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Ulfi Saidata Aesyi
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ijds.unjaya@gmail.com
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ijds.unjaya@gmail.com
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INDONESIA
Indonesian Journal on Data Science
Published by Universitas Jenderal Achmad Yani Yogyakarta
ISSN : 29877423     EISSN : 29877423     DOI : 10.30989
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Data Science (IJDS) adalah Jurnal ilmiah yang memuat hasil penelitian pada ranah data science (Ilmu Data). Cangkupan jurnal meliputi: 1. Big Data 2. Machine Learning 3. Data Mining 4. Deep Learning 5. Artificial Intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 40 Documents
 1   2   3   4 
Tree-based Machine Learning Ensembles and Feature Importance Approach for the Identification of Intrusions in UNR-IDD Dataset OYELAKIN, Akinyemi
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (2024): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i1.1302

Abstract

Detection of intrusions from network data with the use of machine learning techniques has gained great attention in the past decades. One of the key problems in the network security domain is the availability of representative datasets for testing and evaluation purposes. Despite several efforts by researchers to release datasets that can be used for benchmarking attack detection models, some of the released datasets still suffer from one limitation or the other. Thus, some researchers at the University of Nevada released a dataset named UNR-IDD dataset which was argued to be free from some of the limitations of the past datasets. This study proposed Tree-based ensemble approaches for building binary intrusion identification models from the UNR-IDD dataset. Decision Tree algorithms are used as base classifiers in the Extra Trees, Random Forest and AdaBoost-based intrusion detection models. The results of the experimental analyses carried out indicated that the three ensembles performed excellently when feature selection was used compared to when all features were applied. For instance, Extra Trees model achieved an accuracy of 0.97, precision of 0.98, recall of 0.98 and f1-score of 0.98. Similarly, Random Forest model achieved an accuracy of 0.98, precision of 0.98, recall of 0.99 and f1-score of 0.98. Adaboost-based model had an accuracy of 0.96, precision of 0.96, recall of 0.99 and f1-score of 0.98. It was deduced that Random Forest intrusion classification model achieved slight overall best results when compared to the other models built. It is concluded that the three homogeneous ensemble models achieved very promising results while feature importance was used as attribute selection method.
Pemetaan Opini Publik Menggunakan Data Mining: Studi Kasus Naturalisasi Pemain Sepak Bola dengan K-Means dan Naive Bayes Classifier Tegar Agustian; Fresia Nandela, Emilia; A. Sinay, Stani; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (2024): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

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

Abstract

Naturalisasi merupakan salah satu proses yang dilakukan oleh warga asing agar menjadi Warga Negara Indonesia (WNI) yang sah di mata hukum. Saat ini Timnas Indonesia memiliki beberapa pemain naturalisasi . Beberapa kalangan menyambut positif kehadiran mereka, melihatnya sebagai langkah strategis untuk meningkatkan kualitas dan daya saing tim. Namun, ada pula yang merasa skeptis dan meragukan keberlanjutan dukungan terhadap pemain lokal. Data yang diambil dari 3584 komentar YouTube melalui YouTube Data API mencerminkan keragaman opini yang dapat memberikan gambaran lebih mendalam tentang dinamika pandangan publik. Penelitian ini penting dalam konteks pemahaman pandangan masyarakat terhadap naturalisasi pemain sepak bola Timnas. Dengan menggunakan teknik Data Mining, terutama K-Means Clustering dan Naive Bayes Classifier, penelitian ini memberikan wawasan mendalam tentang kelompok-kelompok masyarakat dengan perspektif serupa atau berbeda terkait isu tersebut. Hasil dari proses K-Means Clustering digunakan sebagai label awal untuk melatih model Naive Bayes Classifier. Evaluasi kinerja model dilakukan menggunakan confusion matrix, yang menghasilkan akurasi sebesar 93,17% dan error rate sebesar 6,83%. Proses ini dilakukan terhadap dataset komentar YouTube yang telah diberi label melalui K-Means Clustering. Hasil klasifikasi menggunakan metode Naive Bayes menunjukan bahwa 3328 data komentar setuju dengan adanya naturalisasi pemain dan 256 data komentar tidak setuju.
Metode Latent Dirichlet Allocation Untuk Menentukan Topik Pada Review Drama Korea Alfun Roehatul Jannah; Kristi, Ria; Muhammad Habibi
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (2024): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i1.1345

Abstract

The Hallyu Wave, involving the spread of South Korean culture and popular media, has rapidly grown over the past two decades. In addition to entertainment industries such as K-pop and K-drama, this phenomenon has also extended into the food and K-beauty sectors. Korean dramas, as the core of Hallyu, have become a global phenomenon with a continuously expanding fan base worldwide. A global survey in 2022 indicated that 36 percent of respondents in 26 countries considered Korean dramas very popular in their respective countries. In Indonesia, Korean films and dramas remain favorites, with 72 percent of streaming audiences choosing them on OTT services throughout 2022. Viu dominates as the most popular Korean drama streaming platform with 57 percent usage, followed by Netflix, Telegram, and WeTv. This research focuses on the analysis of Korean drama review data from 2015 to 2023 using the Latent Dirichlet Allocation (LDA) method. The goal is to provide a deep understanding of critical aspects such as acting, storyline, and cinematography. With LDA, this research aims to identify topics related to these elements, offering specific insights into audience preferences. From the conducted research, 10 ideal topics emerged out of 20 existing topics to ensure topic consistency using topic coherence. From the topic coherence results for these 20 topics, it can be concluded that the overall topic score for topic 10 is 0.527, providing ideal results for topic modeling in accordance with the rules.
ANALISIS PROYEKSI KEBUTUHAN TENAGA KERJA BERDASARKAN SKILLS YANG DIBUTUHKAN MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER Nur Azizah Firdausa; Rifanny Br Girsang, Ribka; Oktaviana, Dela; Wahyuningsiam, Astr; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (2024): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i1.1346

Abstract

In August 2023, Indonesia faced an unemployment rate of 7.86 million people, although there is no denying that the percentage of unemployment has decreased from the previous year. The data is categorized into four groups, namely unemployment involves those who are looking for work, trying to set up a business having trouble landing a job, and even those who have worked but have not started. The Covid-19 pandemic changed the paradigm of work to remote, but the need for job information remains key. Labor demand projections provide long-term insights into promising sectors and fields, guiding job seekers to develop skills according to labor market trends. This research was conducted using naive bayes classification, which is a text classification method that relies on the likelihood of keywords to compare training and testing documents. This classification method is expected to help reduce unemployment rates and align individual skills with industry needs, contributing to education and training policies to make smart career decisions in the digital era.
ANALISIS TRANSFER DATA PADA JARINGAN TERDAMPAK ARP SPOOFING MENGGUNAKAN METODE ARP POISONING DAN STATISTIK DESKRIPTIF sudaryanto; Dwi Nugraheny
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (2024): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i1.1375

Abstract

This Computer network security issues are very important and need to be considered in the development of computer networks. Networks connected to network devices are usually vulnerable to hacking. Hacking is an activity that allows a person or group to change or take data for personal gain. The aim of this research is to carry out testing and analysis to determine the condition and measure the level of security of the ITDA Yogyakarta intra-campus information system and computer network. Describe security gaps and measure the level of security that needs to be immediately repaired so that it can help correct failures in maintaining the security of ITDA Yogayakarta intra-campus information systems and networks. This research uses descriptive statistics with 20 PC units as samples. There were four tests in this study with a total success of 16 out of 20 samples. From the results of Arp spoofing on the local network, it can be concluded that after the local network is infiltrated by an attacker using the ARP spoofing method, the target traffic will be redirected to the attacker's device. This can allow attackers to monitor and understand the contents of data traffic on the local network. Changing the attacker's MAC address is very necessary because if the MAC is not replaced then network traffic will not be redirected to the attacker's device.
Analisis Sentimen Transfer Pemain Klub La Liga Spanyol Pada Bursa Transfer Musim Dingin Eropa Di Twitter Ahmad Adita Shiddiq; Aris Wahyu Murdiyanto; Arif Himawan
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.859

Abstract

Dari beberapa kompetisi Sepak Bola yang ada, Liga Champions UEFA yang paling digemari oleh masyarakat. Pada tahun 2022 bursa transfer pemain Eropa dibuka, bursa transfer yang dilakukan merupakan cara jangka pendek untuk memperbaiki tim dalam mengejar prestasi sepak bola Dengan media sosial sebagai wadah komunitas, para penggemar sepak bola dapat juga menyalurkan opini, informasi dan berita tentang klub kesayangan kepada masyarakat. Opini masyarakat terhadap transfer pemain Liga Spanyol memiliki peranan penting. Dengan dilakukannya analisis sentimen terhadap opini, dapat dijadikan suatu pola prediksi penilaian masyarakat terhadap transfer pemain serta dapat memberikan saran kepada tim sepak bola terkait bursa transfer pemain pada periode musim selanjutnya. Membuat analisis sentiment penggemar sepak bola terhadap transfer pemain Liga Spanyol apakah bersifat positif dan negatif. Metode Naïve Bayes Classifer (NBC) dalam penelitian ini dipilih dikarenakan pada algoritma NBC dapat melakukan proses pengolahan data diskrit dan data kuantitatif dengan menggunakan sampel yang relative sedikit dan juga perhitungan pada algoritma NBC lebih cepat. Pengambilan data berupa topik mengena keyword “Transfer La Liga”, “Transfer Real Madrid”, “Transfer Barcelona”, “Transfer Liga Spanyol” dan “Transfer Copa Del Ray”. Data tweet di ambil dari periode 1 Januari 2020 sampai dengan 31 Mei 2022, dengan jumlah data total 11.282. Pada penelitian telah berhasil mendapatkan akurasi dengan nilai 81,67 % pada data training dan 85 % untuk data testing. Pada penelitian ini berhasil membuat model analisis sentimen berupa file.pickle yang dimana untuk melakukan klasifikasi dan prediksi pada data tweet untuk mendapatkan sebuah hasil sentimen positif dan negative. Penelitian ini telah berhasil mendapatkan akurasi dengan nilai 81,67 % pada data training dan 85 % untuk data testing.Hasil analisis sentimen akhir dalam klasifikasi penelitian ini bernilai “Sentimen Negatif”
Analisis Sentimen Di Media Sosial Twitter Dengan Studi Kasus Vaksinasi Covid-19 Nufia Alfi Rohyana; Aris Wahyu Murdiyanto; Kharisma
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.861

Abstract

With the COVID-19 pandemic, the World Health Organization or WHO conducted research and research trials on the COVID-19 vaccine. The Indonesian government has made several policies, one of which is the "Mass Vaccination Program". However, the COVID-19 vaccination program in the field received mixed responses in the community, there were those who supported the vaccine program and some who rejected the vaccine program. In this study, researchers conducted research on sentiment analysis on the opinion of vaccination programs against anti-vaccine community groups based on Twitter social media data using the Naïve Bayes Classifier algorithm to provide information on opinion assessments that lead to positive and negative sentiments. Objective: The purpose of this study is to find out the public perception of AntiVaccine against the COVID-19 Vaccination Program in Indonesia. This study uses the Naïve Bayes Classification. The use of the Naïve Bayes Classifier (NBC). This research uses tweets obtained from Twitter with the keywords/hashtags “Anti Covid-19 Vaccines” or by collecting data based on accounts related to news about vaccination programs such as @ The Ministry of Health of the Republic of Indonesia. Data collection was carried out in the period August 2021-December 2021, with a total of 889 data. This study has succeeded in obtaining an accuracy of 72 % for testing. The result of the final sentiment analysis in the classification of the Anti-Vaccine group in this study is "Negative Sentimen".
Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches Rizky Fauzi Akbar; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.896

Abstract

The National Social Security Agency (BPJS) for Employment is the Social Security Administering Agency with the goal of ensuring that each participant or member of the family receives adequate necessities. In its implementation, there is information that is spread, particularly on Twitter, regarding the Ministry of Health's decision, namely regarding Old Age Security (JHT), which can only be distributed/taken after the participant turns 56 years old, causing both pros and cons among the public. Based on unanalyzed tweets on Twitter, it is necessary to do extensive research to collect relevant information based on netizens' viewpoints. This research describes sentiment analysis of tweets from Twitter using the terms JHT, BPJSTK, and BPJS, which yield 4154 data tweets. We employ two approaches in this study: Lexicon Based and Nave Bayes Classifier. According to this study, the accuracy of the testing data is 92% for the Lexicon Based and 95% for the Nave Bayes Classifier. This study concluded that the JHT at BPJS Employment received unfavorable attitudes and negative reactions among users who addressed the rejection of new restrictions where JHT, could only be dispensed or taken when participants at BPJS Employment were 56 years old.
K-Nearest Neighbor and Naive Bayes Classifier Methods for Expedition Service Comparison Analysis of User Sentiments Nurul Hikmah; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.899

Abstract

Depending on the service chosen, expedition is one of the freight forwarding companies that operate in the domestic market. The availability of expedition services can make it easier for traders to transfer items to purchasers who conduct online transactions, as well as encourage shipping businesses to collaborate with online dealers. The JNE, JNT, and Pos Indonesia excursions were utilized in this study. The goal of this project is to develop an analytical model that will make it simpler for online merchants to find collaborators for effectively and securely transporting their goods. Based on user sentiment on the social networking site Twitter, this study uses sentiment analysis. With the keywords "JNT, JNE, and Pos Indonesia," this study compares the accuracy results using the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. According to this study, testing accuracy for the NBC method was 80% and training accuracy was 83%. While the accuracy of the KNN approach is 68%. According to public opinion, the JNE expedition is the best one for distributing products, scoring 68.58% in favor of it and 30.64% against it.
Topic Modeling dan Social Network Analysis digunakan untuk mencari keterkaitan topik pada Tweet Pembahasan Saham Muhammad, Ilham Zulqarnain; Puji Winar Cahyo
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 1 (2023): Indonesian Journal on Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v1i1.900

Abstract

Pada tahun 2020, jumlah orang yang melakukan trading di Indonesia mengalami peningkatan meskipun terjadi pandemic covid19. Dalam hitungan jumlah investor pada tahun 2020 mencapai 3.5 juta investor sedangkan pada tahun 2021 meningkat menjadi 7.5 investor. Melalui adanya peningkatan ini, maka jumlah posting tentang saham dan tutorial mengenai trading saham di media sosial meningkat cukup drastis. Maka penelitian ini mencoba untuk melakukan analisis keterkaitan topik pembicaraan saham pada sosial media Twitter dengan menggunakan integrasi topic modelling dan Social Network Analysis (SNA). Proses pembagian topik ideal menggunakan coherence measurement menentukan sebanyak 5 topik ideal. Melalui lima topik yang dihasilkan dari topic modelling tersebut kemudian dilakukan analisis menggunakan SNA sehingga menghasilkan nilai degree centrality, betweeness centrality, dan closeness centrality yang sama pada setiap topik. Nilai tersebut diantaranya: 4 untuk nilai degree centrality, 0.4 untuk betweeness centrality dan 1 untuk closeness centrality. Melalui hasil tersebut maka perlunya evaluasi dalam pembentukan SNA dengan menggunakan topic modeling. Evaluasi tersebut salah satunya bisa dilakukan melalui identifikasi pada tweet yang memiliki kesamaan pembahasan meskipun dengan penulisan redaksi yang berbeda, atau dapat dilakukan dengan cara menambah variasi data dengan cara memperlama waktu pengambilan.

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