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Lestari, Santi Arum Puspita
Universitas Buana Perjuangan Karawang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
OPTIMAL STUDY OF REAL-ESTATE PRICE PREDICTION MODELS USING MACHINE LEARNING Maulana, Ikhsan; Siregar, Amril Mutoi; Lestari, Santi Arum Puspita; Faisal, Sutan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2565

Abstract

Everyone wants a place to live, especially close to work, shopping centers, easy transportation, low crime rates and others. Pricing must also pay attention to external factors, not just the house. Determining this price is sometimes difficult for some people. Therefore, the aim of this research is to predict real-estate prices by taking these factors into account. Prediction results are very useful for sellers who have difficulty determining prices and also for prospective buyers who are confused when making financial plans to buy a house in the desired neighborhood. The dataset used in this research was obtained from Kaggle and consists of 506 samples with 14 attributes. Several machine learning algorithms, such as Extra Trees (ET), Support Vector Regression (SVR), Random Forest (RF), eXtreme Gradient Boosting (XGB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), and CatBoost, used to predict real-estate prices. This research uses Principal Component Analysis (PCA) for feature selection techniques in data sets after the preprocessing phase and before model building. The highest accuracy model obtained is CatBoost with GridSearchCV, this model has been cross validated so there is very little chance of overfitting when given new data. The SVR model with a poly kernel uses a Principal Component (PC) of 10 and GridSearchCV gets an R2 Score of 0.87, a very large number close to the score of CatBoost with GridSearchCV.
IMPLEMENTATION OF DIABETES PREDICTION MODEL USING RANDOM FOREST ALGORITHM, K-NEAREST NEIGHBOR, AND LOGISTIC REGRESSION Pratama, Rio; Siregar, Amril Mutoi; Lestari, Santi Arum Puspita; Faisal, Sutan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2593

Abstract

Diabetes is a serious metabolic disease that can cause various health complications. With more than 537 million people worldwide living with diabetes in 2021, early detection is crucial to preventing further complications. This research aims to predict the risk of diabetes using machine learning algorithms, namely Random Forest (RF), K-Nearest Neighbor (KNN), and Logistic Regression (LR), with the diabetes dataset from UCI. Previous research has explored a variety of algorithms and techniques, with results varying in accuracy. This research uses a dataset from Kaggle which consists of 768 data with 8 parameters, which are processed through pre-processing and data normalization techniques. The model was evaluated using metrics such as accuracy, confusion matrix, and ROC-AUC. The results showed that Logistic Regression had the best performance with 77% accuracy and AUC 0.83, compared to KNN (75% accuracy, AUC 0.81) and Random Forest ( 74% accuracy, AUC 0.81). These findings emphasize the importance of appropriate algorithm selection and good data pre-processing in diabetes risk prediction. This study concludes that Logistic Regression is the most effective method for predicting diabetes risk in the dataset used.
SENTIMENT ANALYSIS OF THE SAMBARA APPLICATION USING THE SUPPORT VECTOR MACHINE ALGORITHM Firdaus, Thoriq Janati; Indra, Jamaludin; Lestari, Santi Arum Puspita; Hikmayanti, Hanny
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2673

Abstract

Rapid technological developments have opened up new opportunities for public services by utilizing digital application innovations. One example is the West Java Samsat Mobile (SAMBARA) designed by the West Java Provincial Revenue Agency (BAPENDA). The SAMBARA application is expected to accelerate annual vehicle tax payment obligations, but several reviews on the Playstore show user dissatisfaction with SAMBARA's performance. This study aims to conduct a sentiment analysis of SAMBARA application reviews using the Support Vector Machine algorithm. SAMBARA user review data on Google Playstore was collected using the python programming language google play scraper library on google colabolatory resulting in 1620 data on January 2, 2024. The data pre-processing stage involves various steps such as data cleaning, lowercase conversion, tokenization, stemming, stop words removal, normalization, and the use of the TF-IDF method. The data is then labeled positive and negative, positive for reviews with scores of 4 and 5 and negative labels for reviews with scores of 1 to 3. The Support Vector Machine (SVM) algorithm is used for classification, a well-known method for accurate classification. Model evaluation was conducted using a confusion matrix to calculate the precision, recall, and F1-Score values. The evaluation results provide an overview of the performance of the classification algorithm in grouping user reviews into positive and negative categories. The evaluation results show that the SVM algorithm provides quite good performance with an accuracy value of 88.75%, precision 87.51%, recall 81.25%, and F1-Score 83.71% which can be the basis for improving the quality of service of the SAMBARA application. Because the Sambara application has a negative sentiment of 73.4%, it can be concluded that it still gets a bad rating in terms of use.
CLASSIFICATION OF RICE PLANTS AFFECTED BY RATS USING THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM Nofie Prasetiyo; Baihaqi, Kiki Ahmad; Lestari, Santi Arum Puspita; Cahyana, Yana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1949

Abstract

In the era of Indonesia's agrarian economy which is supported by the agricultural sector, rice plants play an important role in meeting food needs. However, pest attacks, especially field mice, can cause significant losses in rice production. To overcome this, this research proposes the use of the Support Vector Machine (SVM) algorithm with the Particle Swarm Optimization method in predicting rat pest attacks on rice plants. This research involves the process of collecting data from drone photos to identify affected agricultural land. The preprocessing stage involves changing colors from RGB to GRAY and zoom augmentation. Feature extraction is carried out using Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP). Testing was carried out involving the SVM/SVC model and performance evaluation was carried out using accuracy, precision and recall metrics. The preprocessing test results showed an increase in performance with training accuracy of 68.33%. However, the actual prediction on the original image results in a low accuracy of around 25%. However, image testing after involving the entire process, including preprocessing and model prediction, shows a higher level of accuracy, reaching around 90%.
INTRODUCTION NATIONAL IDENTIFICATION NUMBER AND NAME ON ID CARD USING OCR (OPTICAL CHARACTER RECOGNITION) METHOD Holila, Holila; Pratama, Adi Rizky; Lestari, Santi Arum Puspita; Indra, Jamaludin
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2242

Abstract

This study examines the use of Optical Character Recognition (OCR) methods for the automatic recognition and extraction of text from images of Identity Cards (KTP). The aim is to provide an effective solution to the problems of document forgery and duplication, particularly in the use of KTP as an identity verification tool. Utilizing the Tesseract library, this research involves preprocessing steps such as conversion to grayscale, perspective transformation, and noise reduction to enhance OCR accuracy. Testing was conducted with 50 different KTP images using Python programming, achieving an Optical Character Recognition accuracy rate of 91%. Additionally, tests conducted with a dataset of 50 KTP images containing NIK and name variables showed that all images were successfully detected with an accuracy rate of 90%. This study confirms that the OCR method is effective in reading text from KTP images in real-time, thus it can be implemented for automatic identity verification.
Co-Authors Adi Susilo Afga Ilyasa Ahmad Fauzi Ahmad Fauzi Anisa Itiawanti Baihaqi, Kiki Ahmad Banafshah Shafa Deden Wahiddin Dwi Sulistya Dwi Sulistya Kusumaningrum Faisal, Sutan Fauzi Ahmad Muda Firdaus, Thoriq Janati Firmansyah Maulana Fitria Nurapriani Hanny Hikmayanti Handayani Hartini, Dina Holila, Holila Indra, Jamaludin Intan Murni Pratiwi Iyan Khoerniyah Jamiluddin, Fathir Jorke Alkawiyu Juwita, Ayu Ratna Kusumaningrum, Dwi Sulistya Lutfi Aziz Maulana, Ikhsan Muhammad Ashi Dicky Niki Galih Prasetyo Nofie Prasetiyo Nurapriani, Fitria Nurmayanti, Trisya Nurofik Pratama, Adi Rizky Pratiwi, Sinta Amanda Rahmat Rahmat Ridwan, Ridwan Rishma Maudyna Roby Rupiat Rohana, Tatang Siregar, Amril Mutoi Sukamto, Ika Sumiyarsi Sutan Faisal Tohirin Al Mudzakir Wahiddin, Deden Wahyu Setio Aji Wilda Amalia Yaman, Nuurul Izzati Yana Cahyana Yulia Ningsih