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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 79 Documents
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PENGARUH USER INTERFACE RESPONSIVE MOBILE WEBSITE TERHADAP JUMLAH VISITOR WEBSITE PANDANHOUSE.COM Sinata, Frans
Jurnal Algoritma, Logika dan Komputasi Vol 6, No 2 (2023): Jurnal ALU, September 2023
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v6i2.4771

Abstract

The Human Centered Design (HCD) method is one of the methods in design and development that focuses on users according to their needs, habits and capabilities. The problem that is often faced is to access information on the pandanhouse website in terms of appearance that does not yet support the appearance of responsive web design on smartphone devices. The initial stages in this method start from the observation stage which aims to find out the problems faced by users in accessing the pandanhouse.com website to the testing stage to find out if the solutions provided can be understood and easily used by users. At the testing stage, the user is given tasks to interact directly with access to information on the Pandanhouse website through a smartphone device, as the final result of the test, it is expected that the user is sufficiently familiar and user friendly on the website on the responsive mobile website feature.
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.
PERAN INTERAKSI MANUSIA-KOMPUTER DALAM EVALUASI ANTARMUKA APLIKASI PEMESANAN MAKANAN ONLINE MELALUI SYSTEM USABILITY SCALE Haryanti, Marta Lenah; Figo, Russel; Indradjaja, Reynaldi; Susanto, Cornelius
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 2 (2024)
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i2.7658

Abstract

Kemajuan teknologi informasi dan komunikasi telah memengaruhi berbagai aspek kehidupan, termasuk cara memesan makanan secara daring. Interaksi Manusia-Komputer (IMK) menjadi komponen penting dalam pengembangan antarmuka aplikasi pemesanan makanan online yang efektif dan efisien. Tujuan dari penelitian ini adalah untuk mengembangkan rancangan desain dan mengevaluasi antarmuka aplikasi pemesanan makanan online dengan menggunakan pendekatan User-Centered Design (UCD) yang berfokus pada kebutuhan dan preferensi pengguna. Evaluasi usability yang diterapkan pada penelitian ini menggunakan System Usability Scale (SUS), yang bertujuan untuk menilai kemudahan penggunaan serta kepuasan pengguna terhadap aplikasi. Hasil evaluasi SUS menunjukkan skor sebesar 83,5, yang mengindikasikan bahwa usability aplikasi sangat baik, dengan mayoritas responden berusia antara 17 hingga 25 tahun. Kuesioner yang digunakan dalam penelitian ini juga dinyatakan valid dan reliabel, dengan nilai rhitung di atas 0,334 dan nilai Cronbach's Alpha sebesar 0,714. Hasil penelitian menunjukkan bahwa aplikasi yang dirancang berhasil memenuhi kebutuhan pengguna, memberikan pengalaman pengguna yang memuaskan, dan dapat diterima dengan baik oleh pengguna. Hal ini ditunjukkan oleh skor tinggi pada kategori Adjective yaitu ‘Excellent’, tingkat Acceptability yang dinilai ‘Acceptable’, serta Net Promoter Score (NPS) yang berada pada kategori ‘Promoter’. Penelitian ini menyimpulkan bahwa penerapan prinsip IMK dalam desain aplikasi pemesanan makanan online dapat meningkatkan kualitas antarmuka dan kepuasan pengguna secara signifikan.
APLIKASI SIMULASI PERCOBAAN WARNA PEWARNA BIBIR BERBASIS ANDROID MENGGUNAKAN TEKNOLOGI AUGMENTED REALITY Yosephine, Maria; Sinata, Frans; Agung, Halim
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 2 (2024)
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i2.6035

Abstract

Kecantikan merupakan hal penting bagi wanita, namun terdapat banyak masalah seputar kecantikan yang dialami wanita. Salah satu masalah yang kerap dialami adalah pemilihan warna pada pewarna bibir yang tidak sesuai, sehingga justru membuat penampilan tampak tidak maksimal. Hal ini biasanya terjadi dikarenakan tidak semua warna pewarna bibir cocok di semua warna kulit, sehingga pemilihan warna dalam produk pewarna bibir sangat krusial. Berdasarkan hasil analisa dari permasalahan yang didapatkan, diputuskan akan dibuat aplikasi yang dapat mensimulasikan percobaan pewarna bibir secara real-life dengan teknologi Augmented Reality (AR). Metode yang digunakan adalah Face Tracking dengan algoritma Concurrent Odometry and Mapping (COM) untuk melacak lalu menciptakan representasi virtual dari wajah. Algoritma COM memiliki akurasi tinggi dalam pelacakan dengan hasil yang stabil dan konsisten, sehingga walaupun terjadi pergerakan kepala hasil akan tetap stabil dan konsisten. Berdasarkan dari hasil uji coba implementasi metode untuk simulasi percobaan warna bibir, implementasi berhasil dilakukan dan simulasi dapat berjalan secara lancar dalam real-life.  Sehingga pengguna dapat menggunakan filter pewarna bibir yang tersedia dalam aplikasi untuk mencari tahu warna yang sesuai dengan kulit mereka. Hasil uji coba dari aplikasi menunjukkan bahwa aplikasi mampu memberikan
ENGINE HEALTH MONITORING PADA SEPEDA MOTOR BERBASIS ARDUINO MENGGUNAKAN ALGORITMA FUZZY INFERENCE SYSTEM TSUKAMOTO Joshua, Leonardo; Mulyana, Teady Matius Surya
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 2 (2024)
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i2.7765

Abstract

 Many motors suddenly stop working due to oil leaks in carburetor motors. Lubricant oil leaks can result in decreased engine performance, piston damage to breaking down in the middle of the road. Therefore, to prevent early engine damage, lubricant oil leak notification is needed. Leakage at a certain level can still be tolerated, but up to a certain level, the leak can no longer be tolerated, so good notification is needed to determine the severity of the oil leak. To determine how severe the oil leak is in the engine, precise measurements are needed. One solution offered is to use fuzzy Tsukamoto in determining lubricant oil leaks Based on temperature and origin parameters in the actual conditions that occur. In this study, it will be determined how much lubricant oil leakage occurs in motorcycles that have a carburetor system. Where the input to be used is temperature and smoke. With the respective membership degrees are normal, moderate and severe. While the output produced is in the form of repair recommendations, where for moderate repair suggestions it has a value that determines how much it needs to be repaired. While normal output does not need to be repaired, while the highest output is a suggestion to be repaired immediately without delay. The use of Tsukamoto fuzzy in determining the severity of leaks produces a fairly good accuracy of 96.67% of motorcycles that experience lubricant oil leaks can be detected correctly. 3.33% of errors lie in other causes that are not as specific.

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