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All Journal Journal of Environmental Engineering and Sustainable Technology JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Indonesian Journal of Electrical Engineering and Computer Science Community Empowerment Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI)
Indriati Indriati
Fakultas Ilmu Komputer, Universita Brawijaya, Malang, Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences

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Prediksi Kebutuhan Air PDAM Kota Malang Menggunakan Metode Fuzzy Time Series Dengan Algoritma Genetika Khaira Istiqara; Muhammad Tanzil Furqon; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Water is one of the basic needs of living things derived from natural resources. The Government provides a regional water company called Perusahaan Daerah Air Minum (PDAM) to fullfil the clean water needs of the people of Indonesia, one of which is located in Malang. PDAM water needs prediction system serves to predict the water needs of the people of Malang, so water needs will be guaranteed in the future. Variable used is PDAM water usage data from 2008-2013. Genetic algorithms are used to optimize the subset of universe in fuzzy time series. Search solution uses real-coded chromosome representation, then processed with genetic operator (crossover, mutation and selection). Method of genetic operator used is one-cut-point crossover, uniform mutation and elitism selection. The result of testing genetic algorithm parameter values, obtained the optimal population size is 360, the length of chromosome is 60, the best combination of crossover rate and mutation rate are 0.4 and 0.2, and the number of optimal generation is 550. Based on the best genetic algorithm parameter value, obtained the prediction result with the error value (MAPE) is 2.266776%. These results showed a good predictive ability with low error values.
Analisis Sentimen Dengan Query Expansion Pada Review Aplikasi M-Banking Menggunakan Metode Fuzzy K-Nearest Neighbor (Fuzzy k-NN) Nanda Cahyo Wirawan; Indriati Indriati; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 1 (2018): Januari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this digital era, bussiness grow significantly by using digital application. Banking is one field of business that utilizes the current technological advances very well. Mobile banking is one of the most popular digital banking products, because it is not as complicated as SMS banking or internet banking. In order to face the strict banking business, every company applying feedback from their customers. Now customers can use the review feature that provided by apps store. There's a lot of reviews that received every day, and it takes some time to knowing what kind of review is that. Systems with machine learning are expected to save time to sort out textual data that containing polarity. The system's machine learning in this study was made using fuzzy k-nearest neighbor (fuzzy k-NN) method. The fuzzy k-NN method is a combined method between fuzzy logic and the k-Nearest Neighbor algorithm. The weighting method for processing textual data into numerical data that can be computed is using TF-IDF method with Cosine similarity to calculate the distance between data. The output of this system is the classified data review. Based on the results of the tests, this system produces the best F-Measure is 0.9273 and the worst is 0.8349.
Klasifikasi Dokumen Tumbuhan Obat Menggunakan Metode Improved K-Nearest Neighbor Arinda Ayu Puspitasari; Edy Santoso; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The high utilization rates of medicinal plants is leading to increase the studies on it. Those studies certainly require documentation that contains information about medicinal plants. The large and scattered documentation cause difficulties in searching for information about medicinal plants. To overcome these problems a system that can classify the document automatically is needed to make the information search work more effective and efficient. K-Nearest Neighbor is the algorithm often used to classify text, but has a weakness in accuracy because of the fixed k values for each category. K values is the amount of the closest training data to the test data. Improved k-Nearest Neighbour is the algorithm used in this study to overcome the problem where the different k values will be applied based on the amount of the training data for each category. The average accuracy for the k values testing is 70,99%. The training data variation testing shows that the bigger amount of training data the higher average accuracy will be. The unbalanced data testing showed that the balance data training category has 1,9% better accuracy than the unbalanced category.
Implementasi Metode Analytic Hierarchy Process - Weighted Product Untuk Rekomendasi Hunian Ideal (Studi Kasus: Kota Malang) Rizaldy Aditya Nugraha; Indriati Indriati; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The purpose of this study is to help prospective house buyers in getting the recommendation for an ideal house to be purchased. Prospective house buyers that were looking for a house of their dreams still found it difficult to obtain the appropriate recommendation suitable with their desires. Therefore, this study was conducted to create a decision support system application of ideal house recommendation to facilitate a prospective house buyer in obtaining an ideal house recommendation. The input data used on this system is a weight priority measure for each criteria and sub criteria of the house specified by the prospective house buyer. Then these input data are calculated by using analytic hierarchy process - weighted product method. The analytic hierarchy method is used to obtain the criteria and sub criteria weight which is then used for the calculation of weighted product method. The final result of this system is the rank order of ideal house recommendation. The test performed on this system is done on the pairwise comparison matrices with 80% accuracy.
Penerapan Sentimen Analisis Acara Televisi Pada Twitter Menggunakan Support Vector Machine dan Algoritma Genetika sebagai Metode Seleksi Fitur I Made Budi Surya Darma; Rizal Setya Perdana; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rating is one approach method that can be used to find out about audience satisfaction of a TV show. In Indonesia, rating was calculated by using AGB Nielsen services. However, rating that AGB Nielsen produced was based on the people watching bahavior in 10 major cities in Indonesia. Therefore, rating in Indonesia requires another method to get the watching behavior of the whole people in Indonesia. Twitter, can be used to get Indonesia people watching behavior. Through the published tweets, it can be applied the process of extracting information by using classification techniques to get the opinions. One of the classification techniques that can be applied to text categorization is the Support Vector Machine (SVM) it`s suitable for multiple dimension data. By optimizing the features that will be used, it can provide optimal results with less features used. One of the feature selection methods that can be applied to SVM is the genetic algorithm (GA). System calculates the rating, based on positive and negative sentiments about the TV show and divided by the population of the tweet used. The rating comparison test that produced by AGB Nielsen and system shows an average error value of 0.562. In testing the accuracy before and after the feature selection method is applied, showed results with average error value 0.62%.
Implementasi Metode Dempster-Shafer untuk Diagnosis Penyakit pada Tanaman Kedelai Rahmat Arbi Wicaksono; Nurul Hidayat; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 3 (2018): Maret 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Soybean is one of the main sources of food commodities in Indonesia that not only serves as raw materials for the food industry but also non-food industries. But the lack of knowledge of farmers of soybeans crops about the various symptoms and types of diseases that attack soybean plants are problems that have a negative impact on soybean cultivation. Therefore needed a system that can solve problem of soybean disease diagnosis quickly and precisely. In this research, the writer will implement Dempster-shafer method to diagnose soybean plant disease. This soybean plant diagnosis system can detect 5 types of diseases with 16 symptoms. The results of accuracy tested on 25 data cases obtained an accuracy of 92%, so it can be said that the system works well enough and can be applied.
Peramalan Harga Saham Menggunakan Metode Support Vector Regression (SVR) dengan Particle Swarm Optimization (PSO) Vera Rusmalawati; Muhammad Tanzil Furqon; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 5 (2018): Mei 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the advantages of investing in stocks is capital gains, which is the benefits from stock trading. The use of stock price forecasting will increase profits from stock sale and purchase transactions because shareholders can know when the right time to sell or buy a particular stock. Support Vector Regression (SVR) is one of the methods used for forecasting, it can recognize patterns of time series data and can provide good forecasting results when the parameters of importance can be determined well as well. So we need an optimization method to determine SVR parameters so that SVR can be optimally applied in stock price forecasting. One of the optimization algorithms that can be used is Particle Swarm Optimization (PSO). Stock price forecasting using SVR with PSO optimization uses MAPE method to evaluate forecasting results. Based on the test that has been done, the value of MAPE obtained is 0.8195% with fitness of 0.5496 with the optimal parameters obtained is the number of particles 40, iteration PSO 40, iteration SVR 1000, parameter range of C 100 - 500, the parameter range of ɛ 0, 0001 - 0,001, parameter range of σ 0,001 - 2, parameter range of γ 0,00001 - 0,001, parameter range of λ 0,001 - 0,1, and comparison of training data and test data from 2016 BCA Bank share data is 90%: 10%.
Implementasi Algoritme Fuzzy K-Nearest Neighbor untuk Penentuan Lulus Tepat Waktu (Studi Kasus : Fakultas Ilmu Komputer Universitas Brawijaya) Andhika Satria Pria Anugerah; Indriati Indriati; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 4 (2018): April 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Along with the increasing interest of studying in the collage, therefore the data of student graduation which is filed will keep increasing. However, those data could be in a very large amount if it is processed manually, therefore it is needed to apply the student graduation classification which able to classify the graduation data based on the determined parameters. There are some ways to classify the object that have been developed, one of them is Fuzzy K-Nearest Neighbor. Fuzzy K-Nearest Neighbor is one of the methods which is used to classify the object by calculating the membership degree in each class. The experiment of Fuzzy K-Nearest Neighbor is done toward the problem of time of student graduation which is categorized into graduate on time and graduate out of time. In this experiment, Fuzzy K-Nearest Neighbor is used to identify the students based on the achievement index that they have got. Based on the experiment results, Fuzzy K-Nearest Neighbor is able to get an accuracy score around 98%. This accuracy is from the given weight of the membership in each output class. This is able to minimize the doubtful in determining the output class
Sistem Pakar Diagnosis Penyakit Hepatitis Menggunakan Metode Dempster Shafer Ayu Tifany Novarina; Edy Santoso; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hepatitis is an inflammation of the liver. Inflammation is characterized by elevated liver enzyme levels, due to liver membrane damage or damage. There are two factors that cause the factors of infection and non-infectious factors. There are 5 main hepatitis viruses, referred to as types A, B, C, D and E. These 5 types are of greatest concern because of the burden of illness and death they cause and the potential for outbreaks and epidemic spread.There are many other viruses that potentially cause hepatitis such as adenoviruses, herpes simplex, HIV, rubella, and others. The problems that often occur today is still a lot of ordinary people who lack understanding of health. In fact, not infrequently people do not realize when they get the disease because they do not know the symptoms that cause patients late to handle early. In this study the problems are solved by creating a system by implementing the Dempster-Shafer method to diagnose the types of hepatitis disease suffered by humans, so the system is expected to assist users in diagnosing hepatitis disease in misery since early. Based on the results of system accuracy testing with 20 data samples obtained an accuracy of 90%. Inaccuracy of 10% is caused by several things, among others, the subjectivity of the expert in determining the disease and in the calculations performed using the Dempster-shafer method that uses the highest value without any optimization of the density value on any symptoms.
Aplikasi Berbasis M-KNN untuk Mendukung Keputusan Perekrutan Pemain yang Sesuai dengan Kebutuhan Tim Sepakbola Deny Stevefanus Chandra; Mardji Mardji; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 6 (2018): Juni 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Football is each team or squad trying to control the ball, inserting the ball into the opposing goal as much as possible, and try to break the opponent's attack to protect or keep his goal so as not to concede the ball. From the explanation it can be seen that the purpose of playing football is to score numbers or goals. Each player has a different function that is the attacker or the front player serves as an attacker, therefore a 3 front player is required to be able to score against the opponent's goal. Then the midfielder or midfielder serves as a ball feeder or it could be a midfielder in charge of assisting the attacker to insert the ball into the goal. In addition, there is also a defender or defender who serves to keep the goal defense from attack the opponents. However, in addition to serving as defensive, defender or more often called a defender can also be tasked to assist the attack. Because each player has a function or task of each different, of course it affects the kicks of each player depending on the position they have. MkNN is a development of the k-Nearest Neighbor (kNN) method. MkNN class labels on test data based on the validated training data and weight of each training data, not just based on the nearest distance as done on kNN. MkNN provides a greater opportunity for training data that has high validity, so the classification is not too affected on data that is less stable or have low validity. The result of MkNN calculation done by decision support system is same with result of calculation manually. The accuracy of this decision support system application in determining the player's position gets 90% results.
Co-Authors Abdul Azis Adjie Sumanjaya Abel Filemon Haganta Kaban Achmad Arwan Achmad Burhannudin Achmad Ridok Ade Wahyu Muntizar Adella Ayu Paramitha Adinugroho, Sigit Afif Musyayyidin Aghata Agung Dwi Kusuma Wibowo Agus Wahyu Widodo Ahmad Afif Supianto Ahmad Fauzan Rahman Ahmad Nur Royyan Aisyah Awalina Alaikal Fajri Nur Alfian Alfita Nuriza Alvin Naufal Wahid Anak Agung Bagus Arisetiawan Andhika Satria Pria Anugerah Andre Rino Prasetyo Anggara Priambodo Jhohansyah Anjelika Hutapea Annisa Selma Zakia Ardhimas Ilham Bagus Pranata Arief Andy Soebroto Arifin Kurniawan Arinda Ayu Puspitasari Arthur Julio Risa Ashshiddiqi Arya Perdana Avisena Abdillah Alwi Ayu Tifany Novarina Bagus Abdan Aziz Fahriansyah Bayu Rahayudi Benita Salsabila Berlian Bidari Ratna Sari B Beta Deniarrahman Hakim Billy Sabilal Binti Najibah Agus Ratri Binti Robiyatul Musanah Brian Andrianto Budi Darma Setiawan Candra Ardiansyah Candra Dewi Chandra Ayu Anindya Putri Choirul Anam Daneswara Jauhari Dea Zakia Nathania Deny Stevefanus Chandra Deri Hendra Binawan Desy Andriani Desy Wulandari Dewi Syafira Dhaifa Farah Zhafira Dhony Lastiko Widyastomo Diajeng Ninda Armianti Dian Eka Ratnawati Dina Dahniawati Dinda Adilfi Wirahmi Durrotul Fakhiroh Dwi Suci Ariska Yanti Dyah Ayu Wulandari Edo Ergi Prayogo Edy Santoso Eka Putri Nirwandani Enggar Septrinas Erma Rafliza Fajar Pradana Faradila Puspa Wardani Fardan Ainul Yaqiin Febriana Ranta Lidya Febrina Sarito Sinaga Fera Fanesya Ferdi Alvianda Feri Angga Saputra Firda Oktaviani Putri Firda Priatmayanti Firhad Rinaldi Saputra Fitra Abdurrachman Bachtiar Frans Agum Gumelar Galuh Fadillah Grandis Ghiffary Rizal Hamdhani Guedho Augnifico Mahardika Hilmy Khairi Idris I Made Budi Surya Darma Imam Cholissodin Indah Mutia Ayudita Indriya Dewi Onantya Inosensius Karelo Hesay Jeffrey Junior Tedjasulaksana Jeowandha Ria Wiyani Joda Pahlawan Romadhona Tanjung Junda Alfiah Zulqornain Katherine Ivana Ruslim Khaira Istiqara Khalisma Frinta Kornelius Putra Aditama Ksatria Bhuana Lailil Muflikhah Liana Shanty Wato Wele Keaan Liana Shinta Dewi Liana Shinta Dewi Linda Pratiwi Ludgerus Darell Perwara Lusiyana Adetia Isadi Luthfi Mahendra M. Aasya Aldin Islamy M. Ali Fauzi Mahdarani Dwi Laxmi Mahendra Okza Pradhana Mardji Mardji Marinda Ika Dewi Sakariana Marji Marji Mentari Adiza Putri Nasution Merry Gricelya Nababan Moch Bima Prakoso Mochamad Havid Albar Purnomo Mohamad Alfi Fauzan Mohammad Birky Auliya Akbar Mohammad Fahmi Ilmi Mohammad Imron Maulana Muhammad Abdurasyid Muhammad Fauzan Ziqroh Muhammad Hakiem Muhammad Mauludin Rohman Muhammad Reza Ravi Muhammad Tanzil Furqon Muhammad Yudho Ardianto Nadya Oktavia Rahardiani Nana Nofiana Nanda Ajeng Kartini Nanda Cahyo Wirawan Ni Made Gita Dwi Purnamasari Ni Made Gita Dwi Purnamasari Nihru Nafi' Dzikrulloh Nirmala Fa'izah Saraswati Novanto Yudistira Novia Agusvina Nur Intan Savitri Bromastuty Nurdifa Febrianti Nurina Savanti Widya Gotami Nurudin Santoso Nurul Hidayat Nurul Muslimah Pengkuh Aditya Prana Prais Sarah Kayaningtias Pratitha Vidya Sakta Puteri Aulia Indrasti Putra Pandu Adikara Putri Rahma Iriani Putu Amelia Vennanda Widyaswari Putu Rama Bena Putra Rachmad Ridlo Baihaqi Rahma Chairunnisa Rahmat Arbi Wicaksono Rakhman Halim Satrio Randy Cahya Wihandika Ratih Karika Dewi Ratna Tri Utami Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rien Difitria Rifki Akbar Siregar Rilinka Rilinka Riska Dewi Nurfarida Riski Nova Saputra Riyant Fajar Riza Cahyani Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Haqmanullah Pambudi Rizky Nur Ariyanti Sabrina Hanifah Salsabila Rahma Yustihan Sigit Adinugroho Sinta Kusuma Wardani Siti Robbana Sutrisno Sutrisno Swandy Raja Manaek Pakpahan Tania Malik Iryana Tania Oka Sianturi Tasya Agiyola Thio Marta Elisa Yuridis Butar Butar Titus Christian Vera Rusmalawati Wayan Firdaus Mahmudy Yane Marita Febrianti Yobel Leonardo Tampubolon Yudha Ananda Kresna Yudha Irwan Syahputra Yudha Prasetya Anza Yuita Arum Sari Yulia Kurniawati Zahra Swastika Putri