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DIAGNOSA PENYAKIT DISK HERNIA DAN SPONDYLOLISTHESIS MENGGUNAKAN ALGORITMA C5 Ulfi Saidata Aesyi; Taufaldisatya Wijatama Diwangkara; Riyanto Tri Kurniawan
Telematika Vol 16, No 2 (2019): Edisi Oktober 2019
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v16i2.3181

AbstractThe vertebral Column or spine is a sequence of bones from the neck to the tail owned by the vertebrates. The function of the spine is to protect the spinal nerves. The spine may experience malfunction if it is caused by abnormalities and diseases such as Disk Hernia and Spondylolisthesis. Based on the problem then it takes a system that can recognize or identify the disease Disk Hernia and Spondylolisthesis that attack the spine. So that it can be diagnosed with early spinal diseases. In building this system using C 5.0 algorithm. This research uses 310 data from the UCI machine Learning, where there are three classification classes of Normal, Hernia disks, and Spondylolisthesis. The results showed that the C 5.0 algorithm was able to identify with accuracy of 79%. Then the resulting decision tree C 5.0 algorithm is maximized by using AdaBoost algorithm, so the accuracy increases to 83%.Keywords : Vertebral Column, C 5.0 algorithm, AdaBoost algorithmVertebral Column atau tulang belakang merupakan sebuah rangkaian tulang dari leher ke ekor yang dimiliki oleh vertebrata. Fungsi dari tulang belakang adalah untuk melindungi syaraf tulang belakang. Tulang belakang dapat mengalami kegagalan fungsi jika disebabkan kelainan dan penyakit seperti Disk Hernia dan Spondylolisthesis. Berdasarkan masalah tersebut maka dibutuhkan suatu sistem yang dapat mengenali atau mengidentifikasi penyakit Disk Hernia dan Spondylolisthesis yang menyerang tulang belakang. Sehingga dapat dilakuakn diagnosa penyakit tulang punggung secara dini. Dalam membangun sistem ini menggunakan algoritma C5.0. Penelitian ini menggunakan 310 data dari UCI Mechine Learning, dimana terdapat tiga kelas klasifikasi yaitu Normal, Disk Hernia, dan Spondylolisthesis. Hasil penelitian menunjukkan bahwa algoritma C5.0 mampu melakukan identifikasi dengan akurasi sebesar 79%. Kemudian pohon keputusan yang dihasilkan Algoritma C5.0 dimaksimalkan dengan menggunakan Algoritma AdaBoost, sehingga akurasi meningkat menjadi 83%.

Prediction of Length of Study of Student Applicants Using Case Based Reasoning Ulfi Saidata Aesyi; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.28076

Graduation is important matter in college. Length of study can be used to evaluate curriculum. It affect accreditation score of the sutdy program. Based on Akreditasi Program Studi Magister Buku V Pedoman Penilaian Instrumen Akreditasi 3rd standard there is rule about students and graduation, such as profile of the graduates including average length of study time and gpa (grade point average) of graduates.In this study, system built to predict Gadjah Mada University Master of Computer Science student applicant’s length of study. It used new case with 13 features from applicant that will be predict as new case, then calculate local similarity using euclidean distance and hamming distance while global similarity using nearest neighbor. Maximum value of global similarity taken as solution while revised will be done if it’s value below threshold.Result of this study show that system can help study program to manage educational process. It show 76% accuracy of 50 data.

Analisis Hashtag pada Twitter untuk Eksplorasi Pokok Bahasan Terkini Mengenai Business Intelligence Arif Himawan; Muhammad Rifqi Maarif; Ulfi Saidata Aesyi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 6 No. 2 (2021): Mei 2021
Publisher : UIN Sunan Kalijaga Yogyakarta

The main purpose of this paper is to examine the dominant topics about Business Intelligence in micro-blogging Twitter. There are 7.153 tweets collected from Twitter API. Text mining and natural language processing are used to analyze the dominant topics among those tweets. Computational method used to count the most frequent hashtag that appears together with Business Intelligence hashtag. Twitter users are large and scattered around the world with a diverse range of skills (expertise) that can give a new perspective on a subject that may not be predicted before. For example, for topics related to Business Intelligence, the very dominant general topic discussed in the scientific literature are about data management, as well as for analytics and machine learning data. The result contributes to understanding dominant topics about Business Intelligence that can help researchers to level their research.

Deteksi Dini Mahasiswa Drop Out Menggunakan C5.0 Ulfi Saidata Aesyi; Alfirna Rizqi Lahitani; Taufaldisatya Wijatama Diwangkara; Riyanto Tri Kurniawan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 6 No. 2 (2021): Mei 2021
Publisher : UIN Sunan Kalijaga Yogyakarta

The decline in the number of active students also occurred at the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This greatly affects the profile of study program graduates. So it is necessary to have a system that is able to detect students who are threatened with dropping out early. In this study, the attributes chosen were the student's GPA and the percentage of attendance . This attribute is used to classify students who are predicted to drop out. The research data uses student data from the Faculty of Engineering and Information Technology, Universitas Jenderal Achmad Yani. This study uses the C5.0 algorithm to build a decision tree to assist data classification. The decision tree that was built with 304 data as training data resulted a C5.0 decision tree which had an error rate of 5%. The accuracy results obtained from the 76 test data is 93%.

Analisis Eksploratif Berita Hoax pada Situs Cek Kebenaran Puji Winar Cahyo; Ulfi Saidata Aesyi
Jurnal Informatika Universitas Pamulang Vol 7, No 2 (2022): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v7i2.13952

The spread of fake news (hoax) through social media is currently quite difficult for the public to distinguish hoax or actual news. News can be categorized as actual if it comes from a trusted source and is supported by valid source clarification. Therefore, the news that has been spread needs to be clarified to check the truth. Currently, news checking sites are available, including turnbackhoax.id and kominfo.go.id. They have a detail of clarification data on the news classified as hoax or actual. Based on the number of online spreading hoaxes, this study seeks to create a Directory Fact Checker platform, which is a news analysis platform that can display distribution data in graphic form within a certain period of time. Exploratory data analysis was applied to hoax data in 2020. The results of the analysis show that Facebook is the first ranked social media that is often used to spread hoax news, followed by Whatsapp in second place. Meanwhile, judging from the categorization of hoaxes, Content Fabrication is the most widely spread category. Content Fabrication is a news category, 100% of the discussion is fake news. Then in the second rank, followed by the Misleading Content category, Misleading Content is a discussion of news whose contents are twisted with the aim of discrediting.

Cosine Similarity untuk Mengukur Tingkat Kesadaran pada Topik Software Security Berbasis Teks Komentar di Media Sosial Youtube Alfirna Lahitani; Ulfi Saidata Aesyi; Noviana Wulandari; Bagas Dwi Santosa
Jurnal Sains dan Informatika Vol. 8 No. 2 (2022): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v8i2.535

Kecenderungan peningkatan celah keamanan siber pada kelemahan perangkat lunak akan mengancam kerahasiaan, integritas, dan keamanan tidak hanya secara infrastruktur tetapi dapat meyerang secara psikologis. Menurut survey APJI 2020 sebagian masyakarat hanya merasa aman saat beraktivitas di internet dan menjalankan software namun kurang peduli bahwa ancaman bisa saja datang tanpa disadari. Apakah kesadaran akan keamanan beraktivitas di internet dan menjalankan software telah diketahui oleh sebagian besar masyarakat indonesia. Kesadaran akan keamanan siber khususnya pada area CyBOK Software Security menjadi perhatian bagi para pengguna, sebagian besar aktivitas siber menggunakan software. Kurangnya edukasi akan kesadaran membuat tidak sedikit para pengguna menjadi korban dari celah keamanan siber. Penelitian ini bertujuan melakukan analisis data berbasis teks pada 100 komentar pengguna di sosial media Youtube untuk mengukur tingkat kesadaran terhadap keamanan siber pada topik software security. Data teks dikolektif, dibersihkan dan ditransformasi menjadi term sehingga siap digunakan untuk proses pembobotan. Pembobotan term menggunakan metode TF-IDF, selanjutnya dilakukan pengukuran derajat kemiripan pada topik software security menggunakan Cosine Similarity. Hasil analisis divisualisasi dalam bentuk derajat awareness yang memodelkan tingkat kesadaran pengguna pada topik software security. Hasil pengukuran menunjukkan 98% pengguna pada kategori â€œkurang awareâ€, 2% pengguna pada kategori â€œcukup awareâ€.

Analisis Pengaruh Meninggalnya Emmeril Terhadap Elektabilitas Ridwan Kamil Menggunakan Naïve Bayes Ulfi Saidata Aesyi; Risky Firmansyach
JIEET (Journal of Information Engineering and Educational Technology) Vol. 6 No. 2 (2022)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jieet.v6n2.p86-89

Twitter merupakan sumber informasi dan media sosial yang sangat populer dikalangan masyarakat, dimana orang-orang bisa berkomunikasi dengan pesan singkat yang disebut tweet. Dengan adanya pemberitaan tentang Eril, banyak masyarakat yang menggunakan Twitter untuk mengunggah opini dan tanggapan mereka mengenai hal ini sehingga menjadi sebuah trending topik atau perbincangan hangat di Twitter. Tanggapan masyarakat terkait Meninggalnya Almarhum Emmeril Kahn bisa memiliki peranan penting. Dengan melakukan analisis sentimen terhadap opini masyarkat kita bisa mengetahui opini-opini dari netizen mengenai Almarhum Emmeril Kahn dan apakah memiliki Pengaruh terhadap Elektabilitas Pemilu Ridwan Kamil, baik berupa tweet dan re-tweet. Dalam penelitian ini menggunakan Metode Naïve Bayes Classification dan dibantu perhitungan TF-IDF ntuk membuat model analisis. Data dikumpulkan menggunakan teknik Scraping dan akan melewati tahap Preprocessing data sebelum dilakukannya penelitian. Didapatkan hasil prediksi sentimen Positif sebanyak 5701 tweet atau sebesar 67.8% dan sentimen Negatif sebanyak 2702 data tweet atau sebesar 32.2%. Dengan melakukan klasifikasi terhadap data yang telah diprediksi sehingga didapatkan nilai presisi masing-masing sentimen Positif 92% dan Negatif 80%, nilai recall masing-masing sentimen Positif 93% dan Negatif 83%. Kemudian nilai akurasi prediksi sebesar 87.9%. Sehingga dapat diketahui bahwa pemodelan yang sudah dibuat cukup bagus, dan banyak terdapat opini positif mengenai Eril dan Ridwan Kamil, sehingga masyarakat bisa mengetahui lebih jauh bagaimana sosok Ridwan Kamil di mata masyarakat sehingga bisa memberikan pengaruh positif terhadap Elektabilitasnya.

Perbandingan LSTM dengan Support Vector Machine dan Multinomial Na ve Bayes pada Klasifikasi Kategori Hoax Puji Winar Cahyo; Ulfi Saidata Aesyi
Jurnal Transformatika Vol 20, No 2 (2023): January
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5880

Hoax is fake news, now massively spread through social media. The impact of hoaxes is that people's misperceptions in understanding of news are very high. With the existence of hoaxes are spreading through social media, it requires the public to think smart when receiving the news. Currently, many ways to prevent hoaxes, right now we have Fact Checker Directory Platform which is a truth platform sourced from several fact check sites. On the truth check platform, every news detected as hoaxes has been categorized into specific type of hoax, manually by the validator. For this reason, this research attempts to automatically categorize the types of hoaxes using comparation of Deep Learning with Machine Learning classifications. Deep Learning uses Long Short Term Memory Network (LSTM), while Machine Learning uses Support Vector Machine (SVM) and Multinomial Naive Bayes. Through the build model process, SVM produces the best accuracy quality of 0.74, Multinomial Na ve Bayes produces an accuracy quality of 0.62 while LSTM displays 0.49. The results of low accuracy in LSTM need to be evaluated on model architecture and data normalization during preprocessing.

ANALISIS TINGKAT KEBERMANFAATAN MYPERTAMINA MENGGUNAKAN K-MEANS CLUSTERING Kharisma Kharisma; Ulfi Saidata Aesyi
Journal of Information System Management (JOISM) Vol. 4 No. 2 (2023): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2023v4i2.967

Pertamina, menerapkan digitalisasi Stasiun Pengisian Bahan Bakar Umum (SPBU) melalui aplikasi mypertamina yang bekerja sama dengan linkaja. Meskipun aplikasi tersebut menawarkan beberapa program yang dapat mempermudah pelanggan, ternyata masyarakat banyak mengomentari aplikasi tersebut sehingga menyebabkan topik mypertamina di twitter menjadi trending topik pada bulan Juli. Akan tetapi komentar tersebut tidak seluruhnya positif. Komentar yang terlalu banyak tentang aplikasi mypertamina ini, menyebabkan sulitnya berbagai kalangan untuk menyimpulkan tingkat kebermanfaatan dari aplikasi tersebut, termasuk pertamina. Sehingga diperlukan penelitian untuk menganalisis tingkat kebermanfaatan aplikasi mypertamina dari banyaknya komentar masyarakat di twitter.Oleh karena itu, penelitian ini mengumpulkan data berupa tweet masyarakat dari twitter tentang aplikasi mypertamina. Data tersebut kemudian dibersihkan dan dihitung kedekatan aktanya menggunakan TF-IDF. Setelah itu data dikelompokkan dengan menggunakan K-Means. Dengan menggunakan coherence score, kluster terbaik ada di kluster 2. Kluster 0 berisi kata keluhan masyarakat terhadap aplikasi my pertamina. Kluster 1 berisi kata umum terkait aplikasi pertamina. Berdasarkan analisis hasil kluster yang diperoleh, maka aplikasi mypertamia mendapatkan banyak keluhan dari masyarakat yang cenderung berpendapat bahwa aplikasi mypertamina tidak bermanfaat untuk masyarakat. Kata Kunci : mypertamina, datamining, klustering, K-Means

Analisis Eksploratif Berita Hoax pada Situs Cek Kebenaran Puji Winar Cahyo; Ulfi Saidata Aesyi
Jurnal Informatika Universitas Pamulang Vol 7, No 2 (2022): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v7i2.13952

The spread of fake news (hoax) through social media is currently quite difficult for the public to distinguish hoax or actual news. News can be categorized as actual if it comes from a trusted source and is supported by valid source clarification. Therefore, the news that has been spread needs to be clarified to check the truth. Currently, news checking sites are available, including turnbackhoax.id and kominfo.go.id. They have a detail of clarification data on the news classified as hoax or actual. Based on the number of online spreading hoaxes, this study seeks to create a Directory Fact Checker platform, which is a news analysis platform that can display distribution data in graphic form within a certain period of time. Exploratory data analysis was applied to hoax data in 2020. The results of the analysis show that Facebook is the first ranked social media that is often used to spread hoax news, followed by Whatsapp in second place. Meanwhile, judging from the categorization of hoaxes, Content Fabrication is the most widely spread category. Content Fabrication is a news category, 100% of the discussion is fake news. Then in the second rank, followed by the Misleading Content category, Misleading Content is a discussion of news whose contents are twisted with the aim of discrediting.

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