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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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Comprehensive Analysis of Sentiment Classification and Toxicity Assessment in Cultural Documentary Videos Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5150

Abstract

This research explores sentiment classification and toxicity assessment in cultural documentary videos through a systematic analysis framework based on the Cross-Industry Standard Process for Data Mining (CRISP-DM). The study evaluates the sentiment polarity of viewer comments by utilizing a diverse array of machine-learning algorithms, including k-NN, DT, NBC, and SVM. It identifies toxic language patterns across multiple videos. Additionally, the research employs SMOTE to address class imbalance issues and enhance model performance. The results reveal high accuracy rates ranging from 72.24% to 96.79% in sentiment classification, indicating the effectiveness of the proposed methodology. Moreover, toxicity analysis unveils varying degrees of toxic language prevalence, with toxicity scores ranging from 0.01270 to 0.09334 across different videos. Despite these achievements, the study acknowledges the inherent limitations of toxicity scoring algorithms in capturing contextual nuances. Overall, this research contributes to understanding sentiment dynamics and toxicity trends in cultural documentary content and underscores the importance of employing advanced machine learning techniques within a structured analytical framework for insightful data interpretation and decision-making.
Comprehensive Analysis of Sentiment and Toxicity Dynamics in Tourist Vlog Reviews: A CRISP-DM Approach Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5154

Abstract

This research employs the CRISP-DM framework to analyze sentiment and toxicity dynamics in tourist vlog reviews thoroughly. The study delves into sentiment classification and toxicity identification nuances by leveraging machine learning algorithms such as k-NN, SVM, NBC, and DT with SMOTE. Utilizing a dataset comprising a substantial number of posts, the analysis reveals varying levels of accuracy across different algorithms. For instance, k-NN and SVM showcase promising accuracy rates of 85.90% and 86.27% in sentiment classification, while NBC and DT with SMOTE yield 72.52% and 71.14%, respectively. Furthermore, the research elucidates the limitations of toxicity analysis, with NBC demonstrating a precision of 64.96% and DT exhibiting lower recall rates. These findings highlight the importance of robust methodologies for understanding sentiment and toxicity dynamics in online content, particularly in tourist vlog reviews.
Perbandingan Performa Klasifikasi Terjemahan Al-Qur'an Menggunakan Metode Random Forest dan Long Short Term Memory Aftari, Dhea Putri; Safaat, Nazruddin; Agustian, Surya; Yusra, Yusra; Afrianty, Iis
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5156

Abstract

This study focuses on the use of the Qur'an as the primary source of Islamic teachings, aiming to facilitate Muslims' understanding of its content. To achieve this, the classification of translated Qur'anic verses was conducted. Two methods that are rarely used for Qur'anic translation data are Random Forest (RF) and Long Short Term Memory (LSTM) due to their ability to process large and complex data. The data used in this study are translations of the Qur'an that have been classified into 15 topics by previous research, but this study will only focus on 6 topics. The objective of this research is to compare the performance of RF and LSTM in classifying Qur'anic translations into 6 different categories. The results show that in the preaching category, LSTM consistently outperformed RF, with an F1-Score of 57.3% and an accuracy of 96.8%, whereas RF achieved an F1-Score of 49.4% and an accuracy of 97.5%. These findings indicate that LSTM has better performance, especially with proper preprocessing, optimal parameter tuning, and balanced data. This study provides important insights into the development of classification models for Qur'anic translation texts, highlighting the importance of proper preprocessing and parameter tuning.
Sistem Pendukung Keputusan Penilaian Kinerja Tenaga Honor Panitia Pengawas Menggunakan Kombinasi Logarithmic Least Squares Weighting dan MABAC Mahendra, Ferdian Jerry; Setiawansyah, Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5158

Abstract

The performance of the honor staff, supervisory committee, division, staff handling violations, dispute resolution is vital in ensuring effectiveness and fairness in handling violations and dispute resolution within the organization. The main problems in evaluating the performance of supervisory committee personnel include subjectivity in the assessment, and lack of transparency in the appraisal process. Subjectivity can arise due to different perceptions of the party making the assessment, which can lead to unfairness in performance evaluation. Non-transparency in the appraisal process can also raise doubts and distrust of the fairness of the performance appraisal process of supervisory staff. DSS performance appraisal of honorary personnel of the supervisory committee using a combination of LLSW and MABAC is to develop a holistic and effective approach in evaluating the performance of honorary personnel in the supervisory committee. This research is to improve objectivity and fairness in performance appraisal, as well as enable decision makers to make more informed and informed decisions in honorary personnel management in the supervisory committee. The combination of LLSW (Logarithmic Least Squares Weighting) and MABAC (Multi-Attributive Border Approximation Area Comparison) can obtain more detailed and objective recommendations in the performance assessment of honor personnel. This process combines a statistical approach (LLSW) to determine attribute weights and a multi-attribute comparative analysis (MABAC) to obtain a final alternative ranking. The results of ranking 3 alternatives using a combination of LLSW and MABAC methods in assessing the performance of honor workers showed that the results for rank 1 were obtained by Yustina with a final function value of 0.152406, rank 2 was obtained by Andri with a final function value of 0.118662, and rank 3 was obtained by Sudrajat with a final function value of 0.094245.
Kombinasi Logarithmic Percentage Change-Driven Objective Weighting dan Complex Proportional Assessment Dalam Penentuan Supplier Perlengkapan Olahraga Pramuditya, Andri; Darwis, Dedi; Setiawansyah, Setiawansyah
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5160

Abstract

Sports equipment suppliers are suppliers that provide a wide range of equipment, clothing, and accessories needed by athletes, sports teams, and fans to support their activities. One of the main problems in choosing a sports equipment supplier is consistent product quality. Companies that choose sports equipment suppliers need to ensure that the products they buy meet the expected quality standards. In addition, the issue of stock availability and reliable delivery times is also a concern, as the inability to get goods on time can interfere with sports activities or businesses that depend on such equipment. The combination of Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) and Complex Proportional Assessment (COPRAS) provides a solid and adaptive framework for selecting sports equipment suppliers that best suit existing needs and priorities. This approach helps decision makers to make more informed and targeted decisions, taking into account the overall impact of each supplier's choice in the sporting goods industry. The results of supplier determination recommendations show the results of the assessment of each supplier, based on calculations using a combination of LOPCOW and COPRAS for the first rank with a utility value of 100 obtained by TRB Suppliers. The results of applying the combination of LOPCOW and COPRAS methods in supplier determination can provide more holistic and accurate results, the combination of these two methods can provide a more complete and detailed view of optimal supplier selection, taking into account dynamic changes in relevant criteria and preferences.
Sistem Pendukung Keputusan Pemilihan Jurusan pada SMA menggunakan Metode Profile Matching Anjani, Yulia Merry; Muttakin, Fitriani; Zarnelly, Zarnelly; Permana, Inggih
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5166

Abstract

Every year, in the process of selecting a new major at ABC High School, a major selection is carried out. This process requires students to identify their interests, talents, and abilities in order to make informed choices. However, this process often takes quite a long time because student data must be processed one by one using various different criteria. Apart from that, the selection of majors is currently based on the highest and lowest scores, with the highest score for the Science major and the lowest score for the Social Sciences major which is considered less efficient. To overcome this problem, the development of a Decision Support System (DSS) is proposed. able to provide recommendations for selecting majors more objectively. This research aims to develop SPK using the profile matching method, which will provide major recommendations based on certain criteria at SMA ABC. The criteria used include PPDB scores, science subject scores, social studies subject scores, mathematics scores, Indonesian language scores, psychological test results, student interests and parental preferences. Based on sample trials, this system recommends 6 students to enter the science department and 4 students to enter the social studies department. This system is expected to help students obtain education that suits their abilities and interests, as well as increase the efficiency of the majors process at SMA ABC.
Sentiment and Toxicity Analysis of Biometric Authentication and Facial Recognition Technology Content Reviews using Cross-Industry Standard Process for Data-Mining Singgalen, Yerik Afrianto
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5167

Abstract

This study investigates sentiment analysis methodologies within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining), aiming to discern the efficacy of various algorithms in sentiment classification tasks. The research uses a structured approach to evaluate SVM, NBC, DT, and K-NN algorithms with the SMOTE oversampling technique, uncovering distinct performance metrics and limitations. Results indicate SVM achieving 59.88% accuracy, NBC at 59.25%, DT with 52.09%, and K-NN obtaining 54.80%, highlighting the differential precision, recall, and f-measure. Additionally, content analysis identifies pertinent themes such as Biometric security, Cloud storage, and Emotion Analysis, enriching sentiment dynamics comprehension. The toxicity scores of analyzed videos reveal nuanced sentiment nuances, with the first video exhibiting Toxicity: 0.13227 and the second scoring Toxicity: 0.12794. This study underscores the significance of informed algorithm selection and evaluation methodologies within CRISP-DM, fostering optimized sentiment analysis outcomes while acknowledging diverse topical nuances.
Pengaruh Penyeimbangan Data Pada Klasifikasi Terjemahan Al-Quran Dengan Metode Naïve Bayes dan Long Short Term Memory Ningsih, Sulistia; Safaat, Nazruddin; Agustian, Surya; Yusra, Yusra; Cynthia, Eka Pandu
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5181

Abstract

The Al Qur'an is a holy book of Muslims which is a guide to life for all mankind. Studying and understanding the translation of the Al-Quran is not easy, one way that can be done is to classify the translation of Al-Quran verses into existing topics. This research uses Naïve Bayes and LSTM methods in the classification process. The data used comes from translation data of the Al-Quran in Indonesian which has been labeled based on multi-class classification. One of the main problems faced is data imbalance. To overcome this problem, data balancing, text preprocessing, feature construction and feature extraction processes were carried out using the Bag of Words (BoW) and TF.IDF techniques. The research results indicate that the most optimal Naïve Bayes model achieved an average accuracy of 55.39% on test data from juz 30, 61.59% on test data from juz 10-20, and 59.53% on test data from juz 25-28. Meanwhile, the most optimal LSTM model yielded an accuracy of 58.02% on test data from juz 30, 59.64% on test data from juz 10-20, and 58.59% on test data from juz 25-28. The main aim of this research is to improve classification performance and compare the accuracy between naïve Bayes and lstm.
Penentuan Penerima Bantuan dengan Program Miskin Berprestasi Menggunakan Metode Combined Compromise Solution (CoCoSo) Sallaby, Achmad Fikri; Kanedi, Indra; Sari, Venny Novita; Alinse, Rizka Tri; Supardi, Reno
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5192

Abstract

Educational assistance programmes from the government and other parties allow people to focus on education without being burdened by costs. In Indonesia, various aid programmes are available, including Bidik Misi for new students and special programmes for active students in need. These programmes aim to help poor, high-achieving students continue their education. However, there are problems in the determination of aid recipients. Aid funds are often misused by some students for hedonistic lifestyles. This is due to a selection system that is not objective and is still influenced by "insider power". Assessments based on students' economic conditions and achievements are often ignored, and priority is given to students who have connections in related institutions. This practice contradicts the purpose of the educational assistance programme, which is supposed to help students with economic limitations. Therefore, this research uses a Decision Support System (SDM). This study involved 15 students as data samples and used 5 assessment criteria. The selection of data samples was carried out by applying the Combined Compromise Solution method, one of the methods in decision making. From the selection process, 3 students were selected who were entitled to receive assistance. They are Siti Humairoh with a final score of 2.5395, Hafid Bangko in second place with a score of 2.2353, and Syfa Zahra in third place with a score of 2.1629. The three students have fulfilled all the requirements and they can proceed to the initial data validation process.
Implementasi Data Mining Untuk Penerima Bantuan PKH Pemerintah dengan Menerapkan Algoritma Klastering K-Medoids Wijaya, Yunan Fauzi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5197

Abstract

In 2019, a very heartbreaking event occurred for the entire world population. Where the consequences of the outbreak caused total paralysis of all activities in the world. The impact of non-performance of economic activities has caused the paralysis of the economy throughout the world, not only affecting small companies but also large companies. Especially in Indonesia itself, during the Covid-19 pandemic, there were many large-scale employee layoffs. The impact of this is increasing the number of family poverty cases in Indonesia. The Family Hope Program (PKH) is a program run by the government through the Ministry of Social Affairs. Even though the PKH program is based on the implementation of the Ministry of Social Affairs, the determination process is carried out by each social service in each region. There are still many families who are poor families who actually do not receive PKH assistance. This problem is caused by the large number of families in an area which requires quite a long process. The determination of poor families determined by the relevant agencies should be able to be seen based on data on previous PKH aid recipients. Data mining is a data mining process, data mining is carried out with the aim of obtaining new information that is valuable and important. Clustering or Clustering is part of data mining which aims to group data. Clustering is the formation of a new cluster from previously existing data. The K-Medoids algorithm is an algorithm for clustering data mining. In the K-Medoids algorithm, a process is carried out based on calculating the closest distance. From the process that has been carried out, it is estimated that there are 2 (two) clusters formed where in cluster 1 there are 7 families included in it. Meanwhile, in cluster 2 there are 8 families included.

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