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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
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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
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.
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
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