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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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Student Ranking Based on Learning Assessment Using the Simplified PIPRECIA Method and CoCoSo Method Sitna Hajar Hadad; Dedi Darwis; Arie Qurania; Ahmad Ari Aldino; Abhishek R Mehta; Yuri Rahmanto; Setiawansyah Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4544

Abstract

The problems that occur in determining the best students based on the learning process of the assessment process are still based on the academic scores of students and have not considered the learning process carried out by students. This study aims to apply the Combined Compromise Solution (CoCoSo) method in ranking students based on learning assessment using criteria of academic progress, problem-solving ability, mastery of skills, independence, motivation and positive attitude, adaptability, and for weighting the criteria used to apply the Simplified PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) weighting method. The Simplified PIPRECIA method is particularly useful in situations where there are diverse criteria to be considered and complex decisions must be made taking into account the preferences and interests of various stakeholders. The Combined Compromise Solution Method is useful when there are conflicts in various criteria that need to be considered in the decision-making process. With this approach, each criterion is weighted and carefully calculated, so that the resulting decisions reflect comprehensive considerations that can meet various requirements and constraints. Based on the results of student rankings based on assessments in learning in the table above, rank 1 was obtained by students with Student ID 1211313 with a final grade of 6.487, rank 2 was obtained by students with Student ID 1211316 with a final grade of 6.402, and rank 3 was obtained by students with Student ID 1211314 with a final grade of 5.814.
Pengenalan Bangunan Bersejarah Pura dengan Menggunakan Local Binary Pattern dan Support Vector Machine Erico Jochsen; Dameethia Angeline; Dyah Erny Herwindiati; Janson Hendryli
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4553

Abstract

One area that has a rich cultural heritage is Bali. Bali is very well known as a very beautiful place and is often visited by tourists in Indonesia and outside Indonesia. Temple buildings in Bali have unique characteristics that reflect the richness of Indonesian culture. So many tourists are interested in vacationing there. However, due to the uniqueness of each temple building there, there is a lack of knowledge about the buildings being seen, so the main aim of this design is to develop a system for recognizing historical temple buildings in Indonesia through building images. More broadly, this design contribution can be applied in the development of similar systems for other historical regions in Indonesia, enriching efforts to preserve and promote cultural heritage nationally. Thus, this design not only paves the way for innovation in the field of image recognition, but also has a positive impact in preserving valuable cultural property. The method used for recognition is Local Binary Pattern as texture feature extraction from temple building images, while Support Vector Machine with a polynomial kernel is used to recognize temple buildings. It is hoped that the combination of these two methods can provide good results in recognizing temple buildings with the correct classification level. The accuracy of this design model using 90 percent training data and 10 percent test data was 45.93 percent, while when using 80 percent training data and 20 percent test data, the accuracy dropped slightly to 43.96 percent. When using 90 percent training data, the recognition of historical buildings produces a precision of 59 percent, a recall value of 71 percent, and an f1-score of 57 percent. On the other hand, with 80 percent training data, the recognition of historical buildings produces 62 percent precision, 72 percent recall value, and 57 percent f1-score.
Analisis Sentimen Aplikasi Primaku Menggunakan Algoritma Random Forest dan SMOTE untuk Mengatasi Ketidakseimbangan Data Riska Aryanti; Titik Misriati; Asriyani Sagiyanto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4562

Abstract

The Primaku application is an application that can be used as a tool to monitor the growth of children under five, this application can be used to collect data on the growth of children under five, apart from that this application can also provide clear information and visualization about the growth of children under five, including nutritional status and growth development In accordance with the standards that have been set, the Primaku application can help parents or health workers in routinely monitoring the growth of children under five and early detecting the potential risk of stunting. Stunting is a growth disorder that occurs in children under five due to malnutrition which is characterized by the child's height. which is shorter than the age standard. Stunting can have a long-term impact on a child's quality of life, such as disrupting physical, cognitive and social development, as well as increasing the risk of chronic disease in adulthood. The primaku application has been widely used, more than 500,000 users have downloaded this application and 44,700 reviews have been given by users to this application, however, reading all the reviews may take time, but if there are few reviews read, then the review results will be biased. Therefore, sentiment analysis aims to overcome this problem by automatically grouping user reviews into positive and negative reviews. Therefore, research on toddler growth detection to determine the public's response to the Primaku application can be of great benefit in efforts to prevent stunting in children under five in Indonesia. In this research, the random forest algorithm with the SMOTE technique was used to carry out sentiment analysis of Primaku application reviews. The random forest algorithm is a machine learning algorithm based on decision trees. The SMOTE technique is used to overcome data imbalance problems and is able to reduce overfitting while increasing the performance of the Random Forest algorithm. The data used in this research is Primaku application review data obtained from scrapping results from the Google Play Store. This data contains comments from application users, namely positive and negative. The results of this sentiment analysis show a deep understanding of user perceptions of the Primaku application. This sentiment analysis can be a basis for further improvement and development of the Primaku application, with a focus on aspects that influence user satisfaction and the research results show that the random forest algorithm with the SMOTE technique can produce quite good accuracy in sentiment analysis of the Primaku application. obtained in this study was 88%.
Analisis Dalam Pendukung Keputusan Penerimaan Supervisor Industri Manufacturing dengan Menerapkan Metode EDAS dan Pembobotan ROC Bernadus Gunawan Sudarsono; A Ahyuna; Triyugo Winarko; Dwi Puspita Anggraeni; Zulfi Azhar
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4563

Abstract

Manufacturing industry supervisor is one of the jobs engaged in processing raw goods into whole or semi-finished goods. Honest and responsible supervisors in the company are needed. Therefore, the company is opening the acceptance of supervisors who have a high responsible spirit. However, in accepting supervisors is not an easy thing because finding people who really have a high leadership spirit and are responsible is not an easy thing, therefore a system is needed that can help the company find a decision. The system is a decision support system and in finding these decisions several criteria are needed including Education, Experience, Leadership Ability, Good Interpersonal and Responsibility. And also in order to get accurate results that deserve to be supervisors can use methods where researchers use EDAS methods and ROC weighting. So in the research conducted by the author obtained the highest ranking found in alternative AD7 on behalf of Bambang with a total value of 1,00000 who was selected to be a supervisor of the manufacturing industry with complete requirements and the highest value.
Data Mining Perbandingan Algoritma K-Nearest Neighbor dan Naïve Bayes dalam Prediksi Penerimaan Beasiswa Ahmad Ilham Kushartanto; Rima Tamara Aldisa
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4566

Abstract

One of the goals of the Indonesian State as stated in the Constitution of the Republic of Indonesia is to make the nation's life more intelligent. The government, through the Ministry of Education, Culture, Research and Technology (Kemendikbudristek), is implementing a 12-year compulsory education program. The Indonesia Smart College Card program will start to be implemented in 2021, where initially this program was called bidikmisi. The Indonesia Smart College Card program is intended to help children who experience economic difficulties or are constrained by the costs of continuing their education at tertiary level. Beneficiaries of the Indonesian Smart College Card program will receive full tuition fees from the start of admission to completion of the course within the agreed time period. This aims to ensure that children who have been selected to become students no longer have to worry or feel afraid about their survival when studying. Placing limits on the number of recipients of the Indonesia Smart Tuition Card for Private Universities (PTS) is a problem that must be resolved properly and carefully. The selection process for prospective students who register at the target university aims to find recipients who are worthy of assistance from the Indonesia Smart College Card program. The selection process for prospective students who register at the target university aims to find recipients who are worthy of assistance from the Indonesia Smart College Card program. The results obtained from the application of the research were that from comparing the results of the K-NN and Naive Bayes algorithms, the same results were obtained for the test data, namely Acceptable.
Penerapan Metode Additive Ratio Assessment (ARAS) dan Ranking of Centroid (ROC) dalam Pemilihan Layanan Akomodasi dan Local Cuisine Yerik Afrianto Singgalen
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4569

Abstract

Salatiga is a gastronomic city with various types of traditional food and drinks and is a recreational tourism city with exciting and crowded mountain natural scenery. The availability of accommodation services and local cuisine adds to visitors' preferences to enjoy local culinary dishes. This study offers an initiative to use the Additive Ratio Assessment (ARAS) method in selecting the best accommodation and restaurant services in Salatiga based on Tripadvisor data. The stages in the calculation process based on the ARAS method are as follows: the stage of determining the value of criteria, weights, alternatives, and optimal values; the stage of converting the value of the criterion into a decision matrix; the stage of normalization of the decision matrix for all criteria; stage of calculating the value of utility; Ranking stage. The calculation results show that in the context of accommodation services, A5 occupies the first position with a Ki value of 0.887, A4 occupies the second position with a Ki value of 0.870, and A1 occupies the third position with a Ki value of 0.849. Meanwhile, in the context of local cuisine, A2 occupies the first position with a Ki value of 0.951, A5 occupies the second position with a Ki value of 0.914, and A4 occupies the third position with a Ki value of 0.854. This shows that ARAS and ROC methods can produce the best recommendations for tourists who want to use accommodation services and enjoy local cuisine in Salatiga City.
Perbandingan Metode ARAS dan EDAS dalam Menghasilkan Rekomendasi Layanan Akomodasi Hotel Yerik Afrianto Singgalen
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4574

Abstract

This study compares the decision support model Additive Ratio Assessment (ARAS) and Evaluation based on Distance from Average Solution (EDAS) in selecting hotels in Semarang City. Meanwhile, the criterion-setting method adopts the Ranking of Centroid (ROC) to set criteria, determine the priority of criteria, and determine the weight of the criteria values. Comparison of the two algorithms needs to be done to compare the results and test the performance of the two algorithms, when developed into a decision support application to generate hotel service recommendations. Meanwhile, the stages in this research are as follows: data collection stage, using TripAdvisor; data processing stage, using ARAS and EDAS models; and data analysis stage. The results of this study show that the ranking results based on the EDAS method show that A1 occupies the first position with an NSP value of 1,000 and an NSN of 0,983. Furthermore, A3 occupies the second position with an NSP value of 0.707 and an NSN of 0.983. Meanwhile, A2 occupies the third position with an NSP value of 0.311 and an NSN of 0.983. Furthermore, the ARAS method ranking results show that A1 occupies the first position with a Si value of 0.171 and a Ki value of 0.994. Furthermore, A3 occupies the second position with a Si value of 0.169 and a Ki value of 0.984. Meanwhile, A2 occupies the third position with a Si value of 0.167 and a Ki value of 0.970. Based on the comparison of EDAS and ARAS methods, it can be seen that both produce the same alternative ranking where Padma Hotel Semarang ranks first, Aruss Hotel Semarang ranks second, and Tentrem Hotel Semarang ranks third. Thus, it can be seen that EDAS and ARAS methods can produce the best hotel recommendations for travelers by considering ratings and reviews on the TripAdvisor platform.
Prediksi Jumlah Perceraian Menggunakan Metode Support Vector Regression (SVR) Eka Suryani Indra Septiawati; Elvia Budianita; Fitri Insani; Lola Oktavia
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4613

Abstract

The increasing number of divorces poses an increasingly significant social challenge in Indonesia, including in the city of Pekanbaru. The impact of these divorces on the adolescent population can have negative effects on their emotional and psychological well-being, as well as their ability to interact socially and engage in the learning process. This study utilizes monthly divorce data from 2015 to April 2023 to conduct time series analysis and applies the Support Vector Regression (SVR) method to predict the number of divorces in the city of Pekanbaru. Three types of SVR kernels, namely linear, polynomial, and radial basis function (RBF), are evaluated and compared to find the kernel with the best Mean Squared Error (MSE) results. Through grid search analysis, optimal parameter values for each kernel are determined. The test results indicate that the SVR model with a polynomial kernel provides more accurate predictions with an MSE of 0.010228, compared to the linear kernel (MSE = 0.012767) and the RBF kernel (MSE = 0.010812).
Perbandingan Algoritma Klasifikasi Data Mining Pada Prediksi Penyakit Diabetes Yunan Fauzi Wijaya; Agung Triayudi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4614

Abstract

Diabetes is a chronic disease that attacks humans. One of the causes of diabetes in humans is that sugar intake is too high which the body cannot balance due to absorption or activities carried out. Diabetes is often considered a common disease among people, but the impacts caused by this disease are very detrimental to humans. Based on this, it is necessary for everyone to know whether they suffer from diabetes or not. Therefore, this problem must be resolved appropriately, where it is necessary to predict whether someone will have diabetes or not. The prediction process is carried out to determine whether someone has diabetes or not by knowing the patterns or possible symptoms that cause someone to suffer from diabetes. In this research, the pattern formation process is based on data stored in the past collected in a dataset. A dataset is a collection of past data that occurred in fact and was then collected over a certain period of time on a large scale. Data mining is a method used to process data based on collections of past data, whether in datasets or others. In data mining, the data processing process is carried out using various techniques, one of which is the solution technique in data mining is classification. In this research, the Naïve Bayes algorithm, the K-Nearest Neighbor (K-NN) algorithm and the C4.5 algorithm will be used. In the data mining classification process, there are 3 (three) algorithms used, namely Naïve Bayes, K-Nearest Neighbor and C4.5. From the results of the tests that have been carried out, the accuracy performance results for the Naïve Bayes algorithm are 75%, accuracy for the K-Nearest Neighbor algorithm by 80.60% and the C4.5 algorithm by 91.80%. In this case, it indicates that the C4.5 algorithm has better performance compared to other algorithms. Therefore, the pattern results produced by the C4.5 algorithm are used to make predictions about diabetes.
Penerapan Data Mining Pada Prediksi Harga Emas dengan Menggunakan Algoritma Regresi Linear Berganda dan ARIMA Yunan Fauzi Wijaya; Agung Triayudi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 1 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i1.4615

Abstract

The development of life has developed very rapidly at this time, one thing that has quite an important influence is the business processes carried out. Investment is a business that is carried out by all levels and also members of society easily and flexibly. Currently, the investment that is very popular with the public is gold. Gold itself is one of the most sought after precious metals at the moment, apart from being used to beautify oneself, gold can also be used as an investment asset. Based on several factors above, many people invest in gold. Investments made in Gold are not investments that have a short period of time but investments that are made over a fairly long period of time. Investing in Gold is done by buying Gold at a cheap price at the moment and then selling it again when the Gold price has risen. However, in the process that occurs, problems often occur, where the problems that occur are related to the price of gold. Where this problem can be solved by making a prediction. Data mining is used in predictions because the prediction process is carried out using data mining based on data processing. Data mining itself is a technique that is widely used today to assist in the problem solving process. In this research, the solution process was carried out using the Multiple Linear Regression algorithm and also ARIMA. In this research, the research process will be carried out by comparing the Multiple Linear Regression algorithm. Comparison of algorithms aims to obtain the most optimal results from implementing the algorithm. In solving using the Multiple Linear Regression algorithm and ARIMA, these two algorithms can help solve prediction problems by producing optimal results. From the process carried out, the Multiple Linear Regression algorithm has an RMSE value of 4902782.346, while the ARIMA algorithm gets a value of 5876287.332. This indicates that the results of the Multiple Linear Regression algorithm are better than the ARIMA algorithm.

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