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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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SENTIMENT ANALYSIS FOR REVIEW MOBILE APPLICATIONS USING NEIGHBOR METHOD WEIGHTED K-NEAREST NEIGHBOR (NWKNN) Indriati Indriati; Achmad Ridok
Journal of Environmental Engineering and Sustainable Technology Vol 3, No 1 (2016)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (622.713 KB) | DOI: 10.21776/ub.jeest.2016.003.01.4

Abstract

Indonesia a potential market for business because of a large number of smartphone users, especially developers of mobile applications. Each application stores allow the user to provide a review of the application used. The review is not only beneficial for prospective users of the application but also beneficial for the application developer. Review of the applications that are influenced by emotion (sentiment) are grouped or classified to determine positive and negative polarization. Therefore, it is necessary to have an application that can perform sentiment analysis for the mobile app reviews using Neighbor-Weighted K-Nearest Neighbor (NWKNN) classification method with high accuracy results. NWKNN method is able to classify mobile application review documents on the balanced data with current value of k = 20 gives the best f-measure average value of 0.9 with ratio of training data and test data 80%: 20%. However, for the imbalanced data with value of k = 45 gives the best f-measure average value of 0.797 with a ratio of training data and test data 80%: 20%. Based on the results, the effect of imbalanced data to  the accuracy of the NWKNN methods by comparing NWKNN and KNN methods, it was found that the value of F-Measure NWKNN method is better than KNN method with gap of 0,27, due to the added weight on class minority overcome misclassification problem on minority class.
PENINGKATAN MANAJEMEN DATA MELALUI SISTEM APLIKASI POSYANDU DI KECAMATAN LOWOKWARU MALANG Lailil Muflikhah; Daneswara Jauhari; Indriati Indriati
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol 23, No 4 (2017): OKTOBER - DESEMBER
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v23i4.8606

Abstract

AbstrakPos Pelayanan Terpadu (Posyandu) merupakan upaya dari masyarakat dalam meningkatkan kesehatan masyarakat dengan memberikan layanan kesehatan dasar secara mudah dan ekonomis.Namun kurangnya partisipasi dari masyarakat akan pentingnya Posyandu mendorong dilakukannya kegiatan pengabdian masyarakat ini dengan membuat sistem aplikasi berbasis android. Sistem ini ditujukan untuk pencatatan selama kegiatan posyandu dan pemberian informasi seputar kegiatan informasi serta perkembangan balita. Adapun tahapan kegiatan dimulai dengan surveykegiatan terkait dengan analisis kebutuhan sistem, perancangan dan implmementasi sistem, tahapan selanjutnya adalah pelatihan terhadap pemakai aplikasi, baik bagi kader posyandu maupun ibu balita. Tahap akhir merupakan evaluasi terhadap tingkat kegunaan sistem bagi petugas dan peserta posyandu. Berdasarkan hasil kuisioner diperoleh bahwasannnya sistem aplikasi tersebut telahmemenuhi kebutuhan untuk dioperasikan secara efektif (83.67% responden), efisien (65% responden) dengankemudahan (89.3% responden) serta 79% menyatakan tingkat kepuasan yang relatif tinggi.Kata kunci: android, kader, kesehatan, posyandu, sistemAbstractIntegrated Service Post (Posyandu) is an effort of the community in improving public health by providing basic health services in effective and efficient. However, the lack of participant on the importanceof Posyandu encourages this community service to develop application system based on android. Thissystem is intended for reserving the data information including cadre of Posyandu and children under five. First, survey activities related to system requirements analysis, design and implementation of the system. The next stage is training on application users, eithercadre orchildren’s parent. The final stage is usability evaluation of application system. Based on questionnaire, the evaluation result is effectively rate of 83.6%, efficiently rate of 65%, easily rate of 89.3%, and satisfaction rate of 79.3%.Keywords: android, cadre, health, posyandu,system
Analisis Sentimen Kebijakan Kampus Merdeka Menggunakan Naive Bayes dan Pembobotan TF-IDF Berdasarkan Komentar pada Youtube Dhaifa Farah Zhafira; Bayu Rahayudi; Indriati Indriati
Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi Vol 2 No 1 (2021): Agustus
Publisher : Fakultas Ilmu Komputer Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/justsi.v2i1.24

Abstract

Kebijakan Kampus Merdeka merupakan salah satu kebijakan baru yang digagas oleh Menteri Pendidikan dan Kebudayaan Republik Indonesia (Mendikbud RI). Kebijakan tersebut tengah ramai disorot publik khususnya pada platform Youtube berkaitan dengan video unggahan Mendikbud di kanalnya. Pada Youtube, opini masyarakat dapat membanjiri kolom komentar dalam sekejap karena kemunculannya sebagai platform pertama yang menawarkan fasilitas konten audio visual. Penelitian ini mencoba menganalisis opini masyarakat yang tertampung dalam kolom komentar Youtube ke dalam klasifikasi sentimen positif dan negatif. Klasifikasi diimplementasikan pada Google Colaboratory yang berbasis bahasa Python dan Jupyter Notebook dengan algoritme Naive Bayes Classifier serta pembobotan kata Term Frequency Inverse Document Frequency (TF-IDF). 5 proses utama dalam penelitian ini yang meliputi pelabelan manual, text preprocessing, pembobotan TF-IDF, validasi data menggunakan k-fold cross validation, dan klasifikasi. Hasil akurasi terbaik sebesar 97% yang didapat dengan menggunakan 900 data latih, 100 data uji, menerapkan pembobotan TF-IDF, dan 10-fold cross validation. Rata-rata akurasi yang didapat dari 10 iterasi pada k-fold cross validation yaitu sebesar 91.8% dengan nilai precision, recall, f-measure sebesar 90.35%, 93.6%, 91.95%. Berdasarkan hasil tersebut, Naive Bayes Classifier cukup baik sebagai alternatif untuk analisis sentimen.
Cyberbullying identification in twitter using support vector machine and information gain based feature selection Ni Made Gita Dwi Purnamasari; M. Ali Fauzi; Indriati Indriati; Liana Shinta Dewi
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1494-1500

Abstract

Cyberbullying is one of the actions that violate the ITE Law where the crime is committed on social media applications such as Twitter. This action is difficult to detect if no one is reporting the tweet. Cyberbullying tweet identification aims to classify tweets that contain bullying. Classification is done using Support Vector Machine method where this method aims to find the dividing hyperplane between negative and positive class. This study is a text classification where more data is used, the more features are produced, therefore this research also uses Information Gain as feature selection to select features that are not relevant to the classification. The process of the system starts from text preprocessing with tokenizing, filtering, stemming and term weighting. Then perform the information gain feature selection by calculating the entropy value of each term. After that perform the classification process based on the terms that have been selected, and the output of the system is identification whether the tweet is bullying or not. The result of using SVM method is accuracy 75%, precision 70.27%, recall 86.66% and f-measure 77.61% on experiment maximum iteration = 20, λ = 0.5, γ = 0.001, ε = 0.000001, and C = 1. The best threshold of information gain is 90%, with accuracy 76.66%, precision 72.22%, recall 86.66% and f-measure 78.78%.
Enhancing the capability of online teaching for elementary school teacher through interactive video making training Yuita Arum Sari; Randy Cahya Wihandika; Sigit Adinugroho; Indriati Indriati; Putra Pandu Adikara
Community Empowerment Vol 7 No 7 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.453 KB) | DOI: 10.31603/ce.6616

Abstract

The strategies and learning mechanisms that have been widely used up until now have changed as a result of the Covid-19 pandemic. Learning from home (BDR) activities are used to replace face-to-face learning activities. Effective implementation of BDR requires information technology skills, especially the use of learning support software. Thus, it is imperative that teachers receive training in the use of learning support software in order to advance their abilities to teach online effectively and efficiently. In this community service, training activities for making creative teaching materials were carried out for elementary school teachers. The creative teaching material is in the form of animation, so that it attracts the interest of students and is expected to increase the effectiveness of online learning. This community service activity begins with a pre-test, continues with the delivery of material, and ends with the provision of a post-test and questionnaire. The evaluation's findings revealed that participants' skills had improved when learning support hardware and instructional videos were introduced.
Optimasi Komposisi Pakan Sapi Perah Menggunakan Algoritma Genetika Durrotul Fakhiroh; Wayan Firdaus Mahmudy; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 1 (2017): Januari 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (802.777 KB)

Abstract

Hambatan terbesar yang dialami oleh peternak sapi perah adalah penggunaan komposisi pakan yang tidak efisien. Dalam sudut pandang ekonomi, biaya untuk pembelian pakan ternak merupakan biaya tertinggi dalam usaha peternakan, sehingga harus ditekan serendah mungkin untuk memaksimalkan pendapatan dengan tetap memperhatikan nutrisi yang dibutuhkan oleh sapi perah. Agar dapat mencapai dua hal tersebut dilakukan optimasi terhadap ransum agar dapat memenuhi kebutuhan nutrisi dengan biaya yang minimal. Algoritma genetika merupakan salah satu metode yang sesuai untuk memecahkan permasalahan optimasi. Representasi yang digunakan adalah real code dimana setiap kromosom mewakili bobot dari bahan pakan, dan panjang kromosom tergantung dari banyaknya bahan pakan. Metode crossover yang digunakan adalah extended intermediete, proses mutasi menggunakan metode random mutation, sedangkan elitism adalah metode yang digunakan dalam proses seleksi. Berdasarkan hasil pengujian yang telah dilakukan, diperoleh parameter optimal yaitu pada populasi 100, generasi 200, serta kombinasi cr dan mr sebesar 0.3 dan 0.3. Hasil akhir yang didapatkan berupa rekomendasi komposisi ransum dengan biaya yang minimal dan kebutuhan nutrisi sapi perah tetap terpenuhi.
Deteksi Autisme pada Anak Menggunakan Metode Modified K-Nearest Neighbor (MKNN) Zahra Swastika Putri; Rekyan Regasari Mardi Putri; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 3 (2017): Maret 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1123.03 KB)

Abstract

Autism is a childhood and developmental disorder that characterized by lack of communication, cognition, imagination and social interaction activities. Many people didn't recognize the symptoms of autism disorder until the first three or seven years of life. Delay, similarities of symptoms and lack of knowledge about autism cause imprecision treatment handling, and increased number of sufferers. Identification of autism differentiated into severe autism, moderate autism, mild autism and non- autism. Modified K-Nearest Neighbor (MKNN) method is a method that enhancing performance of conventional K-Nearest Neighbor method. There're validity of the train data process and weight voting process to robust neighbors of training dataset and strengthen the performance results. Based on variant value of k testing obtained 83.33% accuracy at dissimilarity measure. Based on composition of balance training data testing obtained 90% accuracy at euclidean distance. Based on amount of training data testing obtained 79.17% average accuracy. Based on variation of training data testing obtained 83.33% accuracy at dissimilarity measure. Based on results of such testing accuracy, pointed out that the detection of children's autism using MKNN method have a pretty good degree of accuracy and capable to classify and detection the autism symptoms based on perceived symptoms user input.
Penerapan Metode K-Nearest Neighbor (KNN) dan Metode Weighted Product (WP) Dalam Penerimaan Calon Guru Dan Karyawan Tata Usaha Baru Berwawasan Teknologi (Studi Kasus : Sekolah Menengah Kejuruan Muhammadiyah 2 Kediri) Nihru Nafi' Dzikrulloh; Indriati Indriati; Budi Darma Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (878.441 KB)

Abstract

World of particular employment agencies Vocational High School, many a teacher or school employee who less clever in technology of the current technological developments. Actually, it is in need of teachers and school administration employees who have qualified human resources high in the knowledge of science and technology. The school is in need it is because it affects how do learning on students in school. To meet the desired standards of quality teachers, during The Vocational High School Muhammadiyah 2 Kediri is selection and recruitment of teachers by means of manual employees. The selection has been done manually through the test phase 4 aspects of your application letter and attachments GPA averages, academic test, test general knowledge of science and technology (IPTEK), and interview. The data collection process for the selection still use manual. Therefore, we need a web-based system so that the selection acceptance of new teacher candidates can run more effectively and efficiently. On this website using K-Nearest Neighbor (KNN) and the method of Weighted Product (WP). K-Nearest Neighbor used to determine the weight of each criterion to classify the good or bad. After classifying the KNN method, the selection of prospective teachers will be recruited by the school Vocational High School Muhammadiyah 2 Kediri using Weight Product (WP). Weight Product used to determine the results of the classification by KNN method to perform a ranking in order to take the best results. Tests conducted consisting of, testing the accuracy of the value of K means and accuracy testing of the WP value criteria weighting method. The accuracy of the test results obtained suitability accuracy value by 94%, precision 80%, and recall 80%.
Klasifikasi Tweets Pada Twitter Dengan Menggunakan Metode Fuzzy K-Nearest Neighbour (Fuzzy K-NN) dan Query Expansion Berbasis Apriori Joda Pahlawan Romadhona Tanjung; Mochammad Ali Fauzi; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1372.995 KB)

Abstract

Twitter is a unique conversation tool that allows us to send and receive short messages called tweets in the Twitter community. Tweets are short messages that have a length of 140 characters. Tweets that appear on the homepage are all jumbled into one, posted variety ranging from the economy, sports, technology, automotive, healthcare and others. When users search for a news or information desired, the problem that arises is Twitter user difficult to find tweets. The classification process can be performed to categorize a tweets using an algorithm Fuzzy K-Nearest Neighbour. However, the process of classifying a tweets it is difficult to do because the tweets in the form of short-text. Therefore, before doing the classification process a tweets done preprocessing and word expansion beforehand with Query Expansion algorithms in order to provide maximum results in the classification. In the study conducted to produce the best accuracy by 82%. Best accuracy is obtained when using the Fuzzy KNN method with Query Expansion without preprocessing and threshold for the support value> = 0.15 and the value of confidence> = 1.
Pembangkitan Nilai Belief Pada Dempster-Shafer Dengan Particle Swarm Optimization (PSO) Untuk Penentuan Pasal Kasus Penganiayaan Merry Gricelya Nababan; Rekyan Regasari Mardi Putri; Indriati Indriati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1004.216 KB)

Abstract

The crime against the body and life continues to increase every year, judges as decision makers against criminal defendants have a very important role in providing decisions. However, there are some things that the judge needs to consider in making decisions, so that the problem of uncertainty can be a judge's obstacle. The author applies a method that can solve the problem of this uncertainty is Dempster-shafer (D-S). D-S algorithm has belief value that serves to determine the influence between symptoms obtained from an expert. In this case the expert can not give the value of belief karana must be in accordance with the evidence and real sanctions. So with Particle Swarm Optimization algorithm (PSO) belief value will be raised as well as doing optimization to get maximum results. In accordance with the test conducted from the case data of the penganiaayan obtained maximum belief value based on PSO parameter test. The result of system accuracy calculation by using belief value that has been optimized with D-S on 29 cases of abuse shows accuracy of 13.79%. The result of this accuracy is not maximal due to complex problems with the output (Output) of the system more than one. For further research, we can use Artificial Neural Network (ANN) method or with algorithm Analytic Hierarchy Process (AHP).
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