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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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Menentukan Pola Kelulusan Mahasiswa Berdasarkan Nilai Pada Mata Kuliah Pemograman Dengan Menggunakan Algoritma Apriori buhfi arides hanyodi; Elvia Budianita
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

Universitas Islam Sultan Syarif kasim Riau adalah perguruan tinggi negeri yang terdapat diwilayah Pekanbaru yang memiliki berbagai macam fakultas dan jurusan salah satunya yaitu jurusan Teknik Informatika. Jumlah mahasiswa dan jumlah kelulusan yang ada pada jurusan Teknik Informatika ini cukup banyak, Akan tetapi pihak manajemen Universitas dan pihak jurusan pada Teknik informatika kesulitan untuk memprediksi suatu pola dan tingkat kelulusan dari data mahasiswa yang telah ada pada setiap tahun akademik. Para peneliti Sebagian besar menggunakan teknik data mining untuk menemukan sebuah keteraturan pola atau hubungan set pada data yang berukuran besar. Pada penelitian ini, untuk menentukan pola dan menganalisis kelulusan mahasiswa berdasarkan nilai mata kuliah pemograman dengan menggunakan metode apriori. Penelitian menggunakan data mining dengan menggunakan algoritma apriori. Dalam penelitian ini data yang digunakan berasal dari data gabungan antara data induk mahasiswa dan data kelulusan. Hasil pengujian yang dilakukan menghasilkan pola kelulusan dengan berbagai variasi sesuai dengan atribut learning yang digunakan yaitu jenis kelamin, program studi, Nilai Mata kuliah Pemograman.Kata kunci: Apriori, Data Mining, Mata Kuliah Pemograman, Pola
Analisis Sentimen Tanggapan Masyarakat Terhadap Calon Presiden 2024 Ridwan Kamil Menggunakan Metode Naive Bayes Classifier Neni Sari Putri Juana; Elin Haerani; Fadhilah Syafria; Elvia Budianita
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6168

Abstract

Reaction to public facts about the election of the presidential candidate Ridwan Kamil, which will later be obtained, the data is taken from Twitter based on these problems, it is necessary to do sentiment analysis research. Based on the results of this study, the classification process for the Naïve Bayes Classifier has 3 scenarios for dividing training data and test data, namely with 90%:10% training data, the test data produces an accuary value of 85.43%, a recall value of 100.00%, and a precision of 85.33%. For training data 80%: 20% of the test data produces an accuracy value of 86.38%, a recall of 100.00% and a precision value of 86.38% and for data on the distribution of training data 70%: 30% of the test data produces an accuary value of 84.29 %, 100.00% recall and 84.29% precision. From the tweet data that has been used, there are 1262 positive comments and 242 negative comments. These results prove that the Naïve Bayes classifier is very good for conducting sentiment analysis on Twitter comments about the 2024 presidential candidate Ridwan Kamil. The naïve Bayes classifier process gets the highest accuracy value of 86.38% by dividing the training data 80%:20% test data.
Analisis Sentimen Masyarakat Mengenai Aplikasi Shopeefood Menggunakan Chi Square dan Metode Naive Bayes Classifier Muhammad Rizky Ramadhan; Elvia Budianita; Iwan Iskandar; Muhammad Affandes
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.927

Abstract

In April 2020, Shopee launched a delivery service known as ShopeeFood. This service has grown into a large service platform that is in demand by various age groups. Regarding the ShopeeFood phenomenon, the public provides feedback in the form of ratings and opinions on various social media, one of which is Twitter media. The steps taken to understand the perceptions or sentiments of the ShopeeFood community, data are collected through social media and analyzed using the Naive Bayes Classifier method with the Chi feature. Rectangle. Tweet data is retrieved via the Twitter API using key terms related to ShopeeFood. The dataset contains 1050 data divided into three sentiment classes: positive, negative and neutral. The test results use Chi Square on the Naive Bayes Classifier method with a test ratio of 90:10% and produces an accuracy value of 65.71%, a precision value of 95,00 %, and the recall value is 90,00% and the test results use TF-IDF with a test ratio of 90:10% and produce an accuracy value of 79.05%, a precision value of 95,00%, and a 90,00% recall value. Based on the research results, it can be concluded that the TF-IDF feature gets higher accuracy than the Chi Square feature in the Naive Bayes Classifier method and is successful in classifying tweets with positive, negative, and neutral sentiments.
Analisis Sentimen Tanggapan Masyarakat terhadap Calon Presiden Ridwan Kamil 2024 Menggunakan Metode K-Nearest Neighbor Fatma Hayati; Ellin Haerani; Fadhilah Syafria; Elvia Budianita
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30054

Abstract

Banyaknya berita yang beredar dalam sosial media mengenai Tanggapan Masyarakat Terhadap Calon Presiden Ridwan Kamil 2024  menggugah rasa penasaran penulis untuk memastikan seperti apakah tanggapan masyarakat mengenai calon presiden ridwan kamil, apakah menuai kesan positif atau negatif. Dengan demikian, penulis melakukan analisis sentimen pengguna twitter terhadap Tanggapan Masyarakat Terhadap Calon Presiden Ridwan Kamil 2024 yang dapat digunakan sebagai bahan evaluasi dalam menentukan kebijakan. Penulis menggunakan algoritma K-Nearest Neighbor untuk menentukan sentimen pengguna twitter dengan bantuan phyton yang populer di kalangan Data Scientist. Metode tersebut diterapkan ke 2261 data tweet dengan kata kunci “calon presiden ridwan kamil” yang dikumpulkan pada 20 Desember 2022– 30 Desember 2022, yang mana hasil data bersih dari data tersebut berjumlah 1504 data tweet.  Hasil training model membuktikan bahwa skor akurasi 88,70%, recall 96,92% , dan presisi 90,65% dengan nilai k=3.
Sistem Pendukung Keputusan Penerimaan Beasiswa Gubernur Riau Menggunakan Fuzzy dengan Metode Profile Matching Budianita, Elvia; Syahputra, Armadani
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 2, No 1 (2016): Juni 2016
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (994.676 KB) | DOI: 10.24014/coreit.v2i1.2352

Abstract

Beasiswa Gubernur Riau bertujuan meningkatkan kualitas pendidikan yang ada di Provinsi Riau. Penelitian ini ditujukan kepada beasiswa program D3 dan S1, dengan kriteria penilaian seperti status keluarga, penghasilan wali perbulan, jumlah anak dari wali, jumlah saudara menikah, jumlah saudara kandung kuliah dan belum menikah, biaya semester, semester dan IPK. Sistem ini merupakan Sistem Pendukung Keputusan (SPK) menggunakan fuzzy dengan metode profile matching. Fuzzy sebagai nilai kriterianya menutupi kekurangan profile matching menangani data yang bervariatif menjadi kesuatu nilai antara 0 sampai 1, nilai diproses dengan metode profile matching, menghasilkan sebuah perangkingan penerima beasiswa. Berdasarkan hasil pengujian SPK dari 15 data pemohon tahun sebelumnya, bahwa data 5 terbawah merupakan data yang memang tidak lulus seleksi, artinya hasil perangkingan SPK sesuai dengan yang diharapkan oleh tim penyeleksi Beasiswa Gubernur Riau dan mampu mengurangi subyektifitas penilaian.
Penerapan Learning Vector Quantization Penentuan Bidang Konsentrasi Tugas Akhir (Studi Kasus: Mahasiswa Teknik Informatika UIN Suska Riau) Budianita, Elvia; Arni, Ulti Desi
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 1, No 2 (2015): Desember 2015
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.198 KB) | DOI: 10.24014/coreit.v1i2.1235

Abstract

Penentuan bidang konsentrasi studi tugas akhir diharapkan dapat mempermudah mahasiswa dalam menentukan bidang tugas akhirnya sesuai dengan pola nilai mata kuliah yang diambilnya. Banyaknya bidang tugas akhir membuat mahasiswa merasa bingung menentukan tema tugas akhirnya. Sehingga banyak mahasiswa menentukan bidang konsentrasi studi tugas akhirnya diluar mata kuliah yang mereka ambil. Jika mahasiswa memilih bidang konsentrasi tugas akhir sesuai mata kuliah yang mereka ambil, maka mahasiswa tersebut dapat dengan cepat menyelesaikan tugas akhirnya tanpa harus mempelajari metode terlebih dahulu. Oleh karena itu dibutuhkan sebuah media yang dapat membantu mahasiswa dalam menentukan bidang tugas akhirnya yang sesuai dengan pola nilai mata kuliah yang diambil. Metode yang digunakan yaitu Metode Learning Vector Quantization (LVQ). LVQ adalah metode jaringan syaraf tiruan yang mempelajari pola nilai dan secara otomatis belajar untuk mengklasifikasikan vektorvektor input. Kelas-kelas yang didapatkan sebagai hasil dari lapisan kompetitif ini tergantung pada jarak antara vector input. Jika dua vektor input mendekati sama, maka lapisan kompetitif akan meletakkan kedua vektor input tersebut kedalam kelas yang sama.
Prediksi Jumlah Perceraian Menggunakan Metode Extreme Learning Machine (ELM) Mawadda Warohma; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3581

Abstract

Divorce lawsuits have considerably increased in frequency in Indonesia. According to a Statistics Indonesia estimate, there were 447,743 divorce cases in 2021, up 53.50% from the 291,677 instances that were reported in 2020. According to data from the Pekanbaru Religious Court's Public Relations, there were 1,756 divorce cases conducted in the Pekanbaru region in 2021. Extreme Learning Machine (ELM) is one of the artificial neural network technologies that can forecast. The benefit of this approach is that it has a low error rate and can train data thousands of times faster than typical feedforward algorithms. This study used the Extreme Learning Machine technique to forecast the number of divorces at Bangkinang city's religious court, where 108 divorces are expected to occur between January 2018 and December 2022. The number of neurons in the hidden layer is tested using MSE at random for hidden layer 1, 10, 50, 100, and 200 neurons. The Bangkinang religious court's divorce prediction with the lowest MSE is based on a data comparison of 80%: 20% and produces an up-and-down pattern for the number of divorces predicted for 2023: 164 in January, 66 in February, 72 in March, 74 in April, and 92 in May. If there is an increase in divorce in the upcoming month, the religious court in Kota Bangkinang can use the information that the Extreme Learning Machine can provide to come up with a solution.
Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression Sephia Pratista; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Iis Afrianty
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4260

Abstract

Public Health Centers (Puskesmas) had a crucial role in furnishing society essential healthcare services and medication management. To preempt errors in stock management, a predictive approach is employed. This prediction methodology involves comparing Data Mining techniques utilizing the Simple Linear Regression algorithm and Machine Learning methodologies harnessing the Support Vector Regression algorithm. This research uses Paracetamol 500 mg and Cetirizine drug data from January 2020 to June 2023. The selection of these algorithms is motivated by the continuous nature of the data variables and their temporal span, spanning 42 months (period). The core aim of this study is to evaluate the magnitude of predictive errors using the Mean Absolute Percentage Error (MAPE) methodology. Implementing these methods was effectuated through the programming language Python with an 80%:20% partitioning of training and testing data. Drawing from experimental endeavors conducted concerning Paracetamol 500 mg, the utilization of the Simple Linear Regression algorithm, yields a MAPE score of 20.85%, categorized as 'Moderate,' whereas the application of the Support Vector Regression algorithm generates a MAPE of 18.39%, classified as 'Good.' Otherwise, experimentation on Cetirizine employing the Simple Linear Regression algorithm, employing an identical division of training and testing data, results in a MAPE of 18.39%, also classified as 'Good.' Meanwhile, resorting to the Support Vector Regression algorithm leads to a MAPE of 17.14%, falling under the 'Good' category. Based on the MAPE obtained, the Support Vector Regression algorithm has better prediction results than the Simple Linear Regression algorithm
Prediksi Jumlah Perceraian Menggunakan Metode Multilayer Perceptron Ikhsanul Hamdi; Elvia Budianita; Fadhilah Syafria; Iis Afrianty
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6291

Abstract

Divorce is a situation when a married couple decides to end their relationship and separate legally. The increasing number of cases in divorce cases filed at the Bangkinang Religious Court every month has led to a gradual increase and decrease. This study uses the Multilayer Perceptron (MLP) method and evaluates using Mean Squared Error (MSE) to determine prediction accuracy. The data used is divorce data from the Bangkinang Religious Court from January 2014 to December 2022 collected and processed from the Religious Court office. A total of 102 data in the form of time series data. In this study using MLP which consists of three layers, namely the input layer, hidden layer, and output layer. And using architectural testing consisting of 6-7-1, 6-9-1, and 6-12-1 with learning rate parameters: 0.01, 0.03, 0.09 with a comparison of training and test data 70:30, 80:20, 90 :10. Based on the test results using MSE, the best architecture was obtained, namely by comparing data 90:10 with 6-9-1 architecture, learning rate: 0.03, Epoch: 300, Alpha fixed value: 0.1, MSE results were successfully obtained: 0.01144 and the pattern of the number of splits from January until May 2023 has decreased, thus, this MLP can provide predictive results that help in predicting the number of divorces.
Klasifikasi Sentimen Masyarakat Di Twitter Terhadap Prabowo Subianto Sebagai Bakal Calon Presiden 2024 Menggunakan M-KNN Abdul Halim; Yusra Yusra; Muhammad Fikry; Muhammad Irsyad; Elvia Budianita
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4054

Abstract

Presidential elections are held every five years and each presidential candidate will get support from several political parties to run for candidacy in the election. In a multi-party system, the number of parties participating in the election is very large, so that the perspectives of voters on political actors, including presidential candidates who will advance in the 2024 elections, are varied. The survey results from Polling Indonesia (SPIN) conducted from 7 to 16 October 2022 show that Prabowo Subianto has the highest electability with a score of 31.6%, based on a national leadership survey. In this study, a test was carried out by classifying tweet data from the public collected on the Twitter application from January to December 2022 using the Modified k-Nearest Neighbor method to analyze public sentiment regarding the upcoming election. Data collected as many as 2,100 data with positive and negative categories related to "Presidential Candidate" and "Prabowo Subianto" and the implementation of the Modified k-Nearest Neighbor classification was carried out using Google Colab. Based on the results of the confusion matrix test from the Modified k-Nearest Neighbor classification with three comparisons made (ie comparisons 70%:30%, 80%:20% dan 90%:10%) and using K=3, 5, 7, 9, 11 when testing a comparison of 90:10 at K=3 the highest accuracy results were obtained with a value of 93,3%.
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