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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) SITEKIN: Jurnal Sains, Teknologi dan Industri Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer SISFO Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) JAST : Jurnal Aplikasi Sains dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Kohesi: Jurnal Sains dan Teknologi Journal of Information System and Application Development
Lailil Muflikhah
Fakultas Ilmu Komputer, Universita Brawijaya, Malang, Indonesia
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Analisis Sentimen Kurikulum 2013 Pada Sosial Media Twitter Menggunakan Metode K-Nearest Neighbor dan Feature Selection Query Expansion Ranking Nurul Dyah Mentari; Mochammad Ali Fauzi; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 8 (2018): Agustus 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kurikulum 2013 has become a hot topic that is often discussed by society on Twitter. Twitter is one of the social media that used by a society to talk about a particular subject. This study attempted to analyze tweets about the Kurikulum 2013 by classifying whether it is a positive opinion or a negative opinion. Classification process is done by K-Nearest Neighbor method by using Query Expansion Ranking method for feature selection. There are 4 main processes in this analysis sentiment system that first is text pre-processing, term weighting (TF-IDF), feature selection, and classification. Based on the tests in this study proven that feature selection improve accuracy of systemresults. The best acuracy results of 96.36%was obtained when k = 1 and using a feature selection of 50% ratio. The test results by using selection feature of 50% ratio get higher accuracy than a system does not use the selection feature because some noise features that have been removed.
Penerapan Algoritme Genetika pada Optimasi Fungsi Keanggotaan Sistem Inferensi Fuzzy Tsukamoto untuk Diagnosis Penyakit HIV Yobel Leonardo Tampubolon; Lailil Muflikhah; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One utilization of the fuzzy inference system is to diagnose HIV disease. In fuzzy inference system there is a membership function that plays an important role in solving the problem so that the function must be determined correctly and appropriately. Based on the rules and limitations of symptoms obtained from the expert used to establish the rules required in fuzzy logic to obtain accurate diagnosis of HIV disease. To obtain the right membership function can be done restrictions using Genetic Algorithm that can provide better accuracy results than the previous limitation. Genetic algorithm used can give accuracy about 45% for 24 data tested. Tests conducted using some of the best parameter values ​​there are, population value is 60, the generation is 40, crossover rate is 0.70 and mutation rate is 0.40. The optimization performed on fuzzy logic method using Genetic Algorithm has increased the accuracy about 20%.
Analisis Sentimen Review Barang Berbahasa Indonesia Dengan Metode Support Vector Machine Dan Query Expansion Dimas Joko Haryanto; Lailil Muflikhah; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Shopping an item in online store is a common activity happening to the community now. The rise of time makes someone chooses to shop online rather than having to travel to the store to get what they need. Reviews of each items in an online store can be useful to see how the buyer's previous feedback through a comment. The comments categorized as positive comments or negative comments. Therefore, to overcome the problem then used sentiment analysis reviews of items using Support Vector Machine and Query Expansion method. This research uses 400 data comments that is divided into two comment, that is positive and negative. The method used is Support Vector Macine polynomial kernel with degree two and Query Expansion. Query Expansion is used to expand a word that has synonyms that are not contained in the training data. The final test result yields an average of accuracy is 96,25% with parameter value of learning rate = 0,001, value of lambda = 0,1, value of complexity = 0,01 and maximum iteration is 50. Accuracy of Support Vector Machine and Query Expansion method is better than just using Support Vector Machine method which only gets 94,75% of accuracy.
Analisis Sentimen Review Aplikasi Mobile Dengan Menggunakan Metode Modified K Nearest Neighbour (MK-NN) Ahmad Nur Royyan; Indriati Indriati; Lailil Muflikhah
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

Mobile Applications is a program that runs on mobile devices like Handphone. Especially Smartphone that can accommodate many a program which certainly serves to facilitate users in undergoing their activities. Mobile Banking is required by its users to make transactions that can be done at the ATM. With this application, users do not need to come to the ATM to make transfers or check balances at ATMs. Because it can be done through the Smartphone he has. This is an opportunity for application developers to create Mobile Banking applications that user needs. In the development of this application, They are need the input from users for applications developed in accordance with the needs of its users. Therefore, it takes a method that is able to sort out the sentiments (comments) from the user. Whether the sentiment is included in positive or negative sentiments. In this study, the author used Modified K Nearest Neighbors (MKNN) as the method used to sort the sentiments. The highest accuracy value obtained is 76% for the value of K = 11 with 400 dataset. For 200 training dataset, the highest accuracy is 69% (K=3). And 70% for 300 training dataset and K=3.
Optimasi Komposisi Pakan Kuda Dewasa Menggunakan Algoritme Genetika Rheza Raditya Andrianto; Lailil Muflikhah; Bayu Rahayudi
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

Indonesia is one of a country in the world that has a diversity of livestock population such as cows, goats, sheep, and horses. Over time followed by the modern era, the horse population decreased because of the role of horses in various ways and non-fulfillment of nutrition by the horses feed. In this study tries to implement genetic algorithm to get the composition of feed at a minimal cost but the required nutritional standards remain met so as to maintain the health and stability of the horse breed. The representation used in this study is a real code in which each chromosome initializes the feed ingredients used. The reprodustion method used in this study is extended intermediate crossover combined with reciprocal exchange mutation method, and for the last step elitism selection used as a method for selection. Based on the testing results of this study, the optimal parameters obtained is at 70 population, 250 generation and value combination of crossover rate and mutation rate as 0,5 and 0,5 with the highest fitness 0,18887407184772886. The result obtained in the form of feed composition with a minimum cost based on the nutritional needs of adult horses.
Clustering Pasien Kanker Berdasarkan Struktur Protein Dalam Tubuh Menggunakan Metode K-Medoids Laily Putri Rizby; Marji Marji; Lailil Muflikhah
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 merupakan penyakit yang kerap menjadi momok bagi sebagian besar orang memang telah memakan banyak korban. Semakin berkembangnya zaman semakin banyak virus yang tersebar di masyarakat. Kanker adalah istilah yang digunakan untuk menggambarkan ratusan penyakit berbeda dengan fitur tertentu yang sama. Kanker dimulai dengan perubahan dalam struktur dan fungsi sel yang menyebabkan sel membelah dan menggandakan diri tanpa terkontrol. Umumnya kanker dinamai sesuai organ dan jenisnya tempat pertama kali ia berkembang. Mutasi gen yang paling sering ditemukan pada kanker manusia adalah Gen P53. Gen P53 merupakan gen penekan tumor yang mengkode atau mengekspresikan protein 53. Dari berbagai banyak data yang ada perlu dilakukan proses klusterisasi yaitu pengelompokkan jenis kanker berdasarkan kelasnya. Salah satu metode klustering yang mulai banyak digunakan adalah metode K-Medoids. K-medoids atau dikenal pula dengan PAM (Partitioning Around Medoids) menggunakan metode partisi clustering untuk mengelompokkan sekumpulan n objek menjadi sejumlah k cluster. Algoritma ini menggunakan objek pada kumpulan objek untuk mewakili sebuah cluster. Objek yang terpilih untuk mewakili sebuah cluster disebut medoid. Pada penelitian clustering pasien kanker menggunakan metode K-Medoids ini menunjukkan nilai persentase kualitas cluster sebesar 77% pada percobaan pada nilai k 14 dan menggunakan 116 data.
Implementasi Algoritma Support Vector Machine (SVM) Untuk Penentuan Seleksi Atlet Pencak Silat Eni Hartika Harahap; Lailil Muflikhah; Bayu Rahayudi
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

Pencak Silat is a traditional martial art originating from Indonesia. Along with an evolution of the time pencak silat is not only used to protect and defend themselves from opponents, but also show in a contest. Determination of the final result in selection that still counts the manual becomes main obstacle of the jury when one of the parties can't accept defeat while competing. To overcome these problems required a classification system that is able to classify enrolment acceptance of the pencak silat athletes had eligible pass the support vector machine (SVM) method by the SVM classifying the data into two classes. The data used in this research is 110 whitch is divided into training data and test data with 2 classes of acceptance of selection is pass and not pass. The best accuracy result in this research based on experiment ratio of data 70% : 30%, using kernel Polynomial Degree d = 2 and parameter value λ (lamda) = 0,1, γ (gamma) = 0,0001, ε (Epsilon) = 0.000001, C (Complexity) = 0.00001 and Itermax = 250. Kernel use in this research value of Polynomial Degree 2. The average result of accuracy using SVM method in the classification enrolment of the Pencak Silat athletes is 69,09 %.
Penerapan Analisis Sentimen untuk Menilai Suatu Produk pada Twitter Berbahasa Indonesia dengan Metode Naive Bayes Classifier dan Information Gain Ahmad Wildan Attabi'; Lailil Muflikhah; Mochammad Ali Fauzi
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

Twitter has a major role in the development of social, communication, psychological, marketing and political aspects. Posts Tweet comments or review indirectly will be a review of the assessment on a product. One of the most sought after products sectors today is beauty and skin care products. They look for products that they share with others, so they have a picture that affects their interest on the opinions of others who delivered via Twitter related results after using the product. Sentiment analysis can help in analyzing and classifying into positive and negative terms of twitter-related opinions about product trends and product quality in the public view. Opinions and comments related to Mustika Ratu's products are the subject of this study, citing the economic growth and the large number of users of Musitka Ratu who are companies in the field of beauty skin and beauty care. The Naive Bayes Classifier method is selected for implementation use, and has a fast performance in training, while the addition of Information is required for the feature selection process by reducing the presence of irrelevant words in the data used. The test is performed with 200 data (100 positive documents, and 100 negative documents) using the thresholds : 0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, dan 0.10. The results obtained are adjusted for a difference of 4%, the highest average value if no Information Gain (threshold 0) is 70%, while using Information Gain (threshold 0.01) equal to 74%. This is influenced by several factors such as the amount of data and data that spread from data data and documents. The highest accuracy value is obtained at K1 (threshold 0,02), then K5, K6 (threshold 0.01), and K7 (threshold 0,02 and 0,08) with percentage 85%, while at k with threshold at the lowest point 50%.
Pemilihan Alternatif Simplisia Nabati Untuk Indikasi Gangguan Kesehatan Menggunakan Metode Analytical Network Process (ANP) dan Simple Additive Weighting (SAW) Gessia Faradiksi Putri; Lailil Muflikhah; Sigit Adinugroho
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

Medicinal plants are one of natural resources that is rarely known by Indonesian. Medicinal plants, known as simplicia, have various health benefits. What people know about simplicia is still very low, so they tend to choose modern medicines which costs much more than simplicia. In fact, simplicia is also safe and can save cost. But, because there are so many alternatives, eventually people become confused to choose which one is the most suitable for them. The parameters used to choose simplicia alternatives are the price, taste, availability of materials and nutritious substances. These parameters are used as references for choosing simplicia alternatives. This research uses Analytical Network Process (ANP) and Simple Additive Weighting (SAW) methods which are used for weighting and ranking. The ranking result obtained by ANP and SAW methods has accuracy on fever 40%, diarrhea 50% and cough 40%. The low tendency accuracy is caused by different usage of criteria weighting between target data and outcomes as well as the influence of innerdepence between criteria.
Implementasi Metode Simple Additive Weighting (SAW) Untuk Penentuan Penerima Zakat Hanggar Wahyu Agi Prayogo; Lailil Muflikhah; Satrio Hadi Wijoyo
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

Zakat can be one way done in effort to increase social welfare and was one of the important role in economic community empowerment. Other benefits of zakat is to help reduce poverty and also can reduce the social inequality in society. But the problems encountered by these institutions in distributing zakat is the inaccuracy in choosing a recipient of zakat. The inaccuracy in choosing a zakat recipient occurs because the zakat management agency still provides subjective assessments. It is causing loss to the people who are more eligible to receive the zakat. The SAW (Simple Additive Weighting) is one of the methods in decision support system that can be used to create a system that can determine the recipient of zakat. There are 4 criteria used in determining zakat recipient in this research, there are family status, family income, total dependents and value report card. Based on the accuracy testing that has been done by using 60 test data, obtained the best accuracy of 90%. The SAW method can be applied properly in determining the recipient of zakat.
Co-Authors A. Bachtiar , Fitra Abdurrachman Bachtiar, Fitra Achmad Jafar Al Kadafi, Achmad Jafar Addin Sahirah, Rafifa Adinugroho, Sigit Agung Setiyoaji Agus Ardiansyah Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Nur Royyan Ahmad Wildan Attabi' Akbar Grahadhuita Al Kautsar, Prima Daffa Aldi Bagus Sasmita Aldino Caturrahmanto Anis Zubair, Anis Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiza Dwi Septian Arief Andy Soebroto Ashidiq, Muhammad Fihan Aulia Herdhyanti Bachtiar, Harsya Baharudin B. Baharum Baihaqi, Galih Restu Bajsair, Fath' Hani Sarli Bayu Laksana Yudha Bayu Rahayudi Bening Herwijayanti Bintang, Tulistyana Irfany Brillian Ghulam Ash Shidiq Budi Darma Setiawan Candra Dewi Candra Dewi Daneswara Jauhari Daneswara Jauhari, Daneswara Darma Setiawan, Budi Darmawan, Riski Daud, Nathan Dewi, Buana Dhimas Wida Syahputra Dian Eka Ratnawati Dimas Joko Hariyanto Dimas Joko Haryanto Duwi Purnama Sidik Edy Santoso Edy Santoso Eni Hartika Harahap Eva Agustina Ompusunggu Faris Dinar Wahyu Gunawan Fatimah Az-Zahra, Adinda Feri Eko Herman Fitra Abdurrachman Bachtiar Fitrotuzzakiyah, Shafira Puspa Gessia Faradiksi Putri HANA RATNAWATI Hanggar Wahyu Agi Prayogo Haris, Asmuni Haryanto, Dimas Joko Hinandy Nur Anisa Hoar, Wilhelmina Sonya Ichsan Achmad Fauzi Iftinan, Salsa Nabila Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indriati Indriati Indriati Indriati Issa Arwani Kautsar, Ahmad Izzan Khairunnisa, Alifah Ksatria Bhuana Kukuh Bhaskara Kukuh Haryobismoko Kurnianingtyas, Diva Laily Putri Rizby Luqyana, Wanda Athira Luthfi Afrizal Ardhani M. Ali Fauzi M. Tanzil Furqon, M. Tanzil Marine Putri Dewi Yuliana Marji . Marji Marji Maulana, Muhammad Taufik Maulidiya, Afifulail Maya Nur Muh Arif Rahman Muh Hamim Fajar Muh. Arif Rahman Muhammad Abduh Muhammad Fajri Muhammad Ferian Rizky Akbari Muhammad Rafif Al Aziz MUHAMMAD SYAFIQ Muhammad Tanzil Furqon Muhammad Wafiq Mukhrodi, Dillah Lyra Nashi Widodo Nisa, Lisa N. Novanto Yudistira Nurfansepta, Amira Ghina Nurhidayati Desiani Nurul Dyah Mentari Nurul Hidayat Nurul Hidayat Olivia Bonita Puji Indah Lestari Puspita Sari Putra Pandu Adikara Putri, Rania Aprilia Dwi Setya Rachmad Indrianto Rachmatika, Isnayni Sugma Rafifah Nawawi, Danisha Ramadhan, Galang Gilang Randi Pratama Nugraha Randy Cahya Wihandika Ratih Kartika Dewi Rekyan Regarsari Mardhi Putri Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rendi Cahya Wihandika Rheza Raditya Andrianto Ria Ine Pristiyanti Rika Raudhotul Rizqiyah Riski Darmawan Riyanarto Sarno Rizal Setya Perdana Rizal Setya Perdana Robbiyatul Munawarah Rowan Rowan Rusydi Hanan, Muhammad Satrio Hadi Wijoyo Setiana, Maya Setya Perdana, Rizal Shalsadilla, Shafatyra Reditha Sholeh, Mahrus Sukma, Lintang Cahyaning Supraptoa Supraptoa Surya Dermawan Susanto, Dominicus Christian Bagus Sutrisna, Naufal Putra Sutrisno Sutrisno Sutrisno, Sutrisno Syafruddin Agustian Putra Syarif Hidayatulloh Tahtri Nadia Utami Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Tri Fadilah, Ghina Utaminingrum, Fitri Vianti Mala Anggraeni Kusuma Vidya Capristyan Pamungkas Wahyu Rizki Ferdiansyah Wardana, Dzaky Ahmadin Berkah Warut, Gregorius Batara De Wibowo, Dhimas Bagus Bimasena Wijaya, Nicholas Yobel Leonardo Tampubolon Yogi Suwandy Yulian Ekananta Yunita, W. Lisa Zakiyyah, Rizka Husnun Zanna Annisa Nur Azizah Fareza