Relevant bibliographies by topics / J48 decision tree

Journal articles
Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'J48 decision tree'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'J48 decision tree.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "J48 decision tree"

Lutfiyani, Rizka Safitri, and Niken Retnowati. "IMPLEMENTASI PENDETEKSIAN SPAM EMAIL MENGGUNAKAN METODE TEXT MINING DENGAN ALGORITMA NAÏVE BAYES DAN DECISION TREE J48." Jurnal Komputer dan Informatika 9, no. 2 (2021): 244–52. http://dx.doi.org/10.35508/jicon.v9i2.5304.

Full text

Abstract:

Email cukup populer sebagai salah satu media komunikasi digital. Hal tersebut dikarenakan proses pengiriman pesan dengan email yang mudah. Sayangnya, kebanyakan pesan dalam email adalah email spam. Spam adalah pesan yang tidak diinginkan penerima pesan karena spam biasanya berisi pesan iklan maupun pesan penipuan. Ham adalah pesan yang diinginkan penerima pesan. Salah satu cara untuk menyortir pesan-pesan tersebut adalah dengan melakukan pengklasifikasian pesan email menjadi spam maupun ham. Naïve Bayes dan decision tree J48 ialah algoritma yang dapat digunakan untuk mengklasifikasikan pesan email. Oleh karena itu, penelitian ini bertujuan membandingkan efektifitas algoritma Naïve Bayes dan decision tree J48 dalam penyortiran email spam. Metode yang digunakan adalah text mining. Data yang berisi teks pesan email berbahasa Inggris akan diproses terlebih dahulu sebelum diklasifikasikan dengan Naïve Bayes dan decision tree J48. Tahap pra proses tersebut meliputi tokenisasi, pembuangan stop word list, stemming, dan seleksi atribut. Selanjutnya, data teks pesan email akan diproses dengan algoritma Naïve Bayes dan decision tree J48. Algoritma Naïve Bayes adalah algoritma pengklasifikasi yang berdasarkan pada teori keputusan Bayesian sedangkan algoritma decision tree J48 ialah pengembangan dari algoritma decision tree ID3. Hasil penelitian ini adalah algoritma decision tree J48 mendapat akurasi yang lebih tingggi dari algoritma Naïve Bayes. Algoritma decision tree J48 mendapat 93,117% sedangkan Naïve Beyes memiliki akurasi 88,5284%. Kesimpulan dari penelitian ini adalah algoritma decision tree J48 lebih unggul dibanding Naive Bayes untuk menyortir email spam jika dilihat dari tingkat akurasi masing-masing algoritma.

APA, Harvard, Vancouver, ISO, and other styles

Tundo, Tundo, Shoffan Saifullah, Mesra Betty Yel, Opi Irawansah, Zulfikar Yusya Mubarak, and Andi Saidah. "Prediction of palm oil production using hybrid decision tree based on fuzzy inference system Tsukamoto." Bulletin of Electrical Engineering and Informatics 13, no. 6 (2024): 4182–92. http://dx.doi.org/10.11591/eei.v13i6.7773.

Full text

Abstract:

This research addresses the challenge of optimizing rule creation for palm oil production at PT Tapiana Nadenggan. It deals with the complexity of diverse agricultural variables, environmental factors, and the dynamic nature of palm oil production. The existing problem lies in the limitations of conventional decision tree models—J48, reduced error pruning (REP), and random—in capturing the nuanced relationships within the intricate palm oil production system. The study introduces hybrid decision tree models—specifically J48-REP, REP-Random, and Random-J48—to address this challenge via combination scenarios. This approach aims to refine and update the rule creation process, enabling the recognition of nuanced performance processes within the selected decision tree combinations. To comprehensively tackle this challenge and problem, the study employs Tsukamoto’s fuzzy inference system (FIS) for a sophisticated performance comparison. Despite the complexity, intriguing results emerge after the forecasting process, with the standalone J48 decision tree achieving 85.70% accuracy and the combined J48-REP excelling at 93.87%. This highlights the potential of decision tree combinations in overcoming the complexities inherent in forecasting palm oil production, contributing valuable insights for informed decision-making in the industry.

APA, Harvard, Vancouver, ISO, and other styles

Tundo, Tundo, and Shofwatul 'Uyun. "Perbandingan Decision Tree J48, REPTREE, dan Random Tree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit Menggunakan Fuzzy Tsukamoto." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 3 (2021): 473. http://dx.doi.org/10.25126/jtiik.2021833108.

Full text

Abstract:

<h2 align="center"> </h2>Penelitian ini menerangkan analisis decision tree J48, REPTree dan Random Tree dengan menggunakan metode fuzzy Tsukamoto dalam penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui decision tree mana yang hasilnya mendekati dari data sesungguhnya. Digunakannya decision tree J48, REPTree, dan Random Tree yaitu untuk mempercepat dalam pembuatan rule yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan rule yang digunakan. Berdasarkan data yang digunakan akurasi pembentukan rule dari decision tree J48 adalah 95,2381%, REPTree adalah 90,4762%, dan Random Tree adalah 95,2381%. Hasil dari penelitian yang telah dihitung bahwa metode fuzzy Tsukamoto dengan menggunakan REPTree mempunyai error Average Forecasting Error Rate (AFER) yang lebih kecil sebesar 23,17 % dibandingkan dengan menggunakan J48 sebesar 24,96 % dan Random Tree sebesar 36,51 % pada prediksi jumlah produksi minyak kelapa sawit. Oleh sebab itu ditemukan sebuah gagasan bahwa akurasi pohon keputusan yang terbentuk menggunakan tools WEKA tidak menjamin akurasi yang terbesar adalah yang terbaik, buktinya dari kasus ini REPTree memiliki akurasi rule paling kecil, akan tetapi hasil prediksi memiliki tingkat error paling kecil, dibandingkan dengan J48 dan Random Tree. Abstract<div>This study explains the J48, REPTree and Tree Random tree decision analysis using Tsukamoto's fuzzy method in determining the amount of palm oil production in PT Tapiana Nadenggan's company with the aim of finding out which decision tree results are close to the actual data. The decision tree J48, REPTree, and Random Tree is used to accelerate the making of rules that are used without having to consult with experts in determining the rules used. Based on the data used the accuracy of the rule formation of the J48 decision tree is 95.2381%, REPTree is 90.4762%, and the Random Tree is 95.2381%. The results of the study have calculated that the Tsukamoto fuzzy method using REPTree has a smaller Average Forecasting Error Rate (AFER) rate of 23.17% compared to using J48 of 24.96% and Tree Random of 36.51% in the prediction of the amount of palm oil production. Therefore an idea was found that the accuracy of decision trees formed using WEKA tools does not guarantee the greatest accuracy is the best, the proof of this case REPTree has the smallest rule accuracy, but the predicted results have the smallest error rate, compared to J48 and Tree Random.</div>

APA, Harvard, Vancouver, ISO, and other styles

Tundo, Tundo, and Shofwatul 'Uyun. "Penerapan Decision Tree J48 dan Reptree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit menggunakan Metode Fuzzy Tsukamoto." Jurnal Teknologi Informasi dan Ilmu Komputer 7, no. 3 (2020): 483. http://dx.doi.org/10.25126/jtiik.2020731870.

Full text

Abstract:

Penelitian ini menerangkan penerapan decision tree J48 dan REPTree dengan menggunakan metode fuzzy Tsukamoto dengan objek yang digunakan adalah penentuan jumlah produksi minyak kelapa sawit di perusahaan PT Tapiana Nadenggan dengan tujuan untuk mengetahui decision tree mana yang hasilnya mendekati dari data sesungguhnya sehingga dapat digunakan untuk membantu memprediksi jumlah produksi minyak kelapa sawit di PT Tapiana Nadenggan ketika proses produksi belum diproses. Digunakannya decision tree J48 dan REPTree yaitu untuk mempercepat dalam pembuatan rule yang digunakan tanpa harus berkonsultasi dengan para pakar dalam menentukan rule yang digunakan. Dari data yang digunakan akurasi dari decision tree J48 adalah 95.2381%, sedangkan akurasi REPTree adalah 90.4762%, akan tetapi dalam kasus ini decision tree REPTree yang lebih tepat digunakan dalam proses prediksi produksi minyak kelapa sawit, karena di uji dengan data sesungguhnya pada bulan Maret tahun 2019 menggunakan REPTree diperoleh 16355835 liter, sedangkan menggunakan J48 diperoleh 11844763 liter, dimana data produksi sesungguhnya sebesar 17920000 liter. Sehingga dapat ditemukan suatu kesimpulan bahwa untuk kasus ini data produksi yang mendekati dengan data sesungguhnya adalah REPTree, meskipun akurasi yang diperoleh lebih kecil dibandingkan dengan J48.Abstract<div>This study explains the application of the J48 and REPTree decision tree using the fuzzy Tsukamoto method with the object used is the determination of the amount of palm oil production in the company PT Tapiana Nadenggan with the aim of knowing which decision tree the results are close to the actual data so that it can be used to help predict the amount palm oil production at PT Tapiana Nadenggan when the production process has not been processed. The use of the J48 and REPTree decision tree is to speed up the rule making that is used without having to consult with experts in determining the rules used. From the data used the accuracy of the J48 decision tree is 95.2381%, while the REPTree accuracy is 90.4762%, but in this case the REPTree decision tree is more appropriate to be used in the prediction process of palm oil production, because it is tested with actual data in March 2019 uses REPTree obtained 16355835 liters, while using J48 obtained 11844763 liters, where the actual production data is 179,20000 liters. So that it can be found a conclusion that for this case the production data approaching the actual data is REPTree, even though the accuracy obtained is smaller compared to J48.</div>

APA, Harvard, Vancouver, ISO, and other styles

Ismanto, Heru, Azhari Azhari, Suharto Suharto, and Lincolin Arsyad. "Classification of the Mainstay Economic Region Using Decision Tree Method." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (2018): 1037. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp1037-1044.

Full text

Abstract:

The development of the region cannot be separated from the concept of economic growth and the determination of the mainstay region as a regional center that is expected to have a positive impact on economic growth to the surrounding regions. In fact, the determination of the mainstay region is a difficult thing to do. Some cases of the determination of the mainstay region are mostly on the basis of the prerogative rights of the policy makers without carefully seeing the achievements of the development of a region. The objective of this study is to develop a classification model of the mainstay economic region using computational techniques. The decision tree methods of NBTree and J48 are used in this study and combined with Klassen typology. The results of this study show that J48 algorithm has better accuracy than NBTree in the formation process of decision tree. The accuracy of J48 is higher than NBTree i.e. 68.96%. The comparative result of the classification of the mainstay economic region between Klassen and J48 shows that there is a shift in the class position of the development quadrant. In Klassen classification, there are three regions that are categorized into the mainstay regions with advanced development and rapid growth (K1). Meanwhile, J48 results show that there is no region categorized into K1. However, the mainstay economic region on J48 is based on the level of development with the level below K1, i.e. K2. J48 classification results show that there are ten regencies that are categorized into the mainstay economic regions, namely Biak, Regency of Jayapura, Jayawijaya, Kerom, Merauke, Mimika, Nabire, Ndunga, Yapen, and the Municipality of Jayapura.

APA, Harvard, Vancouver, ISO, and other styles

Garg, Sharvan Kumar, Deepak Kumar Sinha, and Nidhi Bhatia. "Performance of Hoeffding Tree and C4.5 Algorithms to Envisage an Occurrence of Hepatitis–A Liver Disease." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2423–29. http://dx.doi.org/10.1166/jctn.2020.8911.

Full text

Abstract:

Premature forecasting of hepatitis is extremely imperative to save an individual years and take appropriate steps to control the ailment. Decision Tree algorithms have been effectively useful in a variety of fields particularly in medicinal discipline. This manuscript investigates the premature forecasting of hepatitis by means of a variety of decision tree algorithms. In this manuscript, we build up a Hepatitis prediction model that can aid medical experts in envisaging Hepatitis condition supported on the medicinal data of patients. At the outset, we have chosen 19 imperative medicinal attributes viz., age, sex, antivirals, steroid, fatigue, anorexia, malaise, spleen palpable, etc., in addition to one target class. Secondly, we build up a prediction model using Pruned C4.5-J48 Decision Tree, Unpruned C4.5-J48, Reduced Error Pruned C4.5-J48 and Hoeffding Tree algorithms classifier for classifying Hepatitis based on these clinical attributes. Lastly, the precision of Pruned J48 decision tree approach proves to be more superior then the other approaches. Outcome acquired illustrates that Albumin and Ascites are the foremost predictive attributes which provides enhanced classification in opposition to the supplementary attributes.

APA, Harvard, Vancouver, ISO, and other styles

Rahman, Aviv Yuniar. "Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 4 (2021): 688–96. http://dx.doi.org/10.29207/resti.v5i4.3210.

Full text

Abstract:

Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficulty distinguishing the types of lovebirds. This research is needed to improve previous study performance in classifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case, also to reduce the stage of feature extraction to speed up the computational process. Based on available comparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000, and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841, and an accuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 can improve performance and produce the best accuracy.

APA, Harvard, Vancouver, ISO, and other styles

Tundo, Tundo, and Shofwatul 'Uyun. "Konsep Decision Tree Reptree untuk Melakukan Optimasi Rule dalam Fuzzy Inference System Tsukamoto." Jurnal Teknologi Informasi dan Ilmu Komputer 9, no. 3 (2022): 513. http://dx.doi.org/10.25126/jtiik.2022922601.

Full text

Abstract:

Penelitian ini menjelaskan tentang decision tree REPTree dalam membuat suatu rule yang terbentuk dari produksi minyak kelapa sawit di PT Tapiana Nadenggan, yang dipengaruhi oleh faktor banyaknya kelapa sawit, permintaan yang ada, serta persediaan yang tersedia. Konsep dari decision tree REPTree adalah konsep awal dari decision tree J48 yang kemudian mengalami pemangkasan kembali, sehingg rule yang yang terbentuk lebih minimal dan praktis. Rule yang minimal dan praktis belum tentu dapat dikatakan terbaik, untuk membuktikan hal itu perlu adanya uji coba dan pembuktian. Pembuktian yang dilakukan dalam penelitian ini salah satunya dengan menggunakan perbandingan decision tree J48 dan Random Tree dengan tujuan untuk mengetahui optimasi rule yang terbentuk dengan menggunakan metode fuzzy inference system Tsukamoto, setelah dihitung bahwa decision tree REPTree mempunyai Average Forecasting Error Rate (AFER) yang lebih kecil sebesar 23,17% dengan nilai kebenaran 76,83%, sedangkan J48 memiliki tingkat error sebesar 24,96%, dengan nilai kebenaran 75,04%, sementara Random Tree memiliki tingkat error sebesar 36,51%, dengan nilai kebenaran 63,49% pada kasus prediksi produksi minyak kelapa sawit di PT Tapiana Nadenggan. Abstract<div>This research explains about REPTree's decision tree in making a rule that is formed from the production of palm oil in PT Tapiana Nadenggan, which is influenced by factors of the amount of palm oil, existing demand, and available supplies. The concept of the REPTree decision tree is the initial concept of the J48 decision tree which then experiences pruning, so that the rules formed are more minimal and practical. A minimum and practical rule may not be the best, to prove that there is a need for trials and proofs. Proof carried out in this research is one of them by using a comparison of decision trees J48 and Random Tree with the aim to find out the optimization of rules formed using the Tsukamoto system's fuzzy inference method, after calculating that the REPTree decision tree has a more average Forecasting Error Rate (AFER) error tree small of 23.17% with a truth value of 76.83%, while J48 has an error rate of 24.96%, with a truth value of 75.04%, while Random Tree has an error rate of 36.51%, with a truth value of 63, 49% in the case of prediction of palm oil production at PT Tapiana Nadenggan.</div>

APA, Harvard, Vancouver, ISO, and other styles

Maulana, Mohamad Firman, and Meriska Defriani. "Logistic Model Tree and Decision Tree J48 Algorithms for Predicting the Length of Study Period." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 8, no. 1 (2020): 39–48. http://dx.doi.org/10.33558/piksel.v8i1.2018.

Full text

Abstract:

One point to be assessed in the accreditation process in an institution is the length of the student's study period. The Informatics department in XYZ college has been accredited by the national accreditation bureau for higher education (BAN-PT), but the accreditation has the potential to be improved. One thing that affects the accreditation value is many students did not graduate on time. Therefore, the current study used available student data, both academic and non-academic, using data mining. Two model classifications were used, i.e. Logistic Model Tree (LMT) and Decision Tree J48. The study was aimed to compare LMT and Decision Tree J48 algorithm in predicting the length of student’s study and to find out the influence factors. The data were Informatics Engineering students who have graduated in February 2018 to February 2019 (135 records). Results showed that the LMT algorithm produced an accuracy rate of 71% better than Decision Tree J48 (62.8% accuracy) in predicting the length of the student’s study. The factors influencing the length of study of students are temporary grade point average (GPA) of the first semester, temporary GPA of the second semester, organizational status, and employment status.

APA, Harvard, Vancouver, ISO, and other styles

Asim Shahid, Muhammad, Muhammad Mansoor Alam, and Mazliham Mohd Su’ud. "A fact based analysis of decision trees for improving reliability in cloud computing." PLOS ONE 19, no. 12 (2024): e0311089. https://doi.org/10.1371/journal.pone.0311089.

Full text

Abstract:

The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms: AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the Central Processing Unit CPU-Mem Multi classifier has the highest accuracy percentage and the least amount of fault prediction. This holds for the Decision Tree (J48) classifier with an accuracy rate of 89.71% for 80/20, 90.28% for 70/30, and 92.82% for 10-fold cross-validation. Additionally, the Hard Disk Drive HDD-Mono classifier has an accuracy rate of 90.35% for 80/20, 92.35% for 70/30, and 90.49% for 10-fold cross-validation. The AdaBoostM1 classifier was found to have the highest accuracy percentage and the least amount of fault prediction for the HDD Multi classifier with an accuracy rate of 93.63% for 80/20, 90.09% for 70/30, and 88.92% for 10-fold cross-validation. Finally, the CPU-Mem Mono classifier has an accuracy rate of 77.87% for 80/20, 77.01% for 70/30, and 77.06% for 10-fold cross-validation. Based on the primary data results, the Naive Bayes Tree (NB Tree) classifier is found to have the highest accuracy rate with less fault prediction of 97.05% for 80/20, 96.09% for 70/30, and 96.78% for 10 folds cross-validation. However, the algorithm complexity is not good, taking 1.01 seconds. On the other hand, the Decision Tree (J48) has the second-highest accuracy rate of 96.78%, 95.95%, and 96.78% for 80/20, 70/30, and 10-fold cross-validation, respectively. J48 also has less fault prediction but with a good algorithm complexity of 0.11 seconds. The difference in accuracy and less fault prediction between NB Tree and J48 is only 0.9%, but the difference in time complexity is 9 seconds. Based on the results, we have decided to make modifications to the Decision Tree (J48) algorithm. This method has been proposed as it offers the highest accuracy and less fault prediction errors, with 97.05% accuracy for the 80/20 split, 96.42% for the 70/30 split, and 97.07% for the 10-fold cross-validation.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "J48 decision tree"

Sao, Pedro Michael A. "Real-time Assessment, Prediction, and Scaffolding of Middle School Studentsâ€™ Data Collection Skills within Physical Science Simulations." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/168.

Full text

Abstract:

Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students' skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students' developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models' predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students' inquiry skills. The ability to evaluate students' inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students' inquiry.

APA, Harvard, Vancouver, ISO, and other styles

Χαλέλλη, Ειρήνη. "Σχεδίαση και ανάπτυξη ολοκληρωμένου συστήματος δυναμικής ανάλυσης και πρόβλεψης της επίδοσης εκπαιδευόμενων σε συστήματα ανοιχτής και εξ' αποστάσεως εκπαίδευσης". Thesis, 2014. http://hdl.handle.net/10889/8335.

Full text

Abstract:

Η ραγδαία ανάπτυξη και διείσδυση των νέων τεχνολογιών πληροφορίας και επικοινωνίας έχει επιφέρει ριζικές αλλαγές σε όλους τους τομείς της ανθρώπινης δράσης (Castells, 1998). Ιδιαίτερο ενδιαφέρον παρουσιάζει η επιρροή των τεχνολογιών αυτών στον τομέα της εκπαίδευσης. Οι εξελίξεις στον χώρο της τεχνολογίας και επικοινωνίας καθώς και η διάδοση του Internet μετεξέλιξαν αναπόφευκτα την εκπαιδευτική διαδικασία, από το κλασσικό συγκεντρωτικό μοντέλο σε ένα πιο άμεσο και ευέλικτο: η «εξ’ Αποστάσεως Εκπαίδευση» (e-learning) είναι μια εναλλακτική μορφή εκπαίδευσης, που επιδιώκει να καλύψει τους περιορισμούς της παραδοσιακής εκπαίδευσης. Στην παρούσα μεταπτυχιακή διπλωματική εργασία σχεδιάστηκε και υλοποιήθηκε ένα ολοκληρωμένο σύστημα Δυναμικής Ανάλυσης και Πρόβλεψης της επίδοσης των εκπαιδευομένων, για ένα σύστημα εξ΄ αποστάσεως εκπαίδευσης. Η βασική ιδέα εμφορείται από την ανάγκη των ιδρυμάτων εξ΄ αποστάσεως εκπαίδευσης, για την κάλυψη των εκπαιδευτικών αναγκών και την παροχή υψηλής ποιότητας σπουδών. Η εξόρυξη γνώσης για την πρόβλεψη της επίδοσης των εκπαιδευομένων συμβάλλει καθοριστικά στην επίτευξη υψηλής ποιότητας σπουδών. Η ικανότητα και η δυνατότητα πρόβλεψης της απόδοσης των εκπαιδευομένων μπορεί να φανεί χρήσιμη με αρκετούς τρόπους για την διαμόρφωση ενός συστήματος, που θα μπορεί να αποτρέψει την αποτυχία καθώς και την παραίτηση των εκπαιδευομένων. Αξίζει να σημειωθεί ότι στα συστήματα εξ’ αποστάσεως εκπαίδευσης η συχνότητα «εγκατάλειψης» είναι αρκετά υψηλότερη από αυτή στα συμβατικά πανεπιστήμια. Για την πρόβλεψη της επίδοσης των εκπαιδευομένων, η απαιτούμενη πληροφορία βρίσκεται «κρυμμένη» στο εκπαιδευτικό σύνολο δεδομένων (δλδ. βαθμοί γραπτών εργασιών, βαθμοί τελικής εξέτασης, παρουσίες φοιτητών) και είναι εξαγώγιμη με τεχνικές εξόρυξης. Η χρήση μεθόδων εξόρυξης δεδομένων (data mining) στον τομέα της εκπαίδευσης παρουσιάζει αυξανόμενο ερευνητικό ενδιαφέρον. Ο νέος αυτός «αναπτυσσόμενος» τομέας έρευνας, που ονομάζεται «Εκπαιδευτική Εξόρυξη Δεδομένων», ασχολείται με την ανάπτυξη μεθόδων εξόρυξης «γνώσης» από τα εκπαιδευτικά σύνολα δεδομένων. Πράγμα που επιτυγχάνεται με τη χρήση τεχνικών όπως τα δέντρα απόφασης, τα Νευρωνικά Δίκτυα, Naïve Bayes, k-means, κλπ. Η παρούσα εργασία έχει σχεδιαστεί να προσφέρει ένα μοντέλο εξόρυξης δεδομένων χρησιμοποιώντας τη μέθοδο των δέντρων απόφασης, για το σύστημα τριτοβάθμιας εκπαίδευσης στο ανοιχτό πανεπιστήμιο. Η «γνώση» που προκύπτει από τα δεδομένα εξόρυξης θα χρησιμοποιηθεί με στόχο την διευκόλυνση και την ενίσχυση της μάθησης, καθώς επίσης και στη λήψη αποφάσεων. Στην παρούσα εργασία, εξάγουμε «γνώση» που σχετίζεται με τις επιδόσεις των μαθητών στην τελική εξέταση. Επίσης, γίνεται εντοπισμός των ατόμων που εγκαταλείπουν το μάθημα και των μαθητών που χρειάζονται ιδιαίτερη προσοχή και εντέλει δίνει τη δυνατότητα στους καθηγητές να παράσχουν την κατάλληλη παροχή συμβουλών. The rapid development and intrusion of information technology and communications have caused radical changes in all sectors of human’s activity. (Castells, 1998). Of particular interest is the great technology’s influence on education. Due to the adoption of the new technologies, e-learning has been emerged and developed. As a result, distance learning has transformed and new possibilities have appeared. It is remarkable that distance learning became and considered as a scout of the new era in education and contributed to the quality of education: e-learning is trying to cover the limitations of conventional teaching environment. In the present thesis, an integrated system of dynamic analysis and prediction of the performance of students in distance education has been designed and implemented. The initial idea for designing this system came from the higher distance education institutes’ need to provide quality education to its students and to improve the quality of managerial decisions. One way to achieve highest level of quality in higher distance education e-learning system is by discovering knowledge from educational data to study the main attributes that may affect the students’ performance. The discovered knowledge can be used to offer a helpful and constructive recommendations to the academic planners in higher distance education institutes to enhance their decision making process, to improve students’ academic performance, trim down failure rate and dropout rate, to assist instructors, to improve teaching and many other benefits. Dropout rates in university level distance learning are definitely higher than those inconventional universities, thus limiting dropout is essential in university-level distance learning.

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "J48 decision tree"

Khruahong, Sanya, and Pirayu Tadkerd. "Analysis of Scholarship Consideration Using J48 Decision Tree Algorithm for Data Mining." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60816-3_26.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Arslantas, Mustafa Kemal, Tunc Asuroglu, Reyhan Arslantas, et al. "Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_1.

Full text

Abstract:

AbstractSerum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.

APA, Harvard, Vancouver, ISO, and other styles

Saxena, Vivek, Upasna Singh, and L. K. Sinha. "Landslide Susceptibility Mapping Using J48 Decision Tree and Its Ensemble Methods for Rishikesh to Gangotri Axis." In Data Management, Analytics and Innovation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Tien Bui, Dieu, Tien Chung Ho, Inge Revhaug, Biswajeet Pradhan, and Duy Ba Nguyen. "Landslide Susceptibility Mapping Along the National Road 32 of Vietnam Using GIS-Based J48 Decision Tree Classifier and Its Ensembles." In Cartography from Pole to Pole. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32618-9_22.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Luu, Chinh, Duc-Dam Nguyen, Tran Van Phong, Indra Prakash, and Binh Thai Pham. "Using Decision Tree J48 Based Machine Learning Algorithm for Flood Susceptibility Mapping: A Case Study in Quang Binh Province, Vietnam." In Lecture Notes in Civil Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7160-9_195.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Pal, Sujata, Anik Saha, Priyanka Gogoi, and Sunil Saha. "An Ensemble of J48 Decision Tree with AdaBoost and Bagging for Flood Susceptibility Mapping in the Sundarbans of West Bengal, India." In Disaster Risk Reduction. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7707-9_7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Tien Bui, Dieu, Biswajeet Pradhan, Inge Revhaug, and Chuyen Trung Tran. "A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithm and J48 Decision Tree Models in Spatial Prediction of Shallow Landslides at Lang Son City, Vietnam." In Society of Earth Scientists Series. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05906-8_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Brunello, Andrea, Enrico Marzano, Angelo Montanari, and Guido Sciavicco. "J48S: A Sequence Classification Approach to Text Analysis Based on Decision Trees." In Communications in Computer and Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99972-2_19.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Gambhir, Shalini, Yugal Kumar, Sanjay Malik, Geeta Yadav, and Amita Malik. "Early Diagnostics Model for Dengue Disease Using Decision Tree-Based Approaches." In Pre-Screening Systems for Early Disease Prediction, Detection, and Prevention. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7131-5.ch003.

Full text

Abstract:

Classification schemes have been applied in the medical arena to explore patients' data and extract a predictive model.This model helps doctors to improve their prognosis, diagnosis, or treatment planning processes.The aim of this work is to utilize and compare different decision tree classifiers for early diagnosis of Dengue. Six approaches, mainly J48 tree, random tree, REP tree, SOM, logistic regression, and naïve Bayes, have been utilized to study real-world Dengue data collected from different hospitals in the Delhi, India region during 2015-2016. Standard statistical metrics are used to assess the efficiency of the proposed Dengue disease diagnostic system, and the outcomes showed that REP tree is best among these classifiers with 82.7% efficient in supplying an exact diagnosis.

APA, Harvard, Vancouver, ISO, and other styles

Abdul Bujang, Siti Dianah, Ali Selamat, and Ondrej Krejcar. "Predictive Modeling for Student Grade Prediction Using Machine Learning and Visual Analytics." In Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques. IOS Press, 2020. http://dx.doi.org/10.3233/faia200550.

Full text

Abstract:

Data-driven plays an important role in determining the quality of services in institutions of higher learning (HEIs). Increasingly data in education is encouraging institutions to find ways to improve student academic performance. By using machine learning with visual analytics, data can be predicted based on valuable information and presented with interactive visualizations for institutions to improve decision making. Therefore, predicting students’ academic performance is critical to identifying students at risk of failing a course. In this paper, we propose two approaches, such as (i) a prediction model for predicting students’ final grade based on machine learning that interacts with computational models; (ii) visual analytics to visualize predictive models and insightful data for educators. The data were tested using student achievement records collected from one of the Malaysian Polytechnic databases. The data set used in this study involved 489 first semester students in Computer System Architecture (CSA) course from 2016 to 2019. The decision tree algorithms (J48), Random Tree (RT), Random Forest (RF), and REPTree) was used on the student data set to produce the best predictions of the model. Experimental results show that J48 returns the highest accuracy with 99.8 %, among other algorithms. The findings of this study can help educators predict student success or failure for a particular course at the end of the semester and help educators make informed decisions to improve student academic performance at Polytechnic Malaysia.

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "J48 decision tree"

Du, Guang. "Intelligent Assessment of College Students' Mental Health based on Kendall’s Rank Correlation Coefficient Based J48 Decision Tree." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934250.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Sahu, Shailendra, and B. M. Mehtre. "Network intrusion detection system using J48 Decision Tree." In 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015. http://dx.doi.org/10.1109/icacci.2015.7275914.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Al-Khudafi, Abbas M., Hamzah A. Al-Sharifi, Ghareb M. Hamada, Mohamed A. Bamaga, Abdulrahman A. Kadi, and A. A. Al-Gathe. "Evaluation of Different Tree-Based Machine Learning Approaches for Formation Lithology Classification." In International Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/igs-2023-0026.

Full text

Abstract:

Abstract This study aims to assess the effectiveness of several decision tree techniques for identifying formation lithology. 20966 data points from 4 wells were used to create the study's data. Lithology is determined using seven log parameters. The seven log parameters are the density log, neutron log, sonic log, gamma ray log, deep latero log, shallow latero log, and resistivity log. Different decision tree-based algorithms for classification approaches were applied. six typical machine learning models, namely the, Random Forest. Random trees, J48, reduced-error pruning decision trees, logistic model trees, HoeffdingTree were evaluated for formation lithology identification using well logging data. The obtained results shows that the random forest model, out of the proposed decision tree models, performed best at lithology identification, with precession, recall, and F-score values of 0.913, 0.914, and 0.913, respectively. Random trees Random trees came next. With average precision, recall, and F1-score of 0.837, 0.84, and 0.837, respectively, the J48 model came in third place. The HoeffdingTree classification model, however, showed the worst performance. We conclude that boosting strategies enhance the performance of tree-based models. Evaluation of prediction capability of models is also carried out using different datasets.

APA, Harvard, Vancouver, ISO, and other styles

Posonia, A. Mary, S. Vigneshwari, and D. Jamuna Rani. "Machine Learning based Diabetes Prediction using Decision Tree J48." In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS). IEEE, 2020. http://dx.doi.org/10.1109/iciss49785.2020.9316001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Canlas, Ranie Baul, Keno Cruz Piad, Ace Carpio Lagman, et al. "Predicting Faculty Research Productivity using J48 Decision Tree Algorithm." In ICIT 2021: IoT and Smart City. ACM, 2021. http://dx.doi.org/10.1145/3512576.3512624.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Aashkaar, Mohammed, Purushottam Sharma, and Naveen Garg. "Performance analysis using J48 decision tree for Indian corporate world." In 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS). IEEE, 2016. http://dx.doi.org/10.1109/rains.2016.7764377.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Nie, Chun yan, Ju Wang, Fang He, and Reika Sato. "Application of J48 Decision Tree Classifier in Emotion Recognition Based on Chaos Characteristics." In 2015 International Conference on Automation, Mechanical Control and Computational Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/amcce-15.2015.330.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Adnan, Masrur, Riyanarto Sarno, and Kelly Rossa Sungkono. "Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm." In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2019. http://dx.doi.org/10.1109/isemantic.2019.8884282.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Hermawan, Dicky Rahma, Mohamad Fahrio Ghanial Fatihah, Linda Kurniawati, and Afrida Helen. "Comparative Study of J48 Decision Tree Classification Algorithm, Random Tree, and Random Forest on In-Vehicle CouponRecommendation Data." In 2021 International Conference on Artificial Intelligence and Big Data Analytics (ICAIBDA). IEEE, 2021. http://dx.doi.org/10.1109/icaibda53487.2021.9689701.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Senthilnayaki, B., K. Venkatalakshmi, and A. Kannan. "An intelligent intrusion detection system using genetic based feature selection and Modified J48 decision tree classifier." In 2013 Fifth International Conference on Advanced Computing (ICoAC). IEEE, 2013. http://dx.doi.org/10.1109/icoac.2013.6921918.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

Contents

Academic literature on the topic 'J48 decision tree'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Journal articles on the topic "J48 decision tree"

Dissertations / Theses on the topic "J48 decision tree"

Book chapters on the topic "J48 decision tree"

Conference papers on the topic "J48 decision tree"