LPPM - STMIK WIDURI (E-Journals)
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PENGARUH SOCIAL MEDIA MARKETING PADA APLIKASI HALODOC MELALUI PERCEIVED VALUE
Full text linkThe purpose of this research is to see, test and analyze the influence of Social Media Marketing on Percieved Value, Purchase Intention, Willingness to Pay Premium Price, and Electronic Word of Mouth as well as the positive influence of Percieved Value on Purchase Intention, Willingness to Pay Premium Price, and Electronic Word of Mouth and the positive influence of Social Media Marketing on Purchase Intention, Willingness to Pay Premium Price, and Electronic Word of Mouth mediated by Perceived Value. Data was obtained from the results of a questionnaire distributed to 200 respondents who had purchased Halodoc services twice in the last 3 months via social media. Data testing was carried out using Structural Equation Model (SEM) analysis. The results of this research show that Social Media Marketing has a positive influence on Purchase Intention. Social Media Marketing has a positive influence on Willingness to Pay Premium Price. Social Media Marketing has a positive influence on Electronic Word of Mouth. Social Media Marketing has a positive influence on Perceived Value. Perceived Value has a positive influence on Purchase Intention. Perceived Value has a positive influence on Willingness to Pay Premium Price. Perceived Value has a positive influence on Electronic Word of Mouth. Perceived Value mediates the positive influence of Social Media Marketing on Purchase Intention. Perceived Value mediates the positive influence of Social Media Marketing on Willingness to Pay Premium Price. Perceived Value mediates the positive influence of Social Media Marketing on Electronic Word of Mouth
PENGARUH CAPITAL ADEQUACY RATIO, LOAN TO DEPOSIT RATIO, NON-PERFORMING LOAN, BEBAN OPERASIONAL PENDAPATAN OPERASIONAL DAN NET INTEREST MARGIN TERHADAP PROFITABILITAS
Full text linkResearch has the intention of testing the influence of capital adequacy ratio, loan to deposit ratio, non-performing loans, operating expense operating revenue and net interest margin on the profitability of banking companies. This research collects data using financial reports and annual reports from 2021 to 2023 on banking companies listed on the Indonesia Stock Exchange. The number of samples used in the research was 34 banking companies. Descriptive statistical analysis and multiple regression analysis are used as analysis methods. The results show that capital adequacy ratio and net interest margin have a significant positive influence on profitability, while non-performing loans and operating expenses operating revenue have a significant negative influence on profitability. Loan to deposit ratio does not have a significant influence on profitability. Simultaneously, all independent variables have an effect on the dependent variable
ANALISIS SENTIMEN OPINI DEBAT CALON PRESIDEN DENGAN MENGGUNAKAN CLASSIFIER MACHINE LEARNING (STUDI KASUS : PADA DATA TWITTER 2024)
Full text linkThis 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.
APLIKASI TES POTENSI AKADEMIK BERBASIS MOBILE UNTUK SELEKSI PPDB SMK BINAWIYATA SRAGEN
Full text linkThis study aims to explore how effective and how accepted by the community the use of mobile-based academic potential test applications in the PPDB selection process at SMK Binawiyata Sragen. By utilizing technological advances, this application allows evaluation results to be provided instantly and can be accessed by students and parents at any time through an online system that uses a database. This study uses a prototype method, which involves several stages such as Requirement and Analysis, Modeling Quick Design, Construction of Prototype, and Deployment Delivery, Data was collected by conducting literature studies, observations, and interviews, the results of the study revealed that this application provides efficiency and transparency compared to conventional selection methods. In addition, the results of the application evaluation showed positive responses from users, making it an effective tool in the PPDB selection. ABSTRAKPenelitian ini bertujuan untuk mengeksplorasi seberapa efektif dan seberapa diterimanya oleh masyarakat penggunaan aplikasi tes potensi akademik berbasis mobile dalam proses seleksi PPDB di SMK Binawiyata Sragen. Dengan memanfaatkan kemajuan teknologi, aplikasi ini memungkinkan hasil evaluasi diberikan secara instan dan bisa diakses oleh siswa dan orang tua kapan saja melalui sistem online yang menggunakan database. Penelitian ini menggunakan metode prototype, yang melibatkan beberapa tahapan seperti Requirement and Analysis, Modeling Quick Design, Construction of Prototype, dan Deployment Delivery, Data dikumpulkan dengan melakukan studi pustaka, observasi, dan wawancara, hasil penelitian mengungkapkan bahwa aplikasi ini memberikan efisiensi dan transparansi dibandingkan metode seleksi konvensional. Selain itu, hasil evaluasi aplikasi menunjukkan tanggapan positif dari pengguna, menjadikannya sebagai alat yang efektif dalam seleksi PPDB
MENGOPTIMALKAN ORACLE SPASIAL UNTUK ANALISIS KEDEKATAN GEOGRAFIS
Full text linkThe study explores the utilization of Oracle Spatial in the determination of the shortest path between locations. This is very important for the analysis and management of geographic information. Its ability to handle multiple attributes, rasters, and vectors, is greatly enhanced by its support for data management. The objective of the study is to find the closest locations of Binus BB S and Binus Square to investigate the use of Oracle's Geospatial technology. The research entails populating a table with geometry clusters, performing spatial queries, and capturing data. The study utilized SDO_GEOMETERY and SDO_NN to find the Binus Square and BBS campuses' closest locations. The findings reinforced Oracle Spatial's practicality and accuracy for proximity analysis, emphasizing the significance of maintaining and managing such databases. The study also identified an issue which suggests that the product could be improved in the future. The integration of its features with other Oracle applications can provide more effective management and visualization of spatial data. The study highlighted Oracle Spatial's potential to support complex spatial analyses and improve the operations of spatial databases. ABSTRAKPenelitian ini mengeksplorasi pemanfaatan Oracle Spatial dalam penentuan jalur terpendek antar lokasi. Hal ini sangat penting untuk analisis dan pengelolaan informasi geografis. Kemampuannya untuk menangani banyak atribut, raster, dan vektor, sangat ditingkatkan dengan dukungannya terhadap manajemen data. Tujuan penelitian adalah mencari lokasi terdekat Binus BBS dan Binus Square untuk mengetahui penggunaan teknologi Geospasial Oracle. Penelitian ini memerlukan pengisian tabel dengan cluster geometri, melakukan kueri spasial, dan menangkap data. Penelitian ini memanfaatkan SDO_GEOMETERY dan SDO_NN untuk mencari lokasi terdekat kampus Binus Square dan BBS. Temuan ini memperkuat kepraktisan dan akurasi Oracle Spatial untuk analisis kedekatan, menekankan pentingnya memelihara dan mengelola database tersebut. Studi ini juga mengidentifikasi masalah yang menunjukkan bahwa produk tersebut dapat ditingkatkan di masa depan. Integrasi fitur-fiturnya dengan aplikasi Oracle lainnya dapat memberikan pengelolaan dan visualisasi data spasial yang lebih efektif. Studi ini menyoroti potensi Oracle Spatial untuk mendukung analisis spasial yang kompleks dan meningkatkan pengoperasian database spasia
KOMPARASI HARGA TERHADAP HARGA APLIKASI OJEK ONLINE
Full text linkThe development of digital technology has changed people's habits, including in the use of online transportation. Innovation in online transportation services makes it easier for consumers and drivers to find each other's locations find out the driver's identity, vehicle type, and save time. However, competition between online transportation applications often makes consumers compare prices from one platform to another manually. This is time-consuming and makes consumers prefer applications that offer cheaper prices, even though it requires more effort. This study is a price comparison system based on Progressive Web Apps (PWA) that allows consumers to compare prices from several online transportation applications more efficiently. This PWA-based system can be accessed via desktop or mobile devices, providing greater ease of access. In developing this system, the Waterfall method is used, where users are involved from the early stages to provide feedback so that the system can be adjusted to their needs. Comparative analysis is carried out to find similarities and differences in prices between online transportation applications. This study focuses on two main problems: how to design a website that is connected to various online transportation applications, and how the price comparison system works efficiently. The purpose of this study is to provide a solution for consumers who often compare prices between platforms manually, as well as to form a PWA-based price comparison system that facilitates access via mobile devices. The benefits of this research include increasing efficiency for consumers in choosing online transportation services, as well as encouraging healthier competition between online transportation companies through more competitive price offers. The implementation of responsive PWA to compare prices in real-time, provides a better and more efficient user experience. ABSTRAKPerkembangan teknologi digital telah mengubah kebiasaan masyarakat, termasuk dalam penggunaan transportasi online. Inovasi dalam layanan transportasi online memudahkan konsumen dan pengemudi dalam saling menemukan lokasi, mengetahui identitas pengemudi, jenis kendaraan, serta menghemat waktu. Namun, persaingan antar aplikasi transportasi online sering kali membuat konsumen membandingkan harga dari satu platform ke platform lainnya secara manual. Hal ini memakan waktu dan membuat konsumen lebih memilih aplikasi yang menawarkan harga lebih murah, meskipun membutuhkan usaha lebih. Penelitian ini mengembangkan sebuah sistem komparasi harga berbasis Progressive Web Apps (PWA) yang memungkinkan konsumen untuk membandingkan harga dari beberapa aplikasi transportasi online dengan lebih efisien. Sistem berbasis PWA ini dapat diakses melalui desktop maupun perangkat mobile, memberikan kemudahan akses yang lebih luas. Dalam pengembangan sistem ini, digunakan metode Waterfall, di mana pengguna terlibat sejak tahap awal untuk memberikan umpan balik sehingga sistem dapat disesuaikan dengan kebutuhan mereka. Analisis komparatif dilakukan untuk menemukan persamaan dan perbedaan harga antar aplikasi transportasi online. Penelitian ini berfokus pada dua masalah utama: bagaimana merancang sebuah website yang terhubung dengan berbagai aplikasi transportasi online, dan bagaimana sistem komparasi harga bekerja secara efisien. Tujuan penelitian ini adalah memberikan solusi bagi konsumen yang kerap kali membandingkan harga antar platform secara manual, serta membentuk sistem komparasi harga berbasis PWA yang memudahkan akses melalui perangkat mobile. Manfaat dari penelitian ini meliputi peningkatan efisiensi bagi konsumen dalam memilih layanan transportasi online, serta mendorong persaingan yang lebih sehat antar perusahaan transportasi online melalui penawaran harga yang lebih kompetitif. Penerapan PWA yang responsif untuk membandingkan harga secara real-time, memberikan pengalaman pengguna yang lebih baik dan efisien
ANTESEDAN DAN KOSEKUENSI DARI ATTITUDE TERHADAP KOSMETIK HALAL
Full text linkThis research aims to analyze the influence of awareness of halal cosmetics users based on Halal Logo, Halal Brand Image, Halal Awareness, Religiosity, Attitude and Behavioral Intention. Data was obtained through distributing online questionnaires to consumers who use or buy halal cosmetic products. The research sample consisted of 200 respondents selected using purposive sampling techniques. The data analysis method used was Structural Equation Model (SEM) using AMOS 24 software. The results showed that of the nine hypotheses proposed, six hypotheses were accepted and three hypotheses were rejected. The rejected hypothesis is that Halal Logo does not have a positive influence on Attitude, Halal Brand Image does not have a positive influence on Behavioral Intention, and Halal Awareness does not have a positive influence on Behavioral Intention
PENGARUH GOOD CORPORATE GOVERNANCE, CORPORATE SOCIAL RESPONSIBILITY, INTELLECTUAL CAPITAL DAN GREEN BANKING TERHADAP NILAI PERUSAHAAN
Full text linkThe implementation of this research is aimed at evaluating the value of banking companies from 2020 to 2022 that have been listed on the Indonesia Stock Exchange as a result of being influenced by green banking, intellectual capital, corporate social responsibility, and good corporate governance. The data implemented in the study is in the form of sustainability and annual report data available on the official websites of each company and the IDX. The population applied in the study amounted to 47 banking companies. The number of samples used was 36 banking companies selected by applying a purposive sampling method based on certain criteria. The data analysis method applies Eviews-assisted panel data regression analysis. Based on data processing and analysis, it is obtained that corporate governance affects the value of banking companies. While green banking, intellectual capital, and corporate social responsibility do not affect the value of banking companies
PENGARUH CAPITAL STRUCTURE DAN COMPANY GROWTH TERHADAP NILAI PERUSAHAAN DENGAN GOOD CORPORATE COVERNANCE SEBAGAI VARIABEL MODERASI
Full text linkThis research was conducted to analyze the effect of capital structure and company growth on firm value with good corporate governance as a moderating variable. This study is focused on manufacturing companies in the consumer goods sector on the Indonesia Stock Exchange (IDX). Capital Structure and Company Growth are considered as key factors that can affect company value. GCG is also identified as an important element in improving company performance. Quantitative method with hypothesis testing approach is used in this research. The research sample consisted of 83 consumer non-cyclicals sector companies listed on the IDX in the 2021-2022 period. Data obtained from financial reports published by the IDX. Data analysis was performed using descriptive statistics and panel data regression using the fixed effect model (FEM) method. The results of the study show that capital structure and company growth have a significant positive effect on firm value. That is, the better the capital structure and company growth, the higher the company value. However, the implementation of GCG did not strengthen the effect of capital structure and company growth on company value
OPTIMALISASI KLASIFIKASI UJI EMISI SEPEDA MOTOR MENGGUNAKAN ALGORITMA NAÏVE BAYES
Full text linkDense 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%