Garuda - Garba Rujukan Digital

Analisis Sentimen Publik pada Media Sosial Twitter Terhadap Tiket.com Menggunakan Algoritma Klasifikasi Budiman, Budiman; Silvana Anggraeni, Zulmeida; Habibi, Chairul; Alamsyah, Nur
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.17988

Analisis sentimen merupakan proses identifikasi emosional seseorang terhadap suatu objek yang akan menghasilkan sentimen positif, negatif dan netral. Kemajuan teknologi ini tentu memberikan pengaruh terhadap berbagai pelaku bisnis untuk saling mengintegrasikan sistem bisnisnya satu sama lain, salah satunya Tiket.com. Hal tersebut tentu menghasilkan sentimen dari masyarakat Indonesia yang diunggah pada platform media sosial Twitter, sehingga membantu individu maupun organisasi dalam mengambil keputusan. Penelitian ini dilakukan untuk mengetahui klasifikasi sentimen masyarakat Indonesia terhadap Tiket.com menggunakan algoritma Naïve Bayes Classifier (NBC), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) dan Random Forest (RF). Berdasarkan perhitungan data sentimen terhadap Tiket.com terdapat 90.3% sentimen positif dan 9.7% sentimen negatif. Persentase tersebut menunjukkan bahwa Tiket.com cukup berpengaruh positif terhadap penggunanya. Berdasarkan hasil pengujian algoritma klasifikasi, diketahui NBC memperoleh tingkat akurasi sebesar 88%, KNN dengan nilai k = 11 mendapatkan akurasi sebesar 91%, SVM menghasilkan tingkat akurasi sebesar 92%, dan tingkat akurasi RF mencapai 93% dengan n_estimators = 100. Kesimpulan pada penelitian ini, Random Forest merupakan algoritma yang memiliki tingkat akurasi paling tinggi dibanding dengan algoritma klasifikasi lain.

COMPARISON ANALYSIS OF SAW AND SMART METHODS DECISION SUPPORT SYSTEMS IN SELECTIONS ONLINE TRANSPORTATION IN CITY COMMUNITIES BANDUNG Habibi, Chairul; Yasyfa, Irtsa; Budiman, Budiman
International Journal of Global Operations Research Vol. 4 No. 3 (2023): International Journal of Global Operations Research (IJGOR), August 2023
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v4i3.246

Online transportation is transportation based on a particular application, where consumers order a means of transportation through an application system on a smartphone. This change in lifestyle is used by business actors to start business competition in the online transportation business. In the competition regarding price, convenience, driver courtesy, and satisfaction, there are various kinds of online transportation including Gojek, Grab, Maxim, and InDriver. In the end, this becomes a consideration for the selection of online transportation for the people of Bandung City. To overcome these problems, a Decision Support System was developed using 5 criteria including: Ease, Service Quality, Product Quality, Price and Emotional. Comparative analysis between the Simple Additive Weighting (SAW) and Simple Multi Attribute Technique (SMART) methods was conducted to find out how well the level of relevance of each method was to real conditions. Based on the ranking results from the questionnaire of 100 respondents from the Bandung City community, it is known that the distance difference between the rankings in the SAW method is 0.994 and the SMART method is 0.92. Thus, it can be concluded that the SAW method is considered relatively more relevant to be recommended in this type of case than the SMART method.

I-Pos Information System Security Audit Using Framework Control Objectives for Information and Related Technologies 2019 And Information Technology Infrastructure Library 4 Titan Parama Yoga; Habibi, Chairul; Aziz, Nizar Hizbi Abdul
Jurnal Computech & Bisnis (e-journal) Vol. 17 No. 2 (2023): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/jcb.v17i2.236

Information system security is used to protect against cyber attack crimes. Generally, cyber attacks occur because someone wants to intervene in a system to find out the confidentiality and availability of information. PT. Pos Indonesia is a company under the auspices of SOEs engaged in distributing letters and packages. Both domestic package distribution and overseas package distribution. To facilitate the delivery of packages, PT Pos Indonesia developed an information system called I-POS. Based on the results of the researchers' analysis, the I-POS information system is an information system that aims at mail and package delivery transactions, so using the I-POS information system can facilitate the process of delivery transactions, as well as provide accurate, timely, and relevant information. The purpose of this study is to determine the level of maturity of information system security in the field of I-POS information systems at PT. Pos Indonesia, Analyzing the findings and gaps of the level of maturity of the information system security. Based on the results of research that has been conducted through questionnaires using the COBIT 2019 framework with APO13 and DSS05 domains, it was found that the Existing Capability obtained was at level 2 while the expected Capability Level was at level 5 so the Capability Gap produced in these conditions was 3 levels

Sentiment Analysis Of Spotify App In Playstore Using Classification Method Akbar, Imannudin; Berly Bagoes Daniswara; Habibi, Chairul
Jurnal Computech & Bisnis (e-journal) Vol. 19 No. 1 (2025): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/jcb.v19i1.408

Spotify is a globally renowned music streaming program. The program receives a multitude of ratings, both favorable and unfavorable, on the Google Play Store from various users. This study intends to evaluate the sentiment of user evaluations for the Spotify application employing various classification techniques, including Logistic Regression, Random Forest, Support Vector Machine (SVM), C4.5, and Extreme Gradient Boosting (XGBoost). Review data was acquired via web scraping methodologies using the Google Play Scraper API. After this, text preparation was conducted to sanitize the text, enabling the execution of the data. Sentiment analysis was employed to ascertain whether a text expresses favorable or unfavorable opinions. The Random Forest approach, which has been demonstrated to yield optimal outcomes, was employed in this investigation. Testing was performed using training and test data ratios of 80:20%, 70:30%, and 60:40% across hundreds of review datasets. The Random Forest approach, utilizing an 80%:20% data split ratio, produced a precision of 82%, recall of 81%, F1-Score of 81%, and accuracy of 81%, according to the test findings

Strategi Digital untuk Agripreneur 4.0: Meningkatkan Pemasaran, Penjualan, dan Branding dalam Agribisnis Hendra, Acep; Habibi, Chairul; Ramadan, Diki; Mikala, Azka Khafifan
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.142

The development of digital technology presents both challenges and significant opportunities for the agribusiness sector in Indonesia, especially for farmers and small-scale agribusiness entrepreneurs. This community service initiative aims to provide understanding and skills related to digital strategies in marketing, sales, and branding to strengthen the competitiveness of agribusiness products. Through training and mentoring at SMKN PP Lembang, participants are empowered to leverage digital technology, such as social media marketing, SEO optimization, and the use of e-commerce platforms to expand their market reach and increase agribusiness sales. The results of this activity show an increased understanding among participants of the importance of digitalization in agribusiness and the implementation of effective marketing strategies to introduce products to a broader consumer base. However, key challenges faced include limited infrastructure and low digital literacy among some farmers and agribusiness actors. Therefore, ongoing support from the government and private sectors in terms of infrastructure and training is essential to support the digital transformation of the agribusiness sector.

Sentiment Analysis Of Spotify App In Playstore Using Classification Method Akbar, Imannudin; Daniswara, Berly Bagoes; Habibi, Chairul
Jurnal Computech & Bisnis (e-journal) Vol. 19 No. 1 (2025): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Spotify is a globally renowned music streaming program. The program receives a multitude of ratings, both favorable and unfavorable, on the Google Play Store from various users. This study intends to evaluate the sentiment of user evaluations for the Spotify application employing various classification techniques, including Logistic Regression, Random Forest, Support Vector Machine (SVM), C4.5, and Extreme Gradient Boosting (XGBoost). Review data was acquired via web scraping methodologies using the Google Play Scraper API. After this, text preparation was conducted to sanitize the text, enabling the execution of the data. Sentiment analysis was employed to ascertain whether a text expresses favorable or unfavorable opinions. The Random Forest approach, which has been demonstrated to yield optimal outcomes, was employed in this investigation. Testing was performed using training and test data ratios of 80:20%, 70:30%, and 60:40% across hundreds of review datasets. The Random Forest approach, utilizing an 80%:20% data split ratio, produced a precision of 82%, recall of 81%, F1-Score of 81%, and accuracy of 81%, according to the test findings.

Analysis of the Influence of Online Transportation Application Usage Using the EUCS Model in Measuring User Satisfaction in Bandung City: Case Study: InDrive Users Akbar, Imannudin; Anggraeni, Hilma; Habibi, Chairul; Nugraha, Arif Bakti
Jurnal Computech & Bisnis (e-journal) Vol. 18 No. 2 (2024): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/615a5517

The swift advancement of technology has profoundly influenced the transportation industry, especially in digital transportation services. InDrive provides a Peer-to-Peer transportation service for passengers, wherein travel conditions are established through agreements between passengers and drivers. This study seeks to assess user happiness with the InDrive application in Bandung utilizing the End User Computing Happiness (EUCS) model. A quantitative methodology was utilized to gather data via online questionnaires from a sample of 201 active InDrive customers in Bandung who have utilized online transportation services. The hypotheses were tested using SEM-PLS analysis with SmartPLS 3.0 software. The findings indicate that four factors—content, Accuracy, Ease of Use, and Timeliness—positively and significantly affect user satisfaction. One of the five proposed hypotheses was rejected: the Format variable had no significant impact on user satisfaction, evidenced by a t-statistic of 0.701, below the t-table value of 1.972, and a p-value of 0.484, surpassing the significance threshold of 0.05. This research highlights the necessity to improve the aspects that influence user happiness. Enhancing Content, Accuracy, Usability, and Timeliness will enable InDrive to improve service quality and user experience. The minimal influence of the Format variable indicates a necessity for additional research, offering critical insights for players in the online transportation industry to enhance their services and align more closely with user expectations.

Comparative Analysis of Community Sentiment Against the Implementation of Booster Vaccination in Indonesia Using the K-Nearest Neighbor and Naïve Bayes Classifier Methods Budiman, Budiman; Wulandari, Wulandari; Habibi, Chairul
International Journal of Ethno-Sciences and Education Research Vol. 3 No. 3 (2023): International Journal of Ethno-Sciences and Education Research (IJEER)
Publisher : Research Collaboration Community (Rescollacom)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijeer.v3i3.462

Sentiment analysis is a person's opinion or view of a particular object that produces positive, negative, or neutral sentiments. The government's effort during the COVID-19 pandemic is to call for the implementation of a booster vaccination program to the public. Based on this, it produces several public sentiments, some of which are uploaded on the Twitter social media platform, which generate positive and negative sentiments. To find out the classification of public sentiment, the researchers carried out calculations using the K-Nearest Neighbor and Naïve Bayes Classifier methods. Based on the calculation results, it was found that the public sentiment was positive at 98% and negative at 2%. This means that the community is enthusiastic and supports the booster vaccination program. Then the comparison based on the calculation results, namely the K-Nearest Neighbor method with a K value of 3 resulting in an accuracy calculation of 97.33% and using the Naïve Bayes Classifier method, an accuracy calculation of 97.35% can be generated. So it can be seen that using the Naïve Bayes Classifier method has a higher accuracy than the K-Nearest Neighbor method.

Title

Found 8 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 8 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 8 Documents
Search