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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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Identifikasi Penyakit Tanaman Jarak Pagar Menggunakan Metode Fuzzy K-Nearest Neighbor (FK-NN) Eva Agustina Ompusunggu; Dian Eka Ratnawati; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Jatropha (Jatropha curcas L., Euphorbiaceae) is a plant that has many uses that as a raw material medicines and vegetable oil (biodiesel). Jatropha is used to cure various diseases. Some people also make Jatropha as a main ingredient of their livelihoods. However, the quality of Jatropha decreased due to various diseases. Lack of knowledge about the disease of jatropha and do not know how to overcome it became one of the causes. As well as the unavailability of media for the public to know the diseases that attack. To know and make it easier to diagnose diseases that attack jatropha, a system needs to be made. To support this diagnosis used k-nearest neighbor and fuzzy method. The first step of this method is entering training data that contains symptoms. Then classification using the k-nearest neighbor and fuzzy. Then we get the result of this system which is the diagnosis of Jatropha's diseases from nine diseases that there are. Results of tests performed on this study, obtained the highest accuracy by 80%
Sistem Pendukung Keputusan Penentuan Tingkat Keparahan Autis Menggunakan Metode Fuzzy K-Nearest Neighbor Robbiyatul Munawarah; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 7 (2017): Juli 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Autistic or Autistic Spectrum Disorders (ASD) is a general term referring to a neurodevelopmental disorder that is well known among Indonesian. Many researches on autism detection have been done by designing artificial intelligence systems with a variety of techniques used to make it easier for society to predict this kind of disorder. However, we hardly ever seen a system that can determine the severity of autism. In fact, the progress of the research in this field is no longer focused on whether a child is autistic individual or not, but rather to questioning about “Is there anything in autistic children that makes them different from one another?” as the ‘severity' label appear to give them spesific class under certain behaviour they shown. To make it easier to determine the severity of autism, decision support system will be designed using one of data mining method called Fuzzy K-Nearest Neighbor (FK-NN). Fuzzy K-Nearest Neighbor (FK-NN) is K-Nearest Neighbor method combine with Fuzzy theory that gives value of membership on every predicted data.. There are 14 symptoms and 3 types of severity used as a parameter in the development of the system. The output of this decision support system is autism severity level. The results of the system shows that the average maximum accuracy is 90.83% while the average minimum accuracy is 82.50%. Based on those results, the uses of Fuzzy K-Nearest Neighbor (FK-NN) method can be implemented in our daily life.
Implementasi Metode Fuzzy Subtractive Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Vianti Mala Anggraeni Kusuma; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Abstract Forest is the habitat for all kinds of animals and plants, forests have a very big function to maintain the balance of nature, as the supplier of the oxygen requirement for living on earth, and the natural resources that provide a variety of materials for human needs. But at this moment the existence of forest diminishing due to illegal logging by humans or by forest fires are becoming more frequent. Forest fires this gives very bad impact, extinction of some species of plants and animals, the smoke is detrimental to health even low and so forth. So to be able to help deal with the issue made a system that can manage data hotspots (hotspots) with Fuzzy subtractive clustering. Parameter data used in the development of the system: brightness temperature and FRP (Fire Radiative Power). The result of clustering which illustrates the potential of forest fires, which are grouped in the high potential and low potential. The test results showed the best coefficient silhouette value of 0.45 and the results of the cluster is formed by two clusters using radius values ​​0.2, accept ratio 0.5, reject ratio 0.15. The results of the analysis in the determination of the potential for forest fires result is a high potential with an average brightness value of 335.727⁰K, FRP 57.248 and average confidence 83.47%. While medium potential with an average brightness value of 318.934⁰K, FRP 23.330 and average confidence 58.08%. Keywords: Clustering, Hotspot, Fuzzy subtractive clustering, Silhouette Coefficient
Optimasi Pembagian Tugas Dosen Pengampu Mata Kuliah Dengan Metode Particle Swarm Optimization Muhammad Abduh; Rekyan Regasari Mardi Putri; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In forming a lectures schedule, an absolute thing that had to be done is division of labor teaching duties in accordance with their field in order to create an effective teaching and learning activities. At Faculty of Computer Science (FILKOM) Brawijaya University, the assignment process is still manually designed where it requires some substantial time, therefore it needed a right optimization methods in dealing with this case. This problem can be solved by a population-based heuristic methods, Particle Swarm Optimization (PSO) which has been applied in various fields such as scheduling and assignments. The data used in this study is the data division of lecturers teaching tasks as a priority of lecturer's teaching interests to a course. Through the obtained results, it had tested to find the effect of tested parameters on the resulted fitness values. From PSO parameters test results, it obtained the best particle number as 100, best iteration number as 100, and combination of velocity parameter c1 and c2 as 1.5 and 1.5 with resulted fitness value as 94878. From the results of system, the obtained assignment solution gives good results, which is still within the limits of tolerance with decreasingly obtained error values in putting a lecturer on courses that according to their interests.
Rekomendasi Pemilihan Properti Kota Malang Menggunakan Metode AHP-SAW Syafruddin Agustian Putra; Nurul Hidayat; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The increasing of population number in Malang City triggered property developers to answer people's needs by developing and supplying assorted facilities of property. Many criterias should be considered by prospective buyers in selecting a property, for example: price, number of bedrooms, bathrooms, garages, building area, and land area. The numerous considerations referring to some specific criteria lead prospective buyers to take a difficult decision. Regarding to this problem, there are several methods able to resolve the complicacy of buyers in taking decision, which are performing combination of Multi Criteria Decision Making (MCDM) method by using Analytic Hierarchy Process (AHP) as a way to calculate weight of each determined criterion, and Simple Additive Weighting (SAW) as a method used in ranking the criteria. In functional test, the result of 100% represents that the system runs very well as designed. And from the accuracy test, the result is 80.80%. In sum, the AHP-SAW method combination is compatible to be used in selecting property in Malang City
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

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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.
Named Entity Recognition Menggunakan Hidden Markov Model dan Algoritma Viterbi pada Teks Tanaman Obat Agung Setiyoaji; Lailil Muflikhah; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Media to convey information can be through television, radio, social media, and website. Website is a work of someone located in a domain that contains information. The development of websites more and more information is not unstoppable so that the problem arises difficult to find information in accordance with the needs of Internet users, so that the required classification and extraction of information for information on the website. Named Entity Recognition which derives from the extraction of information, NER aims to facilitate the search for information by naming entities on each word in a text. In this research will be done the introduction of four entities namely the NAME, PLACE, SUBSTANCE, and FUNCTION of the text on medicinal plants. The algorithm used Hidden Markov Model (HMM) and Viterbi algorithm. Overall entity recognition count the lowest value with f-measure 0.41, and the highest with f-measure 0.72.
Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity Bening Herwijayanti; Dian Eka Ratnawati; Lailil Muflikhah
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 discussing the online news by using the weighting of tf-idf and cosine of this similarity the previous research reference on online news information using single pass clustering algorithm, where the data to be used comes from the online news website that is kompas.com. Because of the many news that is on the website, so sometimes the news is posted not in accordance with the category. Human error will be the problem of wrong news posting. In addition to posting errors online news groupings are also important for the convenience of users to search for news according to their category. Implementing online news stories using tf-idf and cosine similarities, preprocessing processes ie tokenizing, stopword and stemming can reduce the term process of speeding the weighting of terms using tf-idf and accelerating the cosine process of similarity. The goal is to facilitate human error as well as reduce caution categorization. The value is able to classify news with accreditation rate of 91.25%.
Sistem Pakar Diagnosis Penyakit Pada Kambing Menggunakan Metode Naive Bayes dan Certainty Factor Wahyu Rizki Ferdiansyah; Lailil Muflikhah; Sigit Adinugroho
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

Examination on goats disease periodically is getting less now, so it makes the goats get diseases easily. This makes breeders have difficulty in the first treatment and the don't know what they should do without an expert. The process of diagnosis of diseases on goats can't be done by just anyone because of the type of disease with symptoms have uncertainty. Based on these problems, the author makes an expert system that is able to diagnosis diseases on goats as usually do an expert. This expert system uses Naive Bayes and Certainty Factor method, PHP programming language and MySQL database. Experimental functional test results show all functional requirements can run well. In addition, the results of system accuracy testing using f-measure method is 86,80%. With the amount of accuracy, expert system diagnosis of goats diseases uses Naive Bayes and Certainty Factor method has a good performance.
Implementasi Metode Particle Swarm Optimization-Dempster Shafer untuk Diagnosa Indikasi Penyakit pada Budidaya Ikan Gurami Faris Dinar Wahyu Gunawan; Edy Santoso; Lailil Muflikhah
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

Knowledge of fish breeders of the type of disease that can attack on gouramy fish at the time of cultivation is very small. Prediction indication of disease on gourami fish is an important thing to the succes of cultivation. Prediction of disease obtained from the facts that exist in the cultivation process. Dempster shafer is one of the techniques of artificial intelligence used to predict based on interrelated facts. Dempster shafer method is often used because it is quite easy to implement algorithm. However, the performance of dempster shafer is very dependent on the girlfriend who has a connection with the problem. So, if there is a new fact must first consult to experts. In addition, Dempster shafer does not guarantee specific prediction results because interrelated facts are often general. One approach that can be used to overcome this problem is to apply Particle Swarm Optmization method. Particle Swarm Optimization explores the search space to find initial density values based on particle cost values. . Where the Particle Swarm Optimization method is used to generate density values, and Dempster Shafer as a conclusion of disease indication. In this study using hybrid Particle Swarm Optmization-Dempster Shafer for diagnosis of disease indication on gouramy fish culture. The results obtained from the output of the system with experts achieve 86,5% results.
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