SISFOTENIKA
Jurnal Ilmiah SISFOTENIKA diterbitkan oleh LPPM STMIK Pontianak dan IndoCEISS. Frekuensi Terbit Tengah Tahunan (2 kali dalam setahun, yaitu bulan Januari dan Juli). Topik yang akan dipublikasikan oleh jurnal SISFOTENIKA berhubungan dengan teknologi informasi, komunikasi dan komputer yang berbentuk kumpulan/akumulasi pengetahuan baru, pengamatan empirik atau hasil penelitian, dan pengembangan gagasan atau usulan baru.
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<![CDATA[Skin Cancer Classification Using Random Forest Algorithm ]]>
<![CDATA[Nurul Khasanah]]>;
<![CDATA[Rachman Komarudin]]>;
<![CDATA[Nurul Afni]]>;
<![CDATA[Yana Iqbal Maulana]]>;
<![CDATA[Agus Salim]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1122
<![CDATA[Skin cancer is an excessive lump of skin tissue that affects the skin, has an irregular structure with cell differentiation at various levels in chromatin, nucleus and cytoplasm, is expansive, infiltrative to damage the surrounding tissue, and metastasizes through blood vessels and lymph vessels. Diagnosis of skin cancer by biopsy process is considered less effective because it costs a lot and can injure human skin as a sample. For that, we need a system for classification of skin cancer types that are effective and accurate. The application of machine learning has been widely used in the health sector. One of the machine learning methods is Random Forest. In this study, the histogram color feature extraction will be carried out, the hue moment shape extraction, and the haralick texture extraction. Furthermore, the image will be classified using the Random Forest algorithm. The best accuracy value obtained from the histogram feature extraction process and classification with Random Forest is 0.850822. The novelty of this research is the use of more diverse feature extraction and better accuracy results than previous studies. Future research is expected to use deep learning algorithms with CNN (Convolutional Neural Network) architecture to get better accuracy results and add application designs for the application of models that have been formed in the study so that they can be directly applied by the medical team.]]>
<![CDATA[Comparison of Machine Learning Algorithms for Classification of Drug Groups ]]>
<![CDATA[Purwono Purwono]]>;
<![CDATA[Anggit Wirasto]]>;
<![CDATA[Khoirun Nisa]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1134
<![CDATA[The stages of clinical trials need to be carried out when determining a new drug group for patient management. This stage is considered quite long and requires a lot of money. Medical record system data continues to grow all the time. The data can be analyzed to find a pattern of grouping drugs used in the treatment of patients based on their body condition. Utilization of artificial intelligence (AI) technology can be done to classify drug data used during patient care. Machine learning as a branch of science in the AI field can be a solution to deal with these problems. Machines will learn, analyze, and predict drug requirements quickly with less cost. Based on related research, we contribute to comparing the performance of the best machine learning algorithms that can be used as drug classification models. The results of this study are the accuracy of the support vector machine algorithm is 94.7% while the random forest and decission tree algorithms are 98.2%. This shows that the algorithms that can be considered as a drug classification model are random forest and decision tree. This model needs to be tested on a larger dataset to produce the best accuracy value.]]>
<![CDATA[ClausTher VR: Claustrophobia Therapy using Virtual Reality ]]>
<![CDATA[Joe Yuan Mambu]]>;
<![CDATA[I Gede Purnawinadi]]>;
<![CDATA[Renaldy Luntungan]]>;
<![CDATA[Septian Mottoh]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1126
<![CDATA[Fear or anxiety about a particular situation or object is called phobia, one of the phobias that exists today is Claustrophobia which is a phobia of a closed or small room. The current Claustrophobia therapy method is still traditional, namely by placing the patient in a place that will trigger the patient’s Claustrophobia. This study aims to produce a therapeutic aid and diagnosis of Claustrophobia with Virtual Reality Exposure Therapy method that uses Android-based Virtual Reality and Photogrammetry technology that can be used by medical experts or therapist when conducting therapy to Claustrophobia patients. The research method used is prototyping model. The way to collect data that will be used is by interview and application testing. Researchers used the Unity3D Engine software to create Virtual Reality application and Agisoft Metashape software to create Photogrammetry objects. The application is expected to facilitate the Claustrophobia patients and help people to predict the level of Claustrophobia suffered. The app produced on this research was able to produce the expected virtual environment and had a limited test to user and had similar reaction to a real room. For future research additional rooms and exploration mode may be added, as well as further evaluation to see how this application can be used for therapy tools.]]>
<![CDATA[Cluster Evaluation Weighing Intercomparison Data with Self Organizing Maps Algorithm ]]>
<![CDATA[Arif Fajar Solikin]]>;
<![CDATA[Kusrini Kusrini]]>;
<![CDATA[Ferry Wahyu Wibowo]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1153
<![CDATA[Laboratory intercomparison is one of method to determine the ability and assess the performance of a laboratory. Laboratory performance can be seen from the evaluation of the En ratio’s value, which is a comparison between the difference in the value test of the participant's laboratory with reference’s laboratory and the difference in the square root of the uncertainty value form participant's laboratory and reference’s laboratory. The laboratory is declared equivalent if the En value is in the range of En ≤|1|. Intercomparisons evaluation can also be done by utilizing one of the data mining technologies in the form of clustering. Clustering is done by using self-organizing maps algorithm, which is an unsupervised learning algorithm. The advantage of clustering in evaluating intercomparation data lies in its ability to group data into several clusters that have closeness or similarity in characteristics / traits / characters of data, making it easier for intercomparation organizers to provide analytical recommendations for improving laboratory performance. Intercomparation data are grouped based on the homogeneity between members in one cluster and heterogeneity among the clusters. To get the best number of clusters, evaluation is carried out through three testing methods, pseudo-F statistic, icdrate and davies bouldin index. From several experiments, the largest pseudo-F statistic value was 167.53, the smallest icdrate value was 0.071 and the smallest DBI value was 0.053 for the 1000 g artifact. As for the 200 g artifact, the largest pseudo-F statistic value was 104.86, the smallest icdrate value was 0.289 and the smallest DBI value was 0.306]]>
<![CDATA[Decision Support System for Online Learning Media Selection During the Pandemic Period ]]>
<![CDATA[Fitriyani Fitriyani]]>;
<![CDATA[Yuyi Andrika]]>;
<![CDATA[Melati Suci Mayasari]]>;
<![CDATA[Anisah Anisah]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1127
<![CDATA[Online learning media is one of the uses of technology in the form of software. During the COVID-19 pandemic, the government issued a policy for distance learning, so to facilitate the teaching and learning process between teachers and students who are in different places, online learning media are needed that can be used by educators or students. For this reason, researchers conducted research on online learning media. Alternatives used by researchers include google classroom, zoom free, google meeting free, and WhatsApp group. The criteria used by the researcher are easy to access, according to purpose, time limit for access, interactivity. The method used is the SAW (Simple Additive Weighting) method. This method is a method for calculating the weights of the criteria and alternatives so that the final weight of each alternative is obtained so that it is known which alternative has the highest value weight that can be recommended to decision makers. From the research results obtained WhatsApp group with a weight of 0.75, google classroom with a weight of 0.74, google meeting free with a weight of 0.67, zoom free with a weight of 0.55. It is necessary to do further research using different methods or combining several methods so that the research conducted by this researcher is not the end of research with the same discussion.]]>
<![CDATA[Push Notification Using Firebase Cloud Messaging (FCM) on Employee Attendance Application ]]>
<![CDATA[Abdussalam Abdussalam]]>;
<![CDATA[Bayu Wicaksono]]>;
<![CDATA[Ajib Susanto]]>;
<![CDATA[Sudaryanto Sudaryanto]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1150
<![CDATA[The attendance system uses a tool called a fingerprint like a developing technology, if there is a new technology that is old, it will show its shortcomings, namely making long queues at the time of attendance, having to come to the place of absence, and requiring processing for real time notifications, plus at this time during the Covid-19 pandemic which requires employees to work from home (WFH). The purpose of this study is to produce an android-based attendance system that can be installed on all employees' android phones using Firebase Cloud Messaging (FCM) for real time notification features that will appear on the android phones of the dept heads and staff who use it. This study uses a prototyping system development method with a sequence of stages: needs analysis, design, prototype making, prototype evaluation, testing, implementation, and maintenance. The results of the notification application test successfully appear on the employee side and on the leadership side in the process of applying for leave or permission either approved or rejected on employees' android phones without having to open the application first and this application can be accessed anywhere and anytime to process permits or leave employees and provide real time notifications.]]>
<![CDATA[Diabetic Wound Segmentation Using Masking Contour Image Processing ]]>
<![CDATA[Wien Fitrian Roshandri]]>;
<![CDATA[Ema Utami]]>;
<![CDATA[Agung Budi Prasetio]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1114
<![CDATA[Measuring the wound area in diabetics is still using a manual way with a wound ruler. Whereas the ruler affixed to the wound will become a contaminated agent that can transmit the infection to other recipients. Digital measurement methods are needed to solve the problem. However, clarifying the boundaries between the wound and the skin requires carefulness and high accuracy. For this reason, it has needed an imaging method that can do segmentation between the wound and the skin boundary for diabetic patients based on digital, called digital planimetry. This study uses a masking contour image processing algorithm from the Hue, Saturation, Value (HSV), Then doing iteration five times and gamma filter. So the result of segmentation is formed. This study concludes that the segmentation with this method has not been able to perform the segment properly, and it requires more masking values, but the results of the 5th iteration got a minor error, which is 0.002%. The digital imaging carried out in this study could be developed to be a digital-based diabetic patient wound measurement tool.]]>
<![CDATA[Survey of Chatbot Testing Methods on Social Media to Measure Accuracy ]]>
<![CDATA[Ratna Ayu Sekarwati]]>;
<![CDATA[Ahmad Sururi]]>;
<![CDATA[Rakhmat Rakhmat]]>;
<![CDATA[Miftahul Arifin]]>;
<![CDATA[Arief Wibowo]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1099
<![CDATA[The design of Chatbot aims to facilitate social activities in all areas to be considered. A Chatbot is one type of machine that can communicate with humans using natural language. Chatting is used to communicate, and it is a written conversation. Chatbot is a form of application implementation Natural Language Processing (NLP) that belongs to one branch of artificial intelligence or Artificial Intelligent AI social media now provides a service that allows developers to process and integrate chatbot applications. This paper aims to review the papers that build chatbot applications for various social media using various testing methods. The contribution to this paper is to determine which method can measure the level of chatbot accuracy best. This review paper will choose the equations of the most widely used test methods and social media from various papers, so that further research is expected to implement the right testing methods and use better social media in terms of user experience, features, and services. According to the review papers and papers, the Black-box and System Usability Scale testing methods are the most commonly used in the review papers. This testing method is a type of method that performs testing for the flow and how the chatbot works to achieve functional validation completely.]]>
<![CDATA[Implementation of Deep Learning on Number Recognition in Sign Language ]]>
<![CDATA[Fini Keni Celsia]]>;
<![CDATA[Green Arther Sandag]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1117
<![CDATA[Measuring the wound area in diabetics is still using a manual way with a wound ruler. Whereas the ruler affixed to the wound will become a contaminated agent that can transmit the infection to other recipients. Digital measurement methods are needed to solve the problem. However, clarifying the boundaries between the wound and the skin requires carefulness and high accuracy. For this reason, it has needed an imaging method that can do segmentation between the wound and the skin boundary for diabetic patients based on digital, called digital planimetry. This study uses a masking contour image processing algorithm from the Hue, Saturation, Value (HSV), Then doing iteration five times and gamma filter. So the result of segmentation is formed. This study concludes that the segmentation with this method has not been able to perform the segment properly, and it requires more masking values, but the results of the 5th iteration got a minor error, which is 0.002%. The digital imaging carried out in this study could be developed to be a digital-based diabetic patient wound measurement tool.]]>
<![CDATA[Implementation of Simple Additive Weighting Method in the Best Lecturer Selection Application ]]>
<![CDATA[Hendra Effendi]]>;
<![CDATA[Deri Syabirin]]>;
<![CDATA[Muhammad Oldy Syahputra]]>
<![CDATA[ SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA ]]>
Publisher : <![CDATA[STMIK PONTIANAK ]]>
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DOI: 10.30700/jst.v11i2.1129
<![CDATA[The best lecturer is a form of appreciation that can be given by universities to their lecturers who have carried out the threefold missions of Higher Education (implementing education and teaching, carrying out research and developing science, and carrying out community service) well. This research was conducted at XYZ University Palembang with the aim of developing a web-based application for selecting the best lecturers by applying the Simple Additive Weighting decision-making method. The criteria used are functional position, rank or class, education, length of teaching, number of seminars attended, number of studies, number of published journals and number of community service activities. The software development method used is the prototype method. From the results of the tests carried out, it is known that this application can work and function properly and correctly with a calculation accuracy rate of 100% obtained from comparing the results of calculations manually with the results of application calculations. For further research, it is better to increase the number of criteria used and apply certain methods to determine the weight of the criteria. In addition, it can also make comparisons with other calculation methods in order to find out which method is better in determining the best lecturer.]]>
