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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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Pemanfaatan AI-Language Model Tools untuk Menunjang Copywriting Skill Jurnalis Media Have Fun Erlin Windia Ambarsari; Dudi Parulian; Mohammad Fazrie; Anatasya Aulya Wilatiktah
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.
Optimization of ID3 Structure for Academic Performance Analysis using Ant Colony Algorithm Fathudin, Dedin; Ambarsari, Erlin Windia; Paramita, Aulia
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5353

Abstract

This study investigates the optimization of the ID3 algorithm for academic performance analysis using the Ant Colony Optimization (ACO) method. The primary research problem addressed is the inefficiency and overfitting of traditional ID3 in complex and noisy datasets. Therefore, the ACO method is integrated to enhance the ID3 structure, improving classification accuracy and computational efficiency. The research objectives include developing a decision tree model based on assignment, mid-term, and final exam scores for student performance evaluation. The method combines ID3's decision-making capabilities with ACO's optimization process, which uses pheromone trails to find optimal paths in constructing the decision tree. Temporary results show that the ACO-ID3 model achieves an accuracy of 85% with improved consistency and lower variability compared to the traditional ID3 model, which has an accuracy of 89% but higher variability; this indicates that while traditional ID3 may slightly outperform in accuracy, the ACO-ID3 model provides more stable and reliable performance across different data subsets. The study concludes that ACO-ID3 is a practical and effective tool for academic performance analysis, particularly in cases requiring consistent and reliable classification
Applying IROC Method in Patent Submission Evaluation in Indonesia: A Comparison with MAGIQ and AHP Ambarsari, Erlin Windia; Rahman, Vierhan; Cholifah, Wahyu Nur
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.75

Abstract

This study applies the Improved Rank Order Centroid (IROC) to the Indonesian patent submission process within a Multi-Criteria Decision Making (MCDM) framework. The study evaluates four primary elements in patent assessment: "Patent Description," "Illustration," "Inventor's Ownership Statement," and "Rights Assignment Declaration." Preliminary findings indicate the importance of "Patent Description," followed by the other elements in descending order of significance. The evaluation also encompasses three applicant alternatives, with the Second Applicant emerging as the most favorable. The study further contrasts IROC outcomes with MAGIQ and AHP methodologies. While rank-based techniques like ROC and IROC generally produce similar weight distributions, the AHP method, which employs pairwise comparisons, often displays variations. The research underscores the potential of IROC in determining criterion weights, its comparison within the MAGIQ framework, and its validation through AHP. These insights aim to deepen our understanding of decision-making processes and analysis. The conclusion from comparing IROC results with MAGIQ and AHP indicates that the applicant rankings remain consistent. Therefore, further research is needed to understand the differences between evaluation methods and their impacts and explore the influence of cultural or regional factors in the patent submission process
Penerapan Metode Simple Additive Weighting dalam Penentuan Prioritas Program Pembangunan Daerah Siagian, Jaya Sari Anggraini; Purba, Bister; Ambarsari, Erlin Windia; Rohayani, Hetty
Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 2 (2023): Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v3i2.1542

Abstract

Regional governments and institutions related to preparing regional development programs experience several obstacles in determining regional development work plans, one of which is difficulty in determining development priorities that are in line with regional regulations. The effectiveness and efficiency of implementing regional development programs depends on the accuracy of the data collected. One of the challenges faced by the Medan City Regional Development Planning Agency is the lack of consideration of the priority scale and fairness in the regional development stages, as well as the absence of a system that can help determine the priority scale. Therefore, this research requires a decision support system (DSS). In the SPK, there are many solution methods that can be used according to the needs of each problem. In this research, to determine regional development program priorities, the SAW method will be used. Of the 10 proposed regional programs, only one will be a priority development program. After applying the SAW method in making decisions, the development program that has the highest priority level is the highway construction program with the final result obtained, namely 0.77705.
Implementation of the Preference Selection Index (PSI) Method in Determining the Best Coffee Shop Windarto, Agus Perdana; Mesran, Mesran; Saidah, Fatiyah; Ambarsari, Erlin Windia
Bulletin of Artificial Intelligence Vol 3 No 1 (2024): April 2024
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v3i1.145

Abstract

The decision support system can be claimed to be a personal computer capable of running data into information so that when taking a semi-structured or problem-specific decision, A coffee shop is a place that prioritizes the sale of coffee with a variety of brewing methods, ranging from cold brew, percolator, Turkish coffee, automatic drip, moka pot, tubruk, Arabica, and many more. In this study, the authors used the PSI (preference selection index) method so that the selection of the best coffee shop was carried out by making a decision matrix, normalizing the decision matrix, calculating the mean value of normalized data, determining the variation of preferences, determining storage in preference values, determining the weight of the criteria, and calculating the PSI value so as to find the best alternative. The criteria used in the selection of coffee shops are five: food, drink, service, entertainment, and parking. Then the final result of the best alternative value is A6 as the best coffee shop in Tanjung Morawa, with a result of 3.702 using the PSI method
Decision Support System for Determining the Best Coffee Shop Applying the OCRA Method using ROC Weighting Erlin Windia Ambarsari; Hetty Rohayani; Ade Irma Agustina Lubis; Ridha Maya Faza Lubis
Journal of Computing and Informatics Research Vol 3 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v3i1.970

Abstract

The place of coffee sales, or more commonly known as a coffee shop, not only offers coffee but also serves a variety of hot and cold beverages. Many individuals, especially young people and students, choose to spend their time in modern coffee shops to sit and relax. Currently, coffee shops are often used as places for discussions, exchanging ideas, or simply relieving stress after activities. Coffee shops have become centers of social interaction with adequate service facilities. Although coffee shops are widespread, many people are not careful in choosing them. When choosing a coffee shop, it is important to select one that not only provides a comfortable environment but also serves the best-tasting coffee. The process of choosing the best coffee shop involves considerations such as price, taste quality, service, atmosphere, and cleanliness. To address this challenge, the author deems it essential to implement a Decision Support System (DSS). DSS is a field of science that utilizes technology to assist in problem-solving and accurate decision-making, without being manipulable. In the context of this research, the author uses the OCRA and ROC methods, as both are known as objective and easily understood methods. By applying the OCRA and ROC methods, the research results show that Gen’s Semar Cafe, with a score of 1.594, is selected as the best coffee shop.
Implementasi Metode Simple Additive Weighting (SAW) untuk Sistem Pendukung Keputusan Pemilihan Dosen Favorit Mahasiswa Desyanti, Desyanti; Mesran, Mesran; Windia Ambarsari, Erlin
Journal of Informatics Management and Information Technology Vol. 4 No. 3 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v4i3.406

Abstract

College X in Dumai City always strives to continuously improve internal quality so that it can compete with other universities. One effort to improve quality is by assessing lecturer performance every year. So far, the process of selecting outstanding lecturers or favorite lecturers is still based on the subjectivity of those who choose, so there is a lack of transparency in the selection process. This research discusses the application of the Simple Additive Weighting (SAW) method in a decision support system for selecting favorite lecturers at College X, Dumai. Lecturer performance assessment is based on criteria such as discipline, achievement, behavior, responsibility and communication, involving students as respondents. Data was collected through literature studies, interviews, observations and Likert scale-based questionnaires. The calculation process using the SAW method includes normalization of the decision matrix and ranking based on criteria weights. The research results show that the SAW method provides objective, transparent and systematic results in selecting favorite lecturers. The system developed is able to support universities in improving the quality of lecturers through more structured feedback.
Analisis Faktor Kesuksesan Film dengan Klasterisasi Algoritma Leiden dan Prediksi Pohon Keputusan Ambarsari, Erlin Windia; Mardika, Putri Dina; Bramantia, Agi Candra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8291

Abstract

Abstrak - Penelitian ini bertujuan untuk mengidentifikasi faktor utama yang memengaruhi kesuksesan film blockbuster dengan metode klasterisasi dan prediksi berbasis pembelajaran mesin. Dataset mencakup 430 film blockbuster yang dirilis antara tahun 1977 hingga 2019, dengan variabel utama imdb_rating, film_budget, dan length_in_min. Analisis dilakukan menggunakan pemrograman bahasa R di platform Google Colab. Tahap pertama melibatkan klasterisasi dengan algoritma Leiden, namun hasil menunjukkan bahwa seluruh data tergabung dalam satu klaster, mengindikasikan kesamaan karakteristik di antara film-film tersebut. Selanjutnya, model prediksi kesuksesan film dikembangkan menggunakan algoritma C5.0, dengan hasil akurasi sebesar 82,29%. Analisis menunjukkan bahwa variabel film_budget dan imdb_rating memiliki pengaruh signifikan terhadap kesuksesan film. Pohon keputusan yang dihasilkan menunjukkan bahwa film dengan anggaran lebih dari 142 juta USD dan rating IMDb di atas 7,8 memiliki peluang kesuksesan yang lebih tinggi. Berdasarkan temuan ini, dapat disimpulkan bahwa anggaran produksi dan rating IMDb adalah faktor utama penentu kesuksesan film blockbuster. Rekomendasi bagi industri film adalah memprioritaskan alokasi anggaran yang efektif serta meningkatkan kualitas konten untuk menarik minat pasar. Untuk penelitian lanjutan, disarankan untuk mempertimbangkan variabel tambahan seperti popularitas aktor atau tren genre, serta menggunakan metode pembelajaran mesin lainnya guna memperluas cakupan prediksi.Kata kunci: Film Blockbuster, Klasterisasi Leiden, Algoritma C5.0, Anggaran Produksi, Rating IMDb  Abstract - This study aims to identify the key factors influencing the success of blockbuster films using clustering and machine learning-based prediction methods. The dataset includes 430 blockbuster films released between 1977 and 2019, with primary variables imdb_rating, film_budget, and length_in_min. The analysis was conducted using R programming on the Google Colab platform. The first stage involved clustering with the Leiden algorithm; however, the results indicated that all data merged into a single cluster, suggesting similar characteristics among these films. Subsequently, a model to predict film success was developed using the C5.0 algorithm, yielding an accuracy of 82.29%. The analysis showed that film_budget and imdb_rating significantly impacted film success. The resulting decision tree indicated that films with budgets over 142 million USD and IMDb ratings above 7.8 have a higher likelihood of success. Based on these findings, it can be concluded that production budget and IMDb rating are the primary determinants of blockbuster film success. The recommendation for the film industry is to prioritize effective budget allocation and enhance content quality to attract market interest. For further research, it is suggested to consider additional variables, such as actor popularity or genre trends, and to employ other machine learning methods to expand the scope of prediction.Keywords: Blockbuster Films, Leiden Clustering, C5.0 Algorithm, Production Budget, IMDb Rating
Co-Authors Abdul Karim Ade Irma Agustina Lubis Agi Candra Bramantia Agus Perdana Windarto Anatasya Aulya Wilatiktah Aulia Paramita Bramantia, Agi Candra 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 Lies Sunarmintyastuti 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 Sofiansyah Fadli Sri Agustiani Br Siburian Sri Melati Sagita Subagio, Relo Vierhan Rahman Wahyu Nur Cholifah