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Ulfi Saidata Aesyi
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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
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Smart System Bagian dari Artifical Intellegence dalam Paradigma Keilmuan Laili Wahyunita
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 2 (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.v1i2.1185

Abstract

Artikel ini memuat pembahasan terhadap aspek filsafat sains pada domain smart system. Smart system yang merupakan sub dari Artificial Intellegence (AI) terus berkembang dan semakin banyak diterapkan di berbagai aspek kehidupan. Mulai dari aspek kesehatan, social, politik, budaya, pendidikan, bisnis, budaya, serta aspek lainnya. Melalui kajian filsafat sains baik dari pemikiran Thomas S. Kuhn dan Imre Lakatos, smart system termasuk dalam spesifikasi normal sains dan progresif sains yang ditandai dengan masih akan terus berkembangnya riset di bidang ini. Luasnya aspek pengembangan smart system ini menjadikan potensi riset yang mengandung bias penelitian, etis dan moral yang negative, serta pseudosains. Penulisan artikel ini menggunakan metode studi pustaka atau library research dan pendekatan analisis konten atau content analysis. Dari kajian filsafat sains diketahui upaya pencegahan dari pseudosains dapat dilakukan dengan mengedapankan failsifikasi, metode ilmiah, dan pembuatan aturan dan batasan yang jelas. Bias penelitian dapat dihindari dengan pemilihan metodologi riset yang terpantau dan terawasi melalui pengacakan sample. Nilai etika dan moralitas harus diperhatikan baik sebagai ilmuwan maupun individu dengan mengedapankan etika normative dan terapan.
Klasifikasi Penyakit Hiperkolesterol Menggunakan Algoritma Decision Tree C4.5 Liya, Aprisarita; Supit, Yonal; Muhammad Islah, Andi
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 2 (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.v1i2.1192

Abstract

Hiperkolesterol merupakan faktor risiko penyebab kematian di usia muda. tercatat 4,4 juta kematian akibat hiperkolesterol atau sebesar 7,9% dari jumlah total kematian di usia muda. Hiperkolesterol merupakan salah satu jenis penyakit yang banyak dialami oleh masyarakat Indonesia. Pentingnya mengetahui gejala penyakit stroke sejak dini merupakan pencegahan awal. Maka dari itu, dilakukan penelitian untuk menganalisa data terkait penyebab hiperkolesterol. Adapun atribut yang terlibat dalam penyebab terjadinya hiperkolesterol yakni, usia, jenis kelamin, status merokok, dan index masa tubuh. Diperlukan algoritma tertentu untuk mengklasfikasikan atribut tersebut untuk mengevaluasi kelas suatu objek. Decision tree C4.5 merupakan algoritma yang paling banyak digunakan, dalam kasus ini akurasi dari algoritma Decision tree C4.5 merupakan Algoritma yang paling banyak digunakan, dalam kasus ini akurasi dalam algoritma Decision Tree C4.5 sebesar 80%.
Analisis Prediksi Kematian Pasien Covid-19 di Meksiko Menggunakan Algoritma Random Forest Dennis Fitri Salsabilla Arianti; Latifah Arum Sulistyaningsih; Mohamad Burhanudin
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 2 (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.v1i2.1196

Abstract

This research aims to analyze and predict the deaths of Covid-19 patients in Mexico using the Random Forest algorithm. The data used in this study is sourced from official sources, including the number of cases, symptoms, risk factors, and Covid-19 patient mortality data. The first stage of this research is data preprocessing, where the acquired data is collected, cleaned, and prepared for analysis. Subsequently, data exploration is conducted to understand the characteristics and patterns within the dataset. Then, the Random Forest model is developed to predict the deaths of Covid-19 patients based on relevant factors. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics. The results of this research indicate that the random forest model can provide good predictions for Covid-19 patient deaths in Mexico. The evaluation results show a high level of accuracy and satisfactory performance for the model. These findings can be used as guidance in decision-making and strategic planning to address the Covid-19 pandemic in Mexico. This research contributes significantly to the field of predictive analysis and provides valuable insights in the efforts to manage the Covid-19 pandemic.
Analisis Kepercayaan Masyarakat Tentang Kepolisian Indonesia di Twitter Menggunakan Latent Dirichlet Allocation (LDA) Bagas Dwi Santosa; Nurul Fatimah; Netania Indi Kusumaningtyas; Ulfi Saidata Aesyi; Herdiesel Santoso
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 2 (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.v1i2.1198

Abstract

Kepolisian Negara Republik Indonesia merupakan instansi yang bertugas untuk menjaga ketertiban dan keamanan masyarakat, menerapkan hukum, memberikan perlindungan, dukungan dan layanan kepada warga negara guna menjaga stabilitas dalam negeri. Namun, ditengah peran dari kepolisian itu sendiri, justru banyak kasus yang menyeret beberapa anggota polisi. Hal tersebut yang membuat masyarakat ramai membicarakannya di sosial media, salah satunya Twitter. Bahkan tagar-tagar yang berkaitan dengan kasus lingkup kepolisian juga sempat trending di Twitter. Dari hal tersebut, maka perlu dilakukan analisis terhadap topik kepercayaan masyarakat terhadap kepolisian. Analisis yang dilakukan menggunakan Latent Dirichlet Allocation (LDA). Hasil dari analisis yang dilakukan yaitu kepercayaan masyarakat terhadap kepolisian berkurang atas banyaknya kasus yang dilakukan anggota polisi saat ini. Analisis yang dilakukan menggunakan Latent Dirichlet Allocation (LDA) mengungkapkan bahwa kepercayaan publik terhadap kepolisian telah terkikis secara signifikan akibat banyaknya kasus yang melibatkan anggota kepolisian. Kesimpulan ini didukung oleh prevalensi tagar terkait kepolisian dan diskusi di platform media sosial seperti Twitter.
Analisis Perbandingan Pengukuran Jarak Algoritma K-Nearest Neighbor Dengan Menggunakan Data Breast Cancer Dan Data Heart Disease Herdiesel Santoso; Pratiwi, Linda
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 1 No. 2 (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.v1i2.1200

Abstract

Breast Cancer is a cancerous condition that appears in the breast area. This type of cancer is often experienced by women with a characteristic feature of Breast Cancer, namely the appearance of unusual lumps in the breast area. Heart or Heart Disease is a type of Non-Communicable Disease (PTM): which results in a fairly high mortality rate. Heart Disease is caused by several risk factors including smoking, an unhealthy lifestyle, high cholesterol, hypertension, and diabetes. Based on these facts, an appropriate algorithm is needed to classify Breast Caner and Heart Disease as an effort to prevent an increase in mortality rates due to Breast Cancer and Heart Disease. And the algorithm that will be used is the K-Nearest Neighbor algorithm with 3 distance measurement methods, namely Euclidean distance, Manhattan distance, and Minkowsky distance . From the stages that have been carried out, the final results of the Euclidean distance method obtained an Accuracy value of 80.88% Breast Cancer data at K = 11, and 78.69% heart Disease data at K = 11. The Manhattan distance method obtained an Accuracy value of 89.71% of Breast Cancer data on K=11, and 78.69% of Heart Disease data on K=20.The Minkowsky distance method obtained an Accuracy value of 98.53% of Breast Cancer data on K=11, and 79.41% of Heart Disease data on K=11. This shows that the Minkowsky distance method works more optimally than the Euclidean distance and Manhattan distance methods.
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 | DOI: 10.30989/ijds.v2i1.1318

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.

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