INTI Nusa Mandiri最新文献_第3页

KLASTERISASI ANGKATAN KERJA DI INDONESIA BERDASARKAN USIA MENGGUNAKAN METODE ALGORITMA K-MEANS 使用 K-means 算法按年龄对印度尼西亚劳动力进行分类

INTI Nusa Mandiri Pub Date : 2024-02-05 DOI: 10.33480/inti.v18i2.5056

Ririn Restu Aria

{"title":"KLASTERISASI ANGKATAN KERJA DI INDONESIA BERDASARKAN USIA MENGGUNAKAN METODE ALGORITMA K-MEANS","authors":"Ririn Restu Aria","doi":"10.33480/inti.v18i2.5056","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5056","url":null,"abstract":"The concept of the population is divided into two groups, namely the working age population and the population not working age. Indonesia, which has 34 provinces, has an unequal distribution of labor force due to the level of economic growth that is still not evenly distributed in several sectors. Labor is the most important and influential element in managing and controlling the economic system. In this study the method used in the grouping of provinces was based on the workforce in 34 provinces using the K-Means algorithm. The purpose of grouping data is done to get a province grouping that has a workforce in Indonesia by grouping / clustering into 3 groups based on age groups using the K-Means algorithm. Based on the calculations, the results of cluster 0 were 6 provinces, cluster 1 as many as 3 provinces and cluster 2 were 25 provinces. The K-Means algorithm can be used to understand the workforce problems and make it easier to describe the characteristics or characteristics of each group. Based on these results, the local government can give more attention to the regions with the smallest workforce such as the Province of Central Sulawesi, East Kalimantan, Jambi so that economic growth in various sectors can be increased so that the welfare of the workforce, especially in terms of work in the field of work can be easily obtained.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"30 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANALISA KEPUASAN PENGGUNA WEBSITE TOP UP VOUCHER GAMES ONLINE MENGGUNAKAN WEBQUAL 4.0 在线充值优惠券游戏网站与 Webqual 4.0 的关系分析

INTI Nusa Mandiri Pub Date : 2024-02-05 DOI: 10.33480/inti.v18i2.5036

Shinta Oktaviana R, Rifqi Avriyansyah Prayudha

{"title":"ANALISA KEPUASAN PENGGUNA WEBSITE TOP UP VOUCHER GAMES ONLINE MENGGUNAKAN WEBQUAL 4.0","authors":"Shinta Oktaviana R, Rifqi Avriyansyah Prayudha","doi":"10.33480/inti.v18i2.5036","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5036","url":null,"abstract":"The online gaming industry generates revenue through the sale of games that customers pay to play. In Indonesia, there are numerous programs that offer online game vouchers at different costs and with various payment options. The objective of this study is to identify the variables that influence user satisfaction on web applications that offer online game coupons. The study used the WebQual 4.0 methodology as a tool for evaluating the caliber of web apps. The research was conducted online via the Google Form application. The data collection period spanned from June 11, 2023 to June 29, 2023, and we got 130 respondents. The data was evaluated using descriptive statistical analysis with the aid of SPSS tools. The findings of this study indicate that the variable of Interaction Quality (X3) has a significant role in determining user satisfaction for online game voucher websites, however the factors of Usability (X1) and Information Quality (X2) do not exert any influence on user satisfaction. Therefore, this research shows that hypotheses H1 and H2 are rejected for all data groups. This research provides recommendations to online game voucher application owners to focus on interaction quality variables in further web application development.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139805321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR 使用 Yolo 框架的 cnn 算法在制造企业产品对象检测中的应用

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.5028

A. Maulana, M. Suherman, A. Masruriyah, H. Novita

{"title":"PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR","authors":"A. Maulana, M. Suherman, A. Masruriyah, H. Novita","doi":"10.33480/inti.v18i2.5028","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5028","url":null,"abstract":"Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"507 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139824925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION 用于预测妊娠期的优化方法：随机森林与粒子群优化的融合

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.5031

Imam Nawawi

{"title":"OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION","authors":"Imam Nawawi","doi":"10.33480/inti.v18i2.5031","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5031","url":null,"abstract":"Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"91 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139872703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR 使用 Yolo 框架的 cnn 算法在制造企业产品对象检测中的应用

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.5028

A. Maulana, M. Suherman, A. Masruriyah, H. Novita

{"title":"PENERAPAN ALGORITMA CNN MENGGUNAKAN FRAMEWORK YOLO UNTUK DETEKSI OBJEK PRODUK DI PERUSAHAAN MANUFAKTUR","authors":"A. Maulana, M. Suherman, A. Masruriyah, H. Novita","doi":"10.33480/inti.v18i2.5028","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5028","url":null,"abstract":"Component products used for manufacturing a machine in manufacturing companies have two types of products, type A and B. The problem that often occurs in the industry is product sorting errors due to the traditional sorting process, using human labor. The disadvantages are limited human labor so that fatigue can occur, causing errors in sorting products and losses for the company. Many studies discuss object detection, Industrial problems in the checking process can be approached with the help of this technology. Object detection works to analyze frames with the method of finding objects. There are methods in digital image processing, CNN algorithms which include methods in computer vision. The growing framework makes the CNN algorithm more powerful. YOLO includes a framework based on the CNN algorithm. YOLOv5 detects objects by taking into account the object's confidence value, the output of the detected object is a bounding box on the object. The problem in the industry in the checking process can be approached with the help of this technology. For this reason, this research aims to create a model for product object detection in manufacturing companies. The process carried out is data collection, image annotation, training, testing, evaluation. The images collected were 137 for training data and 34 for validation data totaling 171 image data. The results of the model using YOLOv5 with epoch 1000 get a precision value of 100%, recall 100% and mAP 99%, the product detection results get an average value of 100%.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"36 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139884501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS 用 K-均值法和模糊 C-均值法对印尼东部的气象数据进行聚类

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.5039

Gion Andrian, Desi Arisandi, Teny Handhayani

引用次数: 0

PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES 使用天真贝叶斯方法将 PSO 应用于加密货币评论的情感分析

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.4982

Nita Merlina, Ade Chandra, Nissa Almira Mayangky

引用次数: 0

OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION 用于预测妊娠期的优化方法：随机森林与粒子群优化的融合

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.5031

Imam Nawawi

{"title":"OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION","authors":"Imam Nawawi","doi":"10.33480/inti.v18i2.5031","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5031","url":null,"abstract":"Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"164 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139813052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CLUSTERING DATA METEOROLOGI WILAYAH INDONESIA TIMUR DENGAN METODE K-MEANS DAN FUZZY C-MEANS 用 K-均值法和模糊 C-均值法对印尼东部的气象数据进行聚类

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.5039

Gion Andrian, Desi Arisandi, Teny Handhayani

引用次数: 0

PENERAPAN PSO UNTUK SENTIMEN ANALISIS PADA REVIEW MATA UANG KRIPTO MENGGUNAKAN METODE NAÏVE BAYES 使用天真贝叶斯方法将 PSO 应用于加密货币评论的情感分析

INTI Nusa Mandiri Pub Date : 2024-02-01 DOI: 10.33480/inti.v18i2.4982

Nita Merlina, Ade Chandra, Nissa Almira Mayangky

引用次数: 0