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<![CDATA[ Perbandingan Algoritma K-Means Clustering dengan Fuzzy C-Means Dalam Mengukur Tingkat Kepuasan Terhadap Televisi Dakwah Surau TV ]]>
<![CDATA[Malik, Rio Andika]]>;
<![CDATA[Defit, Sarjon]]>;
<![CDATA[Yuhandri, Yuhandri]]>
<![CDATA[ RABIT Vol 3 No 1 (2018): Januari ]]>
Publisher : <![CDATA[RABIT ]]>
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<![CDATA[Dawah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify and mapping the most appropriate dataset so that it can assist analysing or measuring the level of audience satisfaction toward the dawah television Surau TV. Comparison of clustering algorithm performance with K-Means Clustering modeling and Fuzzy C-Means modeling is based on processing speed and trace value of each RMSE parameter of clustering algorithm. The RMSE result of clustering research using algorithm with K-Means Clustering is 2.09879 and by using algorithm with Fuzzy C-Means model is 2.07911. Fuzzy C-Means modeling speed is faster in conducting the clustering process compared with K-Means Clustering modeling. It can be concluded that clustering with Fuzzy C-Means modeling is able to produce more accurate cluster compared to clustering with K-Means Clustering modeling accuracy
Keywords: Clustering; K-Means; Fuzzy C-Means; Satisfaction rate survey; RMSE]]>
<![CDATA[Perbandingan Algoritma K-Means Clustering dengan Fuzzy C-Means Dalam Mengukur Tingkat Kepuasan Terhadap Televisi Dakwah Surau TV ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Sarjon Defit]]>;
<![CDATA[Yuhandri Yuhandri]]>
<![CDATA[ Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 3 No 1 (2018): Januari ]]>
Publisher : <![CDATA[LPPM Universitas Abdurrab ]]>
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DOI: 10.36341/rabit.v3i1.387
<![CDATA[Da'wah Television Surau TV is a broadcasting media that presents broadcasts around Islam. This media will quickly develop as it presents broadcasting material in meeting the spiritual needs of its viewers. To Increased media development is highly dependent on the satisfaction of the audience in all aspects of broadcast supporting. It is therefore, to measure the level of audience satisfaction as an effort to generate continuous broadcast quality improvement.This research is performing of algorithm clustering comparation with K-Means Clustering modeling and Fuzzy C-Means modeling to classify and mapping the most appropriate dataset so that it can assist analysing or measuring the level of audience satisfaction toward the da'wah television Surau TV. Comparison of clustering algorithm performance with K-Means Clustering modeling and Fuzzy C-Means modeling is based on processing speed and trace value of each RMSE parameter of clustering algorithm. The RMSE result of clustering research using algorithm with K-Means Clustering is 2.09879 and by using algorithm with Fuzzy C-Means model is 2.07911. Fuzzy C-Means modeling speed is faster in conducting the clustering process compared with K-Means Clustering modeling. It can be concluded that clustering with Fuzzy C-Means modeling is able to produce more accurate cluster compared to clustering with K-Means Clustering modeling accuracy Keywords: Clustering; K-Means; Fuzzy C-Means; Satisfaction rate survey; RMSE]]>
<![CDATA[UI/UX Analysis and Design Development of Less-ON Digital Startup Prototype by Using Lean UX ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Marta Riri Frimadani]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v6i6.4454
<![CDATA[The growth of startups in Indonesia continues to experience upward growth. Behind the growth that continues to move up, there is a success rate statistic which is a contradiction behind its development. The startup statistics show that about 90% of startups fail. As many as 75% of unicorn startups believe that a good UI/UX design can increase startup valuations and additional investors' funds. User Interface (UI) and User Experience (UX) are closely related because UX results from UI interactions. Less-On is a provider of private tutoring service providers who serve as an intermediary bridge between teachers and students. This research will be carried out by integrating the processes in the Lean UX method into every process that exists at the stages of software engineering development. The results obtained from this study are a final prototype validated in terms of criticism and suggestions through a questionnaire as a form of Less-On branding. Positive UX and better usability are significant for further development of the prototype private tutor booking application, which plays a vital role in acceptance, satisfaction and efficiency in using this Less-ON application. The UI has good usability for users, with a SUS scoring earn 85.53, which is above average and acceptable.]]>
<![CDATA[Lean UX: Applied PSSUQ to Evaluate Less-ON UI/UX Analysis and Design ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Marta Riri Frimadani]]>
<![CDATA[ International Journal of Advances in Data and Information Systems Vol. 4 No. 1 (2023): April 2023 - International Journal of Advances in Data and Information Systems ]]>
Publisher : <![CDATA[Indonesian Scientific Journal ]]>
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DOI: 10.25008/ijadis.v4i1.1263
<![CDATA[Indonesian start-up growth continues to show upward growth. Behind the upward movement of change are success statistics that develop contradictions. According to start-up statistics, about 90% of start-ups fail. Up to 75% of unicorn start-ups believe his excellent UI/UX design can boost start-up valuations and additional investment capital. Less-On is a tutoring provider that acts as an intermediary between teachers and students. This research is done by integrating the process of Lean UX methodology into each process present in each phase of software development. The results obtained from this research are final prototypes that have been validated in terms of criticism and suggestions through questionnaires in the form of lessons on start-up branding. The positive user experience and excellent usability will help further the development of the tutor booking application prototype. This plays an important role in the acceptance, satisfaction and efficiency of using this Less-ON application. The user interface has excellent usability for users tested using the PSSUQ, with an overall average score of 2.136, indicating system usability, information quality, interface quality, and overall Satisfaction-based demonstrates that the system is highly acceptable.]]>
<![CDATA[Diseminasi Pentingnya Copywriting Untuk Meningkatkan Enggagement Bagi Komunitas Ikan Hias ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Sri Mona Octafia]]>
<![CDATA[ Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 3 No. 4 (2023): Juli 2023 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat ]]>
Publisher : <![CDATA[Indonesian Scientific Journal ]]>
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DOI: 10.59395/altifani.v3i4.461
<![CDATA[Pengelolaan pemasaran dalam era digital terkait dengan penerapan strategi pemasaran digital menggunakan media digital dan internet dengan fokus pada copywriting. Kegiatan pengabdian ini bertujuan untuk memperluas pemahaman akan pentingnya copywriting dalam meningkatkan pemasaran pada Komunitas Ikan Hias di Kelurahan Aia Pacah Padang. Kegiatan ini mencakup serangkaian pelatihan dan pendampingan untuk meningkatkan pengetahuan dan keterampilan copywriting dan pemasaran digital bagi anggota komunitas mencakup prinsip-prinsip copywriting yang efektif, penggunaan bahasa persuasif, penyusunan konten menarik, serta teknik penulisan iklan dan deskripsi produk yang menarik perhatian. Tujuannya agar menciptakan konten pemasaran yang lebih menarik dan persuasif, yang berkontribusi pada pertumbuhan bisnis ikan hias dan menarik minat masyarakat terhadap industri ikan hias di wilayah tersebut. Secara keseluruhan, kegiatan pengabdian ini memberikan sumbangan positif dalam meningkatkan pemahaman dan keterampilan copywriting, serta memperkuat keterlibatan dan pertumbuhan bisnis ikan hias. Keberhasilan kegiatan ini menunjukkan betapa pentingnya copywriting dalam konteks pemasaran digital dan memberikan manfaat yang signifikan.]]>
<![CDATA[Enhancing Soil-Transmitted Helminth Detection in Microscopic Images Using the Chain Code for Object Feature Extraction ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Marta Riri Frimadani]]>;
<![CDATA[Dwipa Junika Putra]]>
<![CDATA[ International Journal of Advances in Data and Information Systems Vol. 4 No. 2 (2023): October 2023 - International Journal of Advances in Data and Information System ]]>
Publisher : <![CDATA[Indonesian Scientific Journal ]]>
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DOI: 10.25008/ijadis.v4i2.1305
<![CDATA[Soil-Transmitted Helminth (STH) infections are a grave global health issue, which involves particularly in countries that are developing with insufficient sanitation and limited access to healthcare. With better intestinal helminth egg detection technology, health facilities in areas with limited resources can identify and treat these infections more promptly. It is necessary to create a strong framework and an effective method to solve this challenge. The outcomes of this study could assist in parasite infection discovery and public health. Chain code-based feature extraction strategy can also be the foundation for the development of comparable approaches for diagnosing various parasitic diseases. Overall, the neural network design used in this study makes the model that is produced a good model that assigns well to never-before-seen data. The significance of image processing technologies in the medical field is shown by this study.]]>
<![CDATA[Pengukuran Tingkat Kepuasan Mahasiswa Terhadap Bahan Ajar Mata Kuliah PTI Menggunakan Algorithma K-Means Clustering ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Wilis Firmansyah]]>
<![CDATA[ Jurnal Janitra Informatika dan Sistem Informasi Vol. 3 No. 2 (2023): Oktober - Jurnal Janitra Informatika dan Sistem Informasi ]]>
Publisher : <![CDATA[Indonesian Scientific Journal ]]>
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DOI: 10.25008/janitra.v3i2.174
<![CDATA[Media pembelajaran memiliki kedudukan yang sangat penting dalam mencapai tujuan pembelajaran secara efektif. Berbagai penelitian yang dilakukan terhadap penggunaan media dalam pembelajaran sampai pada kesimpulan bahwa proses dan hasil belajar setiap siswa menunjukkan perbedaan yang signifikan antara pembelajaran tanpa media dan pembelajaran menggunakan media. Penelitian ini menggunakan ilmu komputasi dan metode numerik dengan pendekatan model formulatif dimana pengolahan algoritma clustering menggunakan pemodelan K-Means memetakan dataset yang paling tepat sehingga dapat membantu menganalisis atau mengukur tingkat kepuasan suatu media pembelajaran. Hasil yang diperoleh dari evaluasi akan memberikan petunjuk kepada dosen tentang bagian mana dari media pembelajaran yang baik dan bagian mana yang kurang baik sehingga belum dapat mencapai tujuan pengembangan media pembelajaran yang dalam hal ini. Dengan menggunakan k-means diperoleh hasil evaluasi media pembelajaran studi kasus mata kuliah Pengenalan Teknologi Informasi menjadi 2 cluster. Dapat disimpulkan bahwa clustering hasil cluster dengan pemodelan K-Means mampu untuk menghasilkan akurasi cluster yang presisi.]]>
<![CDATA[Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Faizal Riza]]>;
<![CDATA[Sarjon Defit]]>
<![CDATA[ Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 1 (2024): Vol. 15, No. 1 April 2024 ]]>
Publisher : <![CDATA[Institute for Research and Community Services, Udayana University ]]>
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DOI: 10.24843/LKJITI.2024.v15.i01.p05
<![CDATA[This research aims to develop a fast and adaptive sentiment evaluation approach related to the use of white paracetamol using a combination of the K-Nearest Neighbors (KNN) algorithm, Synthetic Minority Over-Sampling Technique (SMOTE), and the Receptive Deciding concept. Imbalances in the dataset, where positive sentiment may predominate, are addressed through the use of SMOTE to synthesize minority class samples. The KNN algorithm is applied to build a sentiment classification model, while Receptive Deciding is used to provide adaptive intelligence to changes in sentiment. The SMOTE oversampling process is carried out to achieve class balance, while KNN is used to classify sentiment. Receptive Deciding is applied to increase the model's adaptability to changes in sentiment. The research results show that the integration of the SMOTE, KNN, and Receptive Deciding methods provides an effective approach in assessing sentiment accurately and adaptively. The developed model is able to recognize changes in sentiment over time and provide balanced evaluation results. These findings are expected to contribute to understanding public sentiment towards the use of white paracetamol, as well as being the basis for developing more effective health communication strategies.]]>
<![CDATA[Enrichment of microscopic photographs by utilizing CNN regarding soil-transmitted helminths identification ]]>
<![CDATA[Rio Andika Malik]]>;
<![CDATA[Marta Riri Frimadani]]>;
<![CDATA[Dwipa Junika Putra]]>
<![CDATA[ International Journal of Advances in Applied Sciences Vol 13, No 1: March 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijaas.v13.i1.pp46-53
<![CDATA[Soil-transmitted helminth (STH) infection remains a significant global health challenge, affecting millions of people, particularly in developing countries. A convolutional neural network (CNN) approach to optimize the detection of STH infections in microscopic images. The study aims to assess the effectiveness of the CNN model in identifying and classifying STH worm eggs accurately. The research employs MATLAB as the primary tool for conducting experiments and validation tests. By implementing image preprocessing techniques to enhance image quality and applying precise segmentation methods, the CNN model is trained on a dataset of microscopic images to learn and classify STH infections effectively. The validation test results demonstrate that the CNN model achieved a high accuracy rate of 92.31% in classifying STH infections. This accuracy surpasses traditional methods, which are time-consuming and susceptible to human errors. This study underscores the importance of integrating artificial intelligence, particularly CNN, into the healthcare domain to support detecting and diagnosing diseases requiring specialized expertise, such as STH infections. The findings of this research can serve as a valuable reference for researchers, medical practitioners, and data scientists in leveraging artificial intelligence to enhance the quality of healthcare services, leading to positive impacts on society worldwide.]]>
<![CDATA[Pengukuran Tingkat Kepuasan Mahasiswa Terhadap Pelayanan di Kantin Kampus Menggunakan Algoritma K-means Clusterring ]]>
<![CDATA[Carelsa, Hasnah Vithon]]>;
<![CDATA[Malik, Rio Andika]]>;
<![CDATA[Putra, Dwipa Junika]]>
<![CDATA[ Journal of Information System and Education Development Vol. 1 No. 3 (2023): Journal of Information System and Education Development ]]>
Publisher : <![CDATA[Manna wa Salwa Foundation (Yayasan Manna wa Salwa) ]]>
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<![CDATA[The K-means Clustering algorithm technique is being used in this study to gauge how satisfied students in the Universitas Perintis Indonesia Digital Business programme are with the cafeteria's offerings. The study focuses on customer service characteristics such meal quality, cost, speed of service, cleanliness, and comfort in the cafeteria setting. The goal of the research is to provide deeper insights into student expectations and preferences for cafeteria services by utilising K-means to uncover distinct satisfaction patterns among student groups. When used to measure student satisfaction with cafeteria services, the K-means Clustering method is successful at identifying groups of students who have similar patterns of satisfaction. Some student groups score food quality and cleanliness favourably, according to the clustering data, while other groups may be more critical. In light of the preferences of each student group, cafeteria management can use this data to develop more specialised plans for improving services. The study also shows that using the K-means Clustering method to evaluate customer satisfaction offers a potentially advantageous strategy for enhancing service quality across a variety of service sectors.]]>
