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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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Rekomendasi Kelayakan Peminjam menggunakan Metode Fuzzy Tsukamoto Muh Hamim Fajar; Edy Santoso; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cooperatives is one of the business fields that offer savings and loans. To become a member of the cooperative or to make savings and loans a person must meet the requirements that have been determined by the cooperative. Due to the limited funds owned by the cooperative, the selection of someone to get help is done to reduce payment problems. In this study, calculations were carried out in order to recommend the feasibility of the borrower using the fuzzy tsukamoto method approach. There are several processes using the Fuzzy Tsukamoto method to solve these problems is Fuzzyfication, Tsukamoto's Fuzzy Inference system and defuzzification. In the process of fuzzyfication is to change the real variables into fuzzy variables. Then, the process of the tsukamoto fuzzy inference system is to find the value of -predicate and z, and finally defuzzification is to change the fuzzy variable into a real variable. Based on results of the tests carried out use the Tsukamoto fuzzy method of cooperative data KUD "SRI LESTARI" obtained very good performance results with an accuracy value of 87.88% and an F-1 score of 92.96%.
Analisis Sentimen Twitter terhadap Kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat menggunakan Metode Support Vector Machine Dhimas Wida Syahputra; Bayu Rahayudi; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

COVID-19 or Coronavirus Disease-19 is a virus that infects the respiratory tract with mild to severe symptoms. The government enforces a regulation, namely the Penerapan Pembatasan Kegiatan Masyarakat (PPKM) to reduce the number of positive COVID 19. Pros and cons occur among the community. People usually express opinions on social media, such as Twitter. So that the public can express a public opinion about government policies regarding PPKM. From the public opinion on Twitter, we can analyze the sentiment. Sentiment analysis is used to determine whether a comment is negative or positive. In this case, sentiment analysis determines public comments on PPKM regulations on Twitter social media. The Support Vector Machine (SVM) algorithm is used to find out the sentiment in tweet comments. There are 500 Twitter commentary documents with a comparison of training data and data of 80% and 20%. Parameter search was conducted with the best results on the degree kernel polynomial value 3, the learning rate value 0.0001, and the Complexity 1 value. The results of the K-Fold Cross Validation test using the best parameters, namely the average accuracy of 77.2%, precision 83.3%, recall 68.7%, and F measure 75.11%.
Pencarian Topik dalam Al-Quran Terjemahan Bahasa Indonesia dengan Metode Hybrid Fuzzy C-Means Particle Swarm Optimization (FCM-PSO) Luthfi Afrizal Ardhani; Fitra Abdurrachman Bachtiar; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Diterbitkan di JTSISKOM (Jurnal Teknologi dan Sistem Komputer)
Prediksi Harga Beras menggunakan Metode Least Square Brillian Ghulam Ash Shidiq; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Basic ingredients are something that must be fulfilled in order to support the survival of all humans, one of the main ingredients is rice. Rice is a major commodity of staples needed by humans in several countries in the world. As a result, if the price of the staple food of rice experiences erratic price fluctuations (fluctuations) which can cause a reduction in the purchasing power of people with low incomes, the rice mills stop operating to avoid losses. Therefore, to avoid and reduce the risk of food security problems in the future, we need a system that can be able to predict the prices of rice so that it can help reduce the risk of loss and can ensure that rice prices can be reached by the wider community. In making this system, of course, a periodic data method (time series) is needed that can process real data in order to predict the price of rice staples correctly. In this study the method used is the Least Square method. This method can predict rice prices by using data in the past to be used as forecasting guidelines in the future. From the prediction results, the smallest error search calculation (error) is carried out using MAPE (Mean Absolute Percentage Error). The data used in this study were 132 data. Which consists of 132 training data and 12 test data the latest data. After the prediction process is carried out the result from the prediction prices of rice for 12 months in 2021 are obtained. The results of the prediction of rice prices for 12 months in 2021 are obtained. The test results from this prediction are in the form of the smallest error value (error) which is considered very good in accuracy to predict rice prices using the least square method, which is 5%.
Analisis Sentimen Komentar pada Media Sosial Twitter tentang PPKM Covid-19 di Indonesia dengan Metode Naive Bayes Aldi Bagus Sasmita; Bayu Rahayudi; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The ongoing spread of COVID-19 brings many changes, including Indonesia country. The proper handling for each sector to deal with this pandemic is still ongoing with various efforts including the establishment of policies to intercept the expansion of the virus. The government's policy-setting effort to cut the spread of COVID-19 is the Pemberlakuan Pembatasan Kegiatan Masyarakat, known as PPKM. This policy received various responses from the public through many media, especially digital media through personal social media accounts, especially in the form of comments. Twitter social media platform becomes an effective argumentation space, especially for the phenomenon that is being discussed a lot, including the PPKM policy. Various responses in the form of comments need to be analyzed by sentiment with a classification of positive or negative responses that acts as a sentence filter. The reason of this reasearch on the Naive Bayes method is to determine the value of accuracy in the classification of public sentiment on Twitter social media in response to the PPKM policy carried out by the government in Indonesia. The consequence of the research conducted this time stated that the Naive Bayes Classifier algorithm using the NLTK Filtering Library has the highest accuracy such as Tala Filtering Library and the Combined Stopword Filtering Library. The accuracy results obtained by the NLTK Library Filtering is 77.2%, Tala Filtering Library is 76,6%, and The Combined Stopword Filtering Libray is 75,2%.
Analisis Sentimen Masyarakat Indonesia tentang Vaksin Covid-19 di Twitter dengan menggunakan Metode K-Nearest Neighbors dan Seleksi Fitur Chi Square Ksatria Bhuana; Indriati Indriati; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 3 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The disease outbreak caused by the corona virus (2019-nCov) or commonly called COVID-19 is an outbreak of a disease that causes infection in the lungs. The topic of the COVID-19 vaccine has become a hot topic of discussion for the majority of Indonesian people. One of the biggest social media platforms, Twitter, has become a place for the aspirations of the Indonesian people to express their opinions regarding the COVID-19 vaccine. Therefore, a sentiment analysis system is needed to examine polarities of publics response and to facilitate the data analysis process. The data analyzed comes from the opinion of the Indonesian people on Twitter as many as 1482 tweets with the distribution of training data totaling 1185 and test data totaling 297. The data will then be grouped based on 3 sentiment classes there are negative sentiment class, neutral sentiment, and positive sentiment. Before starting sentiment analysis process, the data set used will be preprocessed including case folding, cleaning, tokenizing, filtering, and stemming. Furthermore, chi square feature selection is applied to eliminate unimportant features or terms, then proceed with TF-IDF weighting. After weighting the TF-IDF, then calculating the cosine similarity, and for the last stage, applying the KNN method approach to find the classification results. The results of the confusion matrix evaluation produce accuracy with a value of 88.5522%, precision with a value of 88.18%, recall with a value of 89.95%, and f-measure with a value of 89.05%.
Penerapan Metode Analytical Hierarchy Process (AHP) dan Metode Simple Additive Weighting (SAW) pada Pembiayaan Anggota (Studi Kasus: Koperasi Simpan Pinjam Pembiayaan Syariah Tunas Artha Mandiri (KSPPS TAM) di Kab. Nganjuk) Hinandy Nur Anisa; Edy Santoso; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 5 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tunas Artha Mandiri Sharia Financing Savings And Loan Cooperative (KSPPS TAM) is a Sharia financial institution in the form of a savings and loan cooperative located in Nganjuk Regency. In the past 3 years, the demand for member financing has increased, because it makes it difficult for cooperatives to determine whether members deserve financing assistance or not. The Simple Additive Weighting Method (SAW) and The Analytical Hierarchy Process (AHP) Method are two of the methods taken for this study. Things are obtained from both methods, namely the optimal level of accuracy and the accuracy of providing member financing. AHP method was chosen because a complex problem was converted into a hierarchy, while the SAW method was chosen because of the weighted addition to the performance rating. The study used 3 criteria, namely income, employment, and the ability to pay installments. Obtaining AHP accuracy with 5 data by 100% and 50 data by 90%. Meanwhile, the accuracy results obtained by the SAW method with 5 data by 80% and 50 data by 78%. The results of the accuracy assessment analysis obtained by the AHP method are more accurate than the SAW method.
Optimasi Gizi Bahan Makanan dan Paket Herbal Kayu India pada Remaja untuk Pencegahan terhadap Covid 19 serta Varian Barunya dalam upaya Meningkatkan Imunitas dan Prestasi menggunakan Algoritma Genetika Aqmal Maulana Tisno Nuryawan; Imam Cholissodin; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 6 (2022): Juni 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Covid-19 pandemic is the most difficult time faced by the community and health workers, prevention of the virus is also increasingly being done to reduce the rate of infection. It is very important to increase the body's resistance or immunity. Therefore, in this study, the problem to be solved is regarding the optimization of nutrition for foodstuffs and herbal packages of Indian wood for adolescents for the prevention of COVID-19 and its new variants in an effort to increase immunity and performance using genetic algorithms. Genetic algorithm is a heuristic search algorithm that uses the mechanism of biological evolution. Some resistance that can be traversed by genetic algorithms is that information will be combined randomly. The following will also be matched Back regarding individuals with previous iterations. Then it will produce a minimum and maximum function to determine the price and get the fitness value as a price reference. The most optimal parameter values are obtained in the generation of 800, using a crossover reproduction of two cut points of 0.5, scrambler mutation value of 0.9, and a population of 125 by obtaining optimal parameter values, then the patient can get the best food ingredients. From the parameter values that have been obtained, the food package is optimally tested with the average nutritional requirement for patient G of 3.53%, patient K of 1.43%, patient E of 3.85%, and patient N of 4.15%, with each price obtained is Rp. 67,945, Rp. 76,397, Rp. 58,853, Rp. 58,195, in the order according to the patient.
Klasifikasi Jamur Dapat Dimakan atau Beracun Menggunakan Naive Bayes dan Seleksi Fitur berbasis Association Rule Mining Muhammad Rafif Al Aziz; Muhammad Tanzil Furqon; Lailil Muflikhah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mushrooms are food commodities that are nutritious for the body. Even mushrooms can be a cure for certain diseases. But not all types of mushrooms are nutritious for the body, there are types of mushrooms that can even be bad for the body or toxic. Therefore the classification of edible and poisonous mushrooms is very important in order to be able to consume the right mushrooms. The classification method used to classify edible or poisonous mushrooms is Naive Bayes with feature selection based on Association Rule. Prior to classification using Naive Bayes, an Association Rule-based feature selection is performed by selecting features in the rule that meet minimum support and minimum confidence. The best accuracy result of mushrooms classification with Naive Bayes and feature selection is 95% with one selected feature. Meanwhile, the seven selected features produce an accuracy of 94%. If without feature selection the resulting accuracy is 95%. Although the accuracy with feature selection is not better than without feature selection, by using feature selection the computational performance of the model becomes more efficient and accuracy only decreases by 1%. This means that feature selection based on Association Rule and classification using Naive Bayes is successful in classifying mushrooms.
Peramalan Kasus Positif COVID-19 di Jawa Timur menggunakan Metode Hybrid ARIMA-LSTM Rowan Rowan; Lailil Muflikhah; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

COVID-19 (Coronavirus Disease 2019) is a new type of disease related to the same virus family as Severe Acute Respiratory Syndrome (SARS) and several strains of the common cold virus. Along with the increase of positive cases, the resources needed in handling COVID-19 cases also increase. To overcome this problem, anticipatory measures are needed so that the resources needed in handling COVID-19 such as health workers and medicines will be available before positive cases spike. In this study, the method used is the hybrid Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) method. The ARIMA-LSTM model is built by combining the ARIMA (2,1,2) model with the LSTM model which has 4 hidden states and 1 layer. ARIMA model is used to predict the trend value from time series data while LSTM model is used to complete the ARIMA model forecasting by predicting the time series residual value. Based on testing, the ARIMA-LSTM model achieved high accuracy, especially in short-term forecasting with an error rate of 1.8 percent for forecasting cases for the next 3 days.
Co-Authors A. Bachtiar , Fitra Abdurrachman Bachtiar, Fitra Achmad Jafar Al Kadafi, Achmad Jafar Addin Sahirah, Rafifa Adinugroho, Sigit Adnawirya Pratama, Cendikia Agung Setiyoaji Agus Ardiansyah Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Nur Royyan Ahmad Wildan Attabi' Ahmad Zaki Akbar Grahadhuita Al Kautsar, Prima Daffa Aldi Bagus Sasmita Aldino Caturrahmanto Anis Zubair, Anis Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiza Dwi Septian Argaputri, Maulida Khairunisa Arief Andy Soebroto Ashidiq, Muhammad Fihan Aulia Herdhyanti Bachtiar, Harsya Baharudin B. Baharum Baihaqi, Galih Restu Bajsair, Fath' Hani Sarli Barlian Henryranu Prasetio 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 Edita Rosana Widasari Edy Santoso Edy Santoso Eni Hartika Harahap Eva Agustina Ompusunggu Faris Dinar Wahyu Gunawan Fatimah Az-Zahra, Adinda Feri Eko Herman Firdaus, Nada Fitra Abdurrachman Bachtiar Fitrotuzzakiyah, Shafira Puspa Gessia Faradiksi Putri Gumelar, Dimas 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 Indriati Issa Arwani Kautsar, Ahmad Izzan Khairunnisa, Alifah Kirana, Nareswara Lintang 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 Nabil Auliya, Muhammad Hanif Nashi Widodo Navira Rahma Salsabila Nisa, Lisa N. Noor Fatyanosa, Tirana 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 Yudhonugroho, Wirkananda Bagus Yulian Ekananta Yunita, W. Lisa Zakiyyah, Rizka Husnun Zanna Annisa Nur Azizah Fareza