Reviewerly/exHarmony huggingface dataset:
The Role of Digital Forensic Experts in Cybercrime Investigations in Indonesia Based on The Scopus Research Index

The development of information technology, besides having a positive impact, has negative impacts, such as an increase in cybercrime. Several parties are involved in handling cybercrimes in Indonesia, including expert witnesses in digital forensics. It is hoped that cybercrimes can be uncovered through a digital forensic approach. Research related to cybercrime and Digital Forensics in Indonesia is experiencing increasing growth from 2010 to 2021. A very rapid increase was shown in 2017. Through bibliometric analysis, you can analyze expert witnesses in the field of digital forensics by region in Indonesia, such as Sumatera, Western Java, Central Java, and Eastern Java. An expert witness in digital forensics must have technical skills in operating digital forensic tools and analysis and academic skills in uncovering crimes. The results of this study are very useful for law enforcement officers or lawyers in determining expert witnesses in the field of digital forensics. List of digital forensic experts in Indonesia who have been tested from their scientific studies by conducting various paper publications. So, law enforcement can synergize with digital forensic experts to solve cybercrime. One of the roles of a digital forensic expert is as an expert witness in court. Through research, it is hoped that there will be an increase in awareness of digital forensic experts to conduct scientific publications so that it is easier to be recognized by law enforcement and academics for updating methods and techniques of cybercrime investigation
Sistem Rekomendasi Content-based Filtering Menggunakan TF-IDF Vector Similarity Untuk Rekomendasi Artikel Berita

The population of active students in the Informatics Bachelor Program, Universitas Amikom Yogyakarta, in the odd semester of 2021 is 3,870. Efforts to track interest in the three concentration options were carried out early on through article literacy recommendations. Various articles are produced continuously and provided on an ongoing basis to students. However, the many articles offered daily make students overwhelmed and tend to choose articles that do not match what they want. To help solve this problem, recommender system is developed. A recommender system helps to estimate the prediction value or relevancy of an article and create a ranking according to user's taste. Content-based Filtering technique is used in this research. Using the dataset from Kabar Informatika news portal of University of Amikom Yogyakarta, the developed Content-based Filtering Recommendation System is able to produce Recall@5 score at around 73% and Recall@10 at around 80%.
Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian

The high divorce rate has a negative impact on couples who will file for divorce and also has an extreme impact on children such as psychological disorders of children. The magnitude of the impact of divorce, it is necessary to predict the divorce decision. In this study, the application of the backpropagation method to predict divorce decisions was carried out. The data used is data on divorce decisions from the Pekanbaru Religious Court from 2020 - 2021 totaling 779. The dataset obtained is not balanced with 724 accepted classes and 55 rejected classes, balancing is done by reducing excess classes. The parameters used in this study build 3 architectural models [6-7-1], [6-9-1], [6-12-1], learning rate (0.01, 0.03, 0.09), max epoch and data sharing (70:30), (80:20), (90:10). The results of this study indicate that the best architectural model is in the network architecture [6-9-1] learning rate 0.09 epoch 300 dataset distribution 80% training data and 20% test data the accuracy value is 80% and the Mean Squared Error (MSE) is 0.1402. In this study, the backpropagation method was successful in predicting divorce decisions.
Analisis Kepuasan Pengguna Sistem Informasi E-Campus Menggunakan Metode E-Servqual Dan Model Kano

This study aims to identify the dimensions and service attributes that need to be improved, as well as improvements related to the quality of services provided by the e-campus system to students at UIN Mahmud Yunus Batusangkar. It also aims to identify the quality of services provided by the e-campus system to students. in order to provide advice or consideration to improve the quality of the e-campus system to the campus or developer, which in turn will produce a solution to improve the quality of the e-campus system for user satisfaction. The procedures used include 5 stages are planning, gathering information processing information analysis and the latest reporting review. The research results created calculations using e-servqual procedures of 0.92 ≤ 1, showing that the quality of the services provided could be said to be unable to fulfill students' expectations. If the level of performance is met, it will bring about a very large increase in user satisfaction. But if not fulfilled, it will reduce the level of satisfaction. Whereas the canoe model gets eight attributes included in the must-be category, eight one-dimensional attributes and six attractive attributes. The ranking level obtained is a source of attributes that are prioritized to be improved. Every attribute that has a very large negative value will also be less effective in achieving user satisfaction
Penerapan Algoritma K-Means clustering Untuk Mengelompokkan Provinsi Berdasarkan Banyaknya Desa/Kelurahan Dengan Upaya Antisipasi/Mitigasi Bencana Alam

Natural disasters are one of the natural phenomena that threaten human survival. The negative impacts can be in the form of material or non-material losses. However, with the ability to recognize the early symptoms of a disaster, humans can prepare themselves for disaster. Application of the K-Means clustering Algorithm in Grouping Provinces Based on the Number of Villages / Sub-districts with Anticipation / Mitigation Efforts for Natural Disasters Using the WEKA Application. The data sources for this research were collected based on documents describing the Number of Villages/ Urban According to Natural Disaster Anticipation/Mitigation Efforts produced by the National Statistics Agency. The data used in this study is provincial data which consists of 34 provinces. There are 4 variables used, namely Natural Disaster Early Warning System, Tsunami Early Warning System, Safety Equipment, Evacuation Path. The data will be processed by clustering in 2 clusters, namely clusters with high anticipation/mitigation levels and low anticipation/mitigation levels. The results obtained from the assessment process are that there are 5 (14.71 %) provinces with a high level of anticipation/mitigation and 29 (85.29%) other provinces including a low level of anticipation/mitigation. This can be an input for the government to pay more attention to the Village/Kelurahan based on the clusters that have been carried out
Automatic Abusive Language Detection in Urdu Tweets

Abusive language detection is an essential task in our modern times.Multiple studies have reported this task, in various languages, because it is essential to validate methods in many different languages.In this paper, we address the automatic detection of abusive language for tweets in the Urdu language.The study introduces the first dataset of tweets in the Urdu language, annotated for offensive expressions and evaluates it by comparing several machine learning methods.The Twitter dataset contains 3,500 tweets, all manually annotated by human experts.This research uses three text representation techniques: two count-based feature vectors and the pre-trained fastText word embeddings.The count-based features contain the character and word n-gram, while the pre-trained fastText model comprises word embeddings extracted from the Urdu tweets dataset.Moreover, this study uses four non-neural network models (SVM, LR, RF, AdaBoost) and two neural networks (CNN, LSTM).The study finding reveals that SVM outperforms other classifiers and obtains the best results for any text representation.Character tri-grams perform well with SVM and get an 82.68% of F1 score.The best-performing words n-grams are unigrams with SVM, which obtain 81.85% F1 score.The fastText word embeddingsbased representation yields insignificant results.
Pengembangan Model Sistem Informasi Geografis Angkot Online Berbasis SCRUM: Ujicoba Kelayakan

Angkot drivers can compete with online transportation in getting passengers via smartphones. This application is based on a geographic information system, which is a system that can show the location of a person or place online, using this system to be integrated with public transportation makes it possible to add or pick up passengers more efficiently not only by waiting in one place but can pick up passengers according to the location of the passenger and the transportation route. To overcome the above problems, making applications with a technology theme is one of the right solutions for innovative methods that will be applied to public transportation drivers so they can get passengers efficiently. The purpose of this study is to determine the user's response to the application that has been developed. The research method used is research and development with a 4D development model with “define, design, development, and dissemination stages”. Data collection was carried out by distributing questionnaires to several respondents, while the trial stages carried out included alpha version trials, beta versions trials, Version Release, and Implementation. The results of the study show that the indicators of language, appearance, and accessibility of applications that have been developed are classified as "very feasible". The developed online angkot application is expected to be a solution in dealing with technological developments that can be applied to public transportation
Pemanfaatan Machine Learning dengan Algoritma X-Means untuk Pemetaan Luas Panen, Produktivitas, dan Produksi Padi

Rice plants are essential for the world, especially Indonesia because it is a rice-producing plant that is useful as a staple food for its people. A decreased harvest area, production, and rice productivity can affect food availability. Therefore, this research aims to classify and map the harvested area, production, and productivity of rice in Indonesia based on each province. The research data used in this paper is data on the harvested area (ha), production (tons), and rice productivity (Ku/ha) by Provinces in Indonesia for 2020-2022 obtained from the Indonesian Central Bureau of Statistics website. In this study, the algorithm used is X-Means Clustering with the help of the Rapid Miner application. The results of this study are in the form of grouping or mapping of harvested area, production, and productivity of rice, divided into 3 (three) regions, including 1. Harvested Area (divided into five groups: Very high Harvested Area consists of 3 provinces, High Harvested Area consists of 1 province, Medium Harvest Area consists of 3 Provinces, Low Harvest Area consists of 8 Provinces, and Very low Harvest Area consists of 19 Provinces 2. Rice Production Area (divided into five groups: Very high rice production consists of 3 provinces, Rice production High rice production consists of 1 province, Medium rice production consists of 3 Provinces, Low rice production consists of 8 Provinces, and Very low rice production consists of 19 Provinces 3. Regions of Rice Productivity (divided into five groups: Very high rice productivity consists of 6 provinces, High Rice Productivity consists of 13 provinces, Medium Rice Productivity consists of 7 Provinces, Low Rice Productivity consists of 4 Provinces, and Very Low Rice Productivity consists of 4 Provinces. This can be information for the Indonesian government, especially for the respective provincial governments, to be able to maintain the harvested area, production, and productivity of rice in Indonesia to remain stabel.
Analisis Sentimen Kenaikan Harga BBM Pertamax Pada Media Sosial Menggunakan Metode Naïve Bayes Classifier

Fuel Oil (BBM) is a very vital commodity. Fuel has an important role in people's lives. Because of the importance of fuel in people's lives, fuel is one of the basic needs of the community. The policy of increasing the price of fuel has always been a phenomenon in various media which causes pros and cons in society. The policy of increasing fuel prices has a big impact on society, both direct and indirect consumption. This study aims to explore public opinion, whether it shows negative or positive sentiment in the policy of increasing fuel prices. The increase in Pertamax fuel prices has drawn several opinions from citizens on Facebook social media. Sentiment analysis research was conducted to determine the response to Facebook comments on Brilio.Net accounts in 2022 related to the increase in Pertamax fuel prices with a dataset of 799 data, as well as a comparison of the number of positive, negative, and neutral comments. In addition, in this study to be able to determine the level of performance generated by the nave Bayes classifier method in the test. The author uses 80% of the comment dataset to be used as training data and 20% to be used as test data to be used as machine learning and test data. Then the data is classified by the system using orange data mining tools so as to produce a percentage of positive sentiment as much as 19%, negative sentiment as much as 22% and neutral sentiment as much as 59%. testing with the nave Bayes classifier method obtained the highest percentage accuracy rate of 99% from all datasets.
New Similarity Measures for Item-based Neighborhood Collaborative Filtering

Similarity measures play an important role in many areas to solve a wide variety of problems.In computer science, these measures are used in decision making, information retrieval, data mining, machine learning, and recommender systems.The recommender systems are tools that have proven their utility in filtering large amounts of information and giving recommendations useful for users.Neighborhood collaborative filtering is the most common recommender system approach implemented by cutting-edge companies.A key element of this approach is the similarity measure, which is used to find neighbors with similar tastes to provide recommendations that satisfy users' needs.A drawback of this approach is the lack of user's information to generate proper recommendations.For this reason, it is important to design new similarity measures that can find the most relevant neighbors to generate more accurate recommendations for users with little information about them.This paper designs two new similarity measures that can generate good recommendations with little information about users.These similarity measures have been tested using MovieLens datasets and different rating prediction methods, and they have shown a good performance in comparison with other similarity measures designed to address the recommendation problem.
Peningkatan Kualitas Citra Termal Menggunakan Metode Blind Deconvolution

Temperature is a quantity that indicates the degree of heat of an object. The higher the temperature of an object, the hotter the object. Thermal imagery is an image created with thermal infrared spectrum that utilizes the temperature of an object. Sensing on this spectrum is based on differences in the temperature of the object and its emission power in the image which is reflected by the difference in hue or color. The sensor used in thermal images is noncamera based on scanning. In thermal imagery, there is a problem, namely blurred / blurred images that make it difficult to obtain information about objects. Blurred thermal images are caused by hot spots or rising temperatures. Thermal images often experience problems due to the large amount of diffraction fog in thermal imaging caused by the refractive index decreases as the wavelength increases. Blind deconvolution is a method that can be used to restore images that experience blur effects without having to know the value of the Point Spread Function (PSF). The process of improving the quality of thermal images is carried out using the blind deconvolution method which consists of 3 stages, namely finding the kernel value, looking for the convolution and estimation values ( ). Blind deconvolution method can be applied in improving the quality of thermal images by reducing the blur effect found in the image
Penerapan Algoritma Support Vector Regression untuk Prediksi Jumlah Pasien Covid-19 di Provinsi Riau

In 2019, at the end of December, there was an outbreak of a disease with an unknown cause in Wuhan, Hubei Province, China. The World Health Organization has named the outbreak of the disease as coronavirus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or Covid-19. Covid-19 is a disease outbreak that has spread in various regions of Indonesia, such as in Riau, at PT. Nusantara V Plantation (PTPN V). So we need a way to increase awareness and vigilance, namely by presenting information using Data Mining in predicting the number of cases with thealgorithm Support Vector Regression (SVR). The prediction process is carried out using SVR by specifying the SVR and Kernel Linear parameters. The SVR algorithm can predict the number of Covid-19 patients in the next 30 days so that the Correlation Coefficienti (R) level is 85% and the Mean Square Error (MSE) value is 0.196. From the results of the experiment, there was a decrease in cases of Covid-19 patients at PT. Perkebunan Nusantara V in the next 30 days, with the acquisition of the best minimum sensitivity value of 0.09
Silent Conversation

A nurse imagines a startling and oddly comforting perspective during a code.
Interactive System for Gender Classification

Abstract: A study on the detection of gender, based on voice using artificial neural networks. In today’s fast-moving world Gender classification through voice plays an important role to enhance performance in Speech recognition systems, Forensic investigation, and Marketing. The dataset has 3,168 recorded voice samples of male and female voices. The samples are produced by using acoustic analysis. The primary goal of the proposed model is to automate the system to identify gender based on the audio signals and to test the voice of a human on the spot. Multilayer Perceptron (MLP) with ReLU activation function as a model has been trained to predict gender. Nadam optimizer is used for the optimization of neural networks, K-Nearest Neighbor and Support Vector Machines are trained on the dataset of 3,168 records. The obtained best accuracy is 97% on the given dataset by MLP algorithm. An Interactive web page has been built to test the voice without interruption and to predict its gender. 125 real time samples are tested out of which model could classify every record into male/female. 19 male records are incorrectly classified.
AN ATTACK-RESISTANT LIGHTWEIGHT DIGITAL SIGNATURE BASED ON AN ELLIPTIC CURVE FOR IMPROVED SECURITY IN RESOURCE CONSTRAINED APPLICATIONS

Despite the fact that ECDSA is the most innovative asymmetric digital signature technique, experts are working tirelessly to strengthen it to survive various challenges.Both internal and external attacks can occur from intruders.An end-user, a malware-infected IT component, a physical attacker who operates within the environment's security perimeter, or a physical person who directly interacts with the environment, manages the hardware, or even communicates with the end-user (i.e., a malicious signer).In contrast, an external attack involves the attacker moving outside the signature environment's security boundary, possibly across a network.Attacks on interfaces place more emphasis on the protocols that a device employs to interact with the outside world rather than on the machine itself.The proposed work presents a solution to an improved, lightweight ECDSA which is resistant to MITM, Replay and forgery attacks than its counterparts.The comparison of the proposed ECDSA is compared with its counterpart and cryptanalysis is performed to prove that the proposed ECDSA is more relevant in real time since the Zhong's Method takes 13.28% less time to sign data than the Suggested ECDSA method.The Suggested technique stands out in broader application areas where calculation time is a concern since it requires 8.2% less time than Zhong's Method for Signature verification at the Receiver end.
Designing in Conversation With Dance Practice

We present a long-term collaboration between dancers and designers, centred around the transmission of the century-old repertoire of modern dance pioneer Isadora Duncan. We engaged in a co-design process with a Duncanian dancer consisting of conversations and participation in her transmission of Duncan's choreographies and technique. We then articulated experiential qualities central to Duncan's repertoire and used them to guide the design of the probes, the sounding scarfs. Our probes sonify dancers' movements using temporal sensors embedded in the fabric of the scarfs, with the goal of evoking Duncan's work and legacy. We shared the probes with our Duncan dance community and found that they deepen the dancers' engagement with the repertoire. Finally, we discuss how co-designing with slowness and humility were key to the dialogue created between the practitioners, allowing for seamless integration of design research and dance practice.
Objectify: Better Living Through Anticipatory, Just-for-you 3D Printing!

The ubiquity of 3D printers reveals a problem: people do not know what to use them for. If they do have an idea, they struggle to realize it, despite decades of research into design tools. However, consumption patterns suggest people still desire new objects, and advanced AI and digital fabrication point together toward an easy, post-scarcity future. This satirical advertorial presents Objectify, a program that harvests users' data to create bespoke objects they could want, just in time. Pushing 'cutting edge' AI's promises to their logical conclusion, our implemented pipeline uses AI-based content generation (GPT-3 [4] and Dreamfusion [14]) to ideate, generate 3D models, and print just-in-time objects. Objectify is 'pataphysical software [16]; created objects were non-functional monolothic pieces of blob-like single-color plastic. We discuss implications of the post-scarcity just-for-you vision of technological progress in AI and digital fabrication.
Attitudes of Saudi female students toward the use of mobile devices in learning computer programming: An empirical study

The purpose of this study is to explore the attitudes of Saudi Arabian female students toward mobile learning approaches pertaining to their learning experiences. Our methodology involved two groups–one that was subjected to a traditional teaching approach and the other (treatment group) that was subjected to a teaching approach with an intervention involving the ViLLE visualization tool during a semester in a programming class. We employed the Mobile Learning for Computer Programming framework to evaluate the perception of the use of mobile devices pertaining to the learning experience of female programming students in Saudi Arabia. Overall, the treatment group had positive attitudes toward mobile-based learning. This approach can promote engagement in learning systems, enhance the learning experience, improve the quality of learning, and help explain learner behavior.