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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) Prioritas : Jurnal Pengabdian Kepada Masyarakat Journal of Computing and Informatics Research Kapas: Kumpulan Artikel Pengabdian Masyarakat Journal of Informatics, Electrical and Electronics Engineering Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Bulletin of Artificial Intelligence Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Aksi Kita: Jurnal Pengabdian Kepada Masyarakat International Journal of Informatics and Data Science Jurnal Informatika Dan Tekonologi Komputer Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Jurnal Publikasi Teknik Informatika
Erlin Windia Ambarsari
Program Studi Teknik Informatika, Universitas Indraprasta PGRI
Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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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.
Hybrid Chaos-Isolation Forest Framework for Anomaly Detection in Indonesia’s Public Procurement Ambarsari, Erlin Windia; Desyanti, Desyanti; Fathudin, Dedin
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.137

Abstract

This study proposes and empirically evaluates a Hybrid Chaos-Isolation Forest (HC-iForest) framework for detecting anomalies in Indonesia’s public procurement datasets. The purpose of this research is to address the difficulty of identifying irregular procurement patterns, as existing assessment mechanisms remain largely descriptive and retrospective. The framework integrates chaos-based temporal descriptors—permutation entropy, turning points, and volatility—with statistical indicators to enhance sensitivity to nonlinear and irregular time series. Using monthly procurement data from the Open Contracting Data Standard (OCDS) covering the period from 2019 to 2024, the model identified anomalous fiscal patterns associated with year-end budget adjustments and procurement surges. Empirical evaluation using correlation, ablation, and statistical validation shows that the hybrid model introduces non-redundant anomaly information, achieving a Spearman rank correlation of approximately 0.75 compared to the baseline Isolation Forest, with reduced overlap at intermediate thresholds (Jaccard similarity of 0.20 at the Top 5%). These results confirm that chaos-driven features improve model stability and interpretability. The findings reveal that anomalies are systemic manifestations of institutional and fiscal behavior rather than random deviations. The HC-iForest framework offers a data-driven early-warning mechanism for oversight agencies such as LKPP and ICW, strengthening transparency and accountability in public spending. Future studies may extend this framework through neural or spatiotemporal hybrid architectures to support intelligent and adaptive fiscal monitoring systems
Comparison of Case-Based Reasoning and Hybrid Case-Based Methods in Expert System for Diagnosing Rice Plant Diseases Roznim, Roznim; Mesran, M.Kom, Mesran; Setiawansyah, Setiawansyah; Ambarsari, Erlin Windia
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.132

Abstract

Rice plants are susceptible to various types of diseases that can reduce productivity and quality of the harvest. Therefore, an expert system is needed that can help the disease diagnosis process quickly and accurately. This study compares two approaches in expert systems, namely the Case-Based Reasoning (CBR) method and the Hybrid Case-Based method, to diagnose rice plant diseases based on the symptoms experienced. Data on symptoms and types of diseases were analyzed using both methods to see the level of suitability of the resulting diagnosis. The test results showed that the Hybrid Case-Based method produced a higher level of certainty for all types of diseases compared to the CBR method. For example, Bacterial Leaf Blight disease has a certainty value of 99.5% in the Hybrid method, higher than 83.8% in the CBR method. These findings indicate that the Hybrid method is more effective and accurate in the process of diagnosing rice plant diseases. Thus, an expert system based on the Hybrid Case-Based method is recommended to support decision making in the agricultural sector, especially in early detection of rice diseases
Virtual Learning Berbasis Karakter Virtual Pada SDN Jatimekar I Bekasi Julaeha, Siti; Kustian, Nunu; Ambarsari, Erlin Windia
Kapas: Kumpulan Artikel Pengabdian Masyarakat Vol 4, No 2 (2025)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/ks.v4i2.4441

Abstract

Pembelajaran daring terus berkembang dengan inovasi teknologi, salah satunya melalui penggunaan karakter virtual sebagai media interaktif. Pelatihan ini mengarah pada penerapan virtual learning berbasis karakter virtual di SDN Jatimekar I Bekasi dengan memanfaatkan aplikasi Vroid Studio, 3Tene, dan OBS Studio sebagai alat bantu mengajar bagi guru. Metode yang digunakan meliputi tahap kegiatan, persiapan, dan pelaksanaan. Guru diberikan pelatihan untuk membuat dan mengoperasikan karakter visual yang bergerak secara real-time melalui webcam dan mikrofon, sehingga dapat menampilkan ekspresi wajah dan sinkronisasi suara guna meningkatkan keterlibatan siswa. Hasil implementasi pengabdian masyarakat menggambarkan bahwa penggunaan karakter virtual meningkatkan minat dan partisipasi siswa serta membantu guru menyampaikan materi secara lebih menarik dan responsif. Virtual learning berbasis karakter virtual berpotensi meningkatkan efektivitas pembelajaran daring dan dapat menjadi solusi inovatif dalam Pendidikan Dasar dengan dukungan teknologi dan pelatihan yang memadai.
Hybrid Autoencoder and NiaARM Framework for Flash Viral Detection on YouTube Shorts Erlin Windia Ambarsari; Mercy Hermawati; Dedin Fathudin
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.162

Abstract

YouTube Shorts has rapidly emerged as a dominant short-form video platform, yet small creator channels often experience an unusual viral phenomenon best described as flash viral—a sudden surge of views that peaks within 24 to 48 hours and then collapses almost as quickly. Detecting and explaining this pattern is challenging because traditional statistical detectors miss multivariate signatures, while classical Association Rule Mining (ARM) such as Apriori loses information through mandatory discretization. This study proposes a hybrid framework that combines a semi-supervised Deep Learning Autoencoder with Nature-Inspired Numerical Association Rule Mining (NiaARM) using Differential Evolution and Particle Swarm Optimization. The framework is validated on six temporal snapshots of the Indonesian Boburu YouTube Shorts channel, comprising 63 unique videos (42 active) collected between February 22 and March 19, 2026. Experimental results show that the Autoencoder achieves an F1-score of 0.667 with 100% recall, matching the best classical baseline (Z-Score) while providing learnable representational capacity for future scaling. NiaARM-PSO discovered 3,115 high-quality numerical association rules with a maximum lift of 63.00, compared to only 43 rules and a maximum lift of 2.52 obtained by Apriori, an improvement of approximately 25 times. Traffic source decomposition further revealed that 99.9% of viral views originated from external platforms rather than YouTube's recommendation system, indicating that flash viral on micro-channels is externally driven. This research contributes a methodological framework that simultaneously detects and explains flash viral phenomena in short-form video analytics
Hybrid K-Means Clustering dan MARCOS dalam Sistem Pendukung Keputusan Pemilihan Mahasiswa Berprestasi Berbasis Konsistensi Nilai Akademik Annisa Elfina Augustia; Andreas Adi Trinoto; Erlin Windia Ambarsari
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v4i3.295

Abstract

Penelitian ini bertujuan untuk mengatasi kelemahan sistem seleksi mahasiswa berprestasi yang sering kali hanya mengandalkan nilai akumulatif (IPK) tanpa mempertimbangkan stabilitas performa akademik mahasiswa. Ketimpangan nilai yang ekstrem antara komponen Tugas, UTS, dan UAS dianggap sebagai hambatan bagi dosen pengampu dalam mengevaluasi dinamika belajar yang sesungguhnya. Oleh karena itu, diusulkan sebuah model pendukung keputusan hybrid yang mengintegrasikan algoritma K-Means Clustering dan metode MARCOS. Dalam implementasinya, algoritma K-Means digunakan sebagai penyaring awal untuk mendeteksi anomali data melalui fitur Mean dan Standar Deviasi. Berdasarkan metode Elbow, ditemukan bahwa jumlah klaster optimal adalah dua (k=2), yang membagi 129 data mahasiswa menjadi 80 mahasiswa dengan profil nilai konsisten (Klaster Normal) dan 49 mahasiswa dengan nilai yang fluktuatif (Klaster Anomali). Validasi klaster menunjukkan Silhouette Score sebesar 0,4869. Mahasiswa pada Klaster Normal kemudian diperingkat menggunakan metode MARCOS yang mempertimbangkan bobot kriteria, yaitu Tugas (20%), UTS (30%), dan UAS (50%). Hasil uji sensitivitas melalui Koefisien Korelasi Spearman menunjukkan nilai sebesar 0,9418, yang secara kuantitatif membuktikan bahwa posisi mahasiswa pada peringkat teratas tetap stabil dan tidak tergoyahkan meskipun dilakukan simulasi perubahan bobot kriteria. Penelitian ini menyimpulkan bahwa penggabungan K-Means dan MARCOS menghasilkan sistem evaluasi yang lebih objektif, transparan, dan tahan terhadap bias subjektif, sehingga sangat layak diterapkan untuk menyeleksi kandidat dengan prestasi yang konsisten.
Co-Authors Abdul Karim Ade Irma Agustina Lubis Agi Candra Bramantia Agus Perdana Windarto Andreas Adi Trinoto Annisa Elfina Augustia Aulia Paramita Baehaqi Bramantia, Agi Candra Dedin Fathudin Dedin Fathudin Desyanti Desyanti Desyanti Dodi Siregar Dodi Siregar Dona Katarina Dudi Parulian 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 Mercy Hermawati Mesran, Mesran Mohammad Taufan Asri Zaen Noni Selvia Noni Selvia Nunu Kustian Nunu Kustian Nurfidah Dwitiyanti Nurfidah Dwitiyanti Paramita, Aulia Parulian, Dudi Puji Lestari, Fibria Anggraini Purba, Bister Putri Dina Mardika Putri Dina Mardika Rahman, Vierhan Randi Ramliyana Reflan Revife Purba Relo Subagio Rezkiyana Hikmah Ridha Maya Faza Lubis Rizki Ridwan Rizki Ridwan Roswita Daeli Roznim, Roznim RR. Ella Evrita Hestiandari Saidah, Fatiyah Setiawansyah Setiawansyah Siagian, Jaya Sari Anggraini Siti Julaeha, Siti Sofiansyah Fadli Sri Agustiani Br Siburian Sri Melati Sagita Subagio, Relo Sunarmintyastuti, Lies Vierhan Rahman Wahyu Nur Cholifah Wilatiktah, Anatasya Aulya