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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)
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STMIK AMIKOM Purwokerto
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Journal : Jurnal Riset Informatika

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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.
ENHANCING SLEEP QUALITY PREDICTION THROUGH SMOTE-BASED DATA BALANCING AND HYBRID MACHINE LEARNING MODELS Rahmawati, Ami; Yulianti, Ita; Oktarini Sari, Ani; Nurajizah, Siti; Hikmatulloh
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.456

Abstract

Sleep is a vital aspect in maintaining a person's physical and psychological balance. Poor sleep quality can reduce physical and cognitive performance, increasing the risk of various health problems. This study aims to develop a predictive model for sleep quality based on factors such as lifestyle, stress, daily activities, and caffeine consumption, using XGBoost combined with Recursive Feature Elimination (RFE). XGBoost was chosen for its ability to handle imbalanced datasets and heterogeneous features, while RFE helps simplify the model without losing important information. In the data pre-processing stage, a class imbalance was found, so the Synthetic Minority Over-sampling Technique (SMOTE) process was carried out to balance the proportion of the minority class. The dataset in this study was divided into two parts, namely 80% as training data and 20% as testing data, and validated using cross-validation to ensure generalization. The results show very high model performance with an accuracy of 99.79% on training data, 99.63% on cross-validation, and 99.10% on testing data. This model was then developed into a web application for practical use in analyzing sleep quality prediction. This study emphasizes the methodological contribution of a SMOTE-based hybrid machine learning model and its ready-to-use application implementation, while also opening opportunities for further testing on more diverse datasets and evaluating biases caused by synthetic data.
Co-Authors A Gunawan Adli, Idham Abdi Al Amegia Saputra, Rizal 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, Hikmatulloh Ibhammurrival, M. Napitupulu, Golda TM Oktarini Sari, Ani 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