GARMENT EMPLOYEE PRODUCTIVITY PREDICTION USING RANDOM FOREST

Clothing also means clothing is needed by humans. Besides the need for clothing in terms of function, clothing sales or business is also very potent. About 75 million people worldwide are directly involved in textiles, clothing, and footwear. In this case, a common problem in this industry is that the actual productivity of apparel employees sometimes fails to reach the productivity targets set by the authorities to meet production targets on time, resulting in huge losses. Experiments were conducted using the random forest model, linear regression, and neural network by looking for the values ​​of the correlation coefficient, MAE, and RMSE.  This aims to predict the productivity of garment employees with data mining techniques that apply machine learning and look for the minimum MAE value. The results of testing the proposed algorithm on the garment worker productivity dataset obtained the smallest MAE, namely the random forest algorithm, namely 0.0787, linear regression 0.1081, and 0.1218 neural network

HYBRID OPTIMIZATION METHOD BASED ON GENETIC ALGORITHM FOR GRADUATES STUDENTS

Graduation is a target that must be achieved by students, especially graduating on time will be very important. To determine students who graduate on time or cannot be determined before students reach the final semester and hold a trial, many students who fail to graduate on time cause delays and affect the quality assurance of a tertiary institution. The problem in this research is how to optimize student graduation in order to graduate on time. Therefore, to determine this decision, we conducted a graduation data trial using the SVM method with GA optimization. SVM with accurate learning skills and good generalizations in classifying non-linear data, but SVM is weak in terms of parameter optimization it requires optimization using GA. GA is a method that has evolved to produce a more optimal data. From the results of processing using SVM and GA, we get more optimal results with 86.57%. Then from these results can help students to graduate on time

Sistem Pakar Diagnosa Penyakit Pada Kucing Menggunakan Metode Forward Chaining

Kucing merupakan hewan yang popular dan sangat disukai di kalangan masyarakat baik dalam bentuk fisik yang lucu maupun tingkah laku yang menggemaskan merupakan salah satu alasan yang membuat banyak orang memelihara hewan peliharaan yang satu ini, dan memelihara kucing juga merupakan sunah rasul bagi umat muslim. Kepopuleran memelihara kucing membuat jumlah peminat kucing di Indonesia sangatlah besar, dan berbagai masalahpun akan terjadi ketika pemilik kucing mendapati kucing kesayangannya sakit. Banyak kucing terserang penyakit, kucing peliharaan ataupun kucing liar, penyakit kucing diantaranya: Helminthiasis, Skabies, Ektoparasit, Koksidiosis, Feline Viral Rhinotracheitis, Feline Caliviral disease, Felice Panleukopenia, Earmite. Sistem pakar adalah metode ilmu yang bertujuan untuk menyelesaikan permasalahan yang bisa dibilang cukup rumit, yang biasanya permasalahan itu hanya bisa diatasi oleh para ahli tertentu. Pemelihara kucing yang tidak mengetahui tentang penyakit yang diderita terhadap kucing akan menjadi permasalahan yang besar maka dengan itu dapat dibuatkan dan dibangun suatu sistem pakar. Dengan sistem yang dibuat dan dibangun tersebut dapat membantu dalam mendiagnosis penyakit yang diderita pada kucing dan memberi solusi cara pengobatan dan pencegahannya

AUDIT OF THE REGIONAL DEVELOPMENT PLANNING INFORMATION SYSTEM (SIPD) USING COBIT 5.0 FRAMEWORK

Tasikmalaya Regional Representative Council’s is one of the legislative institutions that runs the local government. In carrying out the duties and functions of the Leadership and Members of the Regional People's Representative Council in the regional development planning sector, the Secretariat of the Regional People's Representative Council of Tasikmalaya City has implemented information technology in the regional development planning process, namely the Regional Development Information System. This information system is inseparable from problems that can hinder the regional planning process. This study aims to find out how effective and efficient the use of this information system is based on the maturity level of COBIT 5. This research only focuses on the domains EDM05, APO03, APO07, BAI09, DSS01, and MEA03. Data collection techniques are carried out through observation, interviews, and the dissemination of questionnaires.  Tasikmalaya Regional Representative Council’s has implemented IT governance at level 3, namely the Established Process. The results of the questionnaire processing obtained an average value of 3. 04 from a value scale of 0 to 5. The results showed weaknesses in the governance of the regional development planning information system found in the APO07 sub-domain which has the lowest maturity level value from other sub-domains, namely 1. 86

Analisis Sentimen AicoGPT (Generative Pre-trained Transformer) Menggunakan TF-IDF

AicoGPT (Generative Pre-trained Transformer) Sentiment Analysis Using TFIDF. The role of artificial intellegence makes it easy to find precise and accurate information, and even solve complex problem models. One of the AI-based breakthroughs is ChatGPT by OpenAI in 2020, followed by the latest version in 2023, GPT-3. Since, several AI technologies similar to mobile versions have emerged, one of which’s AicoGPT. However, the performance of similar applications cannot be relied on, so it’s still necessary to analyze its users' responses, whether they’ll be as amazing or not. So, from these problems, this research aims to analyse 1443 reviews from users of the AicoGPT application on Google Playstore using sentiment analysis techniques using TF-IDF and a comparison of LR and SVM classifications. Of the two trials, producing the best accuracy with SVM, which’s equal to 92%. While LR produces an accuracy of 89%. From this study, it can be concluded briefly that TF-IDF with SVM classification’s suitable for carrying out a sentiment analysis of the dataset

Rancang Bangun Aplikasi Penggajian Menggunakan Framework CI : Studi Kasus : PD. Perkasa 3

Kegiatan yang ada pada perusahaan dagang pada umumnya yaitu pembelian dan penjualan. Selain kedua kegiatan tersebut yang terkadang hampir dilupakan namun merupakan hal vital diantara adalah kegiatan penggajian karyawan. Masalah pemberian gaji merupakan hal yang penting karena mempunyai pengaruh yang sangat besar terhadap semangat kerja para karyawannya. PD. Perkasa 3 memiliki ratusan karyawan yang berbeda sistem perhitungan penggajiannya. Kerumitan pencatatan dan perhitungan penggajian dapat diatasi dengan rancang bangun  sebuah aplikasi penggajian berbasis website yang dibangun menggunakan  framework code igniter dengan bahasa pemrograman PHP dan database MySQL mampu memecahkan masalah mengenai rumitnya pencatatan dan perhitungan penggajian pada PD. Perkasa 3. Saat ini, PD. Perkasa 3 dapat melakukan penggajian dengan cepat dan tepat, pelaporan dan pengarsipan lebih rapi, aman, tidak mudah terbakar, basah dan hilang karena data tersimpan pada database rancang bangun ini dilakukan dengan berdasarkan metode SDLC (Systems Development Lifecycle) yang umum digunakan, yaitu Waterfall

CLASSIFICATION OF LIVER DISEASE BY APPLYING RANDOM FOREST ALGORITHM AND BACKWARD ELIMINATION

Cancer is a type of disease that is not realized by most people because most people associated with this disease lack understanding of cancer itself and are doing early detection of cancer, due to the majority of cancers found at an advanced stage and difficult to overcome to facilitate large expenditure to help cancer. Early detection of liver or liver cancer is very important to overcome the very high risk of death caused by liver or liver cancer. This study aims to help classify liver or liver cancer based on data from routine examination results of patients summarized in the Indian Liver Data Patient (ILDP) dataset. The method used in the classification process in this research is backward elimination modeling for testing optimization and Random Forest algorithm and split validation to validate the model. The results of this study yielded 76.00% and value of AUC 0.758 results. These results indicate that the results of this study are good enough to help classify breast cance

KOMPARASI ALGORITMA NEURAL NETWORK DAN NAÏVE BAYES UNTUK MEMPREDIKSI PENYAKIT JANTUNG

Heart disease is one of the types of deadly diseases whose treatment must be dealt with as soon as possible because it can occur suddenly to the sufferer.  Factors of heart disease that are recognized based on the condition of the body of a sufferer need to be known from an early age so that the risk of possible instant attacks can be minimized or can be overcome in various ways such as a healthy lifestyle and regular exercise that can regulate heart health in the body.  By looking at the condition of a person's body based on sex, blood pressure, age, whether or not a smoker and some indicators that become a person's characteristics of heart disease are described in a study using the Neural Network and Naïve Bayes algorithm with the aim of comparing the level of accuracy to attributes influential to predict heart disease, so the results of this study can be used as a reference to predict whether a person has heart disease or not

IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION BASED MACHINE LEARNING ALGORITHM FOR STUDENT PERFORMANCE PREDICTION

Education plays an important role in the development of a country, especially educational institutions as places where the educational process has an important goal to create quality education in improving student performance. Based on research conducted in the last few decades the quality of education in Portugal has improved, but statistics show that the failure rate of students in Portugal is high, especially in the fields of Mathematics and Portuguese. On the other hand, machine learning which is part of Artificial Intelligence is considered to be helpful in the field of education, one of which is in predicting student performance. However, measuring student performance becomes a challenge since student performance has several factors, one of which is the relationship of variables and factors for predicting the performance of participating in an orderly manner. This study aims to find out how the application of machine learning algorithms based on particle sworm optimization to predict student performance. By using experimental research methods and the results of empirical studies shown in each model, namely random forest, decision tree, support vector machine and particle swarm optimization based neural network can improve the accuracy of student performance predictions

ALGORITMA C4.5 UNTUK MEMPREDIKSI PENGAMBILAN KEPUTUSAN MEMILIH DEPOSITO BERJANGKA

Deposits are one form of investment offered by the Bank or other financial institutions with the nature of regulating and binding according to the rules set by the manager and the investor or commonly called investors. The advantage of being an investor is getting a fee or profit calculated based on the agreed time period at the beginning of the agreement. Whereas for investment fund managers can be used to advance and develop their business and business. Finding and determining potential customers is the first step to running a financial business in the form of this deposit, before the transaction decision is taken which is a favorable decision for both parties, investors or managers, one of the decision-making techniques can be done using Data Mining using the C4.5 Algorithm which is a structured decision-making technique based on input variables so that it can produce the most potential typical information for customers to participate in time deposits