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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Computatio : Journal of Computer Science and Information Systems Faktor Exacta JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Komputasi dan Teknologi Informasi The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Academia Perspectives Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) Prioritas : Jurnal Pengabdian Kepada Masyarakat Journal of Computing and Informatics Research Jurnal Publikasi Teknik Informatika (JUPTI) Journal of Informatics, Electrical and Electronics Engineering Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Bulletin of Artificial Intelligence Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Aksi Kita: Jurnal Pengabdian Kepada Masyarakat International Journal of Informatics and Data Science
Erlin Windia Ambarsari
Program Studi Teknik Informatika, Universitas Indraprasta PGRI
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Optimalisasi Pembuatan Konten dan Campaign Pemasaran Kreatif Melalui Pemanfaatan Generative AI Chat GPT di PT. Alfa Cipta Teknologi Virtual Fazrie, Mohammad; Ambarsari, Erlin Windia; Parulian, Dudi
Aksi Kita: Jurnal Pengabdian kepada Masyarakat Vol. 1 No. 3 (2025): JUNI
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/b05znj35

Abstract

Pelatihan ini bertujuan untuk membantu tim pemasaran PT. Alfa Cipta Teknologi Virtual dalam memanfaatkan Generative AI, khususnya ChatGPT, guna mengoptimalkan proses pembuatan konten dan perencanaan kampanye pemasaran yang lebih kreatif, efektif, dan efisien. Di tengah persaingan digital yang semakin ketat, kemampuan untuk menciptakan konten yang menarik, relevan, dan sesuai dengan karakter audiens menjadi aspek krusial dalam menentukan keberhasilan suatu kampanye. Oleh karena itu, teknologi kecerdasan buatan seperti ChatGPT dapat menjadi solusi strategis untuk mendukung proses kreatif tim secara berkelanjutan. Melalui pelatihan ini, peserta tidak hanya dikenalkan pada dasar-dasar Generative AI, tetapi juga dibekali keterampilan praktis dalam menyusun prompt, menghasilkan berbagai bentuk konten pemasaran (deskripsi produk, caption media sosial, headline iklan, dan lainnya), serta melakukan eksplorasi ide kampanye digital secara sistematis. Selain itu, pelatihan ini membahas integrasi AI ke dalam workflow pemasaran guna meningkatkan engagement pelanggan dan potensi konversi penjualan. Diharapkan, dengan pemahaman dan penerapan teknologi ini, tim pemasaran PT. Alfa Cipta Teknologi Virtual mampu mempercepat proses produksi konten, memperkuat daya saing digital perusahaan, dan menghadirkan komunikasi brand yang lebih responsif serta adaptif terhadap tren pasar yang dinamis.
Clustering of YouTube Viewer Data Based on Preferences using Leiden Algorithm Erlin Windia Ambarsari; Aulia Paramita; Desyanti
International Journal of Informatics and Data Science Vol. 1 No. 2 (2024): June 2024
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v1i2.45

Abstract

This study aims to analyze YouTube viewer engagement patterns by applying the Leiden algorithm for clustering based on user interactions such as likes, dislikes, and subscription behaviors in correlation with video duration. Therefore, the method that we used begins with data cleaning to ensure completeness, followed by selecting relevant features and applying z-score normalization to equalize their contributions. A similarity graph is constructed using cosine similarity, representing instances as nodes and their relationships as edges. The Leiden algorithm is then applied to optimize modularity and extract clusters, with results integrated into the original dataset for analysis. Dimensionality reduction using PCA facilitates cluster visualization, while statistical summaries and distribution plots provide deeper insights into cluster characteristics. Subsequently, we obtained a dataset sourced from the YouTube content creator @ArmanVesona, which includes 237 instances with ten features: Shares, Comments Added, Dislikes, Likes, Subscribers Lost, Subscribers Gained, Views, Watch Time (hours), Impressions, and Click-Through Rate (%). The analysis reveals two distinct clusters: Cluster 0, characterized by lower engagement and stable audience, and Cluster 1, exhibiting higher engagement but higher subscriber churn. The findings highlight the effectiveness of the Leiden algorithm in detecting well-connected communities and provide insights into viewer behavior, aiding in the development of improved content strategies and targeted marketing approaches.
Pengembangan Model Decision Tree Menggunakan Particle Swarm Optimization untuk Klasifikasi Popularitas, Infrastruktur, dan Potensi Pasar Wilayah Jabodetabek Ambarsari, Erlin Windia; Kustian, Nunu; Mardika, Putri Dina
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7216

Abstract

Traditional markets play a vital role in local economies; however, they face challenges related to competitiveness, infrastructure quality, and legal operational status. This study aims to develop a classification model for traditional markets in the Greater Jakarta (Jabodetabek) region based on three main aspects: popularity, infrastructure readiness, and market potential. The model utilizes a Decision Tree (DT) algorithm optimized with Particle Swarm Optimization (PSO) to enhance classification accuracy while maintaining model interpretability. The dataset comprises 1,253 market entries with 15 predictive features. The classification model categorizes markets into popular or unpopular, infrastructure-ready or not-ready, and potential or non-potential groups. Experimental results demonstrate that the model achieves an average accuracy of 97.48%. Key factors influencing the classification outcomes include the number of vendors, the availability of basic facilities (electricity, clean water, toilets, and drainage), the age of the market, and the presence of an official operating license (IUP2T). The findings provide valuable insights for local governments and policymakers to prioritize market revitalization efforts based on data-driven classification results. Furthermore, this study opens future research opportunities to integrate spatial data and real-time market analytics to improve classification accuracy further and support more adaptive and effective policy-making.
Film Popularity Analysis through Combined K-Means Clustering and Gradient Boosted Trees Agi Candra Bramantia; Desyanti; Jeperson Hutahaean; Erlin Windia Ambarsari
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.81

Abstract

The dynamic and competitive nature of the global film industry presents complex challenges in predicting film popularity, as success is shaped by the interplay of production investment, casting decisions, and audience preferences. This research addresses the limitations of previous studies that have focused primarily on direct relationships, such as budget versus box office returns, by introducing an integrated analytical framework that combines K-Means clustering and Gradient Boosted Trees (GBT) with explainable AI techniques. Utilizing the TMDB movie dataset and constructing features such as actor influence and studio power, the study segments films and predicts audience ratings while providing interpretable visualizations. The results reveal four distinct film clusters and demonstrate that actor influence and budget allocation are the most significant predictors of popularity. The proposed model achieves an R² score of 0.75 and a mean squared error of 0.35 in predicting audience ratings, while cluster analysis shows that Blockbuster films reach the highest average ratings (6.76), and Underperforming films the lowest (2.42). By integrating interpretable predictive modeling and interactive scenario tools, this research offers both theoretical advancement and practical value for industry stakeholders. However, the findings are limited by the available metadata and do not account for factors such as marketing or real-time audience trends, suggesting opportunities for future research to expand the analytical framework.
PENGGUNAAN EVENT VIEWER PADA WINDOWS DALAM MENEMUKAN MASALAH Kustian, Nunu; Fathudin, Dedin; Ambarsari, Erlin Windia
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 6, No 1 (2022): SEMNAS RISTEK 2022
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v6i1.5826

Abstract

Perangkat komputer menjadi kebutuhan masyarakat, untuk menyelesaikan pekerjaannya secara sistem yang terintegrasi. Masalah yang sering terjadi adalah pengguna komputer tidak mengetahui kerentanan sistem, diantaranya adalah aktivitas komputer yang tidak wajar; dalam hal ini, program yang tidak seharusnya dijalankan atau ada di komputer. Beberapa tahapan dapat digunakan untuk menganalisis aktivitas tersebut. Oleh karena itu, pada penelitian ini menggunakan Windows Event Viewer untuk Pengguna Sistem Operasi Windows sebagai pemecahan masalahnya. Event Viewer adalah modul snap-in dari Windows; utilitas yang digunakan untuk memeriksa kesalahan di kedua sistem dan aplikasi Windows. Event Viewer di Windows adalah salah satu alat yang digunakan untuk meninjau sistem individual dan administrator untuk memecahkan masalah melalui diagnostik log aktivitas abnormal yang sudah masuk dalam Event Viewer. Metode yang digunakan pada penelitian ini adalah forensik, yang dimana tujuannya adalah untuk menemukan kesalahan sistem berdasarkan skenario yang dibuat pada penelitian ini sebagai ilustrasi implementasi Event Viewer. Hasil yang didapatkan dari penelitian ini adalah Event Viewer dapat mendeteksi siapa saja yang berhasil masuk berdasarkan tanggal dan waktu sehingga perlu membatasi hak akses pada komputer yang digunakan.Kata Kunci: Diagnosis, Event Viewer, Windows
Pemetaan Mosaic Plot dalam Menganalisis Fundamental Saham Perusahaan pada Aplikasi IPOT IndoPremier Securitas Ambarsari, Erlin Windia; Sunarmintyastuti, Lies; Lestari, Fibria Anggraini Puji
Journal of Academia Perspectives Vol 2, No 2 (2022): Journal of Academia Perspectives
Publisher : Universitas Indraprasta PGRI, Jakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jap.v2i2.1124

Abstract

For investors who invest, the selection of issuers is made by considering a company's fundamentals. Several digital securities applications can be used, including Indopremier Sekuritas ipot. However, the analysis is difficult for layman who does not master the basics of finance and business. Therefore, we used a mosaic plot by mapping financial statement data into bar charts. The results showed that the Mosaic plot could present the company's characteristics based on its performance in managing capital from shares so that it can be a reference for investors in considering the selection of issuers based on investment objectives.
Pemanfaatan AI-Language Model Tools untuk Menunjang Copywriting Skill Jurnalis Media Have Fun Ambarsari, Erlin Windia; Parulian, Dudi; Fazrie, Mohammad; Wilatiktah, Anatasya Aulya
Prioritas: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 01 (2024): EDISI MARET 2024
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35447/prioritas.v6i01.890

Abstract

Kegiatan pengabdian masyarakat yang memanfaatkan AI-Language Model Tools seperti ChatGPT dan Copilot telah berhasil mengatasi tantangan dalam jurnalisme digital di Media Have Fun sebagai platform berita yang fokus pada sektor M.I.C.E. Menghadapi keterbatasan waktu anggota untuk menulis artikel berkualitas, kegiatan ini mengintegrasikan teknologi generatif AI untuk meningkatkan efisiensi dan kualitas konten. Melalui bimbingan daring, anggota dilatih menggunakan ChatGPT untuk pengumpulan informasi dan analisis konten, serta Copilot untuk pengambilan data otomatis dan penyesuaian konten, termasuk pengolahan Bahasa. Alhasil, terdapat peningkatan signifikan dalam keterlibatan pembaca, ditandai dengan lonjakan pembaca aktif dan baru, serta interaksi yang lebih tinggi pada situs. Namun, tantangan dalam mempertahankan keterlibatan pembaca menunjukkan kebutuhan untuk strategi konten yang lebih adaptif. Kegiatan ini juga menekankan pentingnya menjaga etika jurnalistik dan menghindari plagiarisme, dengan memastikan originalitas konten. Akhirnya, pengabdian ini tidak hanya meningkatkan kemampuan copywriting anggota tetapi juga menggarisbawahi pentingnya adaptasi teknologi dengan pertimbangan etis untuk kemajuan jurnalisme digital.
Utilizing K-Means Clustering to Understanding Audience Interest in SEO-Optimized Media Content Erlin Windia Ambarsari; Dedin Fathudin; Gravita Alfiani
Journal of Computing and Informatics Research Vol 3 No 2 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i2.1207

Abstract

This study observes k-means clustering for segmenting SEO data to understand audience interests, identifying the elbow method as crucial for determining the optimal number of clusters. It highlights notable differences in content engagement across clusters, emphasizing the need for refined SEO strategies and a deeper understanding of audience segmentation. Despite challenges like SEO's dynamic nature and data reliance, this methodology provides a strong foundation for enhancing content strategies. Future research suggestions include cross-platform data integration, longitudinal studies, sentiment analysis, content experimentation, user experience (UX) focus, and monitoring algorithm updates to develop more adaptive content and SEO strategies aligned with changing audience behaviors.
Decision Support System for Determining the Best School Extracurricular Activities by Combining the ROC and MAUT Methods Jahril; Abdul Karim; Erlin Windia Ambarsari; Agus Perdana Windarto
Journal of Computing and Informatics Research Vol 3 No 3 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i3.1493

Abstract

The various extracurricular activities at school make students confused and difficult to choose which extracurricular activities are more suitable for participation. However, sometimes there are also students choosing extracurricular activities based on many of their friends. Therefore, determining the best school extracurricular activities is the best solution for students as a reference to find which is the best extracurricular activity. The criteria used in this study in choosing the best extracurricular activities are Regional Event Activities, Allocation, Creativity and Talent Channeling. By utilizing SPK, decision makers can make more systematic decisions, based on a deeper understanding of the various alternatives available and relevant criteria. SPK or decision support system is a technique that has the ability to determine a decision using a technical design based on alternatives and predetermined criteria. SPK or decision support system is a technique that has the ability to determine a decision using a technical design based on alternatives and predetermined criteria. In the context of extracurricular school selection, combining the ROC (Rank Order Centroid) and MAUT (Multi-Attribute Utility Theory) methods in a Decision Support System is an interesting approach. The ROC method is used to cluster and rank schools based on certain criteria, while MAUT helps in the calculation of appropriate weights for these criteria. By integrating these two methods, the SPK can provide a more structured guideline in the selection of extracurricular activities that suit students' interests and needs. The research results obtained show that the Futsal alternative is the first recommendation as the best extracurricular with a final value of 0.655086.
Clustering Algoritma Fuzzy Ant Untuk Optimalisasi Penentuan Rute Kemacetan Tanah Abang Ambarsari, Erlin Windia; Khotijah, Siti
Computatio : Journal of Computer Science and Information Systems Vol. 1 No. 2 (2017): Computatio : Journal of Computer Science and Information Systems
Publisher : Faculty of Information Technology, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/computatio.v1i2.1015

Abstract

Tanah Abang merupakan salah satu kecamatan yang terletak di Kota Administrasi JakartaPusat dengan luas wilayah 9,3 Km2. Berdasarkan administrasi pemerintahan, kecamatan TanahAbang terdiri dari 7 kelurahan, yaitu Kelurahan Gelora, Bendungan Hilir, Karet Tengsin,Kebon Melati, Petamburan, Kebon Kacang, dan Kampung Bali. Tanah Abang merupakandaerah yang sebagian besar perkantoran, pusat perbelanjaan dan pemukiman penduduksehingga banyak kendaraan yang lalu lalang sehingga terjadi kemacetan di jalan sudahterbiasa terjadi di Daerah Kecamatan Tanah Abang. Penulis melakukan riset untukmenentukan rute kemacetan di daerah tersebut untuk menganalisa penyebab terjadinyakemacetan dengan menggunakan Metode Algoritma Fuzzy Ant. Penggunaan Algoritma FuzzyAnt memungkinkan pemilihan rute semut lebih cepat mencapai konvergen karena pemilihantersebut menggunakan cluster maksimum Fuzzy C-Means dari 3 cluster keanggotaan sehinggaproses siklus Ant tidak terlalu lama. Hasil yang di dapatkan dari algoritma tersebut untukpencarian rute kemacetan adalah B-E-C-A dikarenakan terdapat parkir sembarangan,perbaikan jalan, maupun penutupan jalan.
Co-Authors Abdul Karim Ade Irma Agustina Lubis Agi Candra Bramantia Agus Perdana Windarto Aulia Paramita Bramantia, Agi Candra Dedin Fathudin Desyanti Desyanti Desyanti Dodi Siregar Dodi Siregar Dona Katarina Dudi Parulian Fathudin, Dedin Fazrie, Mohammad Finata Rastic Andrari Frieyadie Furqoni Yudhistira Gravita Alfiani Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda, Herlinda Hetty Rohayani Jahril Jayawarsa, A.A. Ketut Jeperson Hutahaean Jumaryadi, Yuwan KUSTIAN, NUNU Laurent Nababan Mesran, Mesran Mohammad Taufan Asri Zaen Noni Selvia Noni Selvia Nunu Kustian Nurfidah Dwitiyanti Nurfidah Dwitiyanti Paramita, Aulia Parulian, Dudi Puji Lestari, Fibria Anggraini Purba, Bister Putri Dina Mardika Rahman, Vierhan Randi Ramliyana Reflan Revife Purba Relo Subagio Rezkiyana Hikmah Ridha Maya Faza Lubis Rizki Ridwan Rizki Ridwan Roswita Daeli RR. Ella Evrita Hestiandari Saidah, Fatiyah Setiawansyah Setiawansyah Siagian, Jaya Sari Anggraini Siti Khotijah Sofiansyah Fadli Sri Agustiani Br Siburian Sri Melati Sagita Subagio, Relo Sunarmintyastuti, Lies Vierhan Rahman Wahyu Nur Cholifah Wilatiktah, Anatasya Aulya