Journal of Computer System and Informatics (JoSYC)
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
Articles
443 Documents
<![CDATA[Penerapan Algoritma Bayesian Regulation untuk Estimasi Posisi Cadangan Devisa Indonesia ]]>
<![CDATA[Siti Aisyah]]>;
<![CDATA[Zulkifli Zulkifli]]>;
<![CDATA[Pandu Adi Cakranegara]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2170
<![CDATA[Foreign exchange reserves need to be predicted because it is a significant monetary indicator to show the strength or weakness of a country's economic fundamentals. Therefore, the purpose of this study is to estimate the position of Indonesia's foreign exchange reserves at the end of 2022 and 2023 so that the government has benchmarks and information in determining the right economic policy so that the position of foreign exchange reserves remains stable. The estimation algorithm used in this study is the Bayesian Regulation algorithm, one of the Artificial Neural Network algorithms. The research data used is data on the position of Indonesia's foreign exchange reserves (US$ million) obtained from the economic reports of Bank Indonesia. This research will be analyzed using three network architecture models 4-9-1, 4-18-1, and 4-27-1. Based on the analysis of the three models used, the results show that the 4-27-1 model is the best because it has a lower Mean Square Error (MSE) value of 0.00203297. Another benchmark is seen from more minor epochs (iterations) and faster times than the other two models, even though they both produce a 100% accuracy rate. Thus, it can be concluded that the Bayesian Regulation algorithm is good enough to estimate the position of foreign exchange reserves using the 4-27-1 model. Based on the prediction results, the part of Indonesia's foreign exchange reserves at the end of 2022 and 2023 slightly decreased compared to 2021.]]>
<![CDATA[Peramalan Jumlah Kasus Baru HIV Menurut Provinsi Menggunakan Machine Learning dengan Teknik Levenberg-Marquardt ]]>
<![CDATA[Irfani Zuhrufillah]]>;
<![CDATA[Fitri Anggraini]]>;
<![CDATA[Rizki Dewantara]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2172
<![CDATA[Early detection of HIV is a crucial step to reducing transmission and increasing the success of HIV treatment. The sooner HIV is detected, the sooner treatment can be carried out so that this infection can be controlled and does not develop into AIDS. Therefore, the purpose of this study is to forecast the Number of New HIV Cases in Indonesia based on 34 Provinces so that the government can obtain information early on to determine the right policy to suppress the increasing number of new HIV cases in Indonesia. This research proposes forecasting using a Machine Learning algorithm with the Levenberg-Marquardt technique. The research data is data on the number of new HIV cases by province obtained from the 2021 Indonesian Health Profile book issued by the Ministry of Health of the Republic of Indonesia. This research will be analyzed using three network architecture models, 3-15-1, 3-20-1 and 3-25-1. Based on the analysis of the three models used, the results show that the 3-15-1 model is the best because it produces a higher accuracy level than the other two models, which is 88%. It can be concluded that the Levenberg-Marquardt technique with the 3-15-1 model is quite suitable for forecasting new cases of HIV in Indonesia. Based on the prediction results, the number of new HIV cases by the province in Indonesia at the end of 2022 decreased significantly compared to 2021, which was 24668 compared to 36902 or reduced by around 12 thousand cases.]]>
<![CDATA[Sentiment Analysis Based on Aspects Using FastText Feature Expansion and NBSVM Classification Method ]]>
<![CDATA[Sukmawati Dwi Lestari]]>;
<![CDATA[Erwin Budi Setiawan]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2202
<![CDATA[Telkomsel is a service that the people of Indonesia widely use. Complaints from users referring to Telkomsel's service and signal aspects are often made in Twitter tweets with harsh or good language. This is done because users continue to demand to get better service. Therefore, an aspect-based sentiment analysis technique is needed to determine a person's view of each aspect, such as Telkomsel's service and signal aspects. Aspect-based sentiment analysis is a solution to find out the opinions of Telkomsel users based on their aspects. In its implementation, the NBSVM method is used as a classification model that is proven to work well compared to other methods, namely MNB and SVM. The implementation of the expansion of the FastText feature can affect the level of performance model, and the best results are obtained in the Top 1 feature on the signal aspect and Top 5 on the service aspect with a combination of Twitter corpus and news. In this study, the data used is unbalanced and has been handled by applying SMOTE and AdaBoost techniques to the FastText feature expansion model. Based on the results of the tests that have been carried out, SMOTE can handle data imbalances compared to AdaBoost. The performance results of the FastText feature expansion model after SMOTE are applied to get F1-Score 91.24% in the signal aspect and F1-Score 88.75% in the service aspect.]]>
<![CDATA[Recommender System with User-Based and Item-Based Collaborative Filtering on Twitter using K-Nearest Neighbors Classification ]]>
<![CDATA[Muhammad Shiba Kabul]]>;
<![CDATA[Erwin Budi Setiawan]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2204
<![CDATA[Netflix is one of the most widely used applications for watching movies online. There are various movie titles that can be watched by users, so a recommendation system is needed to help users who feel confused in choosing movie titles. Twitter is a social media used to express ideas, thoughts, and feelings. Not a few Twitter users who conduct movie discussions, with the movie discussion can be converted into a rating that can be used in the recommendation system. Collaborative Filtering is one of the methods of the recommendation system, by recommending based on the similarity between users (user-based) and based on items that have similarities with user-selected items (item-based). In this research, the Collaborative Filtering method is combined with K-Nearest Neighbors classification which obtains an RMSE value for user-based 1.8244 and item-based 0.5449. K-Nearest Neighbors gets 91.22% precision and 91.07% recall for user-based, while item-based gets 89.44% precision and 91.22% recall with the optimal K as a parameter is 3.]]>
<![CDATA[Penerapan Metode Naïve Bayes Dalam Mendiagnosa Penyakit Leptospirosis ]]>
<![CDATA[Rima Tamara Aldisa]]>;
<![CDATA[Sechan Alfarisi]]>;
<![CDATA[Mohammad Aldinugroho Abdullah]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2205
<![CDATA[There are still many people who are less concerned with environmental cleanliness, where this can lead to the accumulation of bad bacteria that cause disease. One of them is Leptospirosis, a disease caused by a bacterial infection, namely the Leptospira strain. Leptospirosis is a disease caused by the laying of bacteria in animals that is transmitted to humans. This disease is often ignored by the public due to lack of understanding about this disease and the high cost of conducting examinations and consulting a doctor or hospital. So that in overcoming this we need a way that is able to help the community in knowing and diagnosing Leptospirosis, one of which is by using an expert system. The expert system used in solving the problem of Leptospirosis is by using the Naïve Bayes method. The application of the Naïve Bayes method in diagnosing Leptospirosis is carried out by collecting data about Leptospirosis where this process aims to find out what symptoms are caused by Leptospirosis. The process of collecting data on this disease is done by interviewing an expert or doctor who handles the problem of Leptospirosis. The results of the diagnosis of Leptospirosis based on the calculation of the Naïve Bayes method with new user data samples get results with a definite level of accuracy where the user experiences Leptospirosis disease with mild symptoms of 63% and the results of the user experiencing Leptospirosis disease with severe symptoms of 37%. Naïve Bayes is able to diagnose with 100% accuracy seen from the total severe and mild symptoms]]>
<![CDATA[Pemetaan Produksi Tanaman Tomat di Indonesia Berdasarkan Provinsi Menggunakan Algoritma K-Means Clustering ]]>
<![CDATA[Syaifuddin Syaifuddin]]>;
<![CDATA[Ramlah Ramlah]]>;
<![CDATA[Irma Hakim]]>;
<![CDATA[Yunida Berliana]]>;
<![CDATA[Nurhayati Nurhayati]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2206
<![CDATA[Tomato is one of the essential horticultural commodity vegetables because it has high economic value. The need for this plant continues to increase along with the increase in population, income levels, and heightened public awareness of the importance of nutritional value. Therefore, this research aims to see and map the production of tomato plants in Indonesia by the province in the form of clusters (grouping). The research data used in this paper is data on tomato production in Indonesia by the province in the last five years (2017-2021) obtained from the District/City Agriculture Service of each province and the Indonesian Central Statistics Agency. The algorithm proposed in this study is K-Means Clustering with the help of RapidMiner. The results of the proposed paper are grouping and mapping of tomato production in Indonesia, which is divided into 5 (five) zones, including the Black Zone (areas with very high tomato production), which consists of 1 province, Green Zone (areas with high production of tomatoes). Which consists of 2 provinces, the Blue Zone (areas with moderate production), which consists of 4 provinces. The Light Blue Zone (areas with low production), which consists of 8 provinces, and the Orange Zone (areas with moderately low production), which consists of 18 provinces.]]>
<![CDATA[Identify User Behavior Based on The Type of Tweet on Twitter Platform Using Gaussian Mixture Model Clustering ]]>
<![CDATA[Ridha Novia]]>;
<![CDATA[Sri Suryani Prasetyowati]]>;
<![CDATA[Yuliant Sibaroni]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2208
<![CDATA[Social media has now become a place for social interaction to exchange information about business, politic, and many other. Twitter is one of the social media platforms that provides services for their users to share information and opinions on certain topics. The topic that will be discussed in this study is about politic by collecting tweet data about the student demonstration movement and SemuaBisaKena campaign. By using the word weighting method TF-IDF Vectorizer and Gaussian Mixture Model Clustering, it is possible to identify whether the user behavior is positive (support) or negative (blasphemy). To achieve the final result, there are several stages that must be passed. Such as data preprocessing, feature extraction using TF-IDF Vectorizer, Gaussian Mixture Model Clustering algorithm and data visualization. The results are there is 1 cluster identified as positive behavior and there are 2 clusters identified as negative behavior.]]>
<![CDATA[Topic Modelling Using Non-Negative Matrix Factorization (NMF) for Telkom University Entry Selection from Instagram Comments ]]>
<![CDATA[Alfajri Alfajri]]>;
<![CDATA[Donny Richasdy]]>;
<![CDATA[Muhammad Arif Bijaksana]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2212
<![CDATA[The development of information technology is increasingly rapid, such as social media, which has much influence. Social media is a place or media used to express and express various opinions on a topic. One example is Instagram. Instagram is a social media platform with many features, such as posting photos, videos, comments, likes, and others. The comments feature that Instagram has contained much public opinion that can be used as data. Nothing but the post on the SMB Telkom University Instagram account about the entrance to the university. In posts about the entrance to Telkom university, many Instagram users comment on the post. This can be convenient for the marketing team to get topics or discussions that most followers need from Telkom University's Instagram account. Therefore, a topic modelling of Instagram users' perceptions of comments posted on the entrance to Telkom university was carried out using the Nonnegative Matrix Factorization (NMF) method. After doing several research scenarios, the best coherent value was obtained with a coherent value of 0.60628 and the best 4 topics.]]>
<![CDATA[Sistem Pendukung Keputusan Dalam Penilaian Kinerja Supervisor Menggunakan Metode COPRAS Dengan Pembobotan ROC ]]>
<![CDATA[Agung Triayudi]]>;
<![CDATA[Fifto Nugroho]]>;
<![CDATA[Andreas Gerhard Simorangkir]]>;
<![CDATA[Mesran Mesran]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v3i4.2214
<![CDATA[Supervisors are direct supervisors of leaders and operators who have duties and responsibilities, namely providing direction or work planning in accordance with existing SOPs in the company and monitoring the work of leaders and operators properly. In this study, so far the company in evaluating the performance of the supervisor is done manually, that is, the company only judges based on the length of time the supervisor has worked. In fact, the supervisor's performance appraisal is an ineffective performance appraisal. Therefore, the company must make several other performance assessments such as Length of Work, Leadership, Communication, Discipline and Attendance. In evaluating the supervisor's performance, a decision support system (DSS) is needed. The method used in this study is the COPRAS method with ROC weighting. The calculation of the COPRAS method with ROC weighting can produce the best alternative, namely alternative A2 on behalf of "Budiman Sianipar, ST" with a value of Ui = 100]]>
<![CDATA[Penerapan Kombinasi Metode ROC dan TOPSIS Pemilihan Karyawan Terbaik Untuk Rekomendasi Promosi Jabatan ]]>
<![CDATA[Lince Tomoria Sianturi]]>;
<![CDATA[Mesran Mesran]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 4 No 1 (2022): November 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v4i1.2215
<![CDATA[Position promotion is a transfer or promotion of an employee in a structure within the organizational structure who has the ability to know skills and a responsible attitude in accordance with qualifications based on standards that exist in a company itself with specific functions. In the process of selecting the best employees, for recommendation for promotion, a measurable decision support system is needed. As in the POLDASU HR BUREAU, which does not yet have a decision support system, which so far has recommended promotions at the North Sumatra Regional Police, there are still obstacles in the criteria for evaluating promotions, so does not rule out the possibility that decision making is done subjectively by only looking at certain aspects and requires a long time in making decisions. In general, the recommendation for promotion is given by each work unit, good assessments from superiors and employee performance in carrying out their duties. For this reason, it is necessary to assess data processing employees who can help companies make decisions related to promotion recommendations. There needs to be supporting criteria for using a decision support system. In this study applied a combination of ROC and TOPSIS methods. The weighting of each criterion is carried out by applying the ROC method, then followed by a ranking process that will select the best alternative from a number of alternatives using the TOPSIS method. The TOPSIS method is a method that uses the principle that the chosen alternative must have the closest distance from the positive ideal solution and the farthest from the negative ideal solution. The results of the Research Recommendations for Promotion of Positions that are appropriate for promotion are alternative A1 with a preference value of 1,497 on behalf of "Hendrik".]]>
