blob Detection and Background Subtraction. Testing done with twenty image by taking smallest error value as a best evaluation. The performance of precision is 93.44%, recall is 77.03% and accuracy is 73.75%. The value of precision, recall and accuracy needs to be increased again by adding test parameters and multiplying datasets with different conditions. The results show that the Blob Detection and Background Subtraction methods can give pretty good results when blob between vehicles is spaced. This method does not provide good results when used in heavy traffic conditions with vehicle bodies sticking together.
Peramalan Harga Cabai Menggunakan Metode High Order Fuzzy Times Series Multifactors Ridho Agung Gumelar; Budi Darma Setiawan; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The daily needs of Indonesian people can not be separated from agricultural commodities such as chili, onion, garlic, tomatoes and others. Some of these agricultural commodities have sharp price fluctuations, such as chili. When the supply of chilli in the market decreases, the price can be soar higher than the normal price. Conversely, when the supply of chili is excessive, the price will be fall well below the normal price. This is influenced by various factors such as the harvest season, the amount of production, the amount of public consumption, the area of the harvest area and others. Therefore we need a method to estimate the price off chili so that it can be used to support decision-making related to price issues. Forecasting is one solution to be able to estimate the price movement of chili commodities. The method used to forecast the price of chili is High Order Fuzzy Times Series Multifactors. In this method the formation of subinterva is done by using Fuzzy C-means. For calculate forecasting error results in this research using Mean Square Error (MSE). Based on the results of the test, the value of training data and orders used in forecasting does not guarantee a low error rate. The results of forecasting the price of chili using the method of High Order Fuzzy Times Series Multifactors get the best MSE results of 20,374.19.
Klasifikasi Video Clickbait pada YouTube Berdasarkan Analisis Sentimen Komentar Menggunakan Learning Vector Quantization (LVQ) dan Lexicon-Based Features Dwi Wahyu Puji Lestari; Rizal Setya Perdana; Putra Pandu Adikara
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

Clickbait is social media content that aims to attract website visitors in order to visit their content by creating clickbait in form of appealing or provoking title but with irrelevant content. It makes the visitor decieved and disappointed, so they usually vent their frustation by writing their positive or negative opinion on the comment section. The document that is used in the research comes from YouTube comments that is related with Indonesian clickbait and non-clickbait content. This research used Learning Vector Quantization (LVQ) method and Lexicon-Based Features as word weighting other than using TF-IDF. This research uses 300 data consisting 2 type of data, training and testing data with the ratio of 70% training data and 30% testing data. The accuracy of the system that is obtained by classification using LVQ without Lexicon-Based Features is 54.54%, 1 precission, 0.1667 recall and 0.2858 f-measure. The result of the accuracy of the system using LVQ and Lexicon-Based Features is 90.91%, 0.8571 precission, 1 recall, and 0.9231 f-measure. The conclution is that LVQ method and Lexicon-Based Features can be used for sentiment classification.
Rekomendasi Rumah Makan Malang Menggunakan Metode Fuzzy Analytical Hierarchy Process dan Technique For Order Preference by Similarity to Ideal Solution Mohammad Toriq; Imam Cholissodin; Putra Pandu Adikara
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

Indonesia is one country with a large population increasing every year in the culinary business. Then a system is needed that can recommend restaurants to customers. This problem can be solved by using Fuzzy Analytical Hierarchy Process and Technique for Order Preference methods by Similarity to Ideal Solution (F-AHP and TOPSIS). The criteria are the number of food menus, restaurant ratings, food menu prices, distance of restaurants and length of time open. This method is divided into 2 stages. The first phase of FAHP is the comparison of criteria matrix, normalization of comparison criteria matrix, weight vector, priority weight, consistency ratio, TFN matrix conversion, fuzzy synthesis matrix, defuzzification vector and ordinate and fuzzy vector normalization. The second stage is TOPSIS from decision making matrix, normalization of decision matrix, weighted normalization matrix, search for positive-negative ideal solution, distance search for ideal positive-negative solution and preference value. The results of the preference value are sorted to produce the recommended restaurant ratings. In this study involved 3 customers who had visited a restaurant. The test uses the Spearman correlation test method in determining the proximity of the results of the ranking system to the manual rating by each customer. The results of testing the level of accuracy of the system rating on customers is low, namely 0.3352, -0.1538 and third -0.3205. This shows a lack of conformity between expert choices on the system because the results of expert ratings are still not based on the specified criteria.
Klasifikasi Pengidap Kanker Payudara Menggunakan Metode Voting Based Extreme Learning Machine (V-ELM) Dheby Tata Artha; Sigit Adinugroho; Putra Pandu Adikara
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

Breast cancer is a malignant tumor that formed by the abnormal growth of breast cells. Every year, breast cancer causes about 2,1 million women to die. To reduce the number of deaths caused by breast cancer, screening can be chosen for prevention efforts. The development of medical technology and information technology, in the medical world, can be used by researchers in their fields to develop early detection models, from routine consultation data and blood analysis. In this study, breast cancer data will be classified using the Voting Based Extreme Learning Machine (V-ELM). This study using Coimbra Dataset Breast Cancer which published on UCI Machine Learning in 2018. It consists of 116 data, 9 features and 2 classes (Healthy Control and Patient). Firstly, the dataset would be normalized, then began the training process of V-ELM with data train. After that, began the testing process of V-ELM with input values from the training process and data test. The ratio between training data and testing data in this study is 80:20. This study tested several parameters and obtained optimal results, including 20 hidden neurons, the value of k for V-ELM is 35 and the activation function with optimal results is the Sigmoid function. By using those optimal parameters, gives accuracy of 89.56%, sensitivity of 96.924% and specificity of 80%.
Co-Authors Adani, Rafi Malik Ade Kurniawan Adinda Chilliya Basuki Adinugroho, Sigit Adiyasa, Bhisma Adriansyah, Rachmat Afrizal Rivaldi Agi Putra Kharisma, Agi Putra Agus Wahyu Widodo Ahmad Fauzi Ahsani Akhmad Sa'rony Al Farisi, Faiz Aulia Al Huda, Fais Albert Bill Alroy Alimah Nur Laili Allysa Apsarini Shafhah Alqis Rausanfita Alvandi Fadhil Sabily Amaliah, Ichlasuning Diah Amar Ikhbat Nurulrachman Ananda Fitri Niasita Anang Hanafi Andina Dyanti Putri Andre Rino Prasetyo Anggraheni, Hanna Shafira Ani Budi Astuti Annisa Alifia Annisa, Zahra Asma Arsya Monica Pravina Aulia Jasmin Safira Aulia Rahma Hidayat Avisena Abdillah Alwi Azhar, Naziha Baliyamalkan, Mohammad Nafi' Barbara Sonya Hutagaol Bayu Andika Paripih Bayu Rahayudi Bryan Pratama Jocom Budi Darma Budi Darma Setiawan Candra Dewi Candra Dewi Dahnial Syauqy Daisy Kurniawaty Danang Aditya Wicaksana Dayinta Warih Wulandari Deri Hendra Binawan Dhanika Jeihan Aguinta Dheby Tata Artha Dian Eka Ratnawati Dika Perdana Sinaga Dimas Fachrurrozi Azam Dwi Suci Ariska Yanti Dwi Wahyu Puji Lestari Dyva Pandhu Adwandha Edy Santosa Eka Dewi Lukmana Sari Elmira Faustina Achmal Evilia Nur Harsanti Faiz Aulia Al Farisi Farid Rahmat Hartono Fattah, Rafi Indra Fayza Sakina Maghfira Darmawan Febriarta, Renaldy Dwisma Ferdi Alvianda Ferly Gunawan Ferly Gunawan Firdaus, Agung Firmansyah, Ilham Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Nuring Bagaskoro George Alexander Suwito Gilang Widianto Aldiansyah Glenn Jonathan Satria Guedho Augnifico Mahardika Haekal, Firhan Imam Hanson Siagian Hendra Pratama Budianto Hernawan, Yurdha Fadhila Hibatullah, Farras Husain Husein Abdulbar Ichsan Achmad Fauzi Ika Oktaviandita Imam Cholisoddin Imam Cholissodin Imam Ghozali Imanuel Juventius Todo Gurning Indah Mutia Ayudita Indriati Indriati Indriati Indriya Dewi Onantya Ivan Fadilla Ivan Ivan Jesika Silviana Situmorang Jojor Jennifer BR Sianipar Jonathan Reynaldo Junda Alfiah Zulqornain Karina Widyawati Karunia Ayuningsih Katherine Ivana Ruslim Khalisma Frinta Krishnanti Dewi Laila Restu Setiya Wati Lailil Muflikhah Laksono Trisnantoro Lubis, Saiful Wardi Lusiyana Adetia Isadi Luthfi Mahendra M. Aasya Aldin Islamy M. Ali Fauzi Maghfiroh, Sofita Hidayatul Makrina Christy Ariestyani Marina Debora Rindengan Maya Novita Putri Riyanto Mayang Arinda Yudantiar Mayang Panca Rini Melati Ayuning Lestari Moch. Khabibul Karim Moh. Dafa Wardana Mohammad Fahmi Ilmi Mohammad Toriq Muh. Arif Rahman Muhammad Faiz Al-Hadiid Muhammad Fajriansyah Muhammad Iqbal Pratama Muhammad Nurhuda Rusardi Muhammad Rizaldi Muhammad Rizky Setiawan Muhammad Tanzil Furqon Muhammad Taufan Muthia Azzahra Nadhif Sanggara Fathullah Nadia Siburian Nanda Agung Putra Nanda Cahyo Wirawan Naufal Akbar Eginda Naziha Azhar Niluh Putu Vania Dyah Saraswati Novan Dimas Pratama Novanto Yudistira Nur Hijriani Ayuning Sari Nurul Hidayat Panjaitan, Mutiharis Dauber Panji Husni Padhila Pengkuh Aditya Prana Prais Sarah Kayaningtias Prakoso, Andriko Fajar Pretty Natalia Hutapea Putri Rahma Iriani Radita Noer Pratiwi Rahma Chairunnisa Raissa Arniantya Randy Cahya Wihandika Randy Cahya Wihandika Randy Ramadhan Ravindra Rahman, Azka Renata Rizki Rafi` Athallah Renaza Afidianti Nandini Restu Amara Rezky Dermawan Rhevitta Widyaning Palupi Ridho Agung Gumelar Riza Cahyani Rizal Maulana, Rizal Rizal Setya Perdana Rizal Setya Perdana Rosy Indah Permatasari Sagala, Revaldo Gemino Kantana Salsabila Insani Salsabila Rahma Yustihan San Sayidul Akdam Augusta Santoso, Nurudin Sigit Adinugroho Sigit Adinugroho Silaban, Gilbert Samuel Nicholas Silvia Ikmalia Fernanda Sindy Erika Br Ginting Sri Indrayani, Sri Sutrisno Sutrisno Tania Malik Iryana Taufan Nugraha Thariq Muhammad Firdausy Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Uke Rahma Hidayah Utaminingrum, Fitri Vergy Ayu Kusumadewi Vinesia Yolanda Vivin Vidia Nurdiansyah Wijanarko, Rizqi Yerry Anggoro Yohana Yunita Putri Yoseansi Mantharora Siahaan Yosua Dwi Amerta Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Yulia Kurniawati Yurdha Fadhila Hernawan Yure Firdaus Arifin Zahra Asma Annisa