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Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 443 Documents
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Analisis Efisiensi Inverter pada Grid-Connected 50 KWp Unpam Viktor Woro Agus Nurtiyanto; Perani Rosyani; Lili Solihin; Wiji Prayogo
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2134

Abstract

The efficiency of grid-tied inverters in converting electrical energy sourced from solar power plants. The purpose of this study is to find out how efficient the Grid-tied inverter is based on the results of field tests, and it is useful as a reference to determine the performance of an inverter connected to the utility grid. Quantitative research methods with direct measurement techniques use the Seaward PV meter measuring instrument and the IsolarCloud monitoring application and control panel to determine the output power absorbed by the load with the research location in the inverter room on the 7th floor of the Pamulang Viktor University campus. The results show that the highest efficiency is achieved with a value of 98.4% where it can be said that the inverter can convert almost all of the energy produced by solar cells, with an average short circuit current (Isc) 16% greater than the maximum current (Imp) and open circuit voltage (Voc) is 21% greater than the maximum power voltage. The results of the analysis also show that the increase in current has no significant effect on the efficiency of the inverter.
Pemodelan Klasifikasi Gaji Menggunakan Support Vector Machine Anas Satria Lombu; Syarif Hidayat; Ahmad Fathan Hidayatullah
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2137

Abstract

It is known that there are currently many types of work in the field. Creativity of the community and economic pressure that is felt makes people have to work hard to be able to meet the needs of life. One way that must be done to be able to continue to survive by working. By working someone can produce wages or salaries so that the necessities of life of a person can be met. Various work that exists raises a problem. In determining the salary or wages of a job. The salary given to someone must be in accordance with the criteria of the worker. Then we need a Machine Learning model to predict a person's salary. In this study, a classification model was made to determine a person to be categorized into salaries above 7 million and salaries below 7 million based on suitable criteria or attributes. This study uses the Python programming language and took 1000 samples from the dataset obtained from Kaggle. The Machine Learning method used is the Support Vector Machine. Then compared to the K-Nearest Neighbors method. In the SVM model the model accuracy was obtained of 87% and 86% for the KNN model. From the results of accuracy, it was found that the SVM model was better than the KNN model in conducting salary classifications based on existing jobs.
Radicalism Speech Detection in Indonesia on Twitter Using Backpropagation Neural Network Method Muhammad Rajih Abiyyu Musa; Yuliant Sibaroni
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2146

Abstract

In this modern era, many people use social media easily and freely. One of the social media used is Twitter. The reason people use Twitter is that they can express their opinion freely. However, this freedom does not always have a positive impact on other Twitter users. One of the negative impacts for users is that they can spread radical content. Therefore, this research aims to detect whether a tweet contains radical elements or not using the backpropagation neural network method. The process is carried out by taking data on Twitter, after which the preprocessing process is carried out. Then the data is processed using imbalanced handling, where the data is divided into oversampling and undersampling data. After the data is divided, the next process is to do stopword and then look for accuracy by comparing different epoch values, namely 100, 150, 200, and 250. The best epoch value obtained is 200, with a final accuracy result of 86%.
Image Detection for Common Human Skin Diseases in Indonesia Using CNN and Ensemble Learning Method Fauzi Dzulfiqar Wibowo; Irma Palupi; Bambang Ari Wahyudi
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2151

Abstract

Skin disease is a common health problem throughout the world which is one of the main causes of global disease. Skin and subcutaneous diseases managed to contribute 1.79% of global diseases and also became the fourth leading cause of the burden of non-fatal diseases and disability in 2013. Indonesia was ranked 29th out of 195 countries in Asia which indirectly contributed to in contributing to the transmission of skin diseases due to several causes such as lack of access to health care services, poor hygiene conditions, and also population density. Based on the information revealed in the book entitled illustrated guide on various skin diseases commonly found in Indonesia, it is stated that skin diseases ranging from herpes, ringworm, chickenpox, scabies, to psoriasis are often found in Indonesia. With current technological advances, it is possible for humans to be able to recognize various skin diseases with the help of the Convolutional Neural Network (CNN) Method. A total of 1203 images containing types of skin diseases such as herpes simplex, pityriasis, psoriasis, tinea corporis, scabies, and also vitiligo will be a class in the classification process, but because most images are still unbalanced and do not have strong object elements, it is necessary to do this. data preparation and data balancing is also needed so that the architectural model will not be difficult to learn. By using k-fold cross validation and carrying out the ensemble method, the results of the model evaluation will be in the form of an accuracy matrix where the results of each model will be compared and it will be determined which model is the best based on the results obtained. The test results that produce Cross Validation show that the RGB image is superior where the accuracy value obtained is 49% and the Grayscale image has an accuracy of 47%. however, when compared with the ensemble results, Grayscale images have superior accuracy results, namely the accuracy results are 93% and RGB images produce only 86.
Analisis Sensitivitas Metode AHP Dan TOPSIS Dalam Pemilihan Objek Wisata di Kabupaten Karangasem I Gede Iwan Sudipa; I Kadek Hardiatama; Christina Purnama Yanti; I Komang Arya Ganda Wiguna
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2152

Abstract

Bali is a well-known tourist destination, but Karangasem is not widely known to the public. Tourist objects are selected as Multi-Attribute Decision Making (MADM). This study analyzes the MADM problem, namely the selection of tourist objects using the Analytical Hierarchy Process (AHP) and The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method. Method testing is done by conducting a sensitivity analysis to determine the most sensitive method in selecting tourist objects. The sensitivity analysis on 10 trials by changing the weight of the criteria by adding values ​​from 1 to 2 shows that the AHP method produces a ranking change of 440 with a percentage of 5.6%. While the TOPSIS method has a ranking change of 292 with a percentage of 3.77%. The results show that the AHP method is more sensitive to changes in weight, so relevant decision-making in selecting Karangasem Regency tourism objects can be carried out using the AHP method.
Rancang Bangun Aplikasi Pemeliharaan Alat Menggunakan QR-Code (Studi Kasus Telkom Property Surabaya Utara) Dhimas Bintang Bagaskara; Bagus Kurniawan; Mohammad Sholik; Fidi Wincoko Putro; Ardian Yusuf Wicaksono; Titus Kristanto; Amirah Diandra
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2153

Abstract

Telkom Property North Surabaya has a responsibility in terms of maintaining and maintaining equipment for all assets of the Telkom Indonesia company in the North Surabaya and Madura areas. Previously, tool maintenance records were written manually, but the Mei-V application has an impact because now tool maintenance recording can be done through an android application by scanning a QR-Code. The Mei-V application was created with the aim of providing a solution so that equipment maintenance records can be carried out through an application that is connected to a direct database. The application uses the Spiral Model method so that it can carry out continuous development in the form of adding functions or changes to suit existing needs. The result of implementing the Spiral Model that can be felt is the flexibility of application development because it can always be monitored and improved at any time. The Mei-V application can provide various useful information for maintenance activities, including detailed information on equipment maintenance, maintenance reports that can be downloaded by supervisors. The Mei-V application testing was carried out with two test methods. Functionality testing is carried out with details of 4 test scenarios and shows a 100% success percentage. While the Usage Test conducted on 5 respondents showed positive results with details of the average score scale of 3.8 out of 5 points.
Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017 Laila Qadrini; Hikmah Hikmah; Megasari Megasari
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2154

Abstract

Bidikmisi is tuition assistance from the government for high school graduates (SMA) or equivalent who have good academic potential but have economic limitations. Different from scholarships that focus on providing awards or financial support to those who excel. The achievement requirements for Bidikmisi are aimed at ensuring that Bidikmisi recipients are selected from those who truly have the potential and willingness to complete higher education. Given that the recipients of this bidikmisi must really be the right person, in this study a classification of the recipients of the 2017 bidikmisi in East Java will be carried out, in this study there is data that is not balanced the "Accepted" class is more than the "Not accepted" class. If the data is not balanced, almost all classification algorithms will produce much higher accuracy for the majority class than for the minority class. Researchers will handle class imbalances. The resampling technique used in research related to the prediction of bidikmisi recipients includes resampling techniques, namely Oversampling, Undersampling and SMOTE using two classification methods, namely SVM and Random Forest. The Oversampling technique was chosen because it does not reduce the amount of data but adds to the dataset that is lacking in the minority class. The Oversampling algorithm used is Synthetic Minority Over-sampling Technique (SMOTE), this algorithm was chosen from several resampling algorithms because SMOTE produces good accuracy and is effective in dealing with unbalanced classes because it reduces overfitting.
Predicting Depressive Disorder Based on DASS-42 on Twitter Using XLNet's Pretrained Model Classification Text Intan Ramadhani; Warih Maharani
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2157

Abstract

Twitter is a free social media site that is not only a place to share posts and multimedia content but also offers its users to express their feelings, emotions, and sentiments about an issue. So with this, it is often found that Twitter users make posts that show how the user's behavior includes mental problems experienced users such as symptoms of depression, anxiety, and stress disorders. Only about half of depression cases can be detected by doctors or other experts, this is because until now, the diagnosis of depression starts from reports of patients, family, or close friends of patients, or also starts from the results of certain tests such as questionnaires. So this research builds a model to predict depression by building a model that predicts whether someone is depressed through tweets on Twitter using the XLNet pre-trained text classification model. Testing is done by removing stemming from the preprocessing stage. Testing is also done by adding hyperparameters for fine-tuning the XLNet model. Testing is also carried out using a dataset that filters out foreign words where foreign data is translated into Indonesian. The data stored is data that uses words based on the KBBI dictionary. Based on the results of model testing that has been carried out using confusion matrix, the model can predict tweets that indicate depression and get an accuracy value of 78.57%.
Penerapan Metode Simple Additive Weighting (SAW) Dalam Sistem Pendukung Keputusan Pemilihan Calon Peserta Pelatihan Kepemimpinan Pengawas Bagi Pejabat Struktural Sri Lestari; Yomi Kusumah
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2161

Abstract

In the nomination of candidates for Pelatihan Kepemimpinan Pengawas (PKP), the Badan Kepegawaian dan Pengembangan Sumber Daya Manusia (BKPSDM) Kota Bekasi is still experiencing problems in determining the nominative to be dispatched. The impact of these problems is in the form of a mismatch of the nominations of potential participants with priorities that must be expedited. Therefore, it is necessary to have a system that uses a database so as to assist the leadership in making decisions to determine and determine the nominative priorities of prospective PKP participants. In this study, the authors propose the application of the Simple Additive Weighting (SAW) method. The data collection method used is the method of observation and interviews. The criteria proposed from the interview results include Age (weight: 0.35), Echelon (weight: 0.25), Group (weight: 0.2), Service Period (weight: 0.15) and Employee Work Target Value (weight: 0.05). The system development method used is the Waterfall method. The system is built using the PHP programming language with MySQL as the database. System design using Unified Modeling Language (UML) and system testing using Black-box Testing. With this decision support system, the leadership will get the results of anyone who has the right to be proposed to take part in the Training.
Evaluation UX Design on GGWP Tourney Application Using HCD and Heuristic Evaluation Methods Heri Setyo Nugroho; Anisa Herdiani; Rosa Reska Riskiana
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2168

Abstract

The rapid developments in technology make the information spread faster, especially in the electronic sports industry (eSports). Game tournament information is essential to gamers since they strive to obtain accurate and easy-to-understand information about esports. The GGWP Tourney application is a mobile application that provides information and means to purchase e-sports competitions online. However, the GGWP Tourney application is still in the development stage so there are still many user experience problems, especially in the usability and journey of the application. Therefore, analysis and evaluation of the usability of the GGWP Tourney application are carried out so that it is in accordance with what users expect. In this study, usability evaluation was carried out using Heuristic Evaluation as a method of evaluating usability values and designing solutions using the Human Centered Design (HCD) method according to user perceptions based on the results of the evaluation carried out. The evaluation has been carried out in two stages, namely the initial stage and the stage after designing the solution design. The results of the initial evaluation found 12 problems and the severity rating value had an average greater than 2 so improvements were made to the application user interface. While the second evaluation stage in the solution design shows the number of problems found only 3 and the severity rating value is less than 2 so that no further repairs are carried out. Based on these results, the heuristic evaluation method can reduce the number of problems in the GGWP Tourney application from 13 problems to 3 remaining problems. This methods be used to determine the usability value of an application that can be used to improve the user experience of the application.

Page 15 of 45 | Total Record : 443

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