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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Economic, Bussines and Accounting (COSTING) INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database Indonesian Journal of Innovation Multidisipliner Research Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Indonesian Journal of Innovation Multidisipliner Research Jurnal Komtika (Komputasi dan Informatika)
Elvia Budianita
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Pendekatan berbasis Machine Learning dan Leksikal Pada Analisis Sentimen Elvia Budianita; Eka Pandu Cynthia
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2022: SNTIKI 14
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pada analisis sentimen terdapat dua pendekatan. Pertama berbasis machine learning dengan melatih data latih pada dataset yang telah dilabelkan secara manual dengan melibatkan seorang pakar atau Annotator. Pendekatan yang kedua adalah berbasis leksikal (Lexicon Based) yang tidak memerlukan pelatihan dataset untuk menemukan polaritas sentiment. Data set komentar yang digunakan adalah mengenai penyedia jasa transportasi online local Maxim di media social Twitter. Data set komentar yang dilabel secara manual akan diklasifikasikan ke dalam kelas positif netral, dan negatif menggunakan metode Support Vector Machine (SVM). Berdasarkan hasil pengujian diperoleh kesimpulan bahwa analisis sentimen untuk kasus Maxim menggunakan pelabelan manual yang dilatih menggunakan metode SVM adalah lebih banyak mengandung kalimat positif sedangkan jika menggunakan pelabelan Lexicon based lebih banyak mengandung kalimat netral
Diagnosa Awal Disgrafia pada Anak Menggunakan Metode Bacpropagation Aji Pangestu Adek; Fadhilah Syafria; Elin Haerani; Elvia Budianita
JURNAL UNITEK Vol. 15 No. 2 (2022): Juli - Desember
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v15i2.391

Abstract

Gangguan belajar merupakan suatu gangguan dasar dalam psikologis yang meliputi penggunaan pemahaman bahasa tulisan. Gangguan belajar yang termasuk dalam klasifikasi gangguan belajar akademik adalah disgrafia. Disgrafia merupakan gangguan khusus dimana anak tidak dapat mengekspresikan pikirannya kedalam bentuk tulisan, karena tidak dapat mengkoordinasikan motorik halusnya untuk menulis dan menyusun kata dengan benar. Diagnosa awal disgrafia pada anak terdiri atas disgrafia dan tidak disgrafia. Diagnosa ini menggunakan 31 variabel inputan menggunakan metode Backpropagation dengan menggunakan data yang berjumlah 150 data. Dari pengujian yang dilakukan didapatkan hasil menggunakan learning rate 0.1 dan 0.01, maks epoch 500 dengan arsitektur jaringan syaraf tiruan 31-31-1 dengan pembagian data 90:10 menghasilkan nilai akurasi sebesar 100% serta pada pembagian data 80:20 menggunakan learning rate 0.1, maks epoch 500 dengan arsitektur jaringan syaraf tiruan 31-31-1 menghasilkan nilai akurasi sebesar 100%. Maka dapat disimpulkan bahwa diagnosa disgrafia pada anak dengan backpropagation dapat dilakukan sangat baik.
Analisis sentimen larangan penggunaan obat sirup menggunakan algoritma naive bayes classifier Fitri Wulandari; Elin Haerani; Muhammad Fikry; Elvia Budianita
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4781

Abstract

The Indonesian government made a policy to stop consuming syrup as a form of prevention against acute kidney failure, which affects many people in Indonesia. However, the policy has caused a lot of comments from the public. These public comments can be found on YouTube, because YouTube has a large data source opportunity to be used as a research material. These comments can be processed directly without using a machine, but it is less effective and efficient. Thus, the comments are processed using machine learning methods. Based on the earlier research, the naive bayes classifier algorithm tends to be simple and easy to use. In addition, this algorithm also has a high accuracy. The amount of data used in this study is 1000 YouTube comment data related to videos regarding the policy of prohibiting the use of syrup medicine, the comments are divided into 2 category, which are positive class and negative class. The results of labeling 1000 comments obtained 704 negative comments and 296 positive comments. Based on the experiments conducted using python programming language, the highest accuracy was obtained at 74% in 70:30 data split. Furthermore, in the balanced dataset (296 positive and 296 negative comments), the highest accuracy was obtained at 64.70% with in 80:20 data split. These results represent that the naive bayes classifier algorithm is good enough at sentiment analysis about the policy of prohibiting the use of syrup drugs.
Analisis sentimen komentar youtube terhadap Anies Baswedan sebagai bakal calon presiden 2024 menggunakan metode naive bayes classifier Chely Aulia Misrun; Elin Haerani; Muhammad Fikry; Elvia Budianita
Computer Science and Information Technology Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i1.4790

Abstract

One of the figures as a presidential candidate is Anies Baswedan, the former governor of DKI Jakarta who received many awards and has an effective work program policy for problems in the DKI Jakarta area. Many comments about Anies Baswedan as a 2024 presidential candidate are found on YouTube social media. Youtube facilitates users to provide comments in response to videos which can be used as sentiment analysis information to find out positive comments and negative comments. The algorithm used in this research is the naïve bayes classifier. There are five main processes in this research, namely data collection, text preprocessing, word weighting (TF-IDF), classification (Naïve Bayes Classifier) and testing. From 1009 comment data on Indonesian-language youtube related to the Anies Baswedan video as a 2024 presidential candidate. Based on the analysis results, there are 610 positive comments and 399 negative comments. The accuracy result using the naïve bayes classifier algorithm is 78% which is obtained by using a comparison of 90% training data and 10% test data.
Klasifikasi Sentiment Review Aplikasi MyPertamina Menggunakan Word Embedding FastText dan SVM (Support Vector Machine) Mustasaruddin Mustasaruddin; Elvia Budianita; M Fikry; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5695

Abstract

The MyPertamina application is a requirement for buying subsidized fuel oil (BBM), namely pertalite and diesel, the goal is that subsidized (BBM) purchases are right on target. The MyPertamina application has received many ratings and comments from the public, both positive and negative, with these comments and ratings expected to help the government as a benchmark in implementing a program. Therefore, this research aims to assess the MyPertamina application by grouping sentiment classes 90:10, 80:20 and 70:30. In this study, the method used is Fasttext and Support Vector Machine (SVM) to review the MyPertamina application. This research uses 8000 data, the data is grouped into three portions of data, with portions of 90:10, 80:20 and 70:30. The best SVM model was obtained with a data portion of 90:10 with a total of 7200 training data and 800 testing data, obtained 80% accuracy, 50% recall and 84% precision without undersampling. Meanwhile, if the amount of data is balanced (undersampling) with the number of positive data 1325, neutral 1325 and negative 1325, that is, with the benchmark of the lowest data value from the sentiment class, an accuracy of 67% is obtained, recall is 69% and precision is 57%. The highest number of sentiment classes from the 90:10 portion of the data is negative, namely 4300, neutral 1575 and positive 1325, because many users found reviews of the MyPertamina application, namely "after updating the MyPertamina application the bugs are getting worse".
Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier M Ikhsan Maulana; Elvia Budianita; Muhammad Fikry; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5854

Abstract

Battle Royale games are games that mix adventure and survival elements with last man standing game modes. One of the most popular battle royale games is the Sausage Man game. The number of complaints such as bugs, cheaters, and FPS which continues to decrease makes the game annoying. The solution is that developers must improve and improve game security so that users feel comfortable playing the game. There are many opinions or reviews from users regarding problems in the game, sentiment analysis will be carried out on the Sausage Man application review data on the Google play store as a process to produce categorization of opinions through reviews. The purpose of the researcher is to carry out a sentiment analysis to see positive, neutral or negative opinions from Sausage Man game users. The stages carried out in this study were data collection using web scraping, data labeling, text preprocessing, document weighting, classification, and evaluation. The results of data labeling using the VADER Lexicon obtained 1089 reviews (36.3%) for positive sentiment, 912 reviews for neutral sentiment (30.4%), and 999 reviews for negative sentiment (33.3%). Classification using the Naïve Bayes Classifier. Evaluation using the Confusion Matrix by dividing 90% training data and 10% test data produces an accuracy of 75%, 79% precision, and 75% recall. For the division of 80% training data 20% of the test data produces an accuracy of 73%, 76% precision and 73% recall. Positive sentences are found more often, but the accuracy is still below 80%.
Penerapan Data Mining untuk Menentukan Penyebab Kematian di Indonesia Menggunakan Metode Clustering K-Means Lili Rahmawati; Alwis Nazir; Fadhilah Syafria; Elvia Budianita; Lola Oktavia; Ihda Syurfi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5912

Abstract

Death in medical science is studied in a scientific discipline called tanatology. death is not only experienced by elderly people, but also can be experienced by young people, teenagers, or even babies. Death can be caused by various factors, namely, due to illness, old age, accidents, and so on. Based on information provided by the World Health Organization (WHO), there are five highest causes of death including ischemic heart disease, Alzheimer's, stroke, respiratory disorders, neonatal conditions. In this study, k-means is used to group causes of death in Indonesia based on the number of deaths that occur to determine the cases of death that have the most impact on the high mortality rate in Indonesia. Knowing what these death cases are will provide early preparation in anticipating the causes of death in Indonesia. The purpose of this study was to classify mortality rates based on the number of causes of death which were included in the low, medium, and high clusters by applying the K-Means method. In this study the authors used the K-Means clustering algorithm to classify death rates in data on causes of death in Indonesia from 2017-2021. The results of this study formed 3 clusters which were evaluated using the Davies Bouldin Index (DBI) in Rapidminer with a value of 0.259. Clustering results from a total of 21 cases obtained high, medium and low clusters. This cluster grouping was obtained according to the number of deaths per case, namely the first cluster (C0) was low with 17 cases, the second cluster (C1) was moderate with 3 cases and the third cluster (C2) was high with 1 case.
Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization Nazir, Alwis; Akhyar, Amany; Yusra, Yusra; Budianita, Elvia
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.33158

Abstract

Abstract. Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).
Analisis Sentimen Review Aplikasi Mypertamina Menggunakan Word Embedding Fasttext Dan Algoritma K-Nearest Neighbor Nanda Sepriadi; Elvia Budianita; Muhammad Fikry; Pizaini
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 15 No 1 (2023): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v15i1.222

Abstract

Pertamina adalah’ perusahaan yang dimilik negara bergerak di bidang energi seperti minyak, gas serta energi baru dan energi terbarukan. Pertamina juga telah menciptakan sebuah aplikasi yang dapat diakses secara mobile yang tersedia di Play Store untuk memudahkan konsumen dalam bertransaksi pembayaran bahan bakar minyak (BBM) yang dapat diakses melalui aplikasi MyPertamina yang dapat di download melalui App Store dan Play Store. Playstore adalah platform Google yang menyediakan aplikasi mobile dan fasilitas pengguna untuk berbagi ulasan. Dalam penelitian ini, pendekatan yang digunakan untuk analisis sentimen adalah menggunakan kamus Lexicon Based dan metode K-Nearest Neighbor. Kamus leksikon yang diterapkan yaitu vader yang memberikan label secara otomatis, sementara metode K-Nearest Neighbor digunakan untuk melakukan pengelompokan atau klasifikasi. Maksud penelitian ini dilakukan adalah untuk memahami bagaimana pengelompokan tanggapan masyarakat terhadap aplikasi MyPertamina dan mengetahui performa terbaik metode K-NN dengan 3 perbandingan data latih dan data uji. Penelitian ini menggunakan data berjumlah 8000 data, data ini kemudian dikelompokkan menjadi tiga bagian data latih dan data uji , dengan bagian 90/10, 80/20 dan 70/30. Diperoleh hasil pelabelan menghasilkan 1405 data dengan label positif, 1698 data dengan label netral, dan 4897 data dengan label negatif. Akurasi tertinggi diperoleh dataset B dengan porsi data 80/20 dengan nilai K = 9 yang menghasilkan akurasi sebesar 73%, presision sebesar 70% dan recall sebesar 73% tanpa mengunakan Undersampling. Sedangkan data yang sama menggunakan Undersampling diperoleh akurasi sebesar 57%, presision sebesar 73% dan recall sebesar 57%. Penurunan akurasi ini disebabkan oleh pengurangan jumlah data training akibat menggunakan undersampling.
Tingkat Kecanduan Game Online Menggunakan Algoritma X-Means Clustering doli fancius silalahi; Elvia Budianita
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2022: SNTIKI 14
Publisher : UIN Sultan Syarif Kasim Riau

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Abstract

Game online adalah sebuah game yang bisa di akses dan bisa dimainkan secara online, dengan artian para pemain harus menggunakan jaringan internet untuk memainkannya.Game online juga dapat dimainkan menggunakan peralatan  gadget milik sendiri atau yang direntalkan, game yang sering dimainkan biasanya bervariasi dan mobile game yang sering dimainkan banyak orang karena mudah untuk mengaksesnya. Kecanduan game online merupakan aktifitas yang dilakukan secara terus menerus dan berkepanjangan yang menimbulkan sikap yang cenderung menarik diri dari kehidupan sosial. Penerapan data mining dengan menggunakan metode clustering dapat dilakukan menggunakan algoritma X - Means. Data kecanduan game online didapat dari pengumpulan data melalui kuisioner yang mengacu kepada  skala Game addict scale (GAS)  yang dijelaskan oleh (Lemmens, Valkenburg & Peter 2009) yang terdiri dari 7 aspek yaitu Relapse, Mood Modification, Conflict, Tolerance, Salience, menarik diri, dan masalah Pengujian pada metode X – Means Dari hasil pengujian dan evaluasi yang telah di lakukan dapat di ketahui bahwa dengan menggunakan metode X-Means dapat mengetahui kelompok data terhadap tingkat kecanduan game online pada mahasiswa jurusan teknik informatika menggunakan tools rapidminer. Dari 300 data responden menunjukan bahwa terdapat dua kelompok kecanduan game online tinggi dan rendah.
Co-Authors Abdul Halim Adzhima, Fauzan Afrianti, Liza Afriyanti, Iis Agnesti, Syafira Agung Syaiful Rahman Agustina, Auliyah Aji Pangestu Adek Akbar, Lionita Asa Akhyar, Amany Al Rasyid, Nabila Alfaiza, Raihan Zia Alfarabi.B, Alif Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Ammar Muhammad Anggi Pranata Aprilia, Tasya Aprima, Muhammad Dzaky Arif Pratama Budiman Azhima, Mohd Baehaqi Berliana, Trisia Intan Boni Iqbal buhfi arides hanyodi Chely Aulia Misrun Damayanti, Elok Desra Rizki Riyandi Dicky Abimanyu Dinyah Fithara Dodi Efendi doli fancius silalahi Dwitama, Raja Zaidaan Putera Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Eka Suryani Indra Septiawati Elin Haerani Elin Haerani Elin Haerani Elin Haerani Ellin Haerani Fadhilah Syafria Fahrozi, Aqshol Al Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Yanto Fikri Utri Amri Fikry Utri Amri Fitri Astuti Fitri Insani Fitri Insani Fitri Insani Fitri Insani Fitri, Anisa Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Habibi, M. Ilham Hara Novina Putri Hariansyah, Jul Hasibuan, Ilham Habibi Ibnu Afdhal Ichsan Permana Putra Ihda Syurfi Ihlal Hanafi Harahap Iis Afrianty Iis Afrianty Ikhsanul Hamdi Indah Wulandari Isra Almahsa, Muhammad Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril jasril jasril jasril Jeki Dwi Arisandi Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati Lola Oktavia M Fikry M Ikhsan Maulana M ridwan Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Matondang, Irfan Jamal Mawadda Warohma Mazdavilaya, T Kaisyarendika Megawati Megawati Meiky Surya Cahyana Mhd. Kadarman Mohd. Ridho Zarkasih Rahim Muhammad Affandes Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Rizky Ramadhan Mulyati, Sabar Mulyono, Makmur Musa Irfan Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Neni Sari Putri Juana Novi Yanti Novi Yanti Novriyanto Novriyanto Nur Iza Nuradha Liza Utami Nurafni Syahfitri Nurfadilah, Nova Siska Okfalisa Okfalisa Pasiolo, Lugas Permata, Rizkiya Indah Pizaini Pizaini Putri, Widya Maulida Rahmad Abdillah Rahmad Kurniawan Ramadani, Repi Ramadhan, Aweldri Ramadhani, Astrid Ramadhani, Siti Reni Susanti Reski Mai Candra Reski Mai Candra Rinaldi Syarfianto Robby Azhar Roni Salambue Rusnedy, Hidayati Said Nurfan Hidayad Tillah Saktioto Saktioto Sephia Pratista Silfia Silfia Siti Sri Rahayu Surya Agustian Suwanto Sanjaya Syahputra, Armadani Ulti Desi Arni, Ulti Desi Wahyuni, Ayu Sri Wang, Shir Li Widodo Prijodiprodjo Wiranti, Lusi Diah Yeni Fariati Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri Zulkarnain Zulkarnain