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Jurnal Algoritma, Logika dan Komputasi
Published by Universitas Bunda Mulia
ISSN : 2620620X     EISSN : 26219840     DOI : http://dx.doi.org/10.30813/j-alu.v1i1.1107
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal Algoritma, Logika dan Komputasi (Jurnal ALU) adalah jurnal Program Studi Teknik Informatika, yang berisikan kumpulan hasil penelitian dosen, penelitian dosen dan mahasiswa, penelitian mahasiswa yang disusun dalam bentuk artikel penelitian. Jurnal Algoritma, Logika dan Komputasi(Jurnal ALU) adalah jurnal Program Studi Teknik Informatika, yang berisikan kumpulan hasil penelitian dosen, penelitian dosen dan mahasiswa, penelitian mahasiswa yang disusun dalam bentuk artikel penelitian.
Arjuna Subject : Ilmu Komputer (semua kategori) - Teori Komputasi dan Matematika
Articles 6 Documents
 1 
Search results for , issue "Vol 7, No 1 (2024): Maret 2024" : 6 Documents clear
GRADIENT BOOSTING TREES UNTUK PEMODELAN DAN PREDIKSI BIAYA KERUGIAN ASURANSI MOBIL Fammaldo, Eric; Lestari, Merryana; Hermawan, Chandra
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.6030

Abstract

Gradient Boosting is a machine learning algorithm that combines several simple parameter functions that aim to predict a fairly accurate information from existing data. In contrast to statistical methods in general, this Gradient boosting provides interpretable information, while requiring little data preprocessing and tuning of parameters. Boosting Gradient can be applied to classify or regress data, complex interaction is modeled simply and minimizes loss of information while in predictor management, so this algorithm is good enough to be used for modeling the cost of insurance loss. This paper presents the GB theory and its application to the problem of predicting '' at-fault '' accidents on auto loss costs using data from Canadian insurance companies. The predictive accuracy of the model is compared to the conventional Generalized Linear Model (GLM) approach.Gradient Boosting is a machine learning algorithm that combines several simple parameter functions that aim to predict a fairly accurate information from existing data. In contrast to statistical methods in general, this Gradient boosting provides interpretable information, while requiring little data preprocessing and tuning of parameters. Boosting Gradient can be applied to classify or regress data, complex interaction is modeled simply and minimizes loss of information while in predictor management, so this algorithm is good enough to be used for modeling the cost of insurance loss. This paper presents the GB theory and its application to the problem of predicting '' at-fault '' accidents on auto loss costs using data from Canadian insurance companies. The predictive accuracy of the model is compared to the conventional Generalized Linear Model (GLM) approach.
IMPLEMENTASI ALGORITMA NAIVE BAYES TERHADAP DIAGNOSA GEJALA PENYAKIT MATA KATARAK BERBASIS WEBSITE Danuputri, Chyquitha; Jonathan, Vincent
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.5974

Abstract

This research have a purpose to analyse an eye disease. One of the many eye diseases is Cataract. The symptoms that are felt can help determine what type of cataract you are felt so that you can find a fast and effective way of eye treatment. For cataract itself, the Expert System uses the Naive Bayes method to classify the symptoms we experienced so that it helps patients and medical personnel to predict what type of cataract they suffer from. A website-based Expert System requires the role of the Informatics Engineering profession to assist health workers. Where the use of the website itself facilitates access for patients / patients and medical personnel in predicting cataracts suffered by patients / patients based on the symptoms felt. If cataract disease is treated early because the type of cataract suffered has been found based on the symptoms felt, then the right healing method can be done quickly so that cataract disease can be treated as soon as possible. So it will increase the success rate and reduce the risk in the eye treatment process.
PREDIKSI KEBANGKRUTAN MENGGUNAKAN JARINGAN SARAF BUATAN Petra, Stradivarius Melvin; Suryantara, I Gusti Ngurah; Tampinongkol, Felliks Feiters
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.6037

Abstract

The worst thing about financial failure is bankruptcy. The bankruptcy of a company can be analyzed from financial statements. the results of financial statement analysis is very useful for corporate leaders and investors to know the true condition of the company. Financial statement analysis can be done by calculating financial ratios. This study uses five variable financial ratios to predict corporate bankruptcy with repeated neural networks that apply Elman model. The sample data used in this study are 50 companies listed on the IDX 2007-2010 period. data is divided into two groups, 80% for training data and 20% for test data. Based on the function obtained from the training data, 10 companies will be tested. The best results from testing show that 9 out of 10 got the correct data. 
IMPLEMENTASI APRIORI PADA PENJUALAN BARANG DENGAN METODE ASOSIASI UNTUK STRATEGI MARKETING Putra, Josef Cristian Adi; Sipayung, Evasaria Magdalena
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.5991

Abstract

Technological developments have led to significant changes in various sectors, including business. The way of trading has also gone digital through e-commerce platforms and social media. Business competition is getting tougher with the emergence of many startups. Entrepreneurs must innovate in order to survive the fierce competition. Association analysis is used in Data mining to find rules for combining items. The advantage of this technique lies in the use of efficient algorithms through high-frequency pattern analysis or frequent pattern mining. This algorithm examines candidate itemsets that evolve from the results of frequency itemsets through support-based pruning, to eliminate insignificant itemsets with a Minimum Support value of 1. The Apriori algorithm association method is used to determine item relationships and identify consumer buying patterns, as well as help entrepreneurs increase product sales. This research proves the effectiveness of the Apriori algorithm in managing transaction data and generating valuable information for companies. This research provides input to companies that want to utilize transaction data to improve business effectiveness. The main goal of the Apriori algorithm is to find itemsets that frequently co-occur in the data. The algorithm adopts a bottom-up approach, where smaller itemsets are analyzed first and larger itemsets are built from smaller itemsets. The steps in creating itemsets using the association method include problem identification, transaction data collection, itemset identification, determining the Minimum Support and confidence values, and establishing association rules. This research develops an application that calculates the Apriori algorithm with the associative method through a calculation table and a summary of the calculation results. After testing, the application shows accurate calculation results and can be checked manually. The drawback of this application is that the notification of errors in the data is only displayed one by one.
MEMPREDIKSI PENINGKATAN H-INDEKS UNTUK JURNAL PENELITIAN DENGAN MENGGUNAKAN ALGORITMA COST-SENSITIVE SELECTIVE NAIVE BAYES CLASSIFIERS Henglie, Reycardo; Purnomo, Yunianto; Ginting, Jusia Amanda
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.6028

Abstract

Machine learning community is not only interested in maximizing classification accuracy, but also in minimizing the distances between the actual and the predicted class. Some ideas, like the cost-sensitive learning approach, are proposed to face this problem. In this paper, we propose two greedy wrapper forward cost-sensitive selective naive Bayes approaches. Both approaches readjust the probability thresholds of each class to select the class with the minimum-expected cost. The first algorithm (CSSNB-Accuracy) considers adding each variable to the model and measures the performance of the resulting model on the training data. The variable that most improves the accuracy, that is, the percentage of well classified instances between the readjusted class and actual class, is permanently added to the model. In contrast, the second algorithm (CS-SNB-Cost) considers adding variables that reduce the misclassification cost, that is, the distance between the readjusted class and actual class. We have tested our algorithms on the bibliometric indices prediction area. Considering the popularity of the well-known h-index, we have researched and built several prediction models to forecast the annual increase of the h-index for Neurosciences journals in a four-year time horizon. Results show that our approaches, particularly CS-SNB-Accuracy, achieved higher accuracy values than the analyzed cost sensitive classifiers and Bayesian classifiers. Furthermore, we also noted that the CS-SNB-Cost always achieved a lower average cost than all analyzed cost-sensitive and cost-insensitive classifiers. These cost sensitive selective naive Bayes approaches outperform the selective naive Bayes in terms of accuracy and average cost, so the cost-sensitive learning approach could be also applied in different probabilistic classification approaches.
MODEL KLASIFIKASI HIBRIDA BARU DARI JARINGAN SYARAF TIRUAN DAN MODEL REGRESI LINIER BERGANDA Valerian, Andre; Honni, Honni
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.6029

Abstract

This paper examines a more accurate and broader classification model and has significant implications in these fields. Combining multiple models or using hybrid models has become common practice to overcome the shortcomings of a single model and can be a more effective way to improve its predictive performance, especially when the models are in very different combinations. In this paper, a new hybridization of artificial neural networks (ANN) is proposed using multiple linear regression models to produce more accurate models than traditional artificial neural networks for solving classification problems. Empirical results show that the proposed hybrid model shows to effectively improve classification accuracy compared to traditional artificial neural networks and also several other classification models such as linear discriminant analysis, quadratic discriminant analysis, and vector machine using benchmarks and real-world application datasets. These datasets vary in number of classes and data sources. Therefore, it can be applied as a suitable alternative approach to solve classification problems, especially when higher forecasting accuracy is required.

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