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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Informasi Cyberku
Ika Novita Dewi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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PENENTUAN TINGKAT KELULUSAN TEPAT WAKTU MAHASISWA STMIK SUBANG MENGGUNAKAN ALGORITMA C4.5 Hermansyah Nur Ahmad; Vincent Suhartono; Ika Novita Dewi
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 1 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (306.758 KB)

Abstract

Timely graduatuion rates in college could not be consideres easy and trivial. Many cases found that the share of the number of students who did not get in and who have completed their studies so that the build up of high numbers of students in every period. I need to know the factors cause students not graduating on time. Classification data mining techniques can be used to predict student graduation rates. The algorithm used is algoritmic C 4.5 with data as much as 200 students study computer engineering programs STMIK Subang. The result of the classification process is evaluated by using the confusion matrix, ROC Curve, Recall. Based on experimental results and evluation is done then it can be inferred that the algorithm C 4.5 accurately applied to determine the level of students graduation. After testing the prediction accuracy resulting from trials reached 95,00% of the classification result generate information in the from of graph in the form of the curve results from the decision tree that is useful for institutions of higher education in taking policy.
Red Onion Customer Relationship Management System Business Process Design Using BPR LC Method Aditya Rahman; Ika Novita Dewi; Farrikh Alzami; Kukuh Biyantama; Muhammad Rizal Nurcahyo; Filmada Ocky Saputra; Rindra Yusianto; Mila Sartika; Firman Wahyudi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8525

Abstract

Customer Relationship Management merupakan sistem yang membantu proses bisnis dalam mengelola hubungan antara perusahaan atau organisasi dengan pelanggan. Akan tetapi Customer Relationship (CRM) jarang ditemui dalam sektor pertanian, terutama pada pertanian bawang merah di Jawa Tengah. Penelitian ini bertujuan untuk merekayasa ulang dan memperbarui proses bisnis yang sedang berjalan guna memperbaiki permasalahan tersebut dengan menggunakan teknologi Machine Learning dan memodelkan proses bisnis dengan Business Procces Modeling Notation (BPMN). Untuk memperlancar tujuan penelitan, penelitian ini menggunakan metode Business Process Reengineering Life Cycle untuk menghasilkan CRM bawang merah. Pada penelitian ini menghasilkan sebuah temuan yaitu proses bisnis yang baru dengan menyertakan teknologi Machine Learning yang ditampung pada aplikasi cluster petani yang telah digambarkan pada BPMN, hal tersebut dilakukan agar menunjang kekurangan dalam kegiatan petani agar lebih menjadi efisien dan optimal serta mendapatkan hasil panen yang diinginkan.
OPTIMIZING RAW MATERIAL INVENTORY MANAGEMENT OF MSME PRODUCT USING EXTREME GRADIENT BOOSTING (XGBOOST) REGRESSOR ALGORITHM: A SALES PREDICTION APPROACH Muhammad Khusni Fikri; Farrikh Al Zami; Ika Novita Dewi; Abu Salam; Ifan Rizqa; Mila Sartika; Diana Aqmala
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.2024.5.2.1487

Abstract

Micro, Small and Medium Enterprises or MSMEs have a very important role for the survival of the economic sector in Indonesia. However, as the development of MSMEs, followed by a series of problems that arise. One of them is the problem of sales, business people have difficulty in determining the number of product sales in the future so that there is often an accumulation of raw materials or unsold products. This study aims to help MSMEs optimize raw material management by predicting product sales using the XGBoost Regressor Algorithm. Recently, the algorithm is very famous in the competition because of its reliability and no one has applied it to predict MSME product sales. Based on several other studies, this algorithm is accurate in predicting a value, such as predicting stock prices and the number of accidents in Bali, Indonesia. This research uses historical product sales data and weather data consisting of air temperature and relative humidity in Semarang Indonesia to train and evaluate the performance of the model. The prediction model was performed with predetermined variables and resulted in MAE 3.0752730568649156, MSE 38.25842541629838, and RMSE 6.185339555456788. In the end, it is concluded that the model built with XGBoost Regressor has a low error rate so that it can accurately predict the sales of an MSME product and optimize the management of raw materials for related products.
Co-Authors Abu Salam Aditya Rahman Alzami, Farrikh Diana Aqmala Farrikh Al Zami Filmada Ocky Saputra Firman Wahyudi Hermansyah Nur Ahmad Ifan Rizqa Kukuh Biyantama Mila Sartika Muhammad Khusni Fikri Muhammad Rizal Nurcahyo Vincent Suhartono Yusianto Rindra