Article Per Year (5 Year)
p-Index From 2020 - 2025
2.225
P-Index
This Author published in this journals
All Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Sains dan Teknologi Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) NUSANTARA: Jurnal Pengabdian Kepada Masyarakat JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA Intelmatics Jurnal Pengabdian Kepada Masyarakat Radisi
Sari, Syandra
Lembaga Penelitian Universitas Trisakti
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Physics Social Sciences

Published : 15 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search

 1   2 
A Web-Based Boarding Management Application Design Maulana, Muhamad Anggi; Syaifudin; Sari, Syandra; Najih, Muhammad
Intelmatics Vol. 4 No. 1 (2024): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v4i1.17649

Abstract

In Indonesia, the rental business of temporary accommodations or boarding houses ('kost') has significantly grown due to the influx of individuals from various cities or regions seeking temporary residence for educational pursuits, work, entrepreneurship, or marriage. Boarding house owners often manage not just one or two rooms but can have dozens or even hundreds of rooms. This extensive scale makes it challenging for boarding house owners to efficiently handle payment data, accurately record information, and report room damages using conventional methods. To address these challenges, an application was developed to streamline data management for boarding house owners, enabling them to efficiently manage their businesses. The data collection methods employed for developing this application included observation, interviews, and literature review, following the waterfall model for software development. The obtained results from this application development facilitate better service management for boarding house owners, enhancing cost and time efficiency while improving the quantity and quality of managed information.
Pelatihan Pembuatan Dashboard Dan Tiktok Bisnis Pada UMKM Shofi Cookies Sari, Syandra; Sugiarto, Dedy; Shofiati, Ratna; Ariwibowo, Anung Barlianto; Gunawan, Muhammad Ichsan; Shan ASP, Putri; Jubaedah, Ida; Firdasari, Elita Wahyu; Nadia, Alya Shafa
NUSANTARA Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 3 (2023): Agustus : Jurnal Pengabdian kepada Masyarakat
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/nusantara.v3i3.1577

Abstract

The Community Service Program (PKM) has successfully conducted a training on Dashboard Development and TikTok Business for the Small and Medium Enterprise (MSMEs) Shofi Group. This training was facilitated by the Informatics Engineering Department of Trisakti University and has received an excellent rating based on feedback from the participants. As part of this training, a visualization dashboard was successfully integrated into the Shofi Group's website, allowing them to monitor sales data more efficiently and effectively. In addition, participants also learned about the use of TikTok Business as a tool for marketing their products. Positive feedback from Shofi Group indicates the significant benefits of this training, in terms of assisting MSMEs to adapt and utilize digital technology in their business development. These results underline the importance of similar programs in supporting SMEs to compete in the digital era. It is hoped that the success of this PKM can lay the foundation for similar initiatives in the future, supporting more MSMEs in leveraging the potential of digital technology.
Analisis Segmentasi Pelanggan Ritel Online Menggunakan K-Means Clustering Berdasarkan Model Recency, Frequency, Monetary (RFM) Yusak Noah Rumapea, Adzriel; Pratiwi, Dian; Sari, Syandra
Jurnal Sains dan Teknologi Vol. 6 No. 3 (2024): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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

Abstract

Customer segmentation is the process of grouping customers based on similar characteristics. It plays a critical role in marketing strategy, enabling businesses to understand consumer behavior and enhance the effectiveness of campaigns. The Recency, Frequency, Monetary (RFM) model and the K-Means Clustering algorithm have proven to be effective in identifying different customer segments. This study uses online retail transaction data from the period 2010-2011. The RFM model is employed to calculate customer value based on recency, frequency, and monetary metrics. Subsequently, the K-Means Clustering algorithm is applied to normalized data to group customers into several segments. The results reveal the existence of three distinct customer segments. These segments, characterized by varying traits, provide a clearer understanding of consumer behavior. By understanding the characteristics of each segment, companies can design more effective and personalized marketing strategies. Furthermore, this study contributes to the advancement of knowledge in the field of data analysis.
Transformasi Digital UMKM Shofi Cookies: Pengembangan Dashboard Penjualan Berbasis API, Database, dan Visualisasi Interaktif menggunakan wpDataTable Sugiarto, Dedy; Sari, Syandra; Ariwibowo, Anung B.; Shofiati, Ratna; Gunawan, Muhamad Ichsan; Octavianus, M Arya
Jurnal Pengabdian kepada Masyarakat Radisi Vol 4 No 3 (2024): Desember
Publisher : Yayasan Kajian Riset dan Pengembangan RADISI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55266/pkmradisi.v4i3.467

Abstract

This community service activity aims to increase the efficiency and accuracy of sales data management at Shofi Cookies through the application of digital technology. The methods used include API-based dashboard development, database integration, and implementation of Custom CRUD Plugins and wpDataTables. The results show significant improvements in operational efficiency, ease of real-time data analysis, and data-driven decision-making capabilities. This program also provides training and mentoring to increase the capacity of human resources in utilizing technology optimally. It is hoped that the success of this activity can become a model for other SMEs in facing the challenges of digitalization in the era of digital transformation.
PELATIHAN PEMANFAATAN GOOGLE SITE UNTUK PEMASARAN PRODUK UMKM Ariwibowo, Anung B; Shofiati, Ratna; Sari, Syandra; Ningsih, Yunia; Salim, Agus; Giffary, Ridho; Zaki, Arviandri
JUARA: Jurnal Wahana Abdimas Sejahtera Volume 6, Nomor 1, Januari 2025
Publisher : Jurusan Teknik Lingkungan Fakultas Arsitektur Lanskap dan Teknologi Lingkungan, Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/94e1j789

Abstract

Kegiatan PKM ini ditujukan bagi pelaku UMKM di wilayah kota Bekasi yang tergabung dalam cluster UMKM di bawah pembinaan Dinas UMKM Kota Bekasi. Cluster ini sudah menjadi peserta dari kegiatan PKM yang dilakukan oleh tim dosen dari Jurusan Teknik Industri dan Teknik Informatika selama beberapa tahun terakhir. Selama pelaksanaan beberapa kegiatan, mereka masih merasakan kebutuhan pendampingan dalam pemanfaatan teknologi digital, terutama untuk mengoptimalkan pemasaran produk-produk hasil karya para anggota cluster UMKM ini. Dari diskusi yang berlangsung selama pelaksanaan kegiatan PKM sebelumnya, salah satu hal yang masih dirasakan perlu dikuasai adalah keterampilan pembuatan dan pemeliharaan blog yang memuat informasi produk-produk hasil karya anggota cluster UMKM. Kegiatan dilaksanakan dalam bentuk pelatihan kepada para pelaku UMKM, namun jika ada masyarakat umum yang berminat untuk mengikuti kegiatan ini tetap diperbolehkan. Hasil pengolahan kuesioner pada peserta pelatihan rata-rata untuk komponen instruktur, materi dan kepuasan terhadap pelaksaan kegiatan mendapatkan skor di atas 4 dari skala 5. Sedangkan peningkatan kemampuan peserta terhadap materi Google Sites mendapat skor rata-rata 3,85.
Analisis Sentimen Masyarakat Di Media Sosial X Terhadap Kemenkes Dengan Naive Bayes dan SVM Andrew Ryandi, Freddy; Pratiwi, Dian; Sari, Syandra
Jurnal Sains dan Teknologi Vol. 7 No. 1 (2025): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

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

Abstract

This study examines public sentiment on social media platform X regarding Indonesia's Ministry of Health during the COVID-19 pandemic, using Naïve Bayes and Support Vector Machine (SVM) algorithms. Posts mentioning the Ministry’s official account (@KemenkesRI) were preprocessed and labeled using the VADER tool. Sentiment classification was performed with TF-IDF word weighting, and both algorithms were evaluated. Results show SVM achieved slightly higher accuracy (79%) than Naïve Bayes (77%), indicating its effectiveness in handling complex language structures, though it requires more computational resources. This research underscores the utility of SVM for analyzing public sentiment on health policies..
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%.
Sentiment Analysis And Topic Modelling Of Candidate News In The 2024 General Election On Twitter Social Media Using Latent Dirichlet Allocation (LDA) Method Ramadhan, Syahrul; Siswanto, Teddy; Sari, Syandra
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/v5i1.21058

Abstract

The use of Twitter as a platform to express public opinion regarding fuel subsidies in Indonesia. Through sentiment analysis using Support Vector Machine method and word-based lexicon, this study reveals that the majority of people are in favour of fuel price increase or subsidy policy change. The sentiment data obtained from this research, which includes positive, neutral and negative sentiments, provides a clear picture of the public's views on the issue. SVM classification method and validation with K-Fold Cross Validation were used to ensure the accuracy of sentiment analysis results obtained from Twitter data. This research is also expected to help society to gauge public opinion on news and candidates in elections. It helps understand how people respond to certain political issues and candidates and the results of sentiment analysis and topic modelling can provide a better understanding of the key issues that matter to voters. This can help candidates and political parties to craft more effective campaign messages and can also be used to detect hoaxes or false information that may spread on social media during elections. This is important for maintaining the integrity of the election.
Integrasi Artificial Intelligence Pada Aplikasi ERP: Systematic Literature Review Siswanto, Teddy; Sari, Syandra; Hartini, Hartini; Teruri, Shabrina
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

Perkembangan aplikasi ERP mencerminkan upaya terus-menerus untuk mengintegrasikan dan menyederhanakan proses bisnis yang kompleks, dengan memanfaatkan teknologi terbaru untuk meningkatkan efisiensi dan efektivitas operasional perusahaan sesuai peningkatan kebutuhan sistem oleh para pengguna. Adanya peningkatan aplikasi ERP membuat membuat kebutuhan pengguna bertambah. Yang menjadi permasalahan kebutuhan pengguna saat ini tidak berhenti sampai disitu saja namun berkembang ingin dapat memprakiraan apa yang akan terjadi kemudian (predictive), lalu kejadian apa yang sering terjadi dan keputusan apa yang sebaiknya dapat diambil (prescriptive) serta proses keberlanjutan dari pengambilan keputusan dalam bisnisnya. Solusi yang dipilih adalah bagaimana ERP menjadi green software, dengan bantuan integrasi Artificial Intelligence (AI) yang memungkinkan sistem ERP untuk tidak hanya bekerja lebih efisien tetapi juga dengan lebih sedikit sumber daya energi, mengurangi emisi karbon, dan mendukung keberlanjutan lingkungan. Metodologi yang digunakan adalah Systematic Literature Review, melalui tahapan formulasi pertanyaan penelitian, strategi pencarian, ekstraksi data, pemetaan data dan analisis data. Adapun pencarian dilakukan melalui database Scopus pada periode Juli 2024. Dari hasil pencarian ditemukan sebanyak 576 paper dan kemudian setelah diseleksi hanya untuk terbitan 5 tahun terakhir dikarenakan perkembangan cepat untuk bidang teknologi informasi maka diperoleh sebanyak 336 paper. Setelah dilakukan pembatasan area berdasarkan subjek, keyword, tipe dokumen dan bahasa yang digunakan maka diperoleh 174 paper. Hasil penelitian menunjukkan penelitian integrasi Artificial Intelligence dalam Enterprise Resource Planning terbagi menjadi 2 (dua) cluster utama yaitu system-process dan application. Integrasi AI dengan ERP, secara signifikan meningkatkan efisiensi operasional dan produktivitas perusahaan dengan otomatisasi tugas-tugas rutin, analisis data yang lebih cerdas, dan pengambilan keputusan yang didukung data secara real-time. AI membantu dalam mengoptimalkan proses bisnis, seperti manajemen rantai pasokan, manajemen inventaris, dan prediksi permintaan dan pengambilan keputusan, yang berkontribusi pada penghematan biaya dan peningkatan kinerja. Dengan memanfaatkan machine learning dan analisis prediktif, AI memberikan wawasan yang lebih mendalam dan akurat dari data ERP, memungkinkan pengambilan keputusan yang lebih cepat dan berdasarkan informasi serta pengetahuan yang lebih baik.
Co-Authors Agus Salim Andrew Ryandi, Freddy Ariwibowo, Anung B Ariwibowo, Anung B Ariwibowo, Anung B. Binti Solihah Dedy Sugiarto Dian Pratiwi Firdasari, Elita Wahyu Giffary, Ridho Gunawan, Muhamad Ichsan Gunawan, Muhammad Ichsan Hartini Hilmi, Hilmi Abdul Gani Hudzafah, Abdullah Ichsan Gunawan, Muhammad Ida Jubaedah Maulana, Muhamad Anggi Maulida Saara, Kharisma Muhammad Fikri Nadia, Alya Shafa Najih, Muhammad Ningsih, Yunia Octavianus, M Arya Rahma Dewi, Vania Ratna Shofiati Shan ASP, Putri Sulaba, Wishnu Abhinaya Syahrul R, Syahrul Syaifudin Teddy Siswanto, Teddy Teruri, Shabrina Tri Astuti, Aulia Rahma Yusak Noah Rumapea, Adzriel Zaki, Arviandri