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All Journal PROCEEDING IC-ITECHS 2014 SMATIKA RESEARCH : Computer, Information System & Technology Management DoubleClick : Journal of Computer and Information Technology JAIM (Jurnal Abdi Masyarakat) International Journal of Advances in Data and Information Systems Jurnal Teknologi dan Sistem Tertanam Journal of Soft Computing Exploration Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Pengabdian Pada Masyarakat Indonesia (JPPMI) SOROT: Jurnal Pengabdian Kepada Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat Asian Journal of Management, Entrepreneurship and Social Science
Yessi Yunitasari
Department Of Informatics Engineering, Universitas PGRI Madiun, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Other

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Enhancing Linear Regression with Forward Selection in Software Effort Estimation Puguh Jayadi; Yessi Yunitasari
Asian Journal of Management, Entrepreneurship and Social Science Vol. 4 No. 04 (2024): Upcoming issues, Asian Journal of Management Entrepreneurship and Social Scien
Publisher : Cita Konsultindo Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Software effort estimation is one of the critical aspects of software project management, but it often faces accuracy issues. Although statistical methods such as Linear Regression have been used, previous research has shown that these models are often inefficient because they involve many variables that may not be relevant. This study aims to improve the performance of Linear Regression models in software effort estimation using Forward Selection feature selection techniques. Two models were compared: the conventional Linear Regression model and the model with Forward Selection. Evaluation metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2). Results show significant improvements in all performance metrics on models with Forward Selection. Notably, the MSE increased from 1.0 to 0, suggesting that this model is more effective in explaining data variability. The use of Forward Selection in Linear Regression models for software effort estimation shows significant performance improvements and is worthy of consideration for further industry research and practice.
Enterprise Architecture Planning Information System at Bus Rental Service Company STT, Latjuba Sofyana; Yunitasari, Yessi; Budi, Belca Setya
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1598

Abstract

In increasing customer satisfaction, convenience and improving the quality of the company. The company engaged in bus rental services is PT. Transaba wants to have a digital platform and an integrated management system in the Company. One way is to do enterprise architecture planning to find out what systems/applications are urgent to create. The EAP framework provides the direction desired by the Company with its stages, including planning initiation, business modeling, current systems and technologies, data architecture and application architecture, and technology architecture. In this planning, the Company's directions and guidance regarding what must be done first to achieve the objectives, namely Procurement and installation of infrastructure, Customer Management System, Reservation management system, Scheduling and route system and Human Resources Training
Application of FFT and KNN Methods for the Process of Identifying Sound Signals Yunitasari, Yessi; Saifulloh, Saifulloh; Harly, Daniswara Andhika Putra
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1599

Abstract

Everyone has a different kind of voice. Sound is a unique thing and has a certain range of frequencies and intensity of sound that can and cannot be heard by humans. we can detect important characteristics of the sound. The Fast Fourier Transform (FFT) algorithm is an algorithm for calculating Discrete Fourier Transform (DFT). Process of Identifying Sound Signals beginning with the sound data was preprocessed, feature extraction using FFT, classification using KNN and finding the nearest distance using the Euclidean distance method, an accuracy result of 79% of the tested data was obtained.
Mapping and Prediction Analysis of Rice Fertilizer Use in Paron District, Ngawi Regency using K-Means and Fuzzy Sugeno Methods Saifulloh, Saifulloh; Yunitasari, Yessi; Welasih, Rusti Dewi
IC-ITECHS Vol 5 No 1 (2024): IC-ITECHS
Publisher : LPPM STIKI Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/ic-itechs.v5i1.1600

Abstract

Clustering and prediction methods are effective tools in overcoming this problem. In this research, the K-Means method was used to map fertilizer needs based on regional characteristics, such as soil type, planting patterns and land productivity levels. This method is able to group regions that have similar characteristics, making it easier to determine optimal fertilizer allocation. Next, the Fuzzy Sugeno method is applied to predict fertilizer needs in the next planting season based on historical data and the results of the grouping that has been carried out. By combining these two methods, it is hoped that the results of this research can provide accurate recommendations for the effective and efficient use of fertilizer. Mapping and predictive analysis on the use of rice fertilizers in Paron District, Ngawi Regency in 12 villages were carried out by referring to the K-Means and Fuzzy Sugeno methods. Mapping with the K-Means Method is done by determining the centroid and clustering, while the prediction mapping using the Fuzzy Sugeno Method is done by determining the variables and fuzzy sets as well as the condition calculation rules. The results obtained are that 2 villages are in the category of low fertilizer use, 3 villages are in the category of medium fertilizer use and 7 villages are in the category of high fertilizer use. Based on the prediction calculation using the Fuzzy Sugeno Method, it was found that the level of error in the comparison of data was 5.36%, meaning that from 100% of the error rate difference, the truth value in calculating the prediction of the use of urea fertilizer using the fuzzy Sugeno method was 94.64%.
Sound detection of gamelan musical instruments using teachable machine Yunitasari, Yessi; Asyhari, Moch Yusuf; Kurniawati, Inung Diah; STT, Latjuba Sofyana
Journal of Soft Computing Exploration Vol. 6 No. 2 (2025): June 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i2.576

Abstract

Gamelan is an instrument of musical expression that has an aesthetic function related to social, moral, and spiritual values. Gamelan consists of a variety of musical instruments that have a unique sound. In this study, the sound detection of nine gamelan musical instruments was carried out using a teachable machine. The gamelan musical instruments detected included gong, kenong, saron, bonang, gambang, kendang, flute, siter, and rebab. The algorithm used is CNN. The CNN algorithm has a fairly good performance for the sound detection process. The test results of the built model show an "acc" value of 25 ranging from 0.99 to 1, which indicates that the model achieves an accuracy rate of 99% to 100% on the training dataset. At the same time, "test accuracy" refers to a measure of the model's effectiveness in predicting data it has not encountered during training. The "test accuracy" score varied from 0. 83, which shows that the validation data has an accuracy of 83%.
Co-Authors Abdul Rozaq Anardani, Sri Andi Rahman Putera Aria Bima, Alim Citra Astuti, Anggun Yuli Budi, Belca Setya Cahyo, Wahyu Nur Cahyono Putro, Moh. Rizal Bagus Eka Resty Novieta Sari Hapsari, Estuning Dewi Harly, Daniswara Andhika Putra Kelik Sussolaikah Kurniawati, Inung Diah Kurniawati, Inung Diah Moch Yusuf Asyhari Muh Nur Luthfi Azis Muh. Lutfi Azis Pratiwi Susanti Puguh Jayadi saifulloh Saifulloh, saifulloh Sanriomi Sintaro STT, Latjuba Sofyana Surya Kharisma Octavian Welasih, Rusti Dewi