cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
jurnal.josyc@gmail.com
Editorial Address
Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
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
 40   41   42   43   44 
Prototipe Sistem Monitoring, Penyaringan dan Pembuangan Asap Rokok Otomatis dalam Ruangan Berbasis NodeMCU ESP-32 Hernawati, Hernawati; Sayuti, Sayuti; Iswanto, Iswanto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6207

Abstract

Cigarette smoke is a source of pollution which contains various substances such as nicotine, tar, carbon monoxide gas (CO) and carbon dioxide gas (CO2) which harm the body's health. Cigarette smoke is inhaled by active smokers and passive smokers, namely people who do not smoke, but are exposed to and inhale cigarette smoke from the surrounding environment. WHO data shows that 8 million deaths were caused by cigarette smoke, 1.2 million of which were passive smokers. Currently, in several places special smoking rooms have been provided, however, cigarette smoke flowing outside the room still causes pollution. Reducing air pollution is crucial and must be provided with a solution, including by filtering the air from smoking rooms. This research aims to build a prototype system for monitoring, filtering and automatically disposing of indoor cigarette smoke. The prototype reduces the level of cigarette smoke in the room until it reaches normal conditions (400-1000 ppm) and filters the cigarette smoke produced from the smoking room before it is released outside the room. The NodeMCU ESP-32 is used as a microcontroller and is equipped with an MQ-2 sensor which is capable of detecting various types of gas including carbon monoxide (CO), carbon dioxide (CO2) and cigarette smoke, as well as activated carbon as the main ingredient in the cigarette smoke filtering process. Activated carbon has high absorption capacity so it is effective as an air purification medium. The system test results succeeded in detecting, monitoring, filtering and disposing of filtered smoke with an average level of filtered cigarette smoke of 249.5 ppm.
Klasifikasi Sentimen Opini terhadap Film Kartini Menggunakan Naive Bayes pada Platform X Ramadhani, Muhammad Al-Fajr; Aryanto, Joko; Untoro, Iwan Hartadi Tri; Sujarwadi, Agus
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6208

Abstract

Film is an audiovisual media that not only serves as entertainment, but can also provide information and touch the emotions of its audience. In the development of the digital era, people tend to use social media to search for films that are popular or trending. One platform that is often used for this purpose is Twitter, which in July 2023 changed its name to ‘X’. This research discusses the sentiment analysis of the film Kartini on the X social media platform, which tells the story of R.A. Kartini's struggle for the rights of Indonesian women. The film became a topic of conversation with various opinions, both positive and negative, circulating on the platform. The amount of public sentiment often creates confusion for potential viewers in determining whether the film is worth watching. These varying views on the film can affect viewers' perceptions, which in turn risks creating a disappointing viewing experience and making viewers feel that the time spent was not worthwhile. Therefore, the researcher aims to classify the sentiment of platform X users towards the film Kartini using the Naive Bayes Classifier method, which works based on Bayes' Theorem to predict the probability of an event based on previous data. This method was chosen due to its ability to efficiently classify sentiment into positive or negative categories. Temporary results on testing data using the naive bayes algorithm and python programming language obtained very good results. The best accuracy result of the emotion dataset obtained is 98% and the best accuracy of the sentiment dataset obtained is 86%.
Penerapan Metode MOORA untuk Optimalisasi Pemilihan Ketua Jemaat Gereja dengan Kriteria Berbasis Penilaian Objektif Lumingkewas, Edwin; Tangka, George Morris William
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6214

Abstract

This study aims to support an objective and transparent selection process for church leadership within the Seventh-day Adventist Church (SDA), recognizing the importance of balanced spiritual and administrative leadership. To achieve this, the study applies the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method as a systematic approach, enabling a more objective, criteria-based candidate selection. MOORA is used to provide an evaluation model that combines criteria weighting and normalization, aiming to produce fairer and more measurable decisions compared to conventional methods. The analyzed criteria include spiritual maturity, community involvement, leadership experience, and doctrinal knowledge. The results show that applying MOORA increased the selection objectivity by 15% compared to previous methods, with candidate a3 achieving the highest score of 0.5708, followed by a1 (0.5663) and a2 (0.5589). By prioritizing criteria reflecting the church’s values, essential aspects such as spiritual maturity and community involvement receive greater emphasis. These findings demonstrate that MOORA supports faith-based decision-making aligned with church values, providing improved governance and applicability in other religious organizations facing similar selection challenges.
Analisis Perbandingan Metode WASPAS dan TOPSIS dengan Menggunakan Pembobotan ROC dalam Sistem Pendukung Keputusan Penentuan Sales Sepeda Motor Terbaik Sudarsono, Bernadus Gunawan; Suhada, Karya; Karim, Abdul
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6222

Abstract

Determining the best motorcycle salesperson is an important aspect in improving sales performance and motivation in automotive companies. This process must be carried out objectively by considering various criteria, such as performance achievement, discipline, teamwork, communication skills and responsibility. However, decision making is often complex because it involves many factors and varying criteria. Therefore, a Decision Support System (DSS) is needed that is able to process and analyze data effectively. This study aims to analyze the comparison of two multi-criteria methods, namely Weighted Aggregated Sum Product Assessment (WASPAS) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), in determining the best motorcycle salesperson. Both methods use Rank Order Centroid (ROC) weighting to give weight to the assessment criteria. Based on the results of the analysis, it can be seen that both methods provide consistent results, although there are differences in the final ranking. The results of the study indicate that the WASPAS and TOPSIS methods are equally effective in determining the best alternative, with differences in the order of priorities that can be used as further consideration by decision makers. The results of the two methods show that the alternative with the highest performance according to both methods is Febriansyah, who is in first place both in the TOPSIS method with a value of 0.796 and in the WASPAS method with a value of 0.810.
Perkiraan Suhu Menggunakan Algoritma Recurrent Neural Network Long Short Term Memory Zahidin, Ilham; Kanata, Bulkis; Akbar, Lalu A. Syamsul Irfan
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6242

Abstract

Air temperature is a critical variable in weather conditions that affects various aspects of human life, including health, agriculture, and the economy. In Indonesia, particularly in Mataram City, which is situated in a tropical region, significant temperature changes can impact sectors such as tourism, agriculture, and daily activities. Accurate temperature forecasting can aid the public, industries, and the government in making more informed decisions, both for short-term and long-term planning. However, weather in tropical regions like Mataram tends to be difficult to predict accurately due to its dynamic nature and the influence of multiple atmospheric factors. Conventional weather prediction methods often fail to capture the complex patterns in historical temperature data, necessitating more advanced methods to improve forecast accuracy. Recurrent Neural Networks (RNNs), particularly the Long Short-Term Memory (LSTM) variant, have proven to be highly effective tools for modeling complex time series data. This algorithm can retain long-term information and recognize patterns in data that change over time, making it well-suited for temperature prediction challenges. In this study, the RNN-LSTM algorithm is applied to forecast temperatures in Mataram City, aiming to improve forecast accuracy and produce results useful for various purposes. The temperature prediction model using the LSTM algorithm involves several steps: data collection, data normalization, splitting data into test and training sets, building the LSTM model by determining the number of epochs, layers, and batch size, and finally, evaluating the model with RMSE. Two parameters, epoch and batch size, influence the LSTM model’s forecasting results in this study. Epochs used in this study are 5, 10, 20, 30, 40, 50, and 100, with a fixed batch size of 32. The LSTM algorithm employs the RMSProp optimizer. The temperature prediction model using the LSTM method achieved the best average accuracy with a batch size of 32 and 50 epochs, yielding an RMSE value of 0.13 and a prediction accuracy of 99.96% in forecasting Mataram City’s temperature for the year 2023.
Klasifikasi Sentimen pada Dataset Terbatas Menggunakan Random Forest dan Word2Vec Fitri, Dina Deswara; Agustian, Surya; Pizaini, Pizaini; Sanjaya, Suwanto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6246

Abstract

Sentiment measurement of public opinion on social media is essential for understanding societal views on various issues, including public figures and political events. This research explores the effectiveness of the Random Forest algorithm with Word2Vec-based word representation for sentiment classification on a limited dataset. The case study involves tweets regarding Kaesang Pangarep as the Chairman of PSI, supplemented by external data related to Covid-19 and general topics. The dataset was processed using cleaning techniques, case folding, stopword removal, stemming, and tokenization. Words in the dataset were represented using the Word2Vec model with a Continuous Bag of Words (CBOW) architecture and a vector dimension of 500. Random Forest was employed to classify sentiment into positive, negative, or neutral categories. In the initial phase, the model was trained using 300 samples per label; however, the results showed unsatisfactory performance with an F1-Score of 49.00% and an accuracy of 50.00%. To improve performance, the dataset was expanded by adding 900 samples from Kaesang and 1,080 samples from external topics. The final results indicated an improvement with an F1-Score of 49.89%, an accuracy of 58.29%, precision of 49.16%, and recall of 56.47%. This research confirms that the use of Random Forest with word representation from Word2Vec can enhance sentiment classification performance, even with a limited dataset, and contributes to the development of sentiment analysis techniques in the field of machine learning.
Addict Coffee Barista Recruitment Decision Support System Using the ARAS Method Mesran, Mesran; Fadillah, Riszki; Wahyu, Riski Ferita
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6249

Abstract

Barista is a person who works in a coffee shop as a delicious coffee maker. So it is necessary to recruit baristas who can work in coffee shops and have responsibilities that not only mix coffee but also have skills in processing coffee beans. The problem in the barista recruitment process is the process of determining barista candidates who are only selected individually so that it is less accurate to get barista candidates who have the expected skills so that it can have an impact on opinions on the coffee shop. So the solution is provided through a decision support system, a highly interactive computer-based system that assists in making a decision to utilise data and models in solving unstructured and semi-structured problems. The method used in making these decisions is the Additive Ratio Assessment Method (ARAS). A total of 11 people who will become data samples and five criteria are used as rules for assessing (selecting). The ARAS method is able to provide maximum results to obtain superior barista recruitment with a result of 5,342, namely A1 as the selected alternative in barista recruitment after going through the method application stage.
Analisis Perbandingan Metode AHP dan MOORA Dalam Pengambilan Keputusan Pemilihan Sertifikasi Kompetensi Sutinah, Entin; Agustina, Nani; Martini, Martini
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6252

Abstract

Bina Sarana Informatika University (UBSI) is a private university in Indonesia with a large number of students across various study programs. This study focuses on the Information Systems program. A challenge was identified in determining the type of competency certification that aligns with students' interests and talents. The objective of this study is to compare two methods, namely AHP and MOORA, to assist students in selecting the appropriate certification. The criteria considered in this research include Student Interest, Market Demand, Certification Cost, Certification Duration, and Career Prospects. The three alternatives evaluated are the Database Proficiency Test, Program Analysis Competency Certification, and Programmer Certification. Data analysis using the AHP method showed that the Database Proficiency Test achieved the highest score of 0.41, followed by the Program Analysis Competency Certification with a score of 0.33, and the Programmer Certification with a score of 0.26. Similarly, data analysis using the MOORA method yielded comparable rankings: the Database Proficiency Test scored 0.303, the Program Analysis Competency Certification 0.287, and the Programmer Certification 0.276. After comparing the two methods, both AHP and MOORA produced the same results, with the Database Proficiency Test ranking first, followed by the Program Analysis Competency Certification and the Programmer Certification. This finding indicates that the Database Proficiency Test is the most preferred certification among students in the Information Systems program.
Knowledge-Based Decision Support System for Determining Types of Agricultural Crops According to Soil Conditions Wibowo, Ferry Wahyu; Sunyoto, Andi; Setiaji, Bayu; Wihayati, Wihayati
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6254

Abstract

Selecting the right crop for a particular land condition is one of the significant challenges in the agricultural sector. Each crop type has specific needs related to environmental factors such as soil type, pH, humidity, rainfall, and temperature. Mistakes in determining the appropriate crop type can result in decreased production, wastage of resources, and losses for farmers. This paper aims to determine the best model for use as a knowledge base to choose suitable plants for soil conditions. Machine learning algorithms were used to identify patterns of relationships between land conditions and the success of certain crop types to assist in selecting suitable crops and then made a knowledge-based decision support system. Algorithms such as Decision Tree, Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) have been applied to solve this problem. In this paper, 30 experiments were conducted to test the stability of the model in determining suitable crops based on land conditions. The results of the experiments showed that the Support Vector Machine (SVM) has a more stable performance than other algorithms, with accuracy values of mean and standard deviation of 1 and 0, respectively.
The Sistem IoT Untuk Monitoring Suhu dan Pengaturan Kelembaban Penetasan Telur Penyu Nurmahsya, Guruh Khedar; Misbahuddin, Misbahuddin; Paniran, Paniran
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6262

Abstract

Turtles are endangered and protected animals today, the turtle population is declining all the time due to various factors, including climate change and global warming. Turtles are TSD or Temperature dependent Sex Determination animals with the development of their embryos dependent on temperature and humidity which requires moisture content in the nest during the incubation period. As a conservation effort carried out at the Kuranji Dalang Conservation, West Lombok aims to design an Internet of Things (IoT)-based system made to monitor temperature and regulate sand moisture. This tool is designed to monitor the environmental temperature in the sand area if the optimum temperature at hatching is 25°C-33 °C using DHT11, and regulates the humidity of the nest sand by 21%-40% using a soil moisture sensor. Eggs during the incubation period of turtles with a nest temperature of 25°C-29 °C will become male hatchlings, while the nest temperature of 29 °C-33 °C will become female hatchlings. This system is controlled using an ESP32 NodeMCU microcontroller whose data is transmitted through a blynk that can be monitored remotely, the tests that have been carried out are able to monitor an average temperature of 29-30 °C, in addition to being able to regulate an average humidity of 27.93. With a water pump watering with a duration of 12 seconds in the morning, 25 seconds in the afternoon and 17 seconds in the afternoon when the sand is dry with a predetermined humidity of 21%-40%.

Page 42 of 45 | Total Record : 443

 40   41   42   43   44 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol 6 No 4 (2025): August 2025 Vol 6 No 3 (2025): May 2025 Vol 6 No 2 (2025): February 2025 Vol 6 No 1 (2024): November 2024 Vol 5 No 4 (2024): August 2024 Vol 5 No 3 (2024): May 2024 Vol 5 No 2 (2024): February 2024 Vol 5 No 1 (2023): November 2023 Vol 4 No 4 (2023): August 2023 Vol 4 No 3 (2023): May 2023 Vol 4 No 2 (2023): Februari 2023 Vol 4 No 1 (2022): November 2022 Vol 3 No 4 (2022): August 2022 Vol 3 No 3 (2022): May 2022 Vol 3 No 2 (2022): Februari 2022 Vol 3 No 1 (2021): November 2021 Vol 2 No 4 (2021): Agustus 2021 Vol 2 No 3 (2021): Mei 2021 Vol 2 No 2 (2021): February 2021 Vol 2 No 1 (2020): November 2020 Vol 1 No 4 (2020): August 2020 Vol 1 No 3 (2020): Journal of Computer System and Informatics (JoSYC) - May 2020 Vol 1 No 2 (2020): Journal of Computer System and Informatics (JoSYC) - Februari 2020 Vol 1 No 1 (2019): Journal of Computer System and Informatics (JoSYC) - November 2019 More Issue