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                <text>Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data</text>
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                <text>The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company so to overcome the problem of high-dimensional data, feature selection is used in selecting the number  of  features.  On the  wdbc  dataset  with  30  features  and  569  data,  feature  selection  is  performed  using  the  Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy, and the use of weighted macros on precision, recall and f1 score, using GA selection  features  has  slightly  higherresults  than  RFE  so  it  is  concluded  that  GA  selection  features  are  better  at  solving problems in high-dimensional data</text>
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                <text>Yoga Pristyanto1, Dipa Wirantanu</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5375/916</text>
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                <text>Departmentof Information System, Facultyof Computer Science, UniversitasAmikom, Yogyakarta, Indonesia</text>
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                <text>The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This research has the main aim of introducing a superiorobject detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involveobstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error  rate,  with  an  average  error  percentage  of  3.15%,  indicating  very  optimal  results.  Using  a  combined  performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but  also  efficiency  in  detection  time  and  power  consumption.  This  research  makes  an  important  contribution  in  presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives</text>
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                <text>Erwin Syahrudin1, Ema Utami2, Anggit Dwi Hartanto</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5529/917</text>
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                <text>Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Yogyakarta, Indonesia</text>
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                <text>FAJAR BAGUS W</text>
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                <text>Mapping Residential Land Suitability Using a WEB-GIS-Based Multi-Criteria Spatial Analysis Approach: Integration of AHP and WPM Methods</text>
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                <text>spatial analysis;MCDM;WPM method;AHP method; cohen's kappa</text>
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                <text>Along with the increase in population and the acceleration of economic growth, the urgent need for additional property that can  serve  as  a  place  for  various  community  activities  also  increases.  It  is typicalfor  big  cities  that  are  the  epicenter  of urbanization to experience a sharp spike in land demand. One area that has excellent accessibility is Sidarjo Regency, which is comparable to SurabayaCity in this regard. This research aims to use Web-GIS to conduct spatial data analysis to identify the most suitable land functions for use in residential areas. Analytic Hierarchy Process (AHP) and Weighted Product Model (WPM) are used in this research for spatial data modeling based on multi-criteria decision-making (MCDM). Parameters were based on distance to the city center, distance to the market, distance to the hospital, distance to public transport, slope, soil type,  and  rainfall  intensity.  The  results  of  spatial  data  modeling  categorize  the  suitability  of  new  settlement  land  into  the categories  of  uninhabitable  and  habitable  land. The  K  value  of  0.27  is  the  outcome  of  a  comparison  test  between  the  two MCDM approaches using Cohen's Kappa coefficient. The test indicates that the K value falls into the category of having a fair strength of agreement between the two methods that came into play</text>
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            <description>An entity primarily responsible for making the resource</description>
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                <text>Anik Vega Vitianingsih1, Choirul Ullum2, Anastasia Lidya Maukar3, Verdi Yasin4, Seftin Fitri Ana Wati5</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/4520/918</text>
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                <text>Informatics Department, Universitas Dr. Soetomo, Surabaya, Indonesia</text>
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                <text> 20-04-2024</text>
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                <text>FAJAR BAGUS W</text>
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                <text>Integration Waterfall and ScrumMethodology in The Development of SIMARGA Web Application</text>
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                <text>This  research  explores  the  integration  of  Waterfall  and  Scrum  methodologies  in  the  development  of  the  SIMARGA  web application.  The  integration  aims  to  maximize  the  strengths  of  each  methodology,  with  Waterfall  contributing  to  planning, analysis,  and  initial  system  structure,  while  Scrum  supports  the  creation  of  Product  Backlog  and  implementation  of  Scrum events.  The  combination  results  in  a  structured  and  methodical  product  management  process,  simplifying  task  lists  and enhancing efficiency. The achievement of good efficiency and effectiveness in task execution is facilitated by leveraging both methodologies, reducing wastage. Progress in product development and team productivity is measured through daily meetings, evaluations, and sprint reviews. Emphasis is placed on fostering strong relationships among the Scrum team, customers, and stakeholders, promoting effective communication and collaboration. However, challenges are identified in team commitment to daily meetings, potentially influenced by their involvement in additional business activities. Future efforts should focuson technological  resource  enhancement  and  software  maintenance  to  achieve  the  initially  outlined  product  goals.  The effectiveness of this product development initiative can be measured using metrics such as views and user data, particularly in Kabupaten Pegunungan Bintang</text>
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            <description>An entity primarily responsible for making the resource</description>
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                <text>Nikson Theys Muay1, Eko Sediyono2, Johan Tambotoh</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5652/920</text>
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                <text>Magister Sistem Informasi, Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana, Salatiga, Jawa Tengah</text>
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                <text>Object detection in digital images has been implemented in various fields. Object detection faces challenges, one of which is rotation problems, causing objects to become unknown. We need a method that can extract features that do not affect rotation and reliable ensemble-based classification. The proposal uses the GLCM-MD (Gray-Level  Co-occurrence  Matrix  Multi-Degrees)  extraction  method  with  classification using  K-Nearest Neighbours(K-NN) and Random Forest (RF) learning as well as Voting Ensemble (VE) from two single classifications. The main goal is to overcome the difficulty of detecting objects when the object experiences rotation which results in significant visualization variations. In this research, the GLCM method is used to produce features that are stable against rotation. Furthermore, classification methods such as K-Nearest Neighbours(KNN), Random Forest (RF), and  KNN-RF  fusion  using  the  Voting  ensemble  method  are  evaluated  to  improve  detection  accuracy.  The experimental results show that the use of multi-degreesand the use of ensemble voting at all degrees can increase the accuracy value, and the highest accuracy for extraction using multi-degreesis 95.95%. Based on test results which show that the use of features of various degrees and the ensemble voting method can increase accuracyfor detecting objects experiencing rotation.</text>
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                <text>Florentina TatrinKurniati1, Hindriyanto Dwi Purnomo2, Irwan Sembiring3, Ade Iriani4</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5597/931</text>
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                <text>Facultyof Information Technology, Universitas Kristen Satya Wacana</text>
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                <text>Facultyof Information Technology, Universitas Kristen Satya Wacana</text>
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                <text>FAJAR BAGUS W</text>
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                  <text>Vol 8 No 2 (2024)</text>
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                <text>IoT-Based Irrigation System Using Machine Learning for Precision Shallot Farming</text>
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                <text>smart farming;WSN sensors; IoT; shallot farming; enrekang</text>
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                <text>Despite the massive production of shallots in EnrekangRegency, South Sulawesi Province, Indonesia, the cultivation method is still very conventional. Shallot cultivation is very challenging because it requires precision irrigation and pest prevention. In  this  research,  we  proposed  a  smart  irrigation  system  to  help  farmers  manage  irrigation  with  more  efficient  water  usage without hampering their pest prevention. The system outcomes were three options: 1) no water needed, 2) water is required and is efficient for watering and 3) water is required but it is not efficient for watering. We used Wireless Sensor Networks and IoT to collect yield parameters, designed a firebase database, and developed a mobile application and a web service embedded with  a  machine  learning  application.  All  applications  interacted  by  using  the  Representational  State  Transfer  Application Programming  Interface.  The  proposed  system  architecture  successfully  gathered  cropland  data  and  distributed  them  to  all applications within the system. Furthermore, we analyzed four supervised learning algorithms (decision trees, random forest, gradient boosting, and K-Nearest neighbor), and the random forest was deployed in the web service because it outperformed other algorithms with an accuracy of 94% and AUC Score of 0.90</text>
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                <text>Mardhiyyah Rafrin1, Muh. Agus2, Putri Ayu Maharani</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5579/919</text>
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                <text>Computer Science,TeknologiProduksiand Industri, Institut Teknologi Bacharuddin Jusuf Habibie, Parepare, Indonesia</text>
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                <text>FAJAR BAGUS W</text>
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                  <text>Vol 8 No 2 (2024)</text>
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            <description>A name given to the resource</description>
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                <text>An Analysis of Meteorological Datain Sumatra and Nearbyusing Agglomerative Clustering</text>
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                <text>agglomerative; climate change; clustering; meteorology; sumatra</text>
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                <text>Sumatra  is  one  of  the  biggest  and  the  second  most  crowded  islands  in  Indonesia.  Sumatrais  also  a  place  of  abundance  of tropical  flora  and  fauna.  This  paper  aims  to  cluster  the  cities  in  Sumatra  and  nearby  based  on  the  meteorology  data.  It implements Agglomerative hierarchical clustering and uses a daily time series dataset from 17 cities from 1 January 2010 to 31  December  2023.  The  dataset  contains  variables  minimum  temperature,  maximum  temperature,  average  temperature, humidity, sunshine duration, and average wind speed. The preprocessing data was dedicated to managing the missing values and data aggregation to create single-form data. The single-form data contains cities and meteorological variables used as an input  for  the  clustering  algorithm,  i.e.  K-Means,  Fuzzy  C-Means,  K-Medoid,  intelligent  K-KMeans,  and Agglomerativeclustering. The Agglomerative clustering outperforms other methods (i.e. K-Means, Fuzzy C-Means, K-Medoid, and intelligent K-KMeans)  and produces Silhouette  scores of  0.11.The  clusters are then  analyzed  to  find  their unique  pattern.The  cut-off when the number cluster is two, Agglomerative hierarchical clustering gathers Aceh, Sabang, Pekanbaru, Padang, and Padang Lawas  in  Cluster  1.  Other  cities,  i.e.,  Nagan  Raya,  Batam,  Jambi,  Bandar  Lampung,  Medan,  Pangkalpinang,  Palembang, Bengkulu,  Belitung,  Tapanuli,  Deli  Serdang,  and  Nias  are  in  Cluster  2.  The resultscan  be  briefly  explained  that  the characteristic  of  Cluster  1  has  a  higher  average  temperature,  lower  humidity,  and  lower  sunshine  duration  than  cities  in Cluster 2. However, Cluster 1 has a lower average minimum temperature than Cluster 2. The pairs of cities which have the most  similarities  are  (Aceh,  Sabang),  (Pekanbaru,  Padang  Lawas),  (Nagan  Raya,  Nias),  (Jambi,  Palembang),  (Bengkulu, Tapanuli),  and  (Medan,  Deli  Serdang). The  annual  trend  in  several  cities  showsthat  there  exists  an  increasing  trend  in minimum temperature, rising sunshine duration, and decreasing wind speed. These are signs of climate change that need a proper handling</text>
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            <description>An entity primarily responsible for making the resource</description>
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                <text>Teny Handhayani1, Irvan Lewenusa</text>
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            <description>A related resource from which the described resource is derived</description>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5663/921</text>
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                <text>Teknik Informatika, Fakultas Teknologi Informasi, Universitas Tarumanagara, Jakarta, Indonesia</text>
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            <description>A point or period of time associated with an event in the lifecycle of the resource</description>
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                <text> 21-04-2024</text>
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            <description>An entity responsible for making contributions to the resource</description>
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                <text>FAJAR BAGUS W</text>
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                  <text>Vol 8 No 2 (2024)</text>
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                <text>Design and Implementation of the Shortest Path Navigation in Samosir District using Branch and Bound Algorithm</text>
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                <text>Samosir has a wide area and several tourist attractions, making it difficult for visitors to explore the island.Not knowing the route can make the journey less fun and waste time.In general, tourists seek to know the fastest path to a tourist location to save time and money while on vacation. As a result, we require an application that will offer directions to the shortest path. This  research  aims  to  develop  a  web-based  application  that  may  display  a  map of  the  shortest  travel  to  a  tourist  site. This website will display a map that marks from the origin point to the destination point. The Branch and Bound algorithm is used to determine the shortest path. The Python libraries OSMnx, Folium, and NetworkX modify paths and show a route map with OpenStreetMap. The error value of the distance between the branch, bound algorithm, and Google Maps is used to get the RMSE accuracy value. The RMSE value is 3.02and MAPE value is 0.0023indicating that the application produced already has  a  good  implementation  prototype.  Furthermore,  there  is  no  significant  distinction  between  the  appearance  of  maps implementing OpenStreetMap and Google Maps</text>
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                <text>Rudy Chandra1, Tegar Arifin Prasetyo2,Ricky Ananda Pardomuan Sitorus3,Pakhomios Havel Situmorang4,Yanti Sopia Ruth Hutasoit5,Trito Exaudi Manik</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5585/922</text>
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                <text>Department of Information Technology, Faculty of Vocational Studies,Institut Teknologi Del, Sitoluama.Indonesia</text>
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                <text>FAJAR BAGUS W</text>
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                  <text>Vol 8 No 2 (2024)</text>
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                <text>Evaluating the Bibit App: The HEART Framework Approach in UX Design</text>
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                <text>With  the  rise  of  investment  applications  like  Bibit  in  Indonesia,  evaluating  user  experience  is  crucial to  improveuser engagement and satisfaction. This study aims to assess the Bibit appuser experience using the HEART framework to identify areas  for  improvement.  A  descriptive  survey  was conducted  usingthe  Slovin  method  to  determine  a  sample  size  of approximately 100 people. The study useda questionnaire distributed throughGoogle Forms that measuresthe five aspects of the HEART frameworkon a Likert scale. Data were analyzed for validity and reliability using IBM SPSS and stored in Google Spreadsheets. Overall, the Bibit app demonstrated high satisfaction, engagement, adoption, retention, and task success among its users, with most categories scoring above the 80% target. However, specific areas, particularlyHappiness 2, Adoption 1, Retention  1,  and  Task  Success  3, requirefurther development  attention.The  Bibit  app  provides  a  generally  satisfying  user experience, with significant potential for improvement in navigation tooltips, investment product development, and app speed and efficiency.</text>
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                <text>Dandi Setya Perdana1, Lintang YuniarBanowosari</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5714/923</text>
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                <text>Postgraduate Program, Gunadarma University, Jakarta, Indonesia</text>
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                <text>24-04-2024</text>
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                <text>FAJAR BAGUS W</text>
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                <text>ENGLISH</text>
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                  <text>Vol 8 No 2 (2024)</text>
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      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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                <text>YOLO-based Small-scaled Model for On-Shelf Availability in Retail</text>
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                <text>YOLO; small-scaled model; on-shelf availability; retail; deep learning</text>
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                <text>On-shelf availability(OSA) in the retail industry plays a very crucial role incontinuous sales. Product unavailability may lead to a bad impression on customers and reduce sales. The retailindustry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advancements in recentdecades, Artificial Intelligence has begun to be applied to support OSA, particularly using object detection technology. In this research, we develop a small-scaled object detection model based on four versions of You Only Look Once (YOLO) algorithm, namely  YOLOv5-nano,  YOLOv6-nano,  YOLOv7-tiny,  and  YOLOv8-nano.  The  developed  model  can  be  used  to  support automatic detection of OSA. A small-scalemodel has developedin the sense of post-practicalimplementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scaled model implementation also offers implementation flexibility. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance</text>
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                <text>Arrie Kurniawardhani1, Dhomas Hatta Fudholi2*, Gabriel Imam Andaru3, Ahmad Azzam Alhanafi4, Nabil Najmudin</text>
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                <text>https://jurnal.iaii.or.id/index.php/RESTI/article/view/5600/925</text>
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                <text>Departmentof Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia</text>
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                <text>25-04-2024</text>
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                <text>FAJAR BAGUS W</text>
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            <description>A language of the resource</description>
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