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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Trikonomika: Jurnal Ekonomi Altasia : Jurnal Pariwisata Indonesia Indonesian Journal of Electrical Engineering and Computer Science
Doni Purnama Alamsyah
Bina Nusantara University
Humanities Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media

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Preferensi Periklanan Digital Dukungannya Pada Minat Berkunjung Turis Indriana Indriana; Dimas Yudistira Nugraha; Rudy Aryanto; Doni Purnama Alamsyah
Altasia Jurnal Pariwisata Indonesia Vol 4 No 2 (2022): Jurnal ALTASIA (Agustus)
Publisher : Program Studi Pariwisata - Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/altasia.v4i2.6846

Abstract

Dalam menarik perhatian turis untuk berkunjung ke destinasi wisata, banyak yang dilakukan oleh manajemen destinasi wisata, diantaranya melaksanakan pemasaran dengan iklan melalui digital platform. Periklanan digital diyakini memberikan dampak besar pada minat turis untuk berkunjung, mengingat perkembangan teknologi semakin meningkat dan pengguna teknologi semakin menyebar. Periklanan digital yang dilakukan oleh manajemen destinasi wisata dapat diakses dengan mudah melalu digital platform terlebih pengguna social media. Dimana social media melekat pada smartphone yang saat ini sudah digunakan dan menjadi bagian pending dari aktifitas pada turis sebelum memilih destinasi wisata. Menelaah dari pentingnya iklan sebagai media pemasaran digital, maka tujuan penelitian ini mengevalusai hubungan dari preferensi turis pada periklnana digital dalam mendukung minat berkunjung turis pada destinasi wisata. Metode penelitian yang digunakan adalah survey, dimana survey dilakukan pada pengguna smartphone yang familiar dengan periklanan digital. Pengguna yang dipilih adalah pengguna yang telah mengetahui adanya distinasi wisata yang akan dikunjungi. Dilakukan penyebaran kuesioner dengan kuantitatif, guna mengumpulkan data dari pengguna dan dianalisis lebih lanjut. Proses pengolahan data menggunakan SPSS untuk mengetahui hubungan antar variabel. Dalam analisisnya dilakukan moderasi dari jenis kelamin pengguna atau turis yaitu pria dan wanina. Hal ini diguanakan untuk mengetahui perilaku turis lebih mendalam. Hasil penelitian dikatahui bahwa preferensi periklanan digital dari turis memberikan dampak pada minat berkunjung turis. Hal ini diketahui dari hubungan positif diantara keduanya yang cukup erat dan dipertegas dengan hasil uji signifikansi melalui uji hipotesis. Temuan lain diketahui bahwa turis yang memiliki jenis kelamin pria lebih dominan beradaptasi dengan periklanan digital, diketahui dari perbandingan hubungan periklanan digital pada minat berkunjung tulis lebih tinggi dibandingkan dengan turis wanita. Temuan dari penelian ini memiliki manfaat untuk dikaji oleh manajemen destinasi wisata, dimana karakteristik dari pengguna digital platform mampu mempengaruhi minat dari turis.
Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin Yudi Ramdhani; Dhia Fauziah Apra; Doni Purnama Alamsyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp192-199

Abstract

Grapes are one of the fruit plants that grow that propagate in certain fields. Grapes can be processed into juice, wine, raisins, and so on. Raisins are dried grapes. Raisins have a distinctive taste and aroma. Raisins are a concentrated and nutritious source of carbohydrates, containing antioxidants, potassium, fiber and iron. To increase the accuracy value, the optimize selection genetic algorithm (GA) is used. This research was conducted modeling using the support vector machine (SVM) and SVM algorithms based on optimize selection GA by using the raisin (raisin varieties) dataset obtained from the UCI machine learning repository. The research dataset is divided into training data and testing data. The data sharing will be carried out using the cross validation and split validation operators. Data validation with 10-Fold-validation on the SVM algorithm has the best level of performance among 5 other algorithms such as; Naïve Bayes, K-nearest neighbor (K-NN), decision tree (DT), neural network, and random forest (RF). The SVM algorithm produces accuracy and area under the curve (AUC) values of 87.11% for accuracy and 0.928 for AUC. Optimization in this study using optimize selection GA. SVM based on optimize selection GA produces accuracy and AUC values of 87.67% for accuracy and 0.930 for AUC.
Heart failure prediction based on random forest algorithm using genetic algorithm for feature selection Yudi Ramdhani; Cakra Mahendra Putra; Doni Purnama Alamsyah
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp205-214

Abstract

A disorder or illness called heart failure results in the heart becoming weak or damaged. In order to avoid heart failure early on, it is crucial to understand the causes of heart failure. Based on validation, two experimental processing steps will be applied to the dataset of clinical records related to heart failure. Testing will be done in the first step utilizing six different classification algorithms, including K-nearest neighbor, neural network, random forest, decision tree, Naïve Bayes, and support vector machine (SVM). Cross-validation was employed to conduct the test. According to the results, the random forest algorithm performed better than the other five algorithms in tests employing the algorithm. Subsequent testing uses an algorithm with the best accuracy value, which will then be tested again using split validation with varying split ratios and genetic algorithms as a selection feature. The value generated from testing using the genetic algorithm selection feature is better than the random forest algorithm alone, which is recorded to produce an accuracy value of 93.36% in predicting the survival of heart failure patients.
WORK-LIFE BALANCE TO DECREASE WORK-FAMILY CONFLICT DURING THE COVID-19 PANDEMIC Ali Amran; Meiliani Luckieta; Doni Purnama Alamsyah; Adi Suparwo
TRIKONOMIKA Vol 21 No 1 (2022): June Edition
Publisher : Faculty of Economics and Business, University of Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.021 KB) | DOI: 10.23969/trikonomika.v21i1.4491

Abstract

This study aims to review the support of work-life balance and work-from-home to work-family conflict. The research method was conducted through a survey of 110 employees from several cities in Indonesia. The Data obtained through the questionnaire was analyzed with the Structural Equation Modeling approach. The analysis tool used in this research is Lisrel. The research results found that work-from-home has an effect on work-life balance, and work-life balance can decrease work-family conflict. Furthermore, work-from-home is not able to directly decrease work-family conflict. The research found that work-family conflict can be indirectly controlled by work-fromhome through the mediation of work-life balance. The importance of work-life balance in mediating work-from-home and work-family conflict raises a mediation model that is a mediation model of work-life balance to decrease work-family conflict. This information is useful for companies in evaluating work-family conflict through the implementation of work-from-home.
Co-Authors Adi Suparwo Ali Amran Cakra Mahendra Putra Dhia Fauziah Apra Dimas Yudistira Nugraha Indriana Indriana Meiliani Luckieta Rudy Aryanto Yudi Ramdhani