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All Journal Jurnal Riset Informatika Abditeknika - Jurnal Pengabdian Kepada Masyarakat Computer Science (CO-SCIENCE) SENTRI: Jurnal Riset Ilmiah Justika : Jurnal Sistem Informasi Akuntansi Journal of Accounting Information System The Indonesian Journal of Computer Science Jurnal Komtika (Komputasi dan Informatika)
Ita Yulianti
STMIK AMIKOM Purwokerto
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Journal : Jurnal Riset Informatika

 1 
PENERAPAN DATA MINING TERHADAP PENJUALAN PIPA PADA CV. GASKINDO SENTOSA MENGGUNAKAN METODE ALGORITMA APRIORI Napitupulu, Golda TM; Oktaviani, Anggi; Sarkawi, Dahlia; Yulianti, Ita
Jurnal Riset Informatika Vol. 1 No. 4 (2019): Periode September 2019
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v1i4.23

Abstract

Gaskindo Sentosa merupakan salah satu perusahaan manufaktur yang bergerak di bidang penjualan pipa. Guna meningkatkan kualitas pelayanan terhadap konsumen, perusahaan tersebut dituntut untuk dapat mengatasi permasalahan yang seringkali muncul diantaranya, kurangnya atau tidak ada (habis) stok persediaan dari jenis pipa yang paling diminati. Hal tersebut dapat disebabkan karena pola perilaku belanja konsumen yang selalu berubah-ubah dan tidak dapat diprediksi. Oleh karena itu, dalam upaya mengatasi permasalahan yang terjadi, penelitian ini dibuat dengan tujuan untuk memprediksi penjualan pipa pada CV. Gaskindo Sentosa dengan menerapkan algoritma apriori sehingga dapat diketahui pola perilaku konsumen dan diharapkan dapat meningkatkan penjualan pada perusahaan tersebut. Adapun untuk data yang digunakan yaitu dengan memanfaatkan data history dari semua transaksi yang pernah terjadi di CV. Gaskindo Sentosa. Dari hasil penelitian ini, diperoleh bahwa algoritma apriori dapat membantu mengembangkan strategi pemasaran untuk memasarkan produk lain dengan menganalisa kelebihan dari nilai jual produk yang paling laris terjual.
Image Segmentation Analysis Using Otsu Thresholding and Mean Denoising for the Identification Coffee Plant Diseases Ami Rahmawati; Yulianti, Ita; Nurajizah, Siti
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.261

Abstract

In Indonesia, coffee is one of the plantation products with a relatively high level of productivity and is a source of foreign exchange income for the country. However, unfortunately, certain factors can threaten productivity and quality in cultivating coffee plants, one of which is rust leaf disease. This disease causes disturbances in photosynthesis, thereby reducing plant yields. Therefore, to maintain and control productivity in coffee cultivation, this research carried out the process of observing coffee leaf images through segmentation using the Otsu Thresholding and Mean Denoising methods. The entire series of processes in this research was carried out using the Python programming language and succeeded in providing output in the form of image comparisons showing areas affected by Rust Leaf disease using the Otsu thresholding method alone and the Otsu thresholding method combined with a non-local means denoising algorithm. The test results prove that the Otsu thresholding method with the non-local means denoising algorithm has a smaller MSE value. It is the most optimal method for handling coffee leaf disease image segmentation with an accuracy level of 88%. It is hoped that this research can support farmers in providing insight into early detection of coffee plant diseases and increasing productivity through visual analysis.
Integration of Adasyn Method with Decision Tree Algorithm in Handling Imbalance Class for Loan Status Prediction Ami Rahmawati; Yulianti, Ita; Mardiana, Tati; Pribadi, Denny
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (761.285 KB) | DOI: 10.34288/jri.v6i3.299

Abstract

Determining the provision of credit is generally carried out based on measuring credibility using credit analysis principles (5C principles). However, this method requires quite a long processing time and is very susceptible to subjective judgments which might influence the final results. This research uses data mining techniques by developing modeling on loan status prediction datasets. The stages in this research include data preprocessing, modeling, and evaluation using accuracy metrics and ROC graphs. In this analysis, it is known that there is a class imbalance in the processed dataset, so an oversampling technique must be carried out. This research uses the ADASYN (Adaptive Synthetic) Oversampling technique to ensure the class distribution is more balanced. Then, the ADASYN technique is integrated with the Decision Tree Algorithm to build a prediction model. The research results show that the two methods can increase prediction accuracy by 12.22%, from 73,91% to 85.22%. This improvement was obtained by comparing the accuracy results before and after using the ADASYN Oversampling technique. This finding is important because it proves that implementing such integration modeling can significantly improve the performance of classification models and provide strong potential for practical application in helping more effective loan status predictions.
DEVELOPMENT OF MANUFACTURING INVENTORY MANAGEMENT SYSTEM USING MATERIAL REQUIREMENT PLANNING METHOD Rahmawati, Ami; Saputra, Rizal Amegia; Yulianti, Ita
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.135

Abstract

Inventory has an important role in business activities. This is because inventory has an effect on changes in the production market and anticipates price changes in the demand for many goods. PT. Barkah Jaya Mandiri is a company engaged in manufacturing where the management of inventory at the company is still done conventionally. This causes various problems such as the occurrence of discrepancies in the stock of goods, discrepancies in data and final reports as well as obstacles in the production process in the event of a shortage or excess of raw materials. (Material Requirement Planning) in order to overcome the problems that occur in the company. The combination of the SDLC model and data collection techniques including observation, interviews and literature study were also carried out in this study in order to achieve the system that will be built to suit the targeted needs. With this system, the management of inventory data at this company can be done easily and accurately and save time compared to the previous system, so that the procurement of manufacturing raw materials is optimal and employee performance is better.
THE EFFECTIVENESS ANALYSIS OF RANDOM FOREST ALGORITHMS WITH SMOTE TECHNIQUE IN PREDICTING LUNG CANCER RISK Yulianti, Ita; Rahmawati, Ami; Mardiana, Tati
Jurnal Riset Informatika Vol. 4 No. 2 (2022): March 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (996.334 KB) | DOI: 10.34288/jri.v4i2.159

Abstract

Abstract When compared with other types of cancer, most of the population with cancer die from lung cancer.A person needs to do a screening test through X-rays, CT scans, and MRI to detect the disease. However, before carrying out the process, the doctor will ordinarily investigate a medical history and physical examination first to study the symptoms and possible risk factors for lung cancer. The lung cancer data set has a class imbalance that affects the performance of the random forest algorithm in predicting the risk of lung cancer. This study aims to employ the SMOTE technique to the random forest algorithm to increase accuracy in predicting lung cancer risk. In this research, data processing and analysis use the Python programming language. The test results show an accuracy value of 88% with an AUC value of 0.93. When employing the random forest method to forecast lung cancer risk, the SMOTE technique is useful in dealing with class imbalances in the data set.
Co-Authors A Gunawan Adli, Idham Abdi Al Ami Rahmawati Anggi Oktaviani ANI OKTARINI SARI, ANI OKTARINI Dahlia Sarkawi Dewi, Rani Kurnia Dwi Nur Aini, Dwi Nur Dwi Retnaningsih, Dwi Endang Supriyanti, Endang Fitrika, Sonia Hikmatulloh, Hikmatulloh Ibhammurrival, M. Napitupulu, Golda TM Panca Saputra, Elin Pribadi, Denny Rahmawati Maulida, Nurul Rahmawati, Ami Ramdiani Azzahra, Dinda Rifa Nurafifah Syabaniah Rizal Amegia Saputra Rusda Wajhillah, Rusda Sahrul, Nizar Saumi Ramdhani, Lis Selowarni, Dati Septiani Nurfauzia K, Tya Siti Nurajizah Sri Melati Putri, Rahma Tati Mardiana, Tati Taufik Hidayatulloh Wahyuni, Ayu Dewi Sri Wahyuni Wahyuningsih Wahyuningsih Yuri Rahayu Yusti Farlina