bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perhitungan Prosentase Kelulusan Emisi Gas Buang Terhadap Kendaraan Bermotor Roda 4 dengan Bahan Bakar Bensin
Authors: Astati Sukawati Ni Ketut Sri , Suryadarmawan I Gusti Agung Gde , Darma Yoga I Wayan Gede , Windisari Ni Komang Ayu 
Published: December 2024

Kebutuhan alat transportasi di Indonesia, terus meningkat seiring dengan bertambahnya jumlah penduduk setiap tahun. Kesadaran masyarakat akan pencemaran udara akibat emisi gas buang kendaraan bermotor di kota – kota besar seperti Kota Denpasar saat ini makin rendah. Salah satu cara pengendalian tersebut adalah dengan melakukan uji emisi gas buang pada kendaraan bermotor beroda empat berbahan bakar bensin di Kota Denpasar tepatnya di kawasan Terminal Ubung. Tujuan utama dari penelitian ini adalah untuk mengetahui prosentase kelulusan emisi gas buang kendaraan bermotor yang berupa CO dan HC produksi tahun < 2007 dan >2007. Berdasarkan penelitian yang telah dilaksanakan, diperoleh hasil pengukuran kadar emisi gas buang yang berupa CO dan HC pada kendaraan roda empat berbahan bakar bensin dengan sistem pembakaran karburator ataupun injeksi tergantung variable tahun produksinya yang melebihi dari ambang batas yang ditentukan, yaitu kadar CO karburator < 2007 berjumlah 47,7 %, kadar CO injeksi produksi < 2007 berjumlah 33,33 %, kadar CO injeksi produksi >2007 berjumlah 10,20 %, kadar HC karburator < 2007 berjumlah 0 %, kadar HC injeksi produksi < 2007 berjumlah 0 %, kadar HC injeksi produksi >2007 berjumlah 8,20%.
ANALISIS SENTIMEN OPINI DEBAT CALON PRESIDEN DENGAN MENGGUNAKAN CLASSIFIER MACHINE LEARNING (STUDI KASUS : PADA DATA TWITTER 2024)
Authors: Purba Rini Widaswari , Waskita Arya Adyaksa , Makshun Makshun 
Published: December 2024

This study aims to analyze public sentiment towards the Indonesian 2024 Presidential Debate using five Machine Learning classification algorithms: Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest, and K-Nearest Neighbors. The data used in this research was sourced from Twitter, a major social media platform with a large and diverse volume of data. The research object is public opinions expressed on Twitter, with the subject of the research being tweets collected using the Twitter API, resulting in 1,300 data points. Data analysis involves text extraction and preprocessing, including data cleaning, tokenization, stemming, and stopword removal. The research results show the following sentiment distribution: 51.55% positive (663 tweets), 14.83% negative (183 tweets), and 34.21% neutral (440 tweets). Among the models, Support Vector Machine and Random Forest demonstrated the highest performance with an accuracy of 81%, while Naïve Bayes had the lowest performance with an accuracy of 62%. Despite variations in performance among the algorithms used, no single method was consistently effective in sentiment classification. This research contributes to mapping public sentiment related to political debates in Indonesia through social media data analysis and provides insights into the effectiveness of Machine Learning algorithms in sentiment analysis. ABSTRAKPenelitian ini bertujuan untuk menganalisis sentimen opini masyarakat terhadap Debat Calon Presiden Indonesia 2024 menggunakan lima algoritma klasifikasi Machine Learning: Naïve Bayes, Decision Tree, Support Vector Machine, Random Forest, dan K-Nearest Neighbors. Data yang digunakan dalam penelitian ini diambil dari Twitter, yang merupakan salah satu platform media sosial dengan volume data yang besar dan beragam. Objek penelitian ini adalah opini publik yang diekspresikan di Twitter, dengan subjek penelitian berupa tweet yang diambil menggunakan Twitter API, menghasilkan 1300 data poin. Analisis data melibatkan proses ekstraksi teks dan preprocessing yang mencakup pembersihan data, tokenisasi, stemming, dan penghapusan stopwords. Hasil penelitian menunjukkan distribusi sentimen sebagai berikut: 51,55% positif (663 tweet), 14,83% negatif (183 tweet), dan 34,21% netral (440 tweet). Dari hasil pemodelan, Support Vector Machine dan Random Forest menunjukkan performa tertinggi dengan akurasi 81%, sedangkan Naïve Bayes memiliki performa paling rendah dengan akurasi 62%. Meskipun terdapat variasi kinerja di antara algoritma yang di gunakan, tidak ada satu metode pun yang sepenuhnya konsisten dalam klasifikasi sentimen. Penelitian ini memberikan kontribusi dalam memetakan sentimen publik terkait perdebatan politik di Indonesia melalui analisis data media sosial. serta memberikan wawasan tentang efektivitas algoritma Machine Learning dalam analisis sentimen.
MEMBANGUN AGENT ENDPOINT DETECTION AND RESPONSE (EDR) MENGGUNAKAN WAZUH DAN VIRUSTOTAL SEBAGAI SISTEM DETEKSI SERANGAN RANSOMWARE LOCKBIT 3.0
Authors: Sholeh Muchammad , Monalisa Almima 
Published: December 2024

Internet users who are still public with cyber threats can be the main target of cyber criminals who want to take away a variety of things that Internet users own, one of which is personal data. Ransomware is one of the most widely used types of cyber attacks today to lock data on the victim's computer and then ask for ransom in the amount of money so that the data can be reopened. The aim of this study is to detect the threat on the server from the LockBit 3.0 ransomware attack using the Wazuh open source platform with the VirusTotal API integration as well as to know the effectiveness of Endpoint Detection and Response (EDR) as a solution in detecting the ransumware attack and obtained the result that Wazuh with the integration of VirusTotal successfully implemented Endpoint detection and response that detects and deletes the LockBit 3.0 file from the server directory within average 0.7 seconds with 3 times trial tests. ABSTRAKPengguna internet yang masih awam dengan ancaman cyber dapat menjadi sasaran utama penjahat cyber yang ingin mengambil berbagai hal yang dimiliki pengguna internet salah satunya adalah data pribadi. Ransomware merupakan salah satu jenis serangan cyber yang paling banyak digunakan saat ini untuk mengunci data pada komputer korban kemudian meminta tebusan dalam jumlah besar agar data dapat dibuka kembali. Tujuan dari penelitian ini adalah dapat melakukan pendeteksian ancaman pada server dari serangan ransomware LockBit 3.0 menggunakan platform Wazuh berbasis open source dengan integrasi API VirusTotal serta mengetahui efesiensi dari Endpoint Detection and Response (EDR) sebagai solusi dalam mendeteksi serangan ransomware dan diperoleh hasil bahwa Wazuh dengan integrasi VirusTotal berhasil menerapkan Endpoint Detection and Response yaitu mendeteksi dan menghapus file ransomware LockBit 3.0 dari direktori server dalam rentang Waktu 0.7 detik dengan melakukan uji coba sebanyak tiga kali.
PENERAPAN METODE AHP DALAM MENENTUKAN LAPTOP TERBAIK UNTUK MAHASISWA TEKNIK INFORMATIKA
Authors: Prasetya Ferdyana Eka , Nugraha Rhendy Diki , Nurcahyo Danang , Fatchan Muhammad , Anshor Abdul Halim 
Published: December 2024

One of the results of this development that is very commonly used by students is the use of electronic technology, such as laptops. In the world of information technology, choosing the right laptop has a very important role, especially for information engineering students. Lack of understanding of the criteria and specifications of laptops, has certainly caused confusion among informatics engineering students in determining the laptop according to their needs, especially because of the various options available, which include various brands, types of laptops, and also different prices. The Analytical Hierarchy Process (AHP) method can be the right solution. This research aims to help informatics engineering students in determining the best laptop choice based on several criteria that have been set. The results of this research successfully used the Analytical Hierarchy Process (AHP) method to help Informatics Engineering students choose the best laptop according to their needs. taking into account criteria such as Price, CPU, RAM, GPU, Storage and Screen Size. The AHP calculation shows that the HP Victus 16-R0017TX is in first place with the highest global priority value of 0.1565, the Ideapad Gaming Ryzen 5 is in second place with a global priority value of 0.1408, and the Asus Vivobook GO 14 is in third place with a global priority value of 0.1362. ABSTRAKSalah satu hasil dari perkembangan ini yang sangat umum digunakan oleh mahasiwa adalah penggunaan teknologi elektronik, seperti laptop. Dalam dunia teknologi informasi, pemilihan laptop yang tepat memiliki peran yang sangat penting, terutama bagi mahasiswa teknik informatika. Kurangnya dalam memahami kriteria dan spesifikasi laptop, tidak diragukan lagi menyebabkan kebingungan di kalangan mahasiswa teknik informatika dalam menentukan laptop sesuai dengan kebutuhan mereka, terutama karena berbagai pilihan yang tersedia, yang mencakup berbagai merek, jenis laptop, dan juga harga yang bervariasi. Metode Analytical Hierarchy Process (AHP) dapat menjadi solusi yang terpat. Penelitian ini bertujuan untuk membantu mahasiswa teknik informatika dalam menentukan pilihan laptop terbaik berdasarkan sejumlah kriteria yang telah ditetapkan. Hasil penelitian ini berhasil menggunakan metode Analytical Hierarchy Process (AHP) untuk membantu mahasiswa Teknik Informatika dalam memilih laptop terbaik sesuai kebutuhan mereka. dengan mempertimbangkan kriteria seperti Harga, CPU, RAM, GPU, Penyimpanan, dan Ukuran Layar. Perhitungan AHP menunjukkan bahwa HP Victus 16-R0017TX berada pada peringkat pertama dengan nilai prioritas global tertinggi sebesar 0.1565, Ideapad Gaming Ryzen 5 berada pada peringkat kedua dengan nilai prioritas global sebesar 0.1408, dan Asus Vivobook GO
IMPLEMENTASI ZERO TRUST NETWORK UNTUK MENINGKATKAN KEAMANAN JARINGAN MENGGUNAKAN FERRUMGATE DENGAN METODE NDLC
Authors: Haeruddin Haeruddin , Favian Felix , Prasetyo Stefanus Eko 
Published: December 2024

As Cyber threats grow increasingly sophisticated, traditional network security measures are no longer adequate to safeguard critical data and resources. This study focuses on enhancing network security through the deployment of a Zero Trust Network utilizing the Ferrumgate platform. Employing the Network Development Life Cycle (NDLC) methodology, the research encompasses requirements analysis, the design of a segmented network, the implementation of Multi-Factor Authentication (MFA) using Google Authenticator, and User activity Monitoring. Findings reveal that Ferrumgate effectively restricts network access to verified Users and devices, significantly reducing risks posed by both internal and external threats. The adoption of MFA enhances security standards, while continuous Monitoring successfully detects and mitigates potential Cyberattacks. In summary, the NDLC-driven implementation of Ferrumgate demonstrated its effectiveness in bolstering network security, with a recommendation for regular evaluations and monitoring to ensure the system remains robust against emerging threats. ABSTRAKDalam menghadapi ancaman Cyber yang semakin kompleks, pendekatan keamanan jaringan tradisional tidak lagi cukup untuk melindungi data dan aset penting. Penelitian ini bertujuan untuk memperkuat keamanan jaringan melalui implementasi Zero Trust Network dengan menggunakan platform Ferrumgate. Pendekatan penelitian dilakukan menggunakan metodologi Network Development Life Cycle (NDLC), yang meliputi analisis kebutuhan, perancangan jaringan yang tersegmentasi, penerapan autentikasi Multi-Factor Authentication – MFA dengan Google Authenticator, serta pemantauan aktivitas pengguna. Hasil penelitian mengindikasikan bahwa Ferrumgate mampu memastikan hanya perangkat dan pengguna yang terverifikasi yang dapat mengakses jaringan, sehingga risiko ancaman internal dan eksternal dapat diminimalkan. Integrasi MFA memberikan peningkatan signifikan pada keamanan, sementara sistem pemantauan berkelanjutan efektif dalam mendeteksi serta mencegah potensi serangan Cyber. Kesimpulannya, penerapan Ferrumgate berbasis NDLC terbukti efektif dalam meningkatakan keamanan jaringan, dengan rekomendasi untuk melakukan evaluasi dan pemantauan berkala guna menjaga efektivitas sistem terhadap ancaman yang terus berkembang.
OPTIMALISASI KLASIFIKASI UJI EMISI SEPEDA MOTOR MENGGUNAKAN ALGORITMA NAÏVE BAYES
Authors: Irwansyah Irwansyah , Dittyata Rizki , Rizal Rizal , Wiyono Wiyono 
Published: December 2024

Dense urban areas with high levels of industrial and transportation activity result in increased air pollutant emissions that threaten air quality and the health of their residents. The issue is the lack of utilization and optimization of motorcycle emission test classification through a machine learning approach. This research aims to utilize motorcycle emission test data and to determine the accuracy, precision, and recall results of the naive Bayes algorithm. The number of datasets used by the researchers is 2409 data points. Based on this data, it is divided into two parts: training data consisting of 1927 data points (80%) and testing data consisting of 482 data points (20%). The results of the motorcycle emission test data can be utilized for classification optimization, and the naive Bayes algorithm can be applied to classify and analyze the accuracy, precision, and recall results of the motorcycle emission test data. The accuracy result is 91.49%, the precision result for the pass classification is 93.72%, and the precision result for the fail classification is 83%, while the recall result for the pass classification is 95.47% and the recall result for the fail classification is 77.57%. ABSTRAK Daerah perkotaan yang padat penduduk dengan tingkat aktivitas industri dan transportasi yang tinggi mengakibatkan peningkatan emisi polutan udara yang mengancam kualitas udara dan kesehatan warganya. Permasalahan belum adanya pemanfaatan dan optimalisasi klasifikasi uji emisi sepeda motor melalui pendekatan machine learning. Penelitian ini bertujuan untuk memanfaatkan data uji emisi sepeda motor dan untuk mengetahui hasil akurasi, presisi, dan recall dari algoritma naïve bayes. Adapun jumlah dataset yang peneliti gunakan sebanyak 2409 data. Berdasarkan data tersebut dibagi menjadi dua yaitu data training sebanyak 1927 data (80%) dan data testing sebanyak 482 data (20%). Hasil penelitian data uji emisi sepeda motor dapat dimanfaatkan untuk optimalisasi klasifikasi dan algoritma naïve bayes dapat diterapkan dalam mengklasifikasi dan menganalisis hasil akurasi, presisi, dan recall dari data uji emisi sepeda motor. Adapun hasil akurasinya sebesar sebesar 91,49%, hasil precision klasifikasi lulus sebesar 93,72% dan hasil precision klasifikasi tidak lulus sebesar 83%, dan hasil recall klasifikasi lulus sebesar 95,47% dan hasil recall klasifikasi tidak lulus sebesar 77,57%.
Zum Ansatz der Bodenbettung zur Bemessung von Kranbahnbalken/On the use of soil bedding for the design of crane beams
Authors: Bergmann Lukas , Liebrecht Klaus , Pucker Tim 
Published: December 2024

Bei der Bemessung von flach gegründeten Stahlbeton-Kranbahnbalken ist die Interaktion zwischen Bauwerk und Boden von entscheidender Bedeutung. Die resultierenden Beanspruchungen des Kranbahnbalkens hängen vom Verhältnis der Biegesteifigkeit des Kranbahnbalkens zur Steifigkeit des Bodens ab.

Die reale Steifigkeit des Kranbahnbalkens kann durch die Verwendung nichtlinearer Stoffmodelle realitätsnah erfasst werden. Die Steifigkeit des Bodens wird in der Regel über die Definition eines Bettungsmoduls in der Berechnung abgebildet.

Vor diesem Hintergrund wird im vorliegenden Artikel ein Ansatz aufgezeigt, wie durch Versuche in situ und zusätzliche geotechnische Berechnungen eine Veränderung des Bettungsmoduls bei zyklischer Belastung abgeschätzt und durch Definition eines Bettungsfaktors auf die Geometrie der Kranbahnbalken übertragen werden kann.

Der Einfluss des infolge zyklischer Beanspruchung veränderlichen Bettungsmoduls auf die Bemessung von flach gegründeten Kranbahnbalken wird abschließend in einem Berechnungsbeispiel dargestellt. Dieses Beispiel bezieht sich auf Kranbahnbalken von Portalkranen im Containerumschlag.
“SNe Ia Twins for Life”: Toward a Precise Determination of H
0
Authors: Ruiz-Lapuente P. , González Hernández J. I. 
Published: December 2024

Abstract
Here we present an approach to the measurement of extragalactic distances using twin SNe Ia, taken from the early down to the nebular phase. The approach is purely empirical, although we can give a theoretical background on why the method is reliable. By studying those twins in galaxies where peculiar velocities are relatively unimportant, we can tackle the H
0 tension problem. Here we apply the method to the determination of the distances to NGC 7250 and NGC 2525, who hosted, respectively, SN 2013dy and SN 2018gv, twins of two different SNe Ia prototypes: SN 2013aa/SN 2017cbv and SN 2011fe. From the study of the SN 2013aa and SN 2017cbv twin pair, by comparing it with SN 2011fe and applying the difference between the SN 2013aa/2017cbv and the SN 2011fe class, we also find a good estimate of the distance to NGC 5643. We have just started to measure distances with the method for the samples in W.L Freedman et al. and A. G. Riess et al. There are differences in measured distances to the same galaxy using Cepheids or TRGBs. In this context of discrepancy, the “twins for life” method is very competitive because it can provide distance estimates with a modulus error of σ

μ
= 0.04 mag. Our findings called for a revision of the distances measured with Cepheids in A. G. Riess et al. (2022). NGC 7250 and NGC 2525 needed better measurements with Cepheids. We have noticed that the Cepheids-based distance obtained with the JWST in A. G. Riess et al. (2024a) for NGC 5643 is in good agreement with what we find, unlike their previous estimate in A. G. Riess et al. (2022). The Hubble tension can arise from the way in which the local SNe Ia sample is linked to the SNe Ia Hubble flow sample. A good calibration of SNe Ia in the local sample is needed and we have started to gather it. We also expect to apply the “twin” SNe Ia comparison from the local sample to that in galaxies with z >0.02–0.03 well into the Hubble flow to obtain a reliable value for H
0. Those distant SNe Ia can be observed with the ELT or the JWST.
High-precision Galaxy Clustering Predictions from Small-volume Hydrodynamical Simulations via Control Variates
Authors: Doytcheva Alexandra , Gerou Filomela V. , Lange Johannes U. 
Published: December 2024

Abstract
Cosmological simulations of galaxy formation are an invaluable tool for understanding galaxy formation and its impact on cosmological parameter inference from large-scale structures. However, their high computational cost is a significant obstacle for running simulations that probe cosmological volumes comparable to those analyzed by contemporary large-scale structure experiments. In this work, we explore the possibility of obtaining high-precision galaxy clustering predictions from small-volume hydrodynamical simulations such as MillenniumTNG and FLAMINGO via control variates. In this approach, the hydrodynamical full-physics simulation is paired with a matched low-resolution gravity-only simulation. By learning the galaxy–halo connection from the hydrodynamical simulation and applying it to the gravity-only counterpart, one obtains a galaxy population that closely mimics the one in the more expensive simulation. One can then construct an estimator of galaxy clustering that combines the clustering amplitudes in the small-volume hydrodynamical and gravity-only simulations with clustering amplitudes in a large-volume gravity-only simulation. Depending on the galaxy sample, clustering statistic, and scale, this galaxy clustering estimator can have an effective volume of up to around 100 times the volume of the original hydrodynamical simulation in the nonlinear regime. With this approach, we can construct galaxy clustering predictions from existing simulations that are precise enough for mock analyses of next-generation large-scale structure surveys such as the Dark Energy Spectroscopic Instrument and the Legacy Survey of Space and Time.
Emergence of the Galaxy Morphology–Star Formation Activity–Clustercentric Radius Relations in Galaxy Clusters
Authors: Hong Sungwook E. , Park Changbom , Mishra Preetish K. , Kim Juhan , Gibson Brad K. , Kim Yonghwi , Few C. Gareth , Pichon Christophe , Shin Jihye , Lee Jaehyun 
Published: December 2024

Abstract
We investigate when and how the relations of galaxy morphology and star-forming activity with clustercentric radius become evident in galaxy clusters. We identify 162 galaxy clusters with total mass

M

tot

cl

>
5
×

10

13

M

⊙

at z = 0.625 in the Horizon Run 5 cosmological hydrodynamical simulation and study how the properties of the galaxies with stellar mass M
* > 5 × 109
M
⊙ near the cluster main progenitors have evolved in the past. Galaxies are classified into disk, spheroid, and irregular morphological types according to the asymmetry and Sérsic index of their stellar mass distribution. We also classify galaxies into active and passive ones depending on their specific star formation rate. We find that the morphology–clustercentric radius relation (MRR) emerges at z ≃ 1.8 as the fraction of spheroidal types exceeds 50% in the central region (d ≲ 0.1R
200). Galaxies outside the central region remain disk-dominated. Numerous encounters between galaxies in the central region seem to be responsible for the morphology transformation from disks to spheroids. We also find that the star formation activity–clustercentric radius relation emerges at an epoch different from that of MRR. At z ≃ 0.8, passive galaxies start to dominate the intermediate radius region (0.1 ≲ d/R
200 ≲ 0.3), and this “quenching region” grows inward and outward thereafter. The region dominated by early-type galaxies (spheroids and passive disks) first appears at the central region at z ≃ 1.8, expands rapidly to larger radii as the population of passive disks grows in the intermediate radii, and clusters are dominated by early types after z ≃ 0.8.
Zeeman Spectral Line Fitting
Authors: Hsu Jui-Teng , Jagannathan Preshanth 
Published: December 2024

Abstract
The Zeeman effect plays a pivotal role as an observational technique for directly assessing the magnitude of magnetic fields within star-forming regions. Over time, Zeeman effect analysis has primarily relied upon software tools such as the Astronomical Image Processing System and MIRIAD. These tools necessitate manual involvement in the analysis and line fitting processes. This study delves into two potential automated methodologies for Zeeman analysis: one grounded in a least squares framework and the other embracing the maximum-likelihood approach. Our investigation reveals that the utilization of Markov Chain Monte Carlo, anchored in the maximum-likelihood framework, results in improved spectral line fitting outcomes with more favorable residuals. Concluding our efforts, we have encapsulated the automated analysis procedure within a software package, called Zeeman_modelling, installable through pip, accompanied by a straightforward command-line interface.
First record of the tetrablemmid armoured spiders (Araneae, Tetrablemmidae) from Xizang, China, with description of a new species
Authors: Zhou Yang , Zhang Xiaoqing , Tong Yanfeng 
Published: December 2024

The spider genus Choiroblemma Bourne, 1980 of the family Tetrablemmidae currently contains two species, C. bengalense Bourne, 1980 and C. rhinoxunum Bourne, 1980. Both species are known from West Bengal, India.
A new species of Choiroblemma is described from Xizang, China, Choiroblemma metok sp. nov. (♂♀). Morphological descriptions, photomicroscopy images of the new species and a distribution map of Choiroblemma species are given.