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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI KLIK: Kajian Ilmiah Informatika dan Komputer
Rima Tamara Aldisa
Universitas Nasional, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Kombinasi Algoritma Sequential Search dan Fitur Autocomplete Pada Aplikasi Arsip (E-Arsip) Perpustakaan Berbasis Web Muhammad Furqon Sjofjan; Fauziah Fauziah; Rima Tamara Aldisa
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3748

Abstract

Files have the capability of being a source of data, therefore the existence of archives in an agency plays an important role in broad activities, given the importance of archives, consideration is needed in data management to ensure agency archives are stored properly and are easy to find. Searching for data through the site has been greatly developed along with the support of improved equipment, data search can be done using the search information stored in the program, search algorithms are a collection of software programs created to make it easier for clients to find and track data. Currently in search enter the word or phrase byuser to find data that is still difficult to find. Therefore we need features that can help users in searching. So this research was made and the application used two algorithms that can help in typing words or phrases namelyAutocomplete in order to assist clients in searching for data, and algorithms Sequential Search to make it easier to find client data. The average run time of the Sequential Search algorithm from 250 user data is 0.00496 seconds, and the word input search process with Autocomplete yields results for 100 percent of the 250 corresponding database data.
Pengenalan Bendera Negara Dengan Fisher Yates- Shuffle Pada Game Edukasi Android Menggunakan Metode GDLC Desty Rahma Fadilla; Fauziah Fauziah; Rima Tamara Aldisa
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3754

Abstract

This research aims to address the negative impact of less educational games by creating an interactive educational game for Android that combines learning about national flags and memorizing flag images, thus enhancing logical thinking skills in children. It is developed using the Flutter framework as the development platform and follows the GDLC method, with alpha testing conducted using the blackbox method. The Fisher-Yates Shuffle algorithm is implemented for shuffling the flag images and the order of elements in the card list. The testing method involves using the Debug Console in Visual Studio Code to verify the success of the shuffling process. The conducted testing, performed six times, demonstrates the algorithm's success in shuffling the card order for each level. In the easy level, the randomized order is 6, 10, 8, 5, 3, 4, 11, 2, 7, 1, 12, 9. In the normal level, the randomized order is 6, 11, 20, 16, 1, 13, 19, 17, 9, 2, 15, 4, 10, 12, 14, 8, 5, 7, 2, 18. In the hard level, the randomized order is 2, 14, 1, 21, 7, 18, 24, 22, 10, 23, 6, 11, 15, 9, 17, 4, 8, 3, 5, 13, 16, 12, 19, 20. The Fisher-Yates Shuffle algorithm successfully generates diverse shuffling of flag images and avoids repeating the random order of cards each time the game starts. Thus, this game can provide stimulation to the brain and encourage children to effectively enhance their object memory skills.
Analisa Perbandingan Metode MAUT dan Metode TOPSIS Dengan Menggunakan Pembobotan ROC Dalam Sistem Pendukung Keputusan Pemilihan Calon Kepala Desa Ahmad Rifqi; Rima Tamara Aldisa
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3829

Abstract

This study aims to carry out a comparative analysis process between the MAUT method and the TOPSIS method using ROC weighting. The MAUT method functions to deal with problems in decision making that link several interrelated attributes, in which the method combines the decision maker's preferences for the relevant attributes by giving a weight value to each attribute. Meanwhile, the TOPSIS method is used to select the best alternative from the many choices based on their relative distances from the ideal solution. In this study, weighting using the ROC method is used to objectively determine attribute weight values. The results of the research conducted show that the MAUT Method and the TOPSIS Method with ROC weighting can give good results in determining village heads. The results of the ranking using the MAUT method are A4 with a value of 0.950. while the ranking results using the TOPSIS method are A4 with a value of 0.917.
Penerapan Metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Dalam Pemilihan Kepala Laboratorium Menggunakan Pembobotan Rank Order Centroid (ROC) Puspa Ayu Sholeha; Rima Tamara Aldisa
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3872

Abstract

In selecting the head of the laboratory, someone who has extensive knowledge in the specific field of the laboratory is needed, as well as good management and leadership skills. They may also be involved in developing new methods, selecting equipment, and developing laboratory policies. However, the challenge in the selection process for a new laboratory head is the high number of applicants and a series of selection stages which are time-consuming, including administrative selection, competency evaluation, and interviews. This causes difficulties for schools in making decisions to get candidates for laboratory heads that match their expectations. In solving problems in laboratory selection, the authors make 5 criteria including qualifications, experience, leadership, communication skills, and responsibility. So this research also needs a system that can help to solve existing problems. A system that can help solve problems is a Decision Support System (SPK). In completing this research, the writer has to find the weight value of the criterion data where the writer uses the ROC method to produce the weight value and after that the writer also searches for the ranking value using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The produce of ranking values in the TOPSIS method, the author must work according to predetermined steps in order to obtain precise and accurate preference values. The ROC and TOPSIS methods really help the writer in generating values accurately. So from the above calculations using the TOPSIS method the chosen head of the laboratory is Zakiyah Alifah with a total value of 1.
Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam Ahmad Rifqi; Rima Tamara Aldisa
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4278

Abstract

Apart from that, there are efforts to provide for the needs of its members as well as financial assistance for education, health and there are also concessions needed by the members. By conducting this customer cluster, it will help the company determine its potential customers so that it can implement the right marketing strategy for each type of existing customer, and will certainly provide benefits for the company in increasing the quality and loyalty of customers towards the company. Data mining has functions, namely prediction, description, classification and clustering functions. Data mining also has many methods for its application, one of these methods is K-Means. The K-Means Clustering algorithm can be implemented in grouping potential customers, especially in savings and loan cooperatives. Based on the data sampling used, the data can be grouped into 2 (two) clusterings.
Penerapan Data Mining Untuk Penjurusan Kelas dengan Menggunakan Algoritma K-Medoids Jhiro Faran; Rima Tamara Aldisa
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4313

Abstract

Class assignments are carried out to focus students on the subjects that will be studied during Senior High School (SMA). Class majors are generally carried out in class of all the main values used in the class majoring process. This is a problem with the class majoring process, where mistakes often occur in the class majoring process. Mistakes regarding class majors made by students will have quite a fatal impact on the student, apart from not being able to change classes, it will also have a laziness effect on the student because it does not match the student's abilities. Solving this problem can be done using a technique called data mining. The solution to this problem is done using clustering. The K-Medoids algorithm is the algorithm used to solve the problems in this research. The process of grouping or forming clusters in the K-Medoids algorithm is based on calculating the closest distance to each object, calculating the closest distance is based on determining the centeroid value first. The K-Medoids algorithm can form 2 (two) clusters according to existing class majors. The results obtained show that there are 3 (three) alternatives included in cluster 1 and also 12 (twelve) alternatives included in cluster 2.
Penerapan Sistem Pendukung Keputusan Dengan Menggunakan Metode EDAS Dalam Seleksi Penerimaan Penyiar Radio Mohammad Aldinugroho Abdullah; Rima Tamara Aldisa
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4393

Abstract

A broadcaster is an individual who is responsible for conveying information, entertainment, and interacting with listeners through radio broadcasts. In the world of radio broadcasting, there are a number of criteria that must be met including education, self-confidence, musical knowledge, vocal quality and communication skills. However, the selection process often faces several challenges, including bias in selection and a lack of transparency and objectivity. Some radio stations still use conventional methods such as direct interviews or written tests, which have proven to be less effective. This is where modern technology and selection methods, such as Decision Support Systems (DSS), can play an important role. SPK is information technology that assists in decision making during the radio announcer selection process, with the aim of increasing the effectiveness and efficiency of decisions. Research using the EDAS (Distance From Average Solution) method has proven that the application of SPK can improve the radio announcer selection process. Radio stations can produce high-quality broadcasters and ensure broadcasts that appeal to listeners. The calculation results using the EDAS method show a value of 1 in the Q1 criterion on behalf of Setyowati Budi, validating the success of the selection process.
Pemilihan Auditor Internal dalam Mengimplementasikan Pendekatan Metode Multi Attribute Utility Theory (MAUT) dan Menerapkan Pembobotan Rank Order Centroid (ROC) Mohammad Aldinugroho Abdullah; Rima Tamara Aldisa
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6795

Abstract

The Internal Auditor of a company is a professional responsible for evaluating, overseeing, and providing independent assessments of the effectiveness of internal controls, accounting systems, and business processes of a company. The primary task of the internal auditor is to assist the company's management in ensuring that its operations adhere to applicable standards, policies, and regulations. The presence of an internal auditor helps the company maintain integrity, accountability, and compliance with relevant standards and regulations. They provide an independent view of the company's performance and aid in enhancing operational efficiency and effectiveness. In the complex business world, selecting a candidate for the role of Internal Auditor poses a challenge. Diverse selection criteria, such as auditing skills, industry knowledge, and integrity, often prove difficult to objectively assess. Decision-making based solely on experience can lead to inconsistent outcomes. The importance of accuracy and objectivity in selecting an Internal Auditor demands a scientific approach. The Multi-Attribute Utility Theory (MAUT) analysis method is employed to address the complexity of criteria. Meanwhile, the Rank Order Centroid (ROC) method is used to assign weights to each criterion. By combining MAUT and ROC in a support system, companies have a more structured and measurable way to select potential Internal Auditors. This approach is expected to help overcome issues in candidate selection that often do not align with the company's needs, and to provide more accurate and objective decisions. The ultimate result obtained by applying the MAUT method is a value of 0.794, which is the highest ranking among the 7 selected alternatives. The highest ranking result associated the seventh alternative, named Poppy Rosana.
Sistem Pendukung Keputusan Perbandingan Metode MOORA Dengan MOOSRA Dalam Pemilihan Hair Stylish Mohammad Aldinugroho Abdullah; Rima Tamara Aldisa
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 1 (2023): September 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6824

Abstract

This study aims to compare the effectiveness of the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) and MOOSRA (Multi-objective Optimization on the Basis of Simple Ratio Analysis) methods in the context of selecting stylish hair at the barbershop. In the growing hair care industry, the selection of stylish hair does not only affect the appearance of the customer but also plays an important role in the image and success of the barbershop itself. Therefore, it is important for barbershop owners to choose the right stylish hair. The MOORA method is known for its ability to solve multi-objective decision-making problems by utilizing ratio analysis. Meanwhile, MOOSRA is another method that focuses on optimization by considering relative preferences. In the context of selecting stylish hair, both can be useful tools in guiding barbershop owners to choose stylish hair according to customer needs and preferences. This research involves collecting data regarding customer preferences and hair stylish characteristics from various barbershops. This data was then analyzed using the MOORA and MOOSRA methods to choose the most suitable hair style for each scenario. The results of the analysis will be compared to assess the relative performance of the two methods in this context. It is hoped that the results of this research will provide valuable insights for barbershop owners and the hair care industry in general. By understanding the advantages and limitations of each method, barbershop owners will be able to make more informed decisions in selecting stylish hair. In addition, this research can also contribute to the development of a methodology in more complex multi-objective decision-making, by providing concrete examples in practical applications. The final results of the calculations of the two methods are proven to produce the same highest ranking result, which is obtained by alternative 1 on behalf of Poppy Sukma.
Analisis Data Mining dalam Komparasi Average Linkage AHC dan K-Means Clustering untuk Dataset Facebook Live Sellers Jhiro Faran; Rima Tamara Aldisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6892

Abstract

Facebook Live is a social media platform owned by Facebook that allows users to broadcast videos directly or live stream via the internet. Users can share moments in real-time with friends, followers, or members of certain groups. The platform allows anyone with a Facebook account to create live video broadcasts from a mobile device or computer equipped with a webcam. Many Micro, Small and Medium Enterprises (MSMEs) use Facebook Live as a tool to sell products or services directly to their audience. This strategy is increasingly popular in direct marketing on social media, especially in countries such as China and Thailand. Sellers on Facebook Live, known as Facebook Live Sellers, broadcast live on the platform to introduce products or services. They explain all the features offered, answer questions from viewers, and encourage them to make a purchase immediately. To increase buyer interest, they often offer special offers or discounts. Facebook Live Sellers can also be considered a form of influencer marketing, where individuals or businesses build a loyal following and use their influence to promote products and services. Despite the potential benefits, Facebook Live Sellers also face challenges. They interact directly with potential buyers, who may sometimes be dissatisfied with the product offered or the way the seller promotes it. Therefore, evaluations such as comments, reactions (such as like, unlike, angry), and other interactions during broadcasts are important. This research aims to group potential buyers' reactions during Facebook Live broadcasts as a strategy to overcome several problems in direct sales via this platform. In addition, grouping by the number of likes and comments can help sellers identify the most active groups of buyers and have the potential to become loyal customers. The number of data samples was determined using the Solvin method so that the dataset that became the data sample was 341 data. The methods used for grouping are K-Means and AHC (Average linkage) with the final results showing that the amount of data grouped into three clusters by both methods is the same, with most of the data being in Cluster 0, namely 98.5% of the total data sample. . Cluster 1 has a small amount of data, namely 0.6%, while Cluster 2 has 0.9% of the data sample.
Co-Authors Ade Priyatna Afid Rozaqi Afif Arofi Agung Triayudi Ahmad Ilham Kushartanto Ahmad Rifqi Akbar Mulia Ardiansyah Andilaw Andilaw Ariel Cahyono Arina Mana Sikana Azizah Azizah Deby Monalisa Simanjuntak Desty Rahma Fadilla Erwin Samudra Fatiyah Saidah Fauziah Fauziah Fifto Nugroho Fikri Fajar Asshiddiqi Iskandar Fitri Jhiro Faran Kasta Yudha Siahaan Kelik Sussolaikah Mawan Br Hutahaean Mesran, Mesran Mohammad Aldinugroho Abdullah Mohammad Aldinugroho Abdullah Mohammad Aldinugroho Abdullah Mohammad Aldinugroho Abdullah Muhammad Faizal Muhammad Furqon Sjofjan Muhammad Naufal Rifqi Nabila Tiara Nuraini Pandu Maulana Puspa Ayu Sholeha Puspa Ayu Soleha Putra Dama Ramadhan Rifando Adeyansa Purba Rossa Sahara Sanwani Sanwani Sardima Siregar Sarpita Laia Sechan Alfarisi Septi Andryana Susi Agustini Sinaga Tiyas Asih Qurnia Putri Triyono, Gandung