Reviewerly/exHarmony huggingface dataset:
Implementasi Data Mining dengan K-Means Clustering untuk Memprediksi Pengadaan Obat

Authors:
raw_author_name: Yusuf Ramadhan Nasution
raw_affiliation_string: Universitas Islam Negeri Sumatera Utara, Medan
raw_author_name: Mhd. Furqan
raw_affiliation_string: Universitas Islam Negeri Sumatera Utara, Medan

Abstract: Community Health Center is one of the institutions that provides healthcare services. To ensure the provision of quality healthcare services, the Community Health Center management must be able to effectively manage medicine inventory to avoid the risks of shortages or excess stock. Therefore, the purpose of this research is to observe and perform clustering of medicine demands at Puskesmas Mandala using the K-Means Clustering technique. The data used includes medicine demand data from January to December 2023 at the health center. In its implementation, the RapidMiner application or software is utilized to perform clustering using the K-Means Clustering algorithm. The available medicine data will be grouped into 3 clusters: cluster 0 for high medicine demands, cluster 1 for moderate medicine demands, and cluster 2 for low medicine demands. Out of the 28 test data used, the results show the first cluster consisting of 24 items, the second cluster consisting of 3 items, and the third cluster consisting of 1 item with a Davies Bouldin Index value of 0.276. From this research, the Puskesmas can continue to procure medicine for the types classified under high-demand clusters to ensure that the medicine needs are consistently met.
An overview of statistical and neural-based line segmentation methods for offline handwriting recognition task

Authors:
raw_author_name: Oleg Yakovchuk
raw_affiliation_string: National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», Ukraine

Abstract: The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.
Prediksi Tingkat Angkatan Kerja Terhadap Pengangguran Terbuka Di Semarang Menggunakan Regresi Linier

Authors:
raw_author_name: Ferian Fauzi Abdulloh

Abstract: Unemployment is a situation where a person who does not have a job is caused by many factors, not only because they are lazy to look for work but mostly in this region of Indonesia unemployment is caused by limited employment opportunities, a lot of competition in the world of work, the large number of the labor force, lack of experience in the world of work, and also too choosy in working. The unemployment that occurs in Semarang is caused by the high number of labor force that makes the unemployment rate more and more. In this research, the author predicts the level of unemployment in Semarang. This research is a quantitative research whose data is taken from BPS Semarang. In this research, the author uses linear regression algorithm. The algorithm is widely used in cases to predict a problem, this research produces an RMSE (Root Mean Square Error) value of 0.07 with an R Square value of 91%. The results obtained can be used as a reference for the government to see the high unemployment rate in Semarang.
UTAPIS Indonesian Word Error Detection Application: Design and Development

Authors:
raw_author_name: Rudi Sutomo
raw_affiliation_string: Universitas Multimedia Nusantara
raw_author_name: Niknik Mediyawati

Abstract: People misspell and incorrectly write Indonesian words without even recognizing it. Poor and incorrect language proficiency is a common cause of word spelling mistakes. Since PT Tribun Digital Online publishes between 600 and 700 articles a day, the U-Tapis website might be of great assistance to journalists in identifying grammatical problems. Three separate cities—Jakarta, Makassar, and Solo—have participated in the testing of the U-tapis website. @ Tribun Digital Online, PT. This department is in charge of using the Flask framework to create the U-tapis website design. This study develops software or applications that are comparatively brief, succinct, and quick using the RAD (Rapid Application Development) technique. In addition to system development methods. The implementation of this method uses the U-tapis Sentence Syntax Error Detection algorithm which functions to detect an algorithm comparing two pieces of data, calculated using a formula called the Jaccard Index to check spelling errors in words. UAT test results produce all the features according to the wishes of the company owner based on the problems faced.
Modeling of Wind Power Generation in Tegal Region using Matlab Simulink

Authors:
raw_author_name: Fahmy Saputri
raw_affiliation_string: Universitas Multimedia Nusantara

Abstract: The increasing energy demands require larger energy production. However, fossil energy sources are depleting, necessitating the use of renewable energy to replace fossil fuels and meet energy needs in Indonesia. Indonesia has a significant wind energy potential. Wind power generation modeling is carried out in one of the cities in Indonesia, namely Tegal. By using Matlab Simulink, it is expected to assist in predicting renewable energy production. Tegal is one of the regions with high wind speeds in Indonesia, specifically at 3 m/s. From the modeling, the optimal blade radius and rotor angular velocity that can generate maximum power at a wind speed of 3 m/s can be determined. Wind power generation in Tegal with a wind speed of 3 m/s has the potential to produce electrical power of 768.55 W.
SISTEM PENYIRAMAN DAN PEMUPUKAN OTOMATIS BERBASIS INTERNET OF THINGS (IOT) DENGAN PEMANTAUAN ANTARMUKA APLIKASI ANDROID

Authors:
raw_author_name: Wiranto Wiranto
raw_affiliation_string: Universitas Sulawesi Barat

Abstract: Technological developments in the field of agriculture are now expanding widely, various modern tools must be owned to assist farmers in cultivating plants. The owners of the chili plants do watering and fertilizing by collecting water or fertilizer and then giving it to the plants one by one. This is not a problem if the plant owner has free time. However, if the plant owner is busy with other things or is having business that makes the plant owner unable to care for the plant, then this becomes a problem because when the plant does not receive sufficient care, the plant will easily wither and die. Seeing the existing problems, this research will design a system that can automatically water and fertilize plants based on IoT with monitoring from an Android application. The design of this system uses an Arduino microcontroller as a controller, the nodeMCU esp2688 wifi module to send data from the sensor to the web server, a soil moisture sensor to determine soil moisture, a real time clock (RTC) for fertilization scheduling and a relay as an automatic switch on the water pump. This system aims to help the activities of farmers who still use physical force to water and fertilize plants. The IoT-based automatic watering and fertilizing system uses the prototype development method, namely by collecting system requirements, building prototyping, evaluating prototyping, coding the system, testing the system, evaluating the system and using the system
WEBSITE SISTEM PAKAR DIAGNOSA PENYAKIT KULIT DENGAN FORWARD CHAINING (STUDI KASUS : PUSKESMAS TANGGULANGIN)

Authors:
raw_author_name: Moh Noor Al Azam
raw_affiliation_string: Universitas Narotama

Abstract: A website-based expert system for diagnosing skin diseases using the Forward Chaining method and the Laravel Framework at the Tanggulangin Health Center. The main focus of research is to improve accessibility, accuracy of diagnosis, and public awareness of skin health. Quantitative approaches and exploratory design are applied through literature studies, interviews, surveys, system design, trials, evaluations, and improvements. The system development process involves collecting data from observations, interviews, and medical data to devise relational rules, tables of disease types, symptoms, and decision trees. The use of the Laravel Framework is the main foundation of system design, including user interface (UI) design, database structure, Forward Chaining inference logic, and diagnostic needs. System trials are conducted with test scenarios to evaluate the system's ability to diagnose various skin disease conditions. Data analysis is carried out quantitatively, especially on test results, to assess system reliability. The results of this study provide an empirical basis for recommendations for further development. Hopefully, this system can make a positive contribution in the process of diagnosing skin diseases at the Tanggulangin Health Center and increase overall public skin health awareness. This system is able to provide a diagnosis of skin diseases based on the symptoms inputted by the user. The evaluation carried out provides a positive picture regarding the effectiveness and satisfaction of users.
Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Authors:
raw_author_name: Sarjon Defit
raw_author_name: Agus Perdana Windarto
raw_author_name: Putrama Alkhairi

Abstract: Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K- NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).
Automatic Text Labeling Method Based on Large Language Models

Authors:
raw_author_name: Henry Dyke A. Balmeo
raw_affiliation_string: Graduate School, University of the East, Manila, Philippines

Abstract: With the increasing demand for large amounts of training data for model development, this paper proposes LLM4Label, an automatic text labeling method based on large language models, to assist human labelers in annotating text data. LLM4Label first selects the most representative seed data using a clustering algorithm based on text similarity. It then constructs prompt dialogues with few-shot prompts to stimulate the language model’s performance on entity labeling tasks, enabling it to automatically and efficiently label more data. Finally, LLM4Label introduces human feedback to correct un- certain labeling results and retrains the model with the corrected annotations. Experiments show that LLM4Label achieves high- quality labeled data at low human labeling cost. The proposed method provides an effective way to obtain sizable and high- quality annotated datasets with minimal manual effort, which can strongly support downstream natural language processing tasks.
Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification

Authors:
raw_author_name: Sarifah Agustiani
raw_author_name: Yan Rianto

Abstract: Colon cancer is a type of cancer that attacks the last part of the human digestive tract. Factors such as an unhealthy diet, low fiber consumption, and high animal protein and fat intake can increase the risk of developing this disease. Diagnosis of colon cancer requires sophisticated diagnostic procedures such as CT scan, MRI, PET scan, ultrasound, or biopsy, which are often time-consuming and require particular expertise. This study aims to classify colon cancer based on histopathological images using a dataset of 10,000 images. This data is divided into 7,950 images for training, 2,000 for testing, and 50 for validation, aiming to achieve effective generalization. The Convolutional Neural Network (CNN) method was applied in this research with a relatively shallow architecture consisting of 4 convolution layers, 2 fully connected layers, and 1 output layer. Research results were evaluated by looking at the accuracy value of 99.55%, precision value of 99.49%, recall of 99.59%, prediction experiments on several images, and loss and accuracy graphs to detect signs of overfitting. However, this research has limitations in determining hyperparameters and layer depth, which was only tested from 1 to 5 convolution layers. Therefore, there are still opportunities for further development, such as applying unique feature extraction before the classification process
Joint Extraction of Entities and Relations Based on Multi-feature Fusion

Authors:
raw_author_name: Shoubin Li
raw_affiliation_string: University of Chinese Academy of Sciences, Beijing, China  and The Institute of Software, Chinese Academy of Sciences, Beijing, China
raw_author_name: Zhiyuan Chang
raw_affiliation_string: The Institute of Software, Chinese Academy of Sciences, Beijing, China
raw_author_name: Yangyang Liu
raw_affiliation_string: University of Auckland, Auckland, New Zealand

Abstract: Joint extraction of entities and relations is essential for understanding massive text corpora. In recent years, the span-based joint models have achieved excellent results in the entity and relation extraction task. However, the previous literature and experimental results suggest that the usage of span-based method in entity and relation extraction may produce more redundant entities, although it can solve the overlapping problem of entities. In order to solve the problem of entity redundancy, this paper proposes a joint extraction model based on multi-feature fusion. The overall network follows the framework as SpERT, which is the state-of-the-art model for joint entity and relation extraction. In addition to the word embedding features in SpERT, the proposed model also considers the part-of-speech features. We believe that the part-of-speech features in entities are helpful for entity recognition and can effectively alleviate the entity redundancy problem. The proposed model is evaluated on two public data sets, CoNLL04 and ADE. The experimental results show that the proposed joint extraction model based on multi-feature fusion significantly outperforms current state-of-the-art methods.
Pembuatan Game Edukasi Bahasa Inggris Untuk Meningkatkan Kosakata Bagi Peserta Tidar

Authors:
raw_author_name: Sidhiq Andriyanto
raw_author_name: Sari Mubaroh

Abstract: Technology is growing rapidly and has affected many aspects of life today. Technology is being used more and more frequently and the most utilized language is English. Tidar English Course provides tutoring for English, but Tidar 7th and 8th grade participants have difficulty in learning and memorizing English vocabulary. To overcome these problems, an application is made to help Tidar 7th and 8th-grade participants learn vocabulary with educational game media. Learning will be packaged into a crossword puzzle game so that it can train their vocabulary by finding the right answer in the crossword puzzle boxes that have been provided. The development method in this research is using Game Development Life Cycle (GDLC), and the testing method uses the User Acceptance Test (UAT). The system testing in this study uses black box testing. User Acceptance Test (UAT) testing submitted to the teacher gets 100% of Tidar 82.44% for grade 7 Tidar participants and 84.22% for grade 8 Tidar participants. It can be concluded that the results of the three User Acceptance Tests (UAT) get the criteria "Very Feasible"
Aplikasi Pengenalan Tempat Dan Bangunan Bersejarah Bangka Belitung Berbasis Mobile

Authors:
raw_author_name: Linda Fujiyanti
raw_affiliation_string: POLMAN BABEL
raw_author_name: Muhammad Setya Pratama
raw_affiliation_string: POLMAN BABEL

Abstract: The historical introduction application is an area that has information about historical heritage. However, not many people know about the history of the Bangka Belitung Islands province. In the current era, mobile applications are growing very rapidly, so it would be very good to create a media in the form of an information application for identifying historical places and buildings in the Bangka Belitung Islands province. The aim of this research is to introduce the history of cultural heritage in the Bangka Belitung Islands province by creating an application that can display images, videos, materials and descriptions of the application. Researchers hope that it will be easier for users to find out and study the application for recognizing historical places and buildings in the Bangka Belitung Islands province.
Sistem Informasi Apotek Happy Berbasis Web

Authors:
raw_author_name: Linda Fujiyanti
raw_author_name: Riki Afriansyah

Abstract: Happy pharmacy is a pharmacy that sells medicine located at street Kp. Asam, Kec. Rangkui, Pangkalpinang City, Bangka Belitung Island 33684. In running its business, Happy Pharmacy still uses a conventional system where all information regarding drug sales and sales reports is still done manually. This causes problems such as the problems that occr at the Happy Pangkalpinang Pharmacy, the author created a web-based pharmacy information system with the aim of making it easier for users to process and provide information regarding pharmacy data and to support the work effectiveness and accuracy of existing pharmacy data. the method that the author useus in building a web-based pharmacy information system uses the waterfall method which consists of several stages such as analysis, design, coding, testing and implementation. Data collection techniques used in the web-based pharmacy information system are interviews, observation and literature study. For system development, the language used PHP,MySQL.
SISTEM PAKAR DIAGNOSIS PENYAKIT MELALUI GENERAL CHECK UP BERBASIS MOBILE APPLICATION ANDROID

Authors:
raw_author_name: Herlina Trisnawati
raw_affiliation_string: Universitas Tama Jagakarsa
raw_author_name: Sakius Octavianus Tarigan
raw_affiliation_string: Universitas Tama Jagakarsa

Abstract: Expert System is a system created by imitating the performance or thought patterns of humans, which is input into an application system with the aim of helping to facilitate human work as done by experts. Android-based Expert System can help users analyze what diseases are present in the body. Expert System for Disease Diagnosis Through General Check Up Based on Android Mobile Application at Bojong Medika Clinic is a mobile-based application system that can be used as a platform for users to record, store medical records, automatically process data from general check-ups, and remind them to do further general check-ups in an effort to make everyone using this application more concerned about their health and avoid various diseases. This application is created using Kodular and supported by Air Table as its database, with additional tools using Firebase. The system development method used is the Waterfall Model, starting from the analysis stage, then followed by design, implementation, and finally testing of the application system.
EVALUASI USABILITAS SISTEM MENGGUNAKAN METODE SYSTEM USABILITY SCALE (SUS) PADA APLIKASI AKHLAQU DENGAN PENERAPAN TEKNIK INDEXING MONG

Authors:
raw_author_name: Nining Rahaningsih
raw_affiliation_string: STMIK IKMI Cirebon
raw_author_name: Raditya Danar Dana
raw_affiliation_string: STMIK IKMI Cirebon

Abstract: The Akhlaqu Application is a web-based Islamic learning platform used by Al-Falah Integrated Islamic Elementary School to facilitate the recording and reporting of students’ activities in developing their memorization. This research aims to assess the level of user satisfaction with the Akhlaqu Application using the System Usability Scale (SUS). The results showed a high level of usability, measured by a System Usability Scale (SUS) score of 89.7, indicating a “very good” quality application that meets high usability standards. In addition, the implementation of text indexes on MongoDB has a positive impact on the speed of transaction data search, especially on complex data searches, improving the efficiency and responsiveness of the application. The implications of this research involve recommendations to continue to improve the usability and performance of the Akhlaqu Application, by maintaining the use of MongoDB index techniques as a reliable solution for optimizing the database. The study concludes that the Akhlaqu Application is an effective tool for managing students’ memorization and recommends further improvements to enhance its usability and performance.
Optimizing Clustering of Indonesian Text Data Using Particle Swarm Optimization Algorithm: A Case Study of the Quran Translation

Authors:
raw_author_name: M Didik R Wahyudi
raw_author_name: Agung Fatwanto

Abstract: The Quran considered the holy book for Muslims, contains scientific and historical facts affirming Islam's truth, beauty, and influence on human life. Consequently, the Quran text and its translations are valuable sources for text mining research, particularly for studying the interrelationship of its verses. One approach to grouping objects using certain algorithms is clustering, with K-Means Clustering being a prominent example. However, clustering results are often suboptimal due to the random selection of centroids. To address this, the study proposes using the Particle Swarm Optimization (PSO) algorithm, which selects centroids based on PSO results. The hybrid PSO algorithm initiates a single iteration of the K-means algorithm. It concludes either upon reaching the maximum iteration limit or when the average shift in the center of the mass vector falls below 0.0001. Evaluation of the clustering results from the three models indicates that the K-Means algorithm produced the lowest Sum of Squared Error (SSE) value of 1032.19. Additionally, the hybrid PSO algorithm generated the highest Silhouette value of 0.258 and the lowest quantization value of 0.00947. Further evaluation using a confusion matrix showed that K-Means clustering had an accuracy rate of 81.7%, K-Means with PSO had 82.5%, and the combination of K-Means with hybrid PSO yielded the highest accuracy rate of 91.1% among the three grouping model.