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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Jurnal Abdimas Le Mujtamak Intelmatics Muhibbul Arabiyah: Jurnal Pendidikan Bahasa Arab
Binti Solihah
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Religion Computer Science & IT Dentistry Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Other

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Journal : Intelmatics

 1 
An ANALYSIS OF OIL SENTIMENT SENTIMENTS ON TWITTER USING SUPPORT VECTOR MACHINE: ANALISIS SENTIMEN SUBSIDI BAHAN BAKAR MINYAK (BBM) DI TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE Ibnu Bilal Marta Prawira; Binti Solihah; Syandra Sari
Intelmatics Vol. 3 No. 1 (2023): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v3i1.16187

Abstract

Twitter is one of the social media platforms used by people in Indonesia. Twitter is often used by its users to express opinions regarding a product, institution or event. From the keyword fuel, fuel subsidy is a keyword that is currently a trending topic because changes in fuel subsidies affect the prices of other staples, to find out the value of sentiment in public opinion, sentiment analysis is one of the methods used is the support vector machine and lexicon based. Lexicon is a labeling method by matching the words contained in the document with the words contained in the dictionary. After labeling, the data is tested using the classification method, the classification stage is carried out after going through the preprocessing phase, where the tweet classification results tend to be positive or negative, using the Support Vector Machine method and validated by K-Fold Cross Validation.This research produced 50,001 data which were divided into 21,561 positive sentiments, 9206 neutral sentiments and 19234 negative sentiments. From these results it can be concluded that the data shows public support for rising fuel prices or changing fuel subsidy prices.
Analysis Of Topic Movement & Conversation Membership On Twitter Using K-Means Clustering Sediyono, Agung; Valentino Hutagalung, Josua; Solihah, Binti
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.21002

Abstract

Humans are born to socialize with each other. Social media is one of the media to be able to socialize with each other. Twitter is one of the social media that contains hundreds of millions of tweets where the tweet contains news, products that are currently popular, even about the daily life of users who can change. Social Context Analysis is a tool to analyze social changes and individual needs in society from time to time. In this study, the author uses the K-means Clustering method to group topics on Twitter. In its implementation, this research is expected to be able to see the occurrence of topic movements and membership movements on Twitter topics.
Analisis Sentimen dan Pemodelan Topik Ulasan PengunjungObjek Wisata Pulau Bali pada Situs Tripadvisor MenggunakanMetode Lexicon-Based dan Latent Dirichlet Allocation (LDA) Aulia, Muhammad Azka; Solihah, Binti; Zuhdi, Ahmad
Intelmatics Vol. 5 No. 1 (2025): January-June
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i1.17619

Abstract

One sought-after type of information by internet users is related to tourist destinations. Hence, the need for information retrieval about a particular tourist spot they plan to visit. This study aims to analyze sentiments and identify the topics in the visitor reviews of Bali Island tourist attractions on TripAdvisor using Lexicon-based and Latent Dirichlet Allocation (LDA) methods. The data used for analysis consists of reviews from various tourist destinations on the island of Bali. For sentiment analysis, the author employs a Lexicon-based approach, focusing on both positive and negative sentiments. To identify the topics in the reviews, the author employs the LDA method to uncover the most frequently discussed topics. From 15,827 dataset, It is found that 87,6% of the responses are positive, 7.9% are negative, and the remaining 4.4% are neutral. As for the topic modeling results, the study identifies four main topics with the best coherence values based on the validation of topics with topic coherence. These four topics are: the first topic discusses experiences in Safari or Safari Park in Bali, the second topic talks about experiences in tourism in Kintamani, Bali, the third topic focuses on experiences in tourism in Nusa Penida, Bali, and the last topic discusses experiences in Scuba Diving activities
PERFORMANCE COMPARISON OF TWITTER SENTIMENT ANALYSIS USING FASTTEXT SVM AND TF-IDF SVM: A CASE STUDY ON ELECTRIC MOTORCYCLES Sulaba, Wishnu Abhinaya; Solihah, Binti; Sari, Syandra
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.18145

Abstract

Electric motorcycles are trending on Twitter as two-wheeled vehicles different from those using fossil fuels. Electric motorcycles rely on batteries charged using electricity. However, there are many opinions about electric motorcycles on social media, especially Twitter. Yet, tweets and comments on Twitter often contain irrelevant words that can affect sentiment analysis. In this study, sentiment analysis was conducted on 8,000 data from Twitter using FastText and TF-IDF as word embedding techniques, along with Support Vector Machine (SVM) as the classification technique. The aim of this research is to compare the performance of SVM using different feature extraction techniques, namely FastText and TF-IDF. The results of this study are expected to be beneficial for electric vehicle manufacturers and individuals interested in electric vehicles. In this comparison, the performance of TF-IDF and FastText feature extraction in sentiment classification with SVM will be evaluated. SVM performance is assessed based on accuracy, precision, recall, and F1-score for each feature extraction technique used. The test results show an average accuracy above 83%, with the highest values being 86% for accuracy, 79% for precision, 52% for recall, and 58% for F1-score.  
COMPARATIVE SENTIMENT ANALYSIS OF VISITOR REVIEWS FOR WATERBOM BALI TOURIST ATTRACTION ON TRIPADVISOR SOCIAL MEDIA USING RANDOM FOREST AND NAÏVE BAYES CLASSIFICATION Hilmi, Hilmi Abdul Gani; Solihah, Binti; Sari, Syandra
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i1.19278

Abstract

With the advancement of technology, especially the internet, the role of the internet as the primary source of information in global life is becoming increasingly crucial. This is particularly true in the context of searching for information about tourist destinations before visiting them. TripAdvisor is a website designed for searching travel destinations and attractions. On this platform, users can provide reviews and see comments from other travelers regarding various tourist destinations, including Waterbom Bali. To gain insights into visitors' perspectives and enhance services for them, the overwhelming number of reviews can be analyzed for sentiment to understand whether travelers' views tend to be positive, negative, or neutral. In this research, the Random Forest and Naïve Bayes methods are employed to conduct sentiment analysis. Scraping data from Waterbom Bali resulted in a dataset of 5750 entries. Despite data imbalance after labeling positive, negative, and neutral sentiments, class imbalance techniques will be applied. The sentiment analysis method, comparing Random Forest and Naïve Bayes, is implemented using the Word2Vec feature extraction method to evaluate its effectiveness. Experimental results show significant differences between the two methods. In Random Forest, after undersampling, an accuracy of 24% was obtained, while oversampling resulted in an accuracy of 98%. Meanwhile, for Multinomial Naïve Bayes, after undersampling, an accuracy of 36% was achieved, and oversampling yielded an accuracy of 97%.
The Opportunity of Ai Technology to Increase The Value Chain of Oil Palm Plantation Sediyono, Agung; Solihah, Binti
Intelmatics Vol. 5 No. 1 (2025): January-June
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i1.22477

Abstract

Indonesia produced 58,4% of worldwide oil palm production, and the contribution of the plantation sub-sector in 2022 is 3,76% of PDB and 30,32% of the Agriculture, Forestry, and Fishery sectors. However, oil palm production in Indonesia is lack of productivity and efficiency compared to other countries, especially Malaysia.  Therefore, this paper tries to explore the opportunities of AI technology to increase the value chain of the oil palm plantation, especially in productivity and efficiency. The scope of exploration started from oil palm seeding, nursery,  planting,  and harvesting. Based on the oil palm plantation value chain review and the previous research works in AI implementation on value chain respectively, it can be concluded that AI technology has been explored to be implemented in oil palm plantations intensively. However, there is still enough room for improvement especially in accuracy rate and adoption feasibility for smallholder planters. Moreover, IoT and drone technology have a big potential to be adopted because the plantation is mostly hard-to-reach areas by humans, for instance high oil palm bunch, long distance journey for inspection and maintenance, wild animal threat, etc.  
The Role of the Project Owner in Agile Project Management: A Case Study of the Kinerjapro Application Development at PT.Menara Indonesia Wibowo, Nurafni Revita; Syaifudin; Solihah, Binti
Intelmatics Vol. 5 No. 2 (2025): July-December
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v5i2.23821

Abstract

This study aims to design a web-based project management information system for monitoring internships at PT Menara Indonesia. The background of the study is the Merdeka Belajar Kampus Merdeka (MBKM) program, which enhances students' competencies in the workforce, as well as the challenges of manual internship monitoring, such as the risk of losing project records, limited storage, and inefficiency in communication and project coordination. The system is developed using the Agile Scrum method, following 5 sprints that include Product Backlog, Sprint Planning, Daily Scrum, Sprint Review, and Sprint Retrospective. The features developed include login, project and task management, messaging system, team member management, and basic report and event features. The Scrum implementation improves operational efficiency and data transparency. The result is a web-based project management information system that overcomes manual monitoring issues, increases efficiency, and facilitates better coordination among teams at PT Menara Indonesia, providing faster and more transparent information access.
Co-Authors Ahmad Hanafi Ahmad Zuhdi Ahmad Zuhdi Alfred Pakpahan Anung B Ariwibowo Ariwibowo, Anung B Aulia, Muhammad Azka Dedy Sugiarto Dimmas Mulya Hilmi, Hilmi Abdul Gani Ibnu Bilal Marta Prawira Is Mardianto, Is Najih, Muhammad Ratna Shofiati Rianti Dewi Sulamet-Ariobimo Sari Irmadani Sari, Syandra Sediyono, Agung Suhartati Agoes Sulaba, Wishnu Abhinaya Syaifudin Syandra Sari Teddy Siswanto Valentino Hutagalung, Josua Wibowo, Nurafni Revita