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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 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
Erlin Windia Ambarsari
Program Studi Teknik Informatika, Universitas Indraprasta PGRI
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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
Effectiveness of Weighting in Assessing Ranking Criteria on the SWOT-MAGIQ Matrix Ambarsari, Erlin Windia; Subagio, Relo; Mesran, Mesran
Bulletin of Informatics and Data Science Vol 3, No 1 (2024): May 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i1.85

Abstract

The Analytical Hierarchy Process (AHP) has been a prominent tool in decision-making, but the Multi-Attribute Global Interference of Quality (MAGIQ) offers an alternative with its unique weighting mechanism. This research delves into the effectiveness of weighting in assessing ranking criteria within the SWOT-MAGIQ matrix. The study contrasts the traditional Rank Order Centroid (ROC) approach with the Improved Rank Order Centroid (IROC), focusing on their application in the SWOT analysis. While ROC provides simplicity, IROC aims for enhanced accuracy by considering variability in rankings. The results indicate nuanced differences, with ROC assigning higher weights to criteria such as "Friendly Staff" (0.3183 vs. IROC’s 0.3125), while IROC prioritizes aspects like "Strong Customer Relationships" more significantly (0.1103 vs. ROC’s 0.1053). The choice between ROC and IROC hinges on the specific needs of the decision-making context, with IROC potentially offering a more detailed perspective in complex scenarios. This research underscores the importance of selecting the appropriate weighting mechanism to ensure informed and strategic decisions within the SWOT-MAGIQ framework
Penerapan Metode Additive Ratio Assement (ARAS) dalam Pemilihan Customer Service Terbaik Sri Agustiani Br Siburian; Mohammad Taufan Asri Zaen; Setiawansyah; Dodi Siregar; Erlin Windia Ambarsari; Yuwan Jumaryadi
Journal of Informatics Management and Information Technology Vol. 3 No. 1 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v3i1.239

Abstract

There are several references or assessments in determining the best customer service, including those based on customer service performance assessments, namely Cross Selling, Greeting Service Recovery, Grooming, and Discipline. In this study the authors used the ARAS method in selecting the best Customer Service. Use the Additive Ratio Assessment (ARAS) method where each criterion is compared to produce the best. The results of the study provide alternative A3 which is the alternative chosen to be the best alternative with a value of 0.2207.
Sistem Pendukung Keputusan Rekomendasi Pengangkatan Karyawan Kontrak Menjadi Karyawan Tetap Menerapkan Metode Multi Objective Optimization on the Basis of Ratio Analysis (MOORA) Laurent Nababan; Roswita Daeli; Dodi Siregar; Erlin Windia Ambarsari; Setiawansyah; Sofiansyah Fadli
Journal of Informatics Management and Information Technology Vol. 3 No. 2 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v3i2.254

Abstract

PT. Indoramah Abadi is a national plastic manufacturing company that focuses on the production of mineral water packaging. Because the amount of goods produced is quite large, the company has a large number of employees, including contract employees and permanent employees. However, the large number of employees can cause problems in determining which contract employees will be appointed as permanent employees. This problem occurs because the company does not have objective criteria in selecting permanent employees, causing jealousy among employees. In addition, the calculation process used is still manual and inaccurate, thus affecting employee performance. To overcome these problems, the authors conducted research using the MOORA method. This method is a method that can assist in making decisions by using complex mathematical calculations that can be used to solve problems in conflicting criteria, namely benefits and costs. The results of the study show that alternative A4 with a value of 0.2106 is the highest score. Therefore, it can be concluded that a contract employee named Aksa Alpindo deserves to be appointed as a permanent employee.
KORELASI BRAND IMAGE DENGAN FITUR APLIKASI TRANSPORTASI ONLINE MENGGUNAKAN PROFILE MATCHING Ambarsari, Erlin Windia; Khotijah, Siti
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 3 No 1 (2018): Volume.3, Nomor.1.April 2018
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3795.108 KB) | DOI: 10.24252/instek.v3i1.4773

Abstract

Aplikasi yang sudah dibuat, diberi brand sebagai identitas aplikasi dan pembeda dengan aplikasi yang sejenis dengan tujuan agar mudah diingat oleh masyarakat sebagai brand image, karena itu aplikasi terdapat fitur-fitur yang ditonjolkan dan menjadi ciri khas pada aplikasi tersebut sehingga dari fitur-fitur tersebut mempengaruhi persepsi membentuk brand image dalam pikiran masyarakat. Diperlukan Profile Matching untuk menganalisis korelasi antara brand image dengan fitur aplikasi transportasi online. Objek yang diambil untuk aplikasi transportasi online adalah Gojek dan Grab, dimana kesimpulan yang didapatkan adalah menurut persepsi kuesioner, Brand Image Grab mendapat nilai bobot tertinggi (4.725) karena berdasarkan fitur-fiturnya Grab adalah aplikasi transportasi dan Gojek adalah aplikasi layanan jasa. Sehingga tidak dapat dibandingkan satu dengan yang lain.Kata Kunci : Aplikasi, Brand Image, Fitur, Profile Matching
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): MEI-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): July 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.
Co-Authors Abdul Karim Ade Irma Agustina Lubis Agi Candra Bramantia Agus Perdana Windarto Aulia Paramita Baehaqi 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 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