bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Kepemilikan Manajerial, Kinerja Keuangan, dan Kualitas Audit terhadap Kecurangan Laporan Keuangan
Authors: Khomariah Oryza Alifia , Khomsiyah Khomsiyah
Published: October 2023

Fraudulent financial reporting can be detrimental to various parties. Therefore, it is essential to identify the factors that can cause fraudulent financial reporting to prevent its occurrence. This study aims to investigate and evaluate how managerial ownership, financial performance, and audit quality affect fraudulent financial reporting. This research used secondary data from company financial and annual reports. Sample used in this study were companies in the consumer goods manufacturing sector listed on the Indonesia Stock Exchange (IDX) between 2019 and 2021. Multiple linear regression analysis and purposive sampling method with 134 samples covering the 2019–2021 period was employed in this research. This study shows that financial performance proxied by leverage and profitability ratio, managerial ownership, and audit quality does not affect fraudulent financial reporting, while financial performance proxied by the liquidity ratios has negative affect towards fraudulent financial reporting
Kebijakan Dividen Ditinjau Dari Kinerja Total Asset Turn Over, Current Ratio, Debt To Equity Ratio Dan Return On Assets
Authors: Vebriyanti Riza , Puspitasari Elen
Published: October 2023

Indonesian companies carry out a dividend policy with investors by dividing dividends annually, but the company retains it in the form of profit to be used as investment financing in the future. This study aims to test and analyze by finding empirical evidence about the effect of total asset turnover, current ratio, debt to equity ratio, and return on assets  on dividend policy in companies listed on the Indonesian Stock Exchange in 2018-2021. Dividend policy is a decision to share profits earned by the company to shareholders as dividends, or to retain them in the form of retained earnings to be used as investment financing in the future. This type of research is classified as a quantitative type and uses secondary data from www.idx.co.id. The number of samples produced by purposive sampling were 76 companies according to the criteria multiplied by 4 years of the manufacturing company period. Multiple linear regression is the hypothesis test used in this study. The results of this study were that the total asset turnover and return on assets had a positive effect on dividend policy, but the current ratio and debt-to-equity ratio variables had no effect on dividend policy.
Peran Moderasi Religiusitas Terhadap Hubungan Antara Dimensi Fraud Pentagon Dengan Kecurangan Akademik
Authors: Simabur Lisda Ariani , M Effendi , Suhandoko Astri  Dwi Jayanti , Zainuddin Zainuddin
Published: October 2023

This study aims to determine and analyze the effect of the Pentagon dimension of fraud on academic fraud. The sample in this study was UPBJJ-UT Ternate students who were conducting lectures during the Covid-19 period as many as 134 respondents. The sampling technique in this study used a saturated sample. Methods of data collection using a questionnaire (questionnaire). Data analysis using path coefficient analysis with the help of the Smart PLS program. Based on the results of the research above, it can be concluded that 1) academic pressure affects academic fraud behavior; 2) opportunity does not affect academic fraud behavior; 3) rationalization affects academic fraud behavior; 4) the ability to influence academic fraud behavior; 5) personal ethics affect academic fraud behavior; Meanwhile, religiosity as a moderator does not affect the relationship between pressure, opportunity, rationalization, ability and personal ethics with academic fraud behavior. Although not large, based on the results of multi-group analysis, religiosity is proven to be moderate which will reduce the level of academic fraud. Religious students are more likely not to commit academic fraud, even though they are under pressure, have opportunities, rationalization, have abilities, and their ethics are still low. Efforts to prevent academic fraud that can be carried out at UPBJJ-UT Ternate include socialization activities that are classified as fraud and the sanctions, convincing students that fraud will be caught and will be announced, ensuring students are not in a situation where they work by fraud, showing evidence that all fraud is proven will be penalized, train students to be able to write without fraud and encourage students to have pride when acting under ethical teachings.
Pengaruh Environmental Performance dan Corporate Social Responsibility Disclosure Terhadap Kinerja Keuangan Pada Perusahaan Kosmetik di BEI
Authors: Alfianah Windah , Rizkianto Ryan Kurnia
Published: October 2023

The purpose of this research is to test and analyze empirically influence environmental performance against the financial performance of cosmetics companies in indonesia and the influence of corporate social responsibility disclosure of financial performance cosmetics companies in Indonesia. This research use a quantitative approach. A subject in this research was cosmetics companies listed on the indonesia stock exchange period 2011-2018. Data presented in the form of descriptive statistics and produce 6 company and 36 samples.
Research carried out by using multiple linear regression analysis environmental performance has a positive influence on the financial performance of corporations and CSR disclosure have a positive influence on financial performance shows that CSR is a form of transparent form of behavior and ethical was an image of corporate sustainbility .CSR can be used as a company to keep the company going concern sustainability in future.
Analisis Sengketa Pajak atas Aset Tidak Berwujud Berupa Goodwill di Indonesia
Authors: Wiharta Mandasari Gusti Ayu , Nuryanah Siti
Published: October 2023

The receiving party in a merger or acquisition can record goodwill, which is subjectively assessed based on appraiser assumptions. Indonesian regulations on goodwill are limited to Article 11A of the Income Tax Law for amortization. Goodwill is almost always a matter of concern between taxpayers, entities claiming amortization, and the tax authority (Directorate General of Taxes). Differences in perception between taxpayers and the tax authority can lead to tax disputes. Tax disputes result in the same losses for both taxpayers and the tax authority. The disputing parties bear high costs (cost of tax compliance and cost of tax collection) to settle tax disputes. This research aims to analyze the causes of tax disputes related to goodwill and recommend solutions to address the recurrence of disputes. This study is a qualitative research with a case study approach. The research is conducted through triangulation, involving content analysis of tax court verdicts from 2018 to 2022 and interviews with tax consultants and the tax authorities. The research findings indicate that there are two main causes of tax disputes related to goodwill. These are differences in the interpretation of tax regulations and the burden of proof related to income generation, maintenance, and collection activities. Based on these findings, the Directorate General of Taxes needs to standardize interpretation differences and harmonize tax regulations to create clear legal certainty that will minimize disputes between the tax authority and taxpayers.
Gender, financial literacy, internal control locus, and economic well-being: The employee viewpoint
Authors: Hadianto Bram , Mariana Ana
Published: October 2023

Economic well-being is related to financial freedom and become a vital topic for employees to perform well in the workplace. Hence, this study examines and analyzes the determinants. Based on prior works, they are gender, financial literacy, and internal control locus. The population is 500 employees of PT. Sinar Indogreen Kencana in Sidoarjo. Furthermore, this study utilizes the Slovin formula with a 10% margin of error to search for the sample size. After calculation based on this formula, its size is 222 employees. For taking them, this study used a simple random technique. Considering this number, the researcher utilizes a covariance-based structural equation model, preceded by validity and reliability examinations and goodness of fit detection. After passing the related tests and detection, this study examines the planned hypotheses and concludes that males have better economic well-being than females. Besides, financial literacy and locus of control have a positive effect on financial well-being. Also, the contribution of these four factors to explaining economic well-being is high, demonstrated by an R-square of 0.821.
Inflation, exchange rate, and stock return: The evidence from the LQ45 index constituents in Indonesia
Authors: Martalena Martalena , Iskandar Dini , Hadianto Bram
Published: October 2023

Economic factors affect companies, especially those listed in the capital market. Consequently, it fundamentally affects investors’ wealth, reflected by the stock price movement. Accordingly, this study aims to investigate and analyze the influence of inflation and exchange rate as related ones on stock return. The population comes from 20 coherent non-financial companies selected as LQ45 index from 2016 to 2022. Then, this study applies the Slovin formula with a 10% error margin to determine the sample size of 17, taken by a simple random sampling method. Also, it uses the regression model with pooling data and t-statistic to examine stock return determinants. Once meeting classical assumptions, this study checks the relationships. Its result demonstrates a positive influence of inflation on stock return, confirming the perspective declaring common stock becomes the hedging tool on inflation. Meanwhile, the IDR/USD exchange rate negatively associates with this return: The more powerful the US Dollar, the more diminished the stock price of non-financial companies belonging to the LQ45 index.
Pengaruh Firm Size, Likuiditas, Leverage Dan Profitabilitas Terhadap Nilai Perusahaan Pada Perusahaan Manufaktur Sub Sektor Barang Konsumsi Yang Terdaftar Di Bursa Efek Indonesia
Authors: Ferdila Ferdila , Mustika Ita , Martina Sri
Published: October 2023

This study examines how business size, liquidity, leverage, and profitability affect firm value in Indonesia Stock Exchange-listed supply products manufacturing companies between 2019 and 2021. Earlier corporate value study had mixed results. Therefore, more research is needed to retest company worth. The survey sampled 47 consumer products manufacturers. For the three-year observation period (2019–2021), 13 business samples were purposefully sampled with 39 observations. The Indonesian Stock Exchange website provided the sample businesses and research data. Research data obtained from the Indonesian Stock Exchange website, where sample businesses were found. The data analysis approach began with descriptive statistics, classical assumption test, multiple regression analysis, and hypothesis testing. Corporate size, liquidity, leverage, and profitability affect firm value. According to research, only profitability increases firm worth significantly. Significant to firm value. Liquidity boosts corporate value but not significantly. Leverage and firm size have minimal impact on firm value
Strategi Peningkatan Kapasitas Pembentukan Modal Pada Kelompok Tani Di Desa Waimital Provinsi Maluku
Authors: Tahitu Meilvis E. , Damanik Inta P. N. , Kembauw Esther
Published: October 2023

Penelitian ini bertujuan menemukan strategi yang sesuai untuk meningkatkan kapasitas kelompok tani di Desa Waimital khususnya untuk membentuk modal kelompok.  Kapasitas pembentukan modal kelompok merupakan salah satu hal penting bagi kelompok tani di Desa Waimital karena berbagai kegiatan kelompok yang terkait dengan usaha tani tidak dapat dilakukan akibat ketiadaan modal kelompok.  Konsekuensinya kelompok tani bersifat stagnan, hanya melakukan rutinitas saja padahal anggota kelompok tani di Desa Waimital adalah petani yang suka mencoba hal-hal baru.  Sebagai populasi adalah seluruh kelompok tani di Desa Waimital, yaitu 12 kelompok tani.  Sebagai responden ditentukan lima orang mewakili setiap kelompok yang terdiri dari ketua, sekretaris, dan tiga anggota kelompok.  Data primer berasal dari responden yang dikumpulkan melalui wawancara berstruktur menggunakan kuesioner. Data primer diantaranya tentang berbagai kegiatan kelompok terkait pembentukan modal kelompok. Data sekunder diperoleh dari berbagai sumber, diantaranya dari Kantor Desa Waimital, Badan Pusat Statistik, dan sumber-sumber lain.  Analisis data dilakukan secara deskriptif kualitatif.  Hasil penelitian menunjukkan bahwa strategi yang sesuai untuk peningkatan kapasitas pembentukan modal kelompok bagi kelompok tani di Desa Waimital adalah dengan meningkatkan pengetahuan, sikap dan keterampilan tentang pembentukan modal kelompok. Ini dapat dilakukan melalui kegiatan sosialisasi, pelatihan, dan pendampingan tentang pembentukan modal kelompok, manajemen pengelolaan modal kelompok dan diikuti dengan dukungan regulasi (kebijakan) yang sesuai, dukungan sarana produksi dan alat serta mesin pertanian.  Strategi yang akan dilakukan membutuhkan kerjasama dan sinergitas dari berbagai pihak terkait, yaitu pihak perguruan tinggi; perbankan; pemerintah desa, kecamatan, dan kabupaten terkait dengan dinas-dinas yang relevan, diantaranya dinas pertanian.
Penggunaan Informasi Pengungkapan Sukarela di Media Sosial: Analisis Technology Acceptance Model (TAM)
Authors: S Soemarno Hidayatullah , Annas Nurul Afiqah , Andriani Wenni
Published: October 2023

This study aims to analyze the determinants of investor behavior while making investment decisions using voluntary disclosure information obtained from social media pages. Using the technology acceptance model, the correlation between belief constructs and behavioral intentions is measured. Perceived ease of use, perceived usefulness, and perceived enjoyment are the three belief constructs used in this study. 180 employees were respondents, and structural equation modelling partial least square was used to evaluate the hypotheses. The results showed that Indonesian investors believe that information regarding voluntary disclosures provided on social media pages will influence their decision to use such information when making investments. The intention is influenced by perceived ease of use, perceived usefulness, and perceived enjoyment. This study also found that perceived enjoyment has an effect on perceived ease of use and perceived usefulness.
Pengaruh Pendapatan Asli Daerah Dan Belanja Modal Pada Peningkatan Indeks Pembangunan Manusia Provinsi DKI Jakarta Tahun 2015 – 2022
Authors: Al Rasyid Harun , K Agus Tri Indah
Published: October 2023

The implementation of regional autonomy and fiscal decentralization gives regional governments the authority to increase regional revenues and realize distributive benefits so that they can determine regional development priorities. This human development concept prioritizes four elements, including productivity, equity, sustainability and empowerment. The aim of this research is to understand how capital expenditure and PAD influence increasing the human development index. DKI Jakarta Province was the location for this research. The 2015-2022 APBD Performance Report and 2015-2022 HDI statistics are secondary data sources used in this research. By using multiple regression analysis, the requirements for traditional hypothesis testing and model feasibility testing are met. Regional initial income does not have a positive impact on the human development index. Capital expenditure does not have a negative impact on the human development index. For simultaneous calculations (F statistical testing), the conclusion is that regional sources of income and capital expenditure simultaneously influence the human development index. Based on hypothesis testing, it shows that PAD and capital expenditure have a positive and significant effect on increasing the human development index. This positive and significant influence means that the more PAD and capital expenditure increase, the higher the HDI will be. This increase was due to the allocation of initial regional income and investment expenditure being used well so that planned activities could be carried out smoothly.
Decision Making for the Best Hospital Evaluation Using the AHP-COPRAS Method
Authors: Akmaludin Akmaludin , Suriyanto Adhi Dharma , Iriadi Nandang
Published: October 2023

The assessment of the Hospital is very important in providing a rating as a measure of accreditation. Hospitals should provide the best service to visiting patients. The purpose of this study is to provide a ranking of hospitals that should be a measure of the appropriateness of a hospital as seen from a number of criteria as a true assessment. The criteria used as a barometer for the assessment consist of ten criteria, namely Administrative Services, Doctor Services, Pharmaceutical Installation Services, Cleanliness, Convenience, Security, Number of Administrative Personnel, Number of Pharmaceutical Personnel, Administrative Complaints, and Pharmacy Installation Complaints. The scoring system that will be applied uses the Analytical Hierarchy Process (AHP) method as a determinant of the weight of each criterion and the Fuzzy assessment uses the Complex Proportional Assessment (COPRAS) method. The two methods will be collaborated as a determinant in the process of giving a rating system to the ten hospitals that are included in the priority assessment. The results obtained from the ranking process of the two AHP and COPRAS methods are seen from the acquisition of the utility value produced, the largest value obtained from the utility gives the best rating. The utility value is obtained from the total assessment of a number of criteria for each hospital and the largest utility rating does not exceed one. The highest value of the utility generated by the RS10 alternative with a utility scale of 0.098 as the best value.
Decision Support for Selection of The Best Teachers Recommendations MCDM-AHP and ARAS Collaborative Methods
Authors: Akmaludin Akmaludin , S. Erene Gernaria , Rinawati Rinawati , Arisawati Ester , Dewi Linda Sari
Published: October 2023

The role of the teacher is very important for the progress of the nation which can increase the dignity of the nation. The quality of education can increase thanks to the support of teachers who have good dedication in developing the learning process, especially in curriculum development. The teacher's biggest contribution is to make students ready to become the nation's credible successors. The purpose of this study is to provide an assessment in the selection process of teachers in an objective and selective manner. Method recommendations that can be raised in this study are collaborative methods that play a role in multi-criteria selection, namely MCDM-AHP and ARAS. Both of these methods can be said to be able to implement a selective and credible selection process for teachers which is carried out through the stages of data conversion and normalization which in turn can determine decision support in the multi-criteria selection process. The results of this study provide the best solution in selecting alternatives with a multi-criteria barometer for measurements with the ARAS method. The resulting decision support for the selection process of twenty teachers resulting in the best assessment can be seen from the optimization function which is based on the maximum value default divisor basis. The results obtained from obtaining the highest utility value of the twenty alternatives, the top three values that can be drawn as support for decision making are owned by A3 with a utility weight of 0.891, followed by the two highest ratings A17 and A14 with respective weights of 0.888 and 0.884.
DESIGN UI/UX E-LEARNING ENGLISH MOBILE USING USER CENTERED DESIGN (UCD) METHOD
Authors: Alamsyah Dwi Rizky , Resmi Mochzen Gito , Jaelani Irsan
Published: October 2023

E-learning English Mobile application design is a Mobile-based application design needed by SMAN 2 Purwakarta for future learning purposes. With this application design SMAN 2 Purwakarta has a picture of learning applications that will facilitate students and teachers in learning and teaching activities.

In this application design research, there are several methods that are widely used by previous researchers. One of the methods used in this research is the User Centered Design (UCD) method which is a new method in system development. UCD is a language that is widely applied in describing designs. The concept of UCD is the user as the center of the system development process, and the goals, the system environment are all based on the user experience.

The results of this study produced a learning application prototype, namely E-learning English Mobile. After testing using the System Usability Scale (SUS). The average value obtained is 78. It can be concluded that the design of the E-Learning English Mobile application is acceptable because it meets the Acceptable category
The Performance of the Equal-Width and Equal-Frequency Discretization Methods on Data Features in Classification Process
Authors: Putri Pramaishella Ardiani Regita , Prasetiyowati Sri Suryani , Sibaroni Yuliant
Published: October 2023

The classification process often needs help with suboptimal accuracy values, which can be attributed to various factors, including the dataset's wide range of attribute values. Discretization methods offer a solution to address these issues. This study aims to compare the effectiveness of Equal-Width and Equal-Frequency discretization methods in enhancing accuracy during the classification process using datasets with varying sizes. The research employs Naïve Bayes, Decision Tree, and Support Vector Machine as classification models, with three datasets utilized: Bandung City Traffic data (3804 records), Bandung City COVID-19 cases data (2718 records), and Bandung City Dengue Fever Disease Index data (150 records). Three experimental scenarios are executed to assess the impact of the two discretization methods on accuracy. The first scenario involves no discretization, the second employs Equal-Width, and the third applies Equal-Frequency discretization. Experimental results indicate significant accuracy improvements post-discretization. The Naïve Bayes model achieved 94% accuracy for the Traffic dataset, while the Decision Tree achieved 71% accuracy for the COVID-19 dataset and an impressive 98% for the Dengue Fever Disease dataset. These outcomes demonstrate that applying Equal-Width and Equal-Frequency discretization methods addresses the challenge of wide attribute value ranges in the classification process.
Densenet Architecture Implementation for Organic and Non-Organic Waste
Authors: Simarmata Allwin M. , Salim Philander , Waruwu Netral Jaya , Jessica Jessica
Published: October 2023

Garbage is the result left over from the process of daily human activities and activities which are considered no longer suitable for use, ranging from household waste to large-scale industrial waste. Therefore, the classification of waste is important because the problem of waste disposal is increasing and the way of processing is wrong. This research focuses on the classification of organic and non-organic waste using the DenseNet architecture. The dataset is processed first and each image in the dataset is resized to 128x128 pixels before being used in the model. We then trained all DenseNet types namely DenseNet121, DenseNet169, DenseNet 201, and compared their performance. Based on the test results, all DenseNet models that were trained were able to produce good accuracy, precision, recall, and F1 scores in garbage classification. In particular, our designed DenseNet121 model achieves 93.1 accuracy, 94.08% precision, 94.00% recall, 94.03% F1 score and 1min 34s training time as the best among other models. These results prove that the DenseNet architecture can be used to classify organic and non-organic waste correctly.
Pneumonia Classification Based on Lung CT Scans Using Vgg-19
Authors: Putra Adya Zizwan , Situmorang D. V. M. , Wahyudi G. , giawa J. P. K. , Tarigan R. A.
Published: October 2023

This research harnesses technology for critical health applications, specifically, pneumonia detection through medical imaging. X-ray photography allows radiologists to visualize the patient's health state, including the detection of lung infections signifying pneumonia. The study's centerpiece is the application of the VGG-19 model in classifying lung CT scan images, helping discern normal from pneumonia-indicative conditions. A comprehensive preprocessing procedure is employed, entailing pixel rescaling and data augmentation techniques. To address data imbalance, a critical issue in machine learning, we incorporate the Synthetic Minority Over-sampling Technique (SMOTE). The developed VGG-19 model demonstrates impressive performance, achieving a 94.6% accuracy rate in classifying lung CT scans. This finding underscores the potential of the VGG-19 model as a reliable tool for pneumonia detection based on lung CT scans. Such a tool could revolutionize the field, providing an efficient and accurate method for early pneumonia diagnosis, thereby allowing for timely treatment.
Classification of E-Commerce Product Descriptions with The Tf-Idf and Svm Methods
Authors: Pakpahan Dagobert , Siallagan Veronika , Siregar Saut
Published: October 2023

The rapidly growing e-commerce sector presents a significant challenge in navigating an abundance of products. Understanding and classifying product descriptions efficiently and accurately is crucial to improving user experience and business operations. This research employed the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm and Support Vector Machine (SVM) for the classification of e-commerce product descriptions into four categories: Electronics, Household Items, Books, and Clothing. The initial phase involved pre-processing of text data which incorporated text cleaning, tokenization, part-of-speech tagging, entity recognition, and conversion into a vector representation. The resulting model was trained and tested using the SVM algorithm. Our model demonstrated a high degree of accuracy, achieving 99.2% during the training phase and 95.7% in the testing phase. This model provides a valuable tool for e-commerce businesses, as it allows for accurate classification of products based on their descriptions. This could lead to improved user navigation and overall user experience on e-commerce platforms.
Implementation of Transfer Learning in CNN for Classification of Nut Type
Authors: Fawwaz Insidini , Sagala Jimmy Deardo , Sijabat Reivaldo Kevin Febriawan , Maringga Novita Marissa
Published: October 2023

Nut has a high nutritional value and is widely used as an ingredient in cooking and snacks. Nut is included in the group of grains and has many types. Each type of nut has different nutritional content. Some types of nuts can also cause allergies or negative reactions in certain people, so it is important to identify the type of nut to be consumed. There are many types of nut that are different from each other, but some of them are similar. This makes it difficult to distinguish between the types of nuts, so there is a need for technology that can accurately identify nut types. Transfer Learning method is used to utilize trained models and applied to nut type classification. The two CNN models used are Inception V3 and Xception. The dataset consists of 11 types of nuts consisting of 1,320 data. The data is divided into 60% for training data and 40% for validation data. Preprocessing is done to ensure the image size is consistent and clarify the focus on the data image to be tested. The training results show that the Xception model is superior to Inception V3, with an accuracy of 86.36% on the validation data, while Inception V3 only reached 74.05%. Xception is able to predict nut types more precisely.
Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method
Authors: Telaumbanua Agustritus Pasrah Hati , Larosa Tri Putra , Pratama Panji Dika , Fauza Ra'uf Harris , Husein Amir Mahmud
Published: October 2023

Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and data augmentation processes need to be considered in the future
Comparative Analysis of CNN and CNN-SVM Methods For Classification Types of Human Skin Disease
Authors: Anggriandi Dendi , Utami Ema , Ariatmanto Dhani
Published: October 2023

Cancer is one of the leading causes of death worldwide, with skin cancer ranking fifth. The skin, as the outermost organ of the body, is susceptible to various diseases, and accurate diagnosis is crucial for effective treatment. However, limited access to dermatologists and expensive skin biopsies poses challenges in achieving efficient diagnosis. Therefore, it is important to develop a system that can assist in efficiently classifying skin diseases to overcome these limitations. In the field of skin disease classification, Machine Learning and Deep Learning methods, especially Convolutional Neural Network (CNN), have demonstrated high accuracy in medical image classification. CNN's advantage lies in its ability to automatically and deeply extract features from skin images. The combination of CNN and Support Vector Machine (SVM) offers an interesting approach, with CNN used for feature extraction and SVM as the classification algorithm. This research compares two classification methods: CNN with MobileNet architecture and CNN-SVM with various kernel types to classify human skin diseases. The dataset consists of seven classes of skin diseases with a total of 21.000 images. The results of the CNN classification show an accuracy of 93.47%, with high precision, recall, and F1-score, at 93.55%, 93.74%, and 93.62%, respectively. Meanwhile, the CNN-SVM model with "poly," "rbf," "linear," and "sigmoid" kernels exhibits varied performances. Overall, the CNN-SVM model performs lower than the CNN model. The findings offer insights for medical image analysis and skin disease classification research. Researchers can enhance CNN-SVM model performance with varied kernel types and techniques for complex feature representations.
Stock Price Correlation Analysis with Twitter Sentiment Analysis Using The CNN-LSTM Method
Authors: Ibnu Sina Muhammad Noer , Setiawan Erwin Budi
Published: October 2023

The intricate interplay between stock prices, reflecting a company's intrinsic value, and multifaceted factors like economic conditions, corporate performance, and market sentiment, constitutes a vital research domain. Grounded in sentiment analysis, our study deciphers public opinions from vast textual data to gauge sentiment, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. We focus on Bank Central Asia (BBCA), a prominent Indonesian banking institution, aiming to forecast stock price fluctuations by analyzing sentiment trends extracted from social media, especially Twitter. Meticulous experimentation, encompassing data segmentation, feature extraction, augmentation, and model refinement, yields significant enhancements in prediction accuracy. The CNN-LSTM model's performance improves from 73.41% to a robust 77.75% accuracy, with F1-scores rising from 73.00% to 75.42%. Importantly, strong correlations emerge between sentiment predictions and actual stock price movements, validated by Spearman correlation coefficients. Positive sentiment exhibits a substantial correlation of 0.745 with stock price changes, while negative sentiment exerts notable influence with a correlation coefficient of 0.691. In summary, our study advances the field of sentiment-driven stock price prediction, showcasing deep learning's effectiveness in extracting sentiment from social media narratives. The implications extend to understanding market dynamics and potentially integrating sentiment-aware strategies into financial decision-making. Future research directions could explore model transferability across financial contexts, real-time sentiment data integration, and interpretability techniques for enhanced practicality in sentiment-driven predictions.
Android-based Automatic Steak Grilling Tool
Authors: Salamah Irma , Syaniah Yunita , Hadi Irawan
Published: October 2023

In an era of rapid technological development, technology is increasingly accessible and easily applied by humans. One of the significant developments is the Internet of Things (IoT), where physical devices such as sensors, equipment, and vehicles are equipped to communicate and interact via the Internet network. The application of IoT has expanded to various sectors, including culinary. In this regard, preparing and presenting food, especially steaks, becomes an exciting focus. There are multiple types of steaks, such as sirloin and tenderloin, and cooking involves various techniques, such as searing and grilling. However, suitability for maturity and risk during cooking is challenging for steak makers and connoisseurs. To overcome this, the application of IoT is needed in an automatic steak roaster to be a promising solution. This research is also equipped with real-time monitoring via an Android application. This aims to ensure proper doneness and consistent results in the steak cooking process. This research makes an automatic steak grill with a success rate of 83%, which shows that the tool's performance and functionality align with expectations. This tool also has an Android application to monitor and control the device remotely efficiently. This research gives confidence that this can be a solution that has been developed and provides significant benefits in roasting steaks with automatic monitoring and operation.
Multidimensional Knapsack 0-1 Solution With Algorithm Evolution Pso-Ga
Authors: Sapoetra Yudistira Arya , Habibi Azwar Riza
Published: October 2023

This paper develops the particle swarm optimization (PSO) method and uses a genetic algorithm (GA) by changing the distribution of articles in the initialization of the initial position. PSO at this time the search and speed of particles will always go to the best solution so that by narrowing the search area will be faster by updating the best position of PSO. While the Genetic algorithm plays a role to get an expanded search area for PSO solutions by utilizing crossover and mutation in GA. So that GA will expand the range of candidates for the best solution in PSO. From each of the advantages of PSO Update and GA will be combined to get Evolutionary PSO-GA (EVPGA) that can minimize error and speed up computation (itation) in finding the best solution. By using the Multidimensional Knapsack data set, the results of EVPGA get an average speed of 24.9s with an error of 1.49%.
Comparison of Algorithms for Sentiment Analysis of Operator Satisfaction Level for Increasing Neo Feeder Applications in PDDikti Higher Education LLDIKTI Region VI Semarang Central Java
Authors: Albab M. Ulil , Utami Ema , Ariatmanto Dhani
Published: October 2023

Sentiment analysis on the satisfaction level of PDDikti operators is very important to find out how PDDikti operators feel after the version of the academic reporting application for higher education was upgraded, namely Neo Feeeder. The increase in the version of this application causes some of the features in it to not function properly. So some academic reporting activities from tertiary institutions experience problems. As a result of this condition, the most felt impact is students, where students experience delays in graduation. Then it is necessary to evaluate through sentiment analysis from PDDikti operators to find out the response from operators and be able to provide positive suggestions to developers from the PDDikti reporting application. This study applies several classification methods for sentiment analysis at once, including the Random Forest algorithm, the Support Vector Machine algorithm, the Multinomial Naïve Bayes algorithm, the Decision Tree algorithm, and the K-Nearest Neighbor algorithm. Of the 5 methods applied, the results of their performance accuracy will be compared. The performance of the highest classification algorithm is the K-Nearest Neighbor (K-NN) algorithm which produces an accuracy value when testing data, which is up to 90% using the oversampling technique in unbalanced classes. While the lowest classification accuracy performance value is in the Multinomial Naïve Bayes (MNB) algorithm with a value of 76%. It is proven that oversampling can help the performance of the classification algorithm to be more optimal. Thus, it should be noted that the balance of data classes is an important factor when applying the classification method.
Improving IT Support Efficiency Using AI-Driven Ticket Random Forest Classification Technique
Authors: Crosley Nathaniel , Wasito Ito
Published: October 2023

This research project aims to improve IT support efficiency at Indonesian company XYZ by using AI-based IT support ticket classification integration. This method involved collecting over 1,000 support tickets from the company's IT ticketing system, GLPI, and pre-processing the data to ensure the quality and relevance of the data for analysis. Claims data is enriched with relevant features, including textual information and categorical attributes such as urgency, impact, and requirement expertise. To improve the ticket preference matrix, AI-based language models, especially OpenAI's GPT-3, are used. These templates help to reclassify and improve the work of IT support teams. In addition, the ticket data is used to train the Random Forest classifier, allowing automatic classification of tickets based on their specific characteristics. The performance of the ticket classification system is evaluated using a variety of metrics, and the results are compared with alternative methods to assess effectiveness. of the Random Forest algorithm. This evaluation demonstrates the system's ability to correctly classify and prioritize incoming tickets. The successful implementation of this project at Company XYZ is a model for other organizations looking to optimize their IT support through AI-driven approaches. By providing simplified ticket classification and admission ticket reclassification based on AI algorithms, this research helps leverage AI technologies to improve IT support processes. Ultimately, the proposed solution benefits both support providers and users by improving efficiency, response times, and overall customer satisfaction.
Optimization of Delay Using Killer Whale Algorithm (KWA) on NB-IoT
Authors: Hadi Muhammad Abdullah , Widodo Agung Mulyo , Firmansyah Gerry , Akbar Habibullah
Published: October 2023

Abstract: NB-IoT is designed to connect IoT devices with low-power, wide-area coverage and efficient costs.  Ensuring optimal data transmission delay is a challenge in NB-IoT implementation. Inadequate coverage can hinder IoT adoption. Optimization balances energy saving and delay trade-off. The Killer Whale Algorithm (KWA) optimizes delay by adjusting repetition variables. KWA addresses dimensions, variable limits. Applying KWA in NB-IoT optimizes transmission, enhancing QoS. Optimizing delay involves reducing latency in uplink data transmission using repetition variables. This study applies KWA to optimize NB-IoT delay. Analysis in Table 4 shows non-linear repetition-distance correlation. Interestingly, delay outcomes exhibit a contrasting relationship. Still, delay remains advantageous, remaining under 1 second even at 10 km, specifically 9.2674 ms (0.0092674 seconds). This thesis aims to optimize delay in NB-IoT network transmission using the Killer Whale Algorithm (KWA), crucial for modern communication networks and IoT applications. Leveraging KWA, the research identifies solutions to reduce transmission delay, enhancing efficiency and meeting IoT communication demands for speed and timeliness
Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16
Authors: Rasyid Irfan , Yudianto Muhammad Resa Arif , Maimunah Maimunah , Purnomo Tuessi Ari
Published: October 2023

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.
Bounding Box and Thresholding in Optical Character Recognition for Car License Plate Recognition
Authors: Sania Wulida Rizki , Sari Christy Atika , Rachmawanto Eko Hari , Doheir Mohamed
Published: October 2023

License plate recognition plays a central role in a variety of application contexts, including traffic management, automated parking, and law enforcement. Among the various approaches available, the Optical Character Recognition (OCR) technique has proven its effectiveness in recognizing characters in license plate images. This study describes an approach for detecting and recognizing vehicle license plates by utilizing the OCR method with Bounding Box, Thresholding, and template matching. In addition, this study uses MATLAB R2022a software as the main tool in developing and implementing the method. The goal is to recognize vehicle license plates from images, describe their characteristics, and generate relevant information. This approach involves a series of image processing steps starting with the pre-processing stage, followed by the process of binarization and license plate segmentation. After successfully isolating the license plate area, isolating the character using a bounding box is performed using image separation techniques. The OCR method is used to recognize license plate characters through comparison using the correlation method. Through a series of experiments on several image datasets, this approach succeeded in showing that out of 20 sampled license plate images, the results obtained were a reading accuracy of 93.55% of 100%, recognizing 13 out of 20 license plate images accurately when tested. Thus, the findings of this research are expected to contribute to the recognition of vehicle license plates that are accurate and efficient, by utilizing image processing techniques and OCR methods implemented using MATLAB R2022a software.
Virtual Space For Virtual Reality Exhibitions With Oculus Quest Devices
Authors: Rusdi Rahman Muhammad , Suyanto M , Ariatmanto Dhani
Published: October 2023

Based on information from the Ministry of Cooperatives and MSMEs, currently the number of MSMEs has reached 64.2 million euros and their share of GDP is 61.07% or 8,573.89 trillion rupiah (Coordinating Ministry for the Economy of the Republic of Indonesia, 2021). The contribution of MSMEs to the Indonesian economy includes the ability to absorb 97 percent of the current total employment and generate 60.4 percent of the total investment. (Ministry of Investment, 2021). VR (Virtual Reality) technology is a technology that allows users to feel in a virtual (virtual) world in visual form and users can interact with a virtual environment simulated by a computer in the form of Android. The focus of this research is a technical way of creating a virtual space or virtual space and 3D objects in displaying products from SMEs to be marketed to consumers with the virtual reality method using the oculus quest device, this research uses the Luther method, a six-stage process for creating multimedia that includes concept, design, material gathering, assembly, testing, and distribution. System testing was carried out using black box testing and usability testing using the SUS Score standardization, with a total of 47 respondents getting an average score of 54%. This average number exceeds 50% of the standard SUS Score Analysis, so the virtual reality exhibition space is categorized as suitable and OK for use by users. And it can also help MSMEs in carrying out virtual reality-based online marketing.
Automatic OEE Data Collection and Alert System for Food Industry
Authors: Sumargo Ruly , Makmur Amelia
Published: October 2023

The constant demand for food and beverages to sustain human life drives fierce competition among manufacturers, focusing on product excellence in terms of timeliness, quality, and pricing. The key to competitiveness depends in optimizing manufacturing processes by efficiently utilizing company resources. To ensure the overall optimization and reliable flow of manufacturing processes, a systematic evaluation process must be used, Overall Equipment Efficiency (OEE) stands out as a prominent performance measurement metric in manufacturing process efficiency. OEE serves as a valuable diagnostic tool, exposing areas for improvement and losses transparently. Accurate OEE measurement necessitates the implementation of an automated data collection system with minimum human dependencies, human intervention, and conducting on-the-fly calculations to informed the stakeholder/user. Data quality and accuracy in OEE measurement is very critical. Low quality and accuracy data could lead to false decision. OEE categorizes losses into six groups loss to pinpoint significant factors for potential improvement. Once OEE could be maintain at high level with high data accuracy and right improvement point, an optimum manufacturing process, and cost effective in manufacturing expenses will be achieve. Base on the result comparison for OEE result before and after the system implementation, positive improvement in OEE could reach 8.06%. This scenario be adopted by other company, and could become a model for 1st phase journey in company digital transformation.