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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Multimedia JAST : Jurnal Aplikasi Sains dan Teknologi TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi (JUST-SI) DEDIKASI PKM Jurnal Informatika dan Multimedia Enrichment: Journal of Multidisciplinary Research and Development Majority Science Journal
Dian Eka Ratnawati
Brawijaya University, Malang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Pengelompokkan Kondisi Komputer pada Bank X di Seluruh Indonesia Menggunakan Metode K-Medoids Clustering Muthia Maharani; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 5 (2021): Mei 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bank X is one of the largest public banks in Indonesia. With the banking process running up to the operational process of Bank X which run every day for 24 hours, a very good quality computer is needed. So the process of upgrading computer must always be done. The large number of computers at Bank X makes the process of upgrading computers is one of Bank X's biggest expenses. Other than that, in several work units at Bank X, the process of upgrading computer is still not effective. From these problems, the research will be conducted in the form of grouping computer conditions at Bank X all over Indonesia using the K-Medoids Clustering method. This research was conducted to classify which computers are really need, need enough, and which computers do not need to upgrade. The results of this grouping are that there are 237 computer in cluster 0 no need to upgrade, 126 computer are in cluster 1 with a very need to upgrade, and 769 computer are in cluster 2 with condition good enough but need to be reviewed because it is quite necessary to do a computer upgrade. For testing using the Silhouette Coefficient, clustering with 2 clusters is the best because it has the highest silhouette score, which is 0.624684967. And also the test for amount of data which showed that 90% of the original data was the minimum presentation for data amount for accurate results. The results of the research will be given to Bank X as a recommendation to find out which computers need to be upgraded so that they can be followed up more quickly.
Rekomendasi Lagu Berdasarkan Lirik Lagu Menggunakan Metode N-gram dan Cosine Similarity Jesika Silviana Situmorang; Putra Pandu Adikara; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Listening to songs is one of the human activities that is often carried out by humans. Song is an art that has a pitch or sound that has elements of poetry sequences and a combination with one or several combinations of musical instruments. The lyrics in the song usually contain several verses that have their own meaning for the songwriter. The development of songs has now progressed and made music and song lovers increasingly like songs or music. This happens because of the smartphone that allows song enthusiasts to listen to songs online and offline. But the number of songs available makes music lovers have limitations in choosing songs in the music player. This problem requires an innovation that makes it easy to search for songs based on lyrics that suit the user (music lover). This problem can be solved in the form of an information acquisition system. Song recommendation model can automatically select songs based on lyrics, making it easier for users to search for the desired song. The research for this song recommendation model used the N-gram method (unigram bigram and trigram) and cosine similarity. Song lyrics will go through the preprocessing stage then Term Frequency - Inverse Document Frequency (TF-IDF) so that the words in the song lyrics are selected first. The system will issue 10 song recommendations. The results of the evaluation of the best song recommendations use Unigram with a Precision@10 value of 0.656 and a Mean Average Precison (MAP@K) value of 0.82914032.
Klasifikasi Tweets Masyarakat yang Membicarakan Layanan GoFood dan GoRide pada GoJek Dimedia Sosial Twitter Selama Masa Kenormalan Baru (New Normal) dengan Metode Naive Bayes Alvian Akmal Nabhan; Bayu Rahayudi; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social media that has long been used and is still a trend today. On Twitter, a lot of users write an opinion on something that is currently being discussed and becomes a trend topic. Early May 2020 the world was shaken by the presence of Coronavirus Disease-19 which causes all life in all parts of the world to be constrained, especially the government sector which has an impact on economic activity has decreased. In Indonesian, a lot of companies have gone bankrupt and must terminate the employment. At that time, the transportation sector companies were the most affected is GoJek online transportation which was successful as a unicorn start-up. GoJek was badly hit by the implementation of the PSBB in various regions. Therefor, the community grouped tweets for existing services on GoJek in social media twitter during the pandemic and new normal . In this study case, the GoJek services used are GoFood and GoRide using Naive Bayes classification model with testing using cross validation and confusion matrix. From this classification, good accuracy result are obtained by testin with 10-fold with mean accuracy 0,94, precission 0,73, recall 0,72 f-measure 0,72.
Analisis Sentimen Opini Publik pada Twitter terhadap Efek Pembelajaran Daring di Universitas Brawijaya menggunakan Metode K-Nearest Neighbor Sobakhul Munir Siroj; Issa Arwani; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

As a result of the Covid-19 virus pandemic, Universitas Brawijaya implemented an online learning system. The implementation of online learning at Brawijaya University has generated a lot of positive and negative opinions on Twitter. This study aims to determine the level of tendency of positive opinions and negative opinions through the sentiment classification process, as well as to find out some of the things that are often complained of in the implementation of online learning at Brawijaya University. The sentiment classification process is carried out using the K-Nearest Neighbor method in the Rapid Miner application. The sentiment classification process is carried out through four main stages, namely the data collection stage, the data pre-processing stage and term weighting, the classification stage, and the testing stage. In addition, a separate calculation of the frequency of occurrence of words is also carried out. In the classification, the results showed that 50.8% of sentiments gave negative opinions, while the remaining 49.2% gave positive opinions. In the process of calculating the frequency of occurrence of words, five words were often complained about, namely the word "offline", "lecturer", "assignment", "quota", and "ukt". In the testing process, a variation of the k value is tested and its effects on accuracy, precision, and recall. In testing, the analysis process of test results, cross validation testing, and feedback stages is also carried out. On the test, the best accuracy, precision, and recall values ​​were 80% for k = 7, 81.48% for k = 7, and 88.89% for k = 23.
K-Nearest Neighbor untuk Memprediksi Pergantian Komputer di Bank X Maliha Athiya Rahmani; Dian Eka Ratnawati; Buce Trias Hanggara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bank X has branches all over indonesia, with operational process running for 24 hours everyday. Computer is certainly one of the most important technology for oprerational needs and banking activity. In order to keep the performance, Bank X has a routine of replacing their computer. This routine has become one of Bank X's biggest expenses. This is beause good quality and complex hardware and software require high cost, and the number of computers owned by Bank X. This replacement routine, has not been carried out effectively. From these problems, the research will be carried out related to the prediction of computer replacement using K-Nearest Neighbor Method. K-Nearest Neighbor method is a simple, fast, easy to understand, effective and accurate method for datasets with large data training. This research was conducted to determine the class of a computer, ‘Yes' or ‘No'. ‘Yes' indicated a computer needs to be replaced, and ‘No' indicates a computer does not need to be replaced. The Data Training used is in the form of 43713 computer data owned by Bank X throughout Indonesia. This method was tested with K-fold Cross Validartion, by testing several K values to get the K value with the highest accuracy. The value of K=4 gets the highest accuracy of 99.6088%. The results of this research is a computer classification management information system in the form of a web. This research is expected to make it easier for Bank X to determine which computers need and do not need to be replaced, in order to be able to make effective decisions in computer replacement.
Prediksi Trend Harga Saham Jangka Pendek berdasarkan Fitur Technical Analysis dengan menggunakan Algoritma Random Forest Michael Eggi Bastian; Bayu Rahayudi; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Stock trading is a daily activity carried out by a stock trader. A stock trader can perform this trading activity on a capital market. In the capital market, you can see a chart that depicts the price movement of a stock in a certain period, also known as "Price Trend". The most important characteristic of a price trend is volatile and irregular changes in direction. Due to its volatile and irregular nature, a problem arises in knowing where the price trend will move. Any mistake in predicting the direction of the trend can cause losses. This study implements the random forest algorithm as a solution model, and technical analysis as a predictive feature to minimize errors in predicting future stock price trends. Based on the test results, the combination of the random forest algorithm and technical analysis is able to minimize errors in predicting price trends with an accuracy of 84% and an f1 score of 88%.
Pengembangan Sistem Informasi E-Tourism sebagai Media Pemasaran (Studi Kasus Kabupaten Trenggalek) Asroru Maula Romadlon; Issa Arwani; Dian Eka Ratnawati
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The Department of Tourism and Culture (DISPARBUD) of Trenggalek Regency is a government agency authorized in terms of tourism. There are various fields that exist in the Trenggalek Regency DISPARBUD, one of which is the marketing field which is responsible for marketing or tourism promotion. DISPARBUD wants to add to its tourism marketing method that does not currently exist with the desire to create a mobile application that can provide information to tourists about tourism objects in Trenggalek Regency. With these problems, DISPARBUD offered to develop an E-Tourism information system. System development in the form of a web application for admins that can be used to view, add, change, and delete tourism object data, as well as a mobile application in the form of Android which will be used by users to access tourism information. The development of the E-Tourism information system was developed using the waterfall method. This system utilizes an API so that the web admin application and the user's mobile application can be connected to each other. Then testing is carried out starting from API testing which results in the API being able to run and being accessed by the user's mobile application, black box testing with validation testing which shows that all functional requirements of the system have been met, compatibility testing shows that web admin applications can be run on different browsers and mobile applications can be run. on different android versions, as well as usability testing with the SUS (System Usability Scale) test which shows this system has received acceptable criteria in the acceptable range and got a B grade scale.
Rekomendasi Pengambilan Asuransi Kecelakaan bagi Driver menggunakan Improve K-Means dengan Inisialisasi Centroid berbasis Sum Square Error dan K-Nearest Neighbor Maulana Syahril Ramadhan Hardiono; Bayu Rahayudi; Dian Eka Ratnawati
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

Carelessness in driving can result in accidents that can harm yourself and others. To reduce losses caused by accidents, one can use insurance services. However, there are still many Indonesians who have not used insurance services due to high premiums. From these problems, a solution is needed to recommend someone to take insurance from the level of driving experience. One of the methods adopted from this research is to improve k-means with centroid initialization based on sum square error and k-nearest neighbor. The data used in this research are 600 data safe drivers, consisting of numeric data and categories. The recommendation process begins with clustering using k-means with the initialization of centroid sum square error which produces an average of 1000 iterations, which is 1660.64. From the SSE process, the optimum centroid was obtained and continued with the k-means process with k-prototype. The use of k-prototype is due to the data consisting of numeric and categorical data. After the clustering process, the distance between the tes data and the centroid is calculated to find the closest cluster. Furthermore, the classification process is carried out on the nearest cluster in order to make the classification process more efficient. The classification process uses KNN which aims to determine whether data is included in the recommendation category and not. From the tesing process the improve k-means method produces the highest average value of 72.83% accuracy, while the KNN method produces an average accuracy value of 47.5%. The results of these calculations use the value of k = 3 and k-fold on cross validation of 5.
Analisis Sentimen Opini Publik pada Media Sosial Twitter terhadap Vaksin Covid-19 menggunakan Algoritma Support Vector Machine dan Term Frequency-Inverse Document Frequency Edgar Maulana Thoriq; Dian Eka Ratnawati; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Social media is a place for people to express their aspirations, ideas, and even their critics. One of the policies made recently by the government is the provision of COVID-19 vaccine. This policy has been widely discussed on Twitter and attracted a lot of diverse opinions in the society. Twitter is a social media that has a fairly large user base in Indonesia, where many users share their opinions regarding the provision of COVID-19 vaccine. Twitter can be a source of data that can be used to conduct sentiment analysis on government policies by classifying tweets (a term for content in Twitter) into positive or negative categories. The classification process is utilizing a classification algorithm, namely Support Vector Machine and term weighting namely Term Frequency - Inverse Document Frequency (TF-IDF) method. This study uses 450 tweets, then testing is carried out using the cross validation method with number of fold = 10. Best performance of the classification algorithm is 86% accuracy, 88% precision, 82% recall, and 85% f-measure. Value of the performance is obtained with value C of 1 and the maximum iteration of 300.
Klasifikasi Penerima Bantuan Program Keluarga Harapan menggunakan Algoritme Decision Tree C4.5 (Studi Kasus: Desa Mlirip Kabupaten Mojokerto) Nabilah Iftah Nella; Nanang Yudi Setiawan; Dian Eka Ratnawati
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

Social assistance is the provision of assistance in the form of money or goods distributed to certain individuals or groups. As a form of responsibility in alleviating poverty, the government allocates costs to handle this, one of which is the Family Hope Program (PKH). PKH is a program to provide cash assistance to poor households. One of the villages that has received PKH assistance from the government is Mlirip village, Mojokerto Regency. With the help of PKH, people with low incomes feel very helpful. Unfortunately, until now PKH is still considered not optimal because many aids are not well targeted and the village head's office does not yet have a reference in choosing a truly worthy family. Classification is carried out to determine the eligibility of PKH beneficiaries so that they are not misdirected. The data used is data for 2020-2021 with a total of 357 data from 7 hamlets in Mlirip village. Based on the results of the confusion matrix, it produces 71.1% accuracy, 90.9% precision, 70.4% recall, and 79.3% f1-score. The results of the classification process are visualized using a dashboard which produces a SUS Score of 87.5 which indicates the dashboard is acceptable to stakeholders
Co-Authors Abdurrahman Airlangga, Aria Abhiram, Muhammad Tegar Achmad Arwan Achmad Ridok Achmad, Riza Putra Adhitya, I Made Yoga Adrian Firmansah, Dani Afif Ridhwan Afrida Djulya Ika Pratiwi Agus Wahyu Widodo Agustin Kartikasari Ahmad Afif Supianto Akbar, Rozaq Aldy Satria Alfa Fadlilah Alifah, Syafira Almira Syawli, Almira Alvian Akmal Nabhan Amonito, Kurnia Ana Mariyam Puspitasari Anak Agung Bagus Arisetiawan Anam, Syaiful Ardhiansyah, Muhammad Hanif Arief Andy Soebroto Arif Pratama Asmoro, Priandhita Sukowidyanti Asroru Maula Romadlon Audia Refanda Permatasari Ayu Dwi Lestari, Cynthia Ayulianita A. Boestari Azizul Hanifah Hadi Bayu Rahayudi Bayu Satriawan, Eka Bayu Septyo Adi Bella Krisanda Easterita Bening Herwijayanti Berton, Freddy Toranggi Buce Trias Hanggara Buce Trias Hanggara Buchori Anantya Firdaus Budi Darma Setiawan Cahyo Gusti Indrayanto Candra Dewi Dany Primanita Kartikasari Darma Setiawan, Budi Darmawan, Riski Davia Werdiastu Denny Manuel Yeremia Sinurat Deny Tisna Amijaya, Fidia Devi Nazhifa Nur Husnina Dewi Yanti Liliana Dhiva Mustikananda Dimas Diandra Audiansyah Dimas Fachrurrozi Azam diniyah, zubaidah Diva, Zahra Djoko Pramono Dwi Ari Suryaningrum Dwi Febry Indarwati Dwi Purwono, Prayoga Dwija Wisnu Brata Dyva Pandhu Adwandha Dzulkarnain, Tsania Dzulkarnain, Tsania - Easterita, Bella Krisanda Edgar Maulana Thoriq Edy Santoso Elfa Fatimah Ema Agasta Entra Betlin Ladauw Eva Agustina Ompusunggu Fadhil, Muhammad Farrasseka Fadila, Putri Nur Faiz Anggiananta Winantoro Fanka Angelina Larasati Fathin Al Ghifari Fatthul Iman Fauzan Dwi Kurniawan, Fauzan Dwi Fauzidan Iqbal Ghiffari Figgy Rosaliana Firdaus, Muhammad Fariz Fitra Abdurrachman Bachtiar Fitri Dwi Astuti Fitria Yesisca Fitria, Tharessa Ghani Fikri Baihaqi glenando Gusti Ngurah Wisnu Paramartha Hadi Wijoyo, Satrio Hamas, radityo Hana Chyntia Morama Hanggara, Buce Trias Hanifa Maulani Ramadhan Haris Haris, Haris Harris Imam Fathoni Hasibuan, Herida Hafni Hasibuan, Raka Ardiansyah Heru Nurwasito Hilal, Khaliffman Rahmat Hilmy Ramadhan, Achmad Zhafran Huda Minhajur Rosyidin I Dewa Gede Ngurah Bramasta Darmawan Ibnu Aqli Ibnu Aqli, Ibnu Ibrahim Kusuma Ilyas, Muhaimin Imam Cholissodin Imam Cholissodin Imam Cholissodin Immanuel Tri Putra Sihaloho Indriati ., Indriati Indriati Indriati Ismiarta Aknuranda Issa Arwani Issa Arwani Isti Marlisa Fitriani Izza, Aisyah Nurul Jesika Silviana Situmorang Jibril Averroes, Muhammad Juan Michel Hesekiel Kartika, Annisa Wuri Kelvin Anggatanata Kevin Renjiro Khairi Ubaidah Khoba, Ahmad Faiz Khofifatunnabilah, Khofifatunnabilah Kirana, Urdha Egha Krishna Febianda Kusuma, Salsabila Azzahra' Zulfa Lailil Muflikhah Leonardo, Ryan Luqman Rizky Dharmawan M. Ali Fauzi Madjid, Marchenda Fayza Maghfiroh, Sofita Hidayatul Mahendra Data Mahendra Data Mala Nurhidayati Maliha Athiya Rahmani Marji . Marji Marji Marji Marji Marji Marji Maulana Syahril Ramadhan Hardiono Michael Eggi Bastian Mochammad Iskandar Ardiyansyah Rochman Moh Fadel Asikin Muh. Arif Rahman MUHAJIR Muhammad Iqbal Mustofa Muhammad Kevin Sandryan Muhammad Reza Utama Pulungan Muhammad Tanzil Furqon Muhyidin Ubaiddillah Muslimah, Fakhriyyatum Muthia Maharani Nabilah Iftah Nella Naily Zakiyatil Ilahiyah Nanang Yudi Setiawan Nanang Yudi Setiawan Nanda Alifiya Santoso Putri Nanda Petty Wahyuningtyas Nilna Fadhila Ganies Norma Desitasari Novirra Dwi Asri Nugraha Perdana, Aditya Nugraheni, Miftakhul Fitria Nur Adli Ari Darmawand Nur Khilmiyatul Ilmiyah Nuraini Anitasari Nuralam, Inggang Perwangsa Nurul Hidayat Nyimas Ayu Widi Indriana Oceandra Audrey Pandu Adikara, Putra Pangestu Ari Wijaya Panjaitan, RE. Miracle Prahesti, Suherni Prakoso, Ricky Pratomo Adinegoro Priyono, Mochammad Fajri Rahmatullah Rendra Puji Indah Lestari Purnomo, Welly Putra Pandu Adikara Putra, Alland Rifqy Putri, Nindy Alya Rachmad, Zikfikri Yulfiandi Raden Rizky Widdie Tigusti Rahma, Dzakiyyah Afifah Rahmah, Yusriyah Raisha, Serefika Raja Farhan Ramadha Pohan Rama Humam Syarokha Randy Cahya Wihandika Rani Metivianis Ratih Diah Puspitasari RE. Miracle Panjaitan Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Indah Rokhmawati, Retno Indah Revi Anistia Masykuroh Rifqi Irfansyah, Nandana Rizal Setya Perdana Rizal Setya Perdana Robiata Tsania Salsabila Aditya Putri Rodiah Rodiah Ryan Leonardo Salsabillah, Dinar Fairus Saparila Worokinasih Saputro, Dimas Sarie, Riza Athaya Rania Satriawan, Eka Bayu Satrio Agung Wicaksono Satrio Hadi Wijoyo Sema Yuni Fraticasari Setiawan, Alexander Christo Setya Perdana, Rizal Setyowati, Andri Shafira Margaretta Sherly Witanto Sherryl Sugiono Sindarto Sigit Pangestu Silvia Ikmalia Fernanda Siregar, Fauziah Syifa R. Siti Fatimah Al Uswah Sobakhul Munir Siroj Sormin, Hartati Penta Angelina Sri Indrayani, Sri Suhhy Ramzini Sukmawati, A'inun Sutrisno Sutrisno Sutrisno, Sutrisno Syaiful Anam Syifa Namira Neztigaty Thifal Fadiyah Basar Titis Sari Kusuma Ulfa Lina Wulandari Utomo, Yoga Cahyo Vina Adelina Welly Purnomo Wibowo, Shinta Dewi Putri Widhy Hayuhardhika Nugraha Putra Wijanarko, Rizqi Winda Fitri Astiti Winurputra, Raihan Wiratama Paramasatya Yahya, Faiz Yolanda Nailil Ula Yudi Setiawan, Nanang Yuita Arum Sari Yunita Dwi Alfiyanti Yure Firdaus Arifin Zahra, Wardah