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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 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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Penerapan Algoritma Apriori Dalam Menentukan Pola Perilaku Dan Gaya Hidup Terhadap Penderita Hipertensi Hara Novina Putri; Elvia Budianita; Fadhilah Syafria; Fitri Insani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4402

Abstract

Abstrak - Data mining merupakan teknik menggali informasi baru dari gudang data, informasi sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah mencapai sebuah tujuan, hal ini membuat setiap orang berlomba untuk memperoleh informasi, demikian juga pada bidang kesehatan terkhusus yang diteliti penulis yaitu penderita hipertensi. Hipertensi merupakan penyakit tidak menular yang prevalensinya sebesar 22% pada kelompok usia  18 tahun pada 2014 dan terus meningkat serta mampu meningkatkan risiko penyakit jantung koroner sebesar 12% dan meningkatkan risiko stroke sebesar 24%. Kebanyakan gejala yang dialami penderita tidak dapat dideteksi secara dini. Karenanya, perlu dilakukan penelitian dalam mendiagnosa pola perilaku dan gaya hidup terhadap penderita hipertensi menggunakan metode algoritma apriori. Data yang didapatkan melalui penyebaran kuisioner di puskesmas Melur dan rumah sakit Aulia Hospital. Atribut yang digunakan pada penelitian ini adalah jenis kelamin, usia, kebiasaan merokok, kebiasaan mengkonsumsi alkohol, intensitas aktifitas fisik, olahraga, dan pola konsumsi makanan. Pada pengujian parameter algoritma apriori dalam mencari pola dengan melihat hasil nilai support dan confidence pada metode algoritma apriori. Pengujian penelitian ini menggunakan tools Tanagra versi 1.4. Dari pengujian 300 data penderita hipertensi menggunakan nilai support 30% dan confidence 85% ditemukan 6 pola/rules dengan lift ratio ≥1.Kata kunci: Hipertensi, Diagnosa, Algoritma apriori, support, confidence, lift ratio Abstract - Data mining is a technique to dig new information from the data warehouse, information is very important and valuable because by mastering information, it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in the field of health, especially those studied by the author, namely people with hypertension. Hypertension is a non-communicable disease whose prevalence was 22% in the age group of ≥ 18 years in 2014 and continues to increase and is able to increase the risk of coronary heart disease by 12% and increase the risk of stroke by 24%. Most of the symptoms experienced by sufferers cannot be detected early.Therefore, it is necessary to conduct research in diagnosing behavioral patterns and lifestyles for hypertension patients using the a priori algorithm method. The data obtained through the distribution of questionnaires at the Melur health center and Aulia Hospital. The attributes used in this study were gender, age, smoking habits, alcohol consumption habits, intensity of physical activity, exercise, and food consumption patterns. In testing the parameters of the a priori algorithm, it is produced in looking for patterns by looking at the results of support and confidence values in the a priori algorithm method. Testing this study using Tanagra tools version 1.4. From testing 300 data on hypertension patients using support values of 30% and confidence of 85% found 6 patterns / rules with an lift ratio of ≥1.Keywords: Hypertension, Diagnosis, Apriori algorithm, support, confidence, lift ratio
Analisa Pola Makan Mahasiswa Penderita Gastritis (Maag) Dengan Menerapkan Metode Frequent Pattern-Growth (FP-Growth) Fitri Astuti; Elvia Budianita; Alwis Nazir; Reski Mai Candra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4447

Abstract

Abstract— Gastritis is an inflammation that occurs in the walls of the stomach. Young and mature age belongs to the category of productive age, where the productive age is more at risk of developing gastritis. This study aims to find the diet of students of Sultan Syarif Kasim Riau Islamic University by applying the fp-growth algorithm. This study used 502 records of data obtained from interviews with several students of the Sultan Syarif Kasim Riau Islamic University. The attributes used are faculty, semester, gender, place to live, busy college schedule solutions, the habit of consuming staple foods, snacks, instant noodles, fast food, spicy food, coffee, soft drinks, and snacks. Based on the results of the implementation of the application that was built and tested using the RapidMiner tools with a minimum support of 6%, and a minimum confidence of 100%, 4 patterns were found with a lift ratio of 1.88. From the 4 association patterns produced, it can be concluded that students with gastritis who have the habit of consuming staple food 2 x / day, spicy food and fast food 2-3 x / week or 4-5 x / week, consume coffee sometimes or 1 x / week, and endure hunger as a solution to a busy college schedule, the student is a student who lives in a boarding house / rented.Keywords : Data Mining, Pattern Association, FP-Growth, Gastritis Disease Abstrak— Gastritis adalah peradangan yang terjadi pada dinding lambung. Usia muda dan dewasa termasuk dalam kategori usia produktif, dimana usia produktif lebih berisiko terkena gastritis. Penelitian ini bertujuan untuk menemukan pola makan mahasiswa Universitas Islam Sultan Syarif Kasim Riau dengan menerapkan algoritma fp-growth. Penelitian ini menggunakan 502 records data yang diperoleh dari hasil wawancara terhadap beberapa mahasiswa Universitas Islam Sultan Syarif Kasim Riau. Atribut yang digunakan adalah fakultas, semester, jenis kelamin, tempat tinggal,  solusi jadwal kuliah padat, kebiasaan mengkonsumsi makanan pokok, makanan ringan, mie instan, fast food, makanan pedas, kopi, minuman bersoda, dan jajanan. Berdasarkan hasil implementasi aplikasi yang dibangun dan pengujian menggunakan tools RapidMiner dengan minimum support 6% dan minimun confidence 100% ditemukan 4 pola dengan lift ratio 1,88. Berdasarkan 4 pola asosiasi yang dihasilkan dapat disimpulkan bahwa bahwa Mahasiswa penderita gastritis yang memiliki kebiasaan mengkonsumsi makanan pokok 2 x/hari, makanan pedas dan fast food  2-3 x/minggu atau 4-5 x/minggu, mengkonsumsi kopi kadang – kadang atau 1 x/minggu, serta menahan lapar sebagai solusi jadwal kuliah yang padat maka mahasiswa tersebut merupakan mahasiswa yang tinggal di kos/kontrakanKata kunci : Data Mining, Pola Asosiasi, FP-Growth, Penyakit Gastritis
Analisis Sentimen Akun Twitter Apex Legends Menggunakan VADER Dicky Abimanyu; Elvia Budianita; Eka Pandu Cynthia; Febi Yanto; Yusra Yusra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 3 (2022): Juni 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i3.4382

Abstract

Abstrak - Pesatnya peningkatan jasa internet saat ini, ada banyak informasi yang dihasilkan dalam jumlah besar secara terus menerus dalam waktu yang singkat. Akhir-akhir ini, analisis sentimen dengan menggunakan ulasan dan pesan telah menjadi topik penelitian yang populer dibicarakan di bidang Natural Language Processing. Selama bertahun-tahun, permainan online telah menjadi suatu aktivitas yang tidak bisa dipisahkan dari sebagian besar orang. Apex Legends adalah salah satu contoh game yang sangat popular di seluruh dunia. Untuk mendapatkan informasi bagaimana pendapat para pemain tentang permainan ini diperlukan analisis sentimen. Pada penelitian ini dilakukan analisis sentimen menggunakan bantuan aplikasi Orange Data Mining dengan metode VADER pada akun twitter Apex Legends menggunakan data sebanyak 500 tweet. Pengujian data dilakukan dengan membandingkan hasil yang didapat menggunakan metode VADER dengan hasil pengujian pakar, yaitu native speaker dari Canada dan Amerika. VADER mengklasifikasikan data yang didapatkan melalui twitter berdasarkan nilai compound yang didapat. Penelitian ini menghasilkan kesimpulan yaitu perbandingan dari pengujian menggunakan VADER dan pengujian pakar tidak berbeda jauh, yang mana total persentase dari penggunaan metode VADER untuk menganalisis sentiment dari twitter ini adalah : Positif = 18%, Negatif = 4,6%, Netral = 73,6%. Sedangkan   hasil pengujian pakar adalah : Positif = 27%, Negatif = 10,8%, Netral = 62,2%.Kata kunci: VADER, Apex Legends, Game, Twitter, Uji Pakar Abstract - With the rapid increase in internet services today, there is a lot of information produced in large quantities continuously in a short time. Recently, sentiment analysis using reviews and messages has become a popular research topic discussed in the Natural Language Processing field. Over the years, online gaming has become an activity that cannot be separated from most of the people. Apex Legends is one example of a game that is very popular around the world. To get information on how the players think about the game, sentiment analysis is needed. In this study, sentiment analysis was carried out using the Orange Data Mining application with the VADER method on the Apex Legends twitter account using 500 tweets (data). Data testing is done by comparing the results obtained using the VADER method with the results of expert testing, native speaker from Canada and America. VADER classifies the data obtained through twitter based on the compound value obtained. This study concludes that the comparison of testing using VADER and expert testing is not much different, where the total percentage of using the VADER method to analyze sentiment from Twitter is : Positive = 18%, Negative = 4,6%, Neutral = 73,6%. While the results of expert testing is : Positive = 27%, Negative = 10,8%, Neutral = 62,2%.Keywords : VADER, Apex Legends, Game, Twitter, Expert Test (Uji Pakar)
Penerapan Algoritma Convolutional Neural Network Arsitektur ResNet-50 untuk Klasifikasi Citra Daging Sapi dan Babi Dodi Efendi; Jasril Jasril; Suwanto Sanjaya; Fadhilah Syafria; Elvia Budianita
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4176

Abstract

Meat is one of the food ingredients needed by humans. The price of pork is cheaper than beef, which has led to the practice of mixing beef with pork for the purpose of making big profits. In plain view, the difference between beef and pork is not striking, so it is difficult for ordinary people to distinguish between them. In terms of color, pork is paler than beef. In terms of texture, beef is stiffer and tougher than pork. In terms of fiber, beef is clearer than pork, so we need a system that can identify the two types of meat. This study uses the Convolutional Neural Network (CNN) algorithm with the ResNet-50 architecture with 3 types of optimizers such as Stochastic Gradient Descent (SGD), Adam, and RMSprop. The dataset used for training first goes through 2 stages of preprocessing, namely cropping and resizing. The results of the study show that the SGD optimizer can outperform the Adam and RMSprop optimizers with 97.83% accuracy, 97% precision, 97% recall, and 97% f1 score with batch size 32, learning rate 0.01, and epoch 50.
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan Ekstraksi Ciri dan Convolutional Neural Network Gusrifaris Yuda Alhafis; Jasril Jasril; Suwanto Sanjaya; Fadhilah Syafria; Elvia Budianita
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4175

Abstract

Cases of mixing beef and pork are still happening today. The increasing demand for beef causes many traders to mix meat to gain more profit. Distinguishing beef and pork can be done by sight and smell, but still has weaknesses. This study uses Deep Learning method for image classification with Convolutional Neural Network architecture EfficientNet-B0. The amount of data is 3,000 images which are divided into 3 classes, beef, pork, and mixed meat. This study uses original image data and image data of Contrast Limited Adaptive Histogram Equalization. The data is divided by the ratio of training data and test data of 80:20. The results of testing the model with the confusion matrix show the highest classification performance with 95.17% accuracy, 92.72% precision, 95.5% recall, and 94.09% f1 score, in the original image data with the use of neurons in the first dense amounting to 256, 32 batch size, 0.01 learning rate, and Adam's optimizer
Implementasi Convolutional Neural Network Untuk Klasifikasi Daging Menggunakan Fitur Ekstraksi Tekstur dan Arsitektur AlexNet Amalia Hanifah Artya; Jasril Jasril; Suwanto Sanjaya; Fadhilah Syafria; Elvia Budianita
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4177

Abstract

The demand for meat began to increase rapidly, causing drastic price changes and causing the existence of scammers to inflate the price of meat to get big profits by mixing beef and pork. Few consumers are aware of the mixing of meat, to distinguish between beef and pork can be seen in terms of color and texture, but this theory still has weaknesses. This research uses the Deep Learning method, namely Convolutional Neural Network with Local Binary Pattern texture extraction feature and AlexNet architecture for meat classification. The research conducted stated that the accuracy of the meat image classification can be measured using various parameters and optimizers. The highest accuracy results obtained from this study were 68.6% accuracy, 62% precision, 57.6% recall, and 59% f1-score using the Stochastic Gradient Descent (SGD) optimizer, 0.01 learning rate, 32 batch size, and 0.9 momentum. Compared to the original dataset, the accuracy of the LBP dataset type is still below the original dataset with the results obtained from the accuracy of the original dataset are 84.1% accuracy, 78.6% precision, 79% recall, and 79% f1-score using the RMSprop optimizer, 0 .0001 learning rate, 32 batch sizes, and momentum So it can be concluded that the AlexNet architecture by setting the existing parameter values can increase the accuracy value.
Pengelompokan Tingkat Kecanduan Game Online Menggunakan Algoritma Fuzzy C-Means Ammar Muhammad; Elvia Budianita
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4511

Abstract

Abstrak - Game online  merupakan aplikasi permainan yang berupa petualangan, pengaturan strategi, simulasi dan bermain peran yang memiliki aturan main dan tingkatan-tingkatan tertentu. Bermain game online  membuat pemain merasa senang karena mendapat kepuasan psikologis. Kepuasan yang diperoleh dari game tersebut akan membuat pemain semakin tertarik dalam memainkannya.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 untuk meneliti tingkat kecanduan game online  dengan menggunakan algoritma Fuzzy C-Means. Dengan menggunakan metode ini kita dapat menentukan jumlah clustering dan dapat diatur  keragaman tingkat kecanduan berdasarkan clusternya, metode ini juga dapat mendeteksi cluster tingkat tinggi serta hubungan antar cluster yang berbeda. Pengujian pada metode menggunakan metode Silhouette Coefficient. Data kecanduan game online didapatkan dari Pengumpulan data melalui kuisioner yang mengacu kepada  skala Game addict scale (GAS). Dari hasil pengujian didapatkan hasil yaitu  148 record pada cluster 1, 50 record pada cluster 2 dan 102 record pada cluster 3.Kata Kunci: Candu, Clustering, Data Mining, Fuzzy C-Means, Game Online Abstract - Online games are game applications in the form of adventure, strategy setting, simulation and role playing that have certain rules and levels. Playing online games makes players feel happy because they get psychological satisfaction. The satisfaction obtained from the game will make players more interested in playingit. Online game addiction is an activity that is carried out continuously and for a long time which causes an attitude that tends to withdraw from social life. Application of data mining using the clustering to examine the level of online game addiction using the Fuzzy C-Means algorithm. By using this method we can determine the number , and can adjust the diversity of addiction levels based on the clusterthis method can also detect clusters high-level clusters . Testing on the method using the Silhouette Coefficient method. Data on online game addiction is obtained from collecting data through a questionnaire that refers to the Game addict scale (GAS). From the test results, the results obtained are  148 records in cluster 1, 50 records in cluster 2 and 102 records in cluster 3.Keywords: Opium, Clustering, Data Mining, Fuzzy C-Means, Online Game 
Penerapan Algoritma Hash Based Untuk Analisis Pola Pemilihan Mata Kuliah Pilihan Jurusan Teknik Informatika UIN Sultan Syarif Kasim Riau Desra Rizki Riyandi; Elvia Budianita; Zulkarnain Zulkarnain
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4449

Abstract

Abstrak - Mata kuliah pilihan merupakan sebuah cara yang digunakan oleh jurusan dalam rangka meningkatkan mutu dan skill mahasiswa. Namun, tidak sedikit mahasiswa yang salah mengambil mata kuliah pilihan karena tidak menyadari potensi dalam dirinya yang mengakibatkan menurunnya prestasi akademik mahasiswa tersebut. Selama ini juga belum ada penyimpanan data yang digunakan sebagai history atau bahan pertimbangan bagi mahasiswa. Asosiasi menjadi salah satu solusi  pencarian pola pada data mining dengan bantuan algoritama hash bashed. Algoritma ini mampu memperbaiki kelemahan algoritma apriori dalam menentukan frequent itemset. Algoritma Hash-based merupakan algoritma yang  menggunakan teknik hashing untuk menyaring keluar itemset yang tidak penting untuk pembangkitan itemset selanjutnya.Aturan pola yang didapatkan dari total data sejumlah 530 data menghasilkan pola akhir 3 itemset dengan pola faktor dosen pengampu, minat tersendiri dan, topik tugas akhir (DS,MN,TA) dengan nilai confidence tertinggi senilai 73%, sehingga menjadi faktor yang paling mepengaruhi mahasiswa dalam memilih mata kuliah pilihan.Kata Kunci: Akademik, Asosiasi, Mahasiswa, Mata Kuliah Pilihan, Hash Bashed Abstract - Elective courses are a method used by majors in order to improve the quality and skills of students. However, not a few students take the wrong elective courses because they do not realize their potential which results in a decline in the student's academic achievement. So far, there is no data storage that is used as history or consideration for students. Association is one of the solutions for finding patterns in data mining with the help of hash bashed algorithms. This algorithm is able to improve the weaknesses of the a priori algorithm in determining frequent itemset. Hash-based algorithm is an algorithm that uses a hashing technique to filter out itemsets that are not important for the next itemset generation. The pattern rules obtained from a total of 530 data produce a final pattern of 3 itemsets with a pattern of supporting lecturer factors, special interests and, the topic of the final project (DS, MN, TA) with the highest confidence value of 73%, so that it becomes the most influencing factor for students in choosing elective courses.Keywords: Academic, Association, Student, Elective Course, Hash Based
Perbandingan Pembobotan Kata Menggunakan Naïve Bayes Classifier Terhadap Analisa Sentimen Permendikbud No 30 Tahun 2021 Jeki Dwi Arisandi; Elvia Budianita; Eka Pandu Cynthia; Febi Yanto; Yusra Yusra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4420

Abstract

Abstrak - Kekerasan seksual di lingkungan Pendidikan mengalami peningkatan kasus dari tahun ke tahun. Menurut data dari Komnas Perempuan periode 2015-2020 kasus kekerasan seksual di lingkungan Pendidikan menunjukkan bahwa lingkungan Pendidikan sudah tidak menjadi tempat yang aman bagi peserta didik. Berdasarkan data kasus yang diadukan kepada komnas perempuan pada tahun 2015-2020 kasus kekerasan seksual tertinggi terjadi di lingkungan Universitas sebanyak 27%, lalu diikuti oleh Pesantren atau Pendidikan berbasis agama sebanyak 19% dan sisanya terjadi di tingkat SMU/SMK sebanyak 15%, SMP 7%, di tingkat TK,SD,SLB dan Pendidikan berbasis Kristen masing-masing sebanyak 3%. Bentuk kekerasan seksual yang terjadi di lingkungan Pendidikan tersebut berupa pemerkosaan, pencabulan, dan pelecehan seksual serta kekerasan psikis dan diskriminasi dengan mengeluarkan siswa dari sekolah. Berbagai kasus tersebut mendorong pihak Kementrian Pendidikan, Kebudayaan, Riset, dan Teknologi Republik Indonesia membuat Peraturan Menteri No 30 Tahun 2021 dengan tujuan untuk menangani berbagai kekerasan seksual yang selama ini masih terjadi di lingkungan Pendidikan. Namun setelah diterbitkannya Peraturan Menteri nomor 30 Tahun 2021 tersebut memunculkan beragam sentimen positif dan negatif dari masyarakat baik itu dari organisasi HAM dan organisasi keagamaan. Opini dari masyarakat tersebut dapat dijadikan bahan evaluasi bagi pemerintah untuk menilai kebijakan yang telah dibuat. Dalam penelitian ini membahas mengenai analisa sentimen Permendikbud no 30 tahun 2021 dengan melakukan perbandingan pembobotan kata menggunakan metode Naïve Bayes Classifier. Langkah awal yang penulis lakukan yaitu pengumpulan data dari media sosial Twitter sebanyak 468 data, kemudian memberikan pelabelan kelas data yang terdiri dari positif, negatif, dan netral lalu melakukan proses pembobotan menggunakan TF-IDF dan TF-RF yang bertujuan untuk melihat perbandingan proses pembobotan kedua metode tersebut. Berdasarkan dari proses dan hasil pengujian Confusion Matrix didapatkan akurasi terbaik dengan rasio 70:30 sebesar 73,94% dengan pembobotan TF-IDF.Kata Kunci: PERMENDIKBUD No 30 Tahun 2021, Kekerasan Seksual, Analisa Sentimen, Twitter, Naïve Bayes Classifier.Abstract - Sexual violence in the educational environment has increased in cases from year to year. According to data from Komnas Perempuan for the 2015-2020 period, cases of sexual violence in the educational environment show that the educational environment is no longer a safe place for students. Based on case data that was reported to Komnas Perempuan in 2015-2020 the highest cases of sexual violence occurred in universities as much as 27%, then followed by Islamic boarding schools or religion-based education as much as 19% and the rest occurred at the high school/vocational level as much as 15%, SMP 7 %, at the level of TK, SD, SLB and Christian-based education each as much as 3%. The forms of sexual violence that occur in the educational environment are in the form of rape, sexual abuse, and sexual harassment as well as psychological violence and discrimination by expelling students from school. These various cases prompted the Ministry of Education, culture, research, and Technology of the Republic of Indonesia to make Ministerial Regulation No. 30 of 2021 with the aim of dealing with various sexual violence that is still happening in the education environment. However, after the issuance of Ministerial regulation number 30 of 2021, it gave rise to various positive and negative sentiments from the community, both from human rights organizations and religious organizations. Public opinion can be used as evaluation material for the government to assess the policies that have been made. This study discusses the sentiment analysis of Minister of Education and Culture No. 30 of 2021 by comparing word weights using the Naïve Bayes Classifier method. The first step that the author took was collecting data from Twitter social media as much as 468 data, then labeling the data classes consisting of positive, negative, and neutral then carrying out a weighting process using TF-IDF and TF-RF which aims to compare the two weighting processes the method. Based on the process and results of the Confusion Matrix test, the best accuracy was obtained with a 70:30 ratio of 73.94% with TF-IDF weighting.Keywords: PERMENDIKBUD No 30 of 2021, Sexual Violence, Sentiment Analysis, Twitter, Naïve Bayes Classifier.
AUTOMATIC CHORUS DETECTION FOR INDONESIAN MUSIC USING REFRAIN DETECTING METHOD (REFRAID) Ichsan Permana Putra; Elvia Budianita; Febi Yanto; Yusra Yusra
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.259

Abstract

Music has become an important part of human life, chorus is part of the musical structure that makes some impression on music, people are generally very familiar with the chorus in music because the chorus is often repeated on music. Automatic chorus detection is a part of Music Information Retrieval which is considered important for building music analysis system with human-like patterns. Refrain Detecting Method (RefraiD) select the chorus by grouping various repeating parts of the music, evaluating the intensity level of the melody from each group, then selecting the group with the highest melodic intensity as the chorus. This paper intends to implement RefraiD in Indonesian pop and dangdut music by downloading 20 pop music videos and 20 dangdut music videos from YouTube then process it with Information retrieval using Python. The results of this paper indicate that the RefraiD method can be used to detect the chorus on Indonesian music with F measure of 91.8% for dangdut music and 91.5% for pop music.
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 Berliana, Trisia Intan Boni Iqbal buhfi arides hanyodi Chely Aulia Misrun Damayanti, Elok Desra Rizki Riyandi Dicky Abimanyu Dodi Efendi doli fancius silalahi Dwitama, Raja Zaidaan Putera Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu 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 Iis Afrianty Iis Afrianty 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 Kurnia Gusti, Siska Lestari Handayani Lestari Handayani Lili Rahmawati Lola Oktavia M Fikry M Ikhsan Maulana M ridwan Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita 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 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 Siska Kurnia Gusti Siti Sri Rahayu Surya Agustian Suwanto Sanjaya Syahputra, Armadani Ulti Desi Arni, Ulti Desi Wahyuni, Ayu Sri Widodo Prijodiprodjo Wiranti, Lusi Diah Yeni Fariati Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri Zulkarnain Zulkarnain