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All Journal Jurnal Media Infotama Jurnal Pseudocode International Journal of Artificial Intelligence Research JUTIM (Jurnal Teknik Informatika Musirawas) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JUKI : Jurnal Komputer dan Informatika GATOTKACA Journal (Teknik Sipil, Informatika, Mesin dan Arsitektur) Jurnal Multidisiplin Dehasen (MUDE) Jurnal Dehasen Untuk Negeri Jurnal Media Computer Science Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Karya Nyata Pengabdian Journal of Engineering and Technology Innovation Jurnal Komputer, Informasi dan Teknologi
Devi Sartika
Dehasen University
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Journal : jurnal media computer science

 1 
Implementation Of Additive Ratio Assessment (Aras) Method For Online Reward Driver Provision M. Iqbal Syahputra; Liza Yulianti; Devi sartika
Jurnal Media Computer Science Vol 1 No 2 (2022): Juli
Publisher : Fakultas Ilmu Komputer Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v1i2.2706

Abstract

Giving rewards to online drivers is one of the annual agendas carried out at Grab Bengkulu Branch to encourage driver motivation and professionalism in improving the quality of work. The obstacles faced in giving these rewards are due to the diversity of educational backgrounds, experiences, competencies and portfolios of the drivers, so we need a system that can assist in the process of giving rewards to drivers so that they do not become sluggish and experience difficulties. In the process of designing the application of this reward decision support system using the ARAS approach. The ARAS method is a method based on the intuitive principle that alternatives must have the largest ratio to produce an optimal solution. The ARAS method performs ranking by comparing the value of each criterion on each alternative by looking at the weights of each to obtain the ideal alternative. The implementation of the system uses the Visual Basic 2010 programming language. From the results of the tests carried out, it can be concluded that the best employee with a value of 0.90, the lowest value of 0.81
Data Mining, Decision Tree, C4.5, Community Satisfaction Index Doan Sefriyansyah; Sapri Sapri; Desi Mahdalena; Devi Sartika
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11541

Abstract

This study aims to apply a data mining method based on the C4.5 Decision Tree algorithm to classify the Public Satisfaction Index (PSI) regarding services at the Central Aceh District Attorney’s Office. Until now, the data from the CSI questionnaires collected has not been optimally utilized because it has only been processed using average calculations, thus failing to provide in-depth information regarding patterns of public satisfaction. The C4.5 Decision Tree method was used because it is capable of producing a classification model that is easy to understand and can identify the service attributes that most influence the level of public satisfaction. The data used in this study were derived from questionnaire results based on seven criteria: service procedures, service information, processing time, staff competence, technological facilities, complaint handling, and integrity. The results indicate that the C4.5 algorithm can be effectively applied to classify levels of public satisfaction and generate decision trees that serve as a basis for evaluation and decision-making.
Application Of The K-Means Algorithm in Clustering Medical Records Of BPJS Participants At Bhayangkara Hospital In Bengkulu Wahyu Rizki Rasuanto; Devi Sartika; Dimas Aulia Trianggana
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.8941

Abstract

Grouping is to separate labels from unknown data and grouping is expected to be able to identify data groups to then be labeled as desired. Cluster analysis is a multivariate analysis technique to find and organize information about variables so that they can be relatively grouped into homogeneous groups or "clusters" can be formed.The purpose of data clustering work can be divided into two, namely grouping for understanding and grouping for use. If the goal is for understanding, the formed groups must capture the natural structure of the data, usually the grouping process in this goal is only an initial process to then be continued with core work such as summarization (average, standard deviation), class labeling in each group to then be used as classification training data and so on. K-Means is one of the clustering algorithms included in the Unsupervised Learning group which is used to divide data into several groups with a partition system. This algorithm accepts input in the form of data without class labels. In the K-Means algorithm, the computer groups the data that is its input without first knowing the target class. The input received is data or objects and k desired groups (clusters).
Group Decision Support System for Selecting Student Study Concentrations Using TOPSIS Muhammad Baluqiah Al Ghazali; Indra Kanedi; Devi Sartika
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9063

Abstract

The use of GDSS in concentration selection using TOPSIS method offers a systematic and data-driven approach to group decision-making. This enables more informed decisions that are widely accepted by all group members, thereby enhancing satisfaction and achieving better outcomes. This new system will be designed to be web-based using PHP and MySQL with computerized programs. Based on the results of the design, implementation, and testing of Group Decision Support System (GDSS) for selecting student concentrations using TOPSIS method, the following conclusions can be drawn: 1.GDSS system that was developed successfully helped the process of selecting student study concentrations objectively and efficiently. By considering various criteria (academic grades, student interests, lecturer recommendations, job opportunities, facilities), the system was able to recommend the right concentration choices. 2.TOPSIS method proved to be effective in calculating concentration alternatives based on the principle of distance from positive and negative ideal solutions. 3.The use of the system accelerates the decision-making process. The average time required to determine a concentration has decreased drastically from approximately 1–2 days (manually) to less than 20 minutes with the system's assistance. 4.The implementation of a web-based GDSS makes the system more flexible and easily accessible to all parties involved, including faculty, students, and administrators
Application Of Fuzzy Algorithm In Decision Making On Honorary Signs Farhaan Fadhlurrahman; Asnawati Asnawati; Devi Sartika
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.9072

Abstract

Decision support system is an information generating system aimed at a particular problem that must be solved by the manager and assists the manager in decision making. Making a decision with many criteria requires a special handling method, especially if the criteria for making a model before the decision is taken. The SMART method is more often used because of its simplicity in responding to the needs of decision makers and analyzing responses. So the formulation of the problem in this study is how to implement the smart method of making honorary decisions. With the aim as comparative data in making decisions on honorary signs for members of the Indonesian National Police, Bengkulu Region. It is hoped that this application will always be up to date so that this application follows the development of Android-based technology with the flutter framework by utilizing the smart algorithm.
The Implementation of Mobile Application for Queue Management and Customer Service at Babe Barbershop Using Agile Method Musa Alhadi Pulungan; Devi Sartika; Ahmad Asyhari
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.10153

Abstract

Babe Barbershop faced manual queuing problems that affected schedule irregularities and customer comfort. This research aims to design and implement a more efficient and structured mobile application for queue management and customer service. Development was carried out at Babe Barbershop (Bengkulu) from February to June 2025 using Agile method with iterative stages from planning to launch, in order to easily adapt to changing needs. The application was developed with Vue.js and Firebase stack; key features include registration/login, service booking with barber and time slot selection, attendance confirmation, walk-in input, barber dashboard, and a public queue page for transparency. Black-box testing showed that all functions worked well according to the scenario: bookings were saved and had a BOOKED status, cancellations updated the status, “Already Present” changed the status and barber queue, and application navigation was smooth without errors. These results confirm that the application supports operational efficiency, reduces waiting times, and improves the service experience at Babe Barbershop..
Co-Authors Achmad Rudi Antoni Adjie Danang Aprianto Agung Pradana Ahmad Arib Alfarisy Ahmad Asyhari Ahmad Meidy Ruslida Alviyan Sahadi Annisa Arsy Tiara Anton Saputra Artika Sari Devi Asnawati Asnawati Aziz Zurahman Bimo Restu Putra Chisillia Mayangsari Dadang Kurniawan Desi Mahdalena Devri Aryansyah Dewi Suranti Dimas Aldi Prandika Dimas Aulia Trianggana Doan Sefriyansyah Dwi Krismo Putra Edo Alwis Eko Febrianto Eko Suryana Farhaan Fadhlurrahman Fauzan Latief Mukmin Feri Hari Feri Feri Saputra Firdhan Satriawan Sakti Fitri Alfarisy Haggy Sandy Prayogy Herlin Herlin Herlina Latipa Sari Ila Yati Beti Intan Permata Sari Jefry Thomas Jumadi, Juju Jusuf Wahyudi Kanedi, Indra Kanedi Khairil Khairil Lena Elfianty Lidya Anggraini Liza Yulianti Lois Kapirosi Lydia Margaretha M. Iqbal Syahputra Marfuatin Muthoharoh Muhamad Lio Meilsandre Muhammad Baluqiah Al Ghazali Muhammad Bintang Firdaus Musa Alhadi Pulungan Nanda Eka Juniawan Nanda Pratama Putra Ockhy Jey Fitter Prahasti Rahmat Hidayat Ricky Zulfiandri Riki Sumansyah Riko Mandala Putra Riska Riska Sapri Sapri Sarah Ulfah Al Amany Siswanto Siswanto Siswanto Sri Narti Tengsi Mayang Nopita Sari Tri Ramadani Venny Novita Sari Wahyu Rizki Rasuanto Yoli Andi Rozzi Yupianti, Yupianti